File size: 141,893 Bytes
6fa4bc9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099 2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112 2113 2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127 2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 2141 2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155 2156 2157 2158 2159 2160 2161 2162 2163 2164 2165 2166 2167 2168 2169 2170 2171 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181 2182 2183 2184 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2198 2199 2200 2201 2202 2203 2204 2205 2206 2207 2208 2209 2210 2211 2212 2213 2214 2215 2216 2217 2218 2219 2220 2221 2222 2223 2224 2225 2226 2227 2228 2229 2230 2231 2232 2233 2234 2235 2236 2237 2238 2239 2240 2241 2242 2243 2244 2245 2246 2247 2248 2249 2250 2251 2252 2253 2254 2255 2256 2257 2258 2259 2260 2261 2262 2263 2264 2265 2266 2267 2268 2269 2270 2271 2272 2273 2274 2275 2276 2277 2278 2279 2280 2281 2282 2283 2284 2285 2286 2287 2288 2289 2290 2291 2292 2293 2294 2295 2296 2297 2298 2299 2300 2301 2302 2303 2304 2305 2306 2307 2308 2309 2310 2311 2312 2313 2314 2315 2316 2317 2318 2319 2320 2321 2322 2323 2324 2325 2326 2327 2328 2329 2330 2331 2332 2333 2334 2335 2336 2337 2338 2339 2340 2341 2342 2343 2344 2345 2346 2347 2348 2349 2350 2351 2352 2353 2354 2355 2356 2357 2358 2359 2360 2361 2362 2363 2364 2365 2366 2367 2368 2369 2370 2371 2372 2373 2374 2375 2376 2377 2378 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2405 2406 2407 2408 2409 2410 2411 2412 2413 2414 2415 2416 2417 2418 2419 2420 2421 2422 2423 2424 2425 2426 2427 2428 2429 2430 2431 2432 2433 2434 2435 2436 2437 2438 2439 2440 2441 2442 2443 2444 2445 2446 2447 2448 2449 2450 2451 2452 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2465 2466 2467 2468 2469 2470 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484 2485 2486 2487 2488 2489 2490 2491 2492 2493 2494 2495 2496 2497 2498 2499 2500 2501 2502 2503 2504 2505 2506 2507 2508 2509 2510 2511 2512 2513 2514 2515 2516 2517 2518 2519 2520 2521 2522 2523 2524 2525 2526 2527 2528 2529 2530 2531 2532 2533 2534 2535 2536 2537 2538 2539 2540 2541 2542 2543 2544 2545 2546 2547 2548 2549 2550 2551 2552 2553 2554 2555 2556 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2567 2568 2569 2570 2571 2572 2573 2574 2575 2576 2577 2578 2579 2580 2581 2582 2583 2584 2585 2586 2587 2588 2589 2590 2591 2592 2593 2594 2595 2596 2597 2598 2599 2600 2601 2602 2603 2604 2605 2606 2607 2608 2609 2610 2611 2612 2613 2614 2615 2616 2617 2618 2619 2620 2621 2622 2623 2624 2625 2626 2627 2628 2629 2630 2631 2632 2633 2634 2635 2636 2637 2638 2639 2640 2641 2642 2643 2644 2645 2646 2647 2648 2649 2650 2651 2652 2653 2654 2655 2656 2657 2658 2659 2660 2661 2662 2663 2664 2665 2666 2667 2668 2669 2670 2671 2672 2673 2674 2675 2676 2677 2678 2679 2680 2681 2682 2683 2684 2685 2686 2687 2688 2689 2690 2691 2692 2693 2694 2695 2696 2697 2698 2699 2700 2701 2702 2703 2704 2705 2706 2707 2708 2709 2710 2711 2712 2713 2714 2715 2716 2717 2718 2719 2720 2721 2722 2723 2724 2725 2726 2727 2728 2729 2730 2731 2732 2733 2734 2735 2736 2737 2738 2739 2740 2741 2742 2743 2744 2745 2746 2747 2748 2749 2750 2751 2752 2753 2754 2755 2756 2757 2758 2759 2760 2761 2762 2763 2764 2765 2766 2767 2768 2769 2770 2771 2772 2773 2774 2775 2776 2777 2778 2779 2780 2781 2782 2783 2784 2785 2786 2787 2788 2789 2790 2791 2792 2793 2794 2795 2796 2797 2798 2799 2800 2801 2802 2803 2804 2805 2806 2807 2808 2809 2810 2811 2812 2813 2814 2815 2816 2817 2818 2819 2820 2821 2822 2823 2824 2825 2826 2827 2828 2829 2830 2831 2832 2833 2834 2835 2836 2837 2838 2839 2840 2841 2842 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855 2856 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2869 2870 2871 2872 2873 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883 2884 2885 2886 2887 2888 2889 2890 2891 2892 2893 2894 2895 2896 2897 2898 2899 2900 2901 2902 2903 2904 2905 2906 2907 2908 2909 2910 2911 2912 2913 2914 2915 2916 2917 2918 2919 2920 2921 2922 2923 2924 2925 2926 2927 2928 2929 2930 2931 2932 2933 2934 2935 2936 2937 2938 2939 2940 2941 2942 2943 2944 2945 2946 2947 2948 2949 2950 2951 2952 2953 2954 2955 2956 2957 2958 2959 2960 2961 2962 2963 2964 2965 2966 2967 2968 2969 2970 | {
"paper_id": "P13-1041",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T09:35:01.300308Z"
},
"title": "The Haves and the Have-Nots: Leveraging Unlabelled Corpora for Sentiment Analysis",
"authors": [
{
"first": "Kashyap",
"middle": [],
"last": "Popat",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "IIT Bombay",
"location": {
"country": "Australia"
}
},
"email": "kashyap@cse.iitb.ac.in"
},
{
"first": "",
"middle": [],
"last": "Balamurali",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "IIT",
"location": {
"settlement": "Bombay"
}
},
"email": "balamurali@cse.iitb.ac.in"
},
{
"first": "Pushpak",
"middle": [],
"last": "Bhattacharyya",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "IIT Bombay",
"location": {
"country": "Australia"
}
},
"email": ""
},
{
"first": "Gholamreza",
"middle": [],
"last": "Haffari",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Monash University",
"location": {}
},
"email": ""
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "Expensive feature engineering based on WordNet senses has been shown to be useful for document level sentiment classification. A plausible reason for such a performance improvement is the reduction in data sparsity. However, such a reduction could be achieved with a lesser effort through the means of syntagma based word clustering. In this paper, the problem of data sparsity in sentiment analysis, both monolingual and cross-lingual, is addressed through the means of clustering. Experiments show that cluster based data sparsity reduction leads to performance better than sense based classification for sentiment analysis at document level. Similar idea is applied to Cross Lingual Sentiment Analysis (CLSA), and it is shown that reduction in data sparsity (after translation or bilingual-mapping) produces accuracy higher than Machine Translation based CLSA and sense based CLSA.",
"pdf_parse": {
"paper_id": "P13-1041",
"_pdf_hash": "",
"abstract": [
{
"text": "Expensive feature engineering based on WordNet senses has been shown to be useful for document level sentiment classification. A plausible reason for such a performance improvement is the reduction in data sparsity. However, such a reduction could be achieved with a lesser effort through the means of syntagma based word clustering. In this paper, the problem of data sparsity in sentiment analysis, both monolingual and cross-lingual, is addressed through the means of clustering. Experiments show that cluster based data sparsity reduction leads to performance better than sense based classification for sentiment analysis at document level. Similar idea is applied to Cross Lingual Sentiment Analysis (CLSA), and it is shown that reduction in data sparsity (after translation or bilingual-mapping) produces accuracy higher than Machine Translation based CLSA and sense based CLSA.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "Data sparsity is the bane of Natural Language Processing (NLP) (Xue et al., 2005; Minkov et al., 2007) . Language units encountered in the test data but absent in the training data severely degrade the performance of an NLP task. NLP applications innovatively handle data sparsity through various means. A special, but very common kind of data sparsity viz., word sparsity, can be addressed in one of the two obvious ways: 1) sparsity reduction through paradigmatically related words or 2) sparsity reduction through syntagmatically related words.",
"cite_spans": [
{
"start": 63,
"end": 81,
"text": "(Xue et al., 2005;",
"ref_id": "BIBREF47"
},
{
"start": 82,
"end": 102,
"text": "Minkov et al., 2007)",
"ref_id": "BIBREF28"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Paradigmatic analysis of text is the analysis of concepts embedded in the text (Cruse, 1986; Chandler, 2012) . WordNet is a byproduct of such an analysis. In WordNet, paradigms are manually generated based on the principles of lexical and semantic relationship among words (Fellbaum, 1998) . WordNets are primarily used to address the problem of word sense disambiguation. However, at present there are many NLP applications which use WordNet. One such application is Sentiment Analysis (SA) (Pang and Lee, 2002) . Recent research has shown that word sense based semantic features can improve the performance of SA systems (Rentoumi et al., 2009; Tamara et al., 2010; Balamurali et al., 2011) compared to word based features.",
"cite_spans": [
{
"start": 79,
"end": 92,
"text": "(Cruse, 1986;",
"ref_id": "BIBREF9"
},
{
"start": 93,
"end": 108,
"text": "Chandler, 2012)",
"ref_id": "BIBREF8"
},
{
"start": 273,
"end": 289,
"text": "(Fellbaum, 1998)",
"ref_id": "BIBREF13"
},
{
"start": 492,
"end": 512,
"text": "(Pang and Lee, 2002)",
"ref_id": "BIBREF34"
},
{
"start": 623,
"end": 646,
"text": "(Rentoumi et al., 2009;",
"ref_id": "BIBREF36"
},
{
"start": 647,
"end": 667,
"text": "Tamara et al., 2010;",
"ref_id": "BIBREF41"
},
{
"start": 668,
"end": 692,
"text": "Balamurali et al., 2011)",
"ref_id": "BIBREF0"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Syntagmatic analysis of text concentrates on the surface properties of the text. Compared to paradigmatic property extraction, syntagmatic processing is relatively light weight. One of the obvious syntagmas is words, and words are grouped into equivalence classes or clusters, thus reducing the model parameters of a statistical NLP system (Brown et al., 1992) . When used as an additional feature with word based language models, it has been shown to improve the system performance viz., machine translation (Uszkoreit and Brants, 2008; Stymne, 2012) , speech recognition (Martin et al., 1995; Samuelsson and Reichl, 1999) , dependency parsing (Koo et al., 2008; Haffari et al., 2011; Zhang and Nivre, 2011; Tratz and Hovy, 2011) and NER (Miller et al., 2004; Faruqui and Pad\u00f3, 2010; Turian et al., 2010; T\u00e4ckstr\u00f6m et al., 2012) .",
"cite_spans": [
{
"start": 340,
"end": 360,
"text": "(Brown et al., 1992)",
"ref_id": "BIBREF7"
},
{
"start": 509,
"end": 537,
"text": "(Uszkoreit and Brants, 2008;",
"ref_id": "BIBREF45"
},
{
"start": 538,
"end": 551,
"text": "Stymne, 2012)",
"ref_id": "BIBREF39"
},
{
"start": 573,
"end": 594,
"text": "(Martin et al., 1995;",
"ref_id": "BIBREF23"
},
{
"start": 595,
"end": 623,
"text": "Samuelsson and Reichl, 1999)",
"ref_id": "BIBREF38"
},
{
"start": 645,
"end": 663,
"text": "(Koo et al., 2008;",
"ref_id": "BIBREF19"
},
{
"start": 664,
"end": 685,
"text": "Haffari et al., 2011;",
"ref_id": "BIBREF15"
},
{
"start": 686,
"end": 708,
"text": "Zhang and Nivre, 2011;",
"ref_id": "BIBREF50"
},
{
"start": 709,
"end": 730,
"text": "Tratz and Hovy, 2011)",
"ref_id": "BIBREF42"
},
{
"start": 739,
"end": 760,
"text": "(Miller et al., 2004;",
"ref_id": "BIBREF27"
},
{
"start": 761,
"end": 784,
"text": "Faruqui and Pad\u00f3, 2010;",
"ref_id": "BIBREF12"
},
{
"start": 785,
"end": 805,
"text": "Turian et al., 2010;",
"ref_id": "BIBREF43"
},
{
"start": 806,
"end": 829,
"text": "T\u00e4ckstr\u00f6m et al., 2012)",
"ref_id": "BIBREF40"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "In this paper, the focus is on alleviating the data sparsity faced by supervised approaches for SA through the means of cluster based features. As WordNets are essentially word clusters wherein words with the same meaning are clubbed together, they address the problem of data sparsity at word level. The abstraction and dimensionality reduction thus achieved attributes to the superior performance for SA systems that employs WordNet senses as features. However, WordNets are manually created. Automatic creation of the same is challenging and not much successful because of the linguistic complexity involved. In case of SA, manually creating the features based on WordNet senses is a tedious and an expensive process. Moreover, WordNets are not present for many languages. All these factors make the paradigmatic property based cluster features like WordNet senses a less promising pursuit for SA.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "The syntagmatic analysis essentially makes use of distributional similarity and may in many circumstances subsume the paradigmatic analysis. In the current work, this particular insight is used to solve the data sparsity problem in the sentiment analysis by leveraging unlabelled monolingual corpora. Specifically, experiments are performed to investigate whether features developed from manually crafted clusterings (coming from WordNet) can be replaced by those generated from clustering based on syntagmatic properties.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Further, cluster based features are used to address the problem of scarcity of sentiment annotated data in a language. Popular approaches for Cross-Lingual Sentiment Analysis (CLSA) (Wan, 2009; Duh et al., 2011) depend on Machine Translation (MT) for converting the labeled data from one language to the other (Hiroshi et al., 2004; Banea et al., 2008; Wan, 2009) . However, many languages which are truly resource scarce, do not have an MT system or existing MT systems are not ripe to be used for CLSA (Balamurali et al., 2013) . To perform CLSA, this study leverages unlabelled parallel corpus to generate the word alignments. These word alignments are then used to link cluster based features to obliterate the language gap for performing SA. No MT systems or bilingual dictionaries are used for this study. Instead, language gap for performing CLSA is bridged using linked cluster or cross-lingual clusters (explained in section 4) with the help of unlabelled monolingual corpora. The contributions of this paper are two fold:",
"cite_spans": [
{
"start": 182,
"end": 193,
"text": "(Wan, 2009;",
"ref_id": "BIBREF46"
},
{
"start": 194,
"end": 211,
"text": "Duh et al., 2011)",
"ref_id": "BIBREF11"
},
{
"start": 310,
"end": 332,
"text": "(Hiroshi et al., 2004;",
"ref_id": "BIBREF16"
},
{
"start": 333,
"end": 352,
"text": "Banea et al., 2008;",
"ref_id": "BIBREF3"
},
{
"start": 353,
"end": 363,
"text": "Wan, 2009)",
"ref_id": "BIBREF46"
},
{
"start": 504,
"end": 529,
"text": "(Balamurali et al., 2013)",
"ref_id": "BIBREF2"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "1. Features created from manually built and finer clusters can be replaced by inexpensive cluster based features generated solely from unlabelled corpora. Experiments performed on four publicly available datasets in three languages viz., English, Hindi and Marathi 1 suggest that cluster based features can considerably boost the performance of an SA system. Moreover, state of the art result is obtained for one of the publicly available dataset.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "2. An alternative and effective approach for CLSA is demonstrated using clusters as features. Word clustering is a powerful mechanism to \"transfer\" a sentiment classifier from one language to another. Thus can be used in truly resource scarce scenarios like that of English-Marathi CLSA.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "The rest of the paper is organized as follows: section 2 presents related work. Section 3 explains different word cluster based features employed to reduce data sparsity for monolingual SA. In section 4, alternative CLSA approaches based on word clustering are elucidated. Experimental details are explained in section 5. Results and discussions are presented in section 6 and section 7 respectively. Finally, section 8 concludes the paper pointing to some future research possibilities.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "The problem of SA at document level is defined as the classification of document into different polarity classes (positive and negative) (Turney, 2002) . Both supervised (Benamara et al., 2007; Martineau and Finin, 2009) and unsupervised approaches (Mei et al., 2007; Lin and He, 2009) exist for this task.",
"cite_spans": [
{
"start": 137,
"end": 151,
"text": "(Turney, 2002)",
"ref_id": "BIBREF44"
},
{
"start": 170,
"end": 193,
"text": "(Benamara et al., 2007;",
"ref_id": "BIBREF4"
},
{
"start": 194,
"end": 220,
"text": "Martineau and Finin, 2009)",
"ref_id": "BIBREF24"
},
{
"start": 249,
"end": 267,
"text": "(Mei et al., 2007;",
"ref_id": "BIBREF26"
},
{
"start": 268,
"end": 285,
"text": "Lin and He, 2009)",
"ref_id": "BIBREF21"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "Supervised approaches are popular because of their superior classification accuracy (Mullen and Collier, 2004; Pang and Lee, 2008) . Feature engineering plays an important role in these systems. Apart from the commonly used bag-of-words features based on unigrams/bigrams/ngrams (Dave et al., 2003; Ng et al., 2006; Martineau and Finin, 2009) , syntax (Matsumoto et al., 2005; Nakagawa et al., 2010) , semantic (Balamurali et al., 2011) and negation (Ikeda et al., 2008) have also been explored for this task. There has been research related to clustering and sentiment analysis. In Rooney et al. (2011) , documents are clustered based on the context of each document and sentiment labels are attached at the cluster level. Zhai et al. (2011) attempts to cluster features of a product to perform sentiment analysis on product reviews. In this work, word clusters (syntagmatic and paradigmatic) encoding a mixture of syntactic and semantic information are used for feature engineering.",
"cite_spans": [
{
"start": 84,
"end": 110,
"text": "(Mullen and Collier, 2004;",
"ref_id": "BIBREF30"
},
{
"start": 111,
"end": 130,
"text": "Pang and Lee, 2008)",
"ref_id": "BIBREF35"
},
{
"start": 279,
"end": 298,
"text": "(Dave et al., 2003;",
"ref_id": "BIBREF10"
},
{
"start": 299,
"end": 315,
"text": "Ng et al., 2006;",
"ref_id": "BIBREF32"
},
{
"start": 316,
"end": 342,
"text": "Martineau and Finin, 2009)",
"ref_id": "BIBREF24"
},
{
"start": 352,
"end": 376,
"text": "(Matsumoto et al., 2005;",
"ref_id": "BIBREF25"
},
{
"start": 377,
"end": 399,
"text": "Nakagawa et al., 2010)",
"ref_id": "BIBREF31"
},
{
"start": 411,
"end": 436,
"text": "(Balamurali et al., 2011)",
"ref_id": "BIBREF0"
},
{
"start": 450,
"end": 470,
"text": "(Ikeda et al., 2008)",
"ref_id": "BIBREF17"
},
{
"start": 583,
"end": 603,
"text": "Rooney et al. (2011)",
"ref_id": "BIBREF37"
},
{
"start": 724,
"end": 742,
"text": "Zhai et al. (2011)",
"ref_id": "BIBREF49"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "In situations where labeled data is not present in a language, approaches based on cross-lingual sentiment analysis are used. Most often these methods depend on an intermediary machine translation system (Wan, 2009; Brooke et al., 2009) or a bilingual dictionary (Ghorbel and Jacot, 2011; Lu et al., 2011) to bridge the language gap. Given the subtle and different ways the sentiment can be expressed which itself manifested as a result of cultural diversity amongst different languages, an MT system has to be of a superior quality to capture them.",
"cite_spans": [
{
"start": 204,
"end": 215,
"text": "(Wan, 2009;",
"ref_id": "BIBREF46"
},
{
"start": 216,
"end": 236,
"text": "Brooke et al., 2009)",
"ref_id": "BIBREF6"
},
{
"start": 263,
"end": 288,
"text": "(Ghorbel and Jacot, 2011;",
"ref_id": "BIBREF14"
},
{
"start": 289,
"end": 305,
"text": "Lu et al., 2011)",
"ref_id": "BIBREF22"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "The goal of this paper, to remind the reader, is to investigate whether superior word cluster features based on manually crafted and fine grained lexical resource like WordNet can be replaced with the syntagmatic property based word clusters created from unlabelled monolingual corpora.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Clustering for Sentiment Analysis",
"sec_num": "3"
},
{
"text": "In this section, different clustering approaches are presented for feature engineering in a monolingual setting.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Clustering for Sentiment Analysis",
"sec_num": "3"
},
{
"text": "A synonymous set of words in a WordNet is called a synset. Each synset can be considered as a word cluster comprising of semantically similar words. Balamurali et al. (2011) showed that WordNet synsets can act as good features for document level sentiment classification. Motivation for their study stems from the fact that different senses of a word can have different polarities. To empirically prove the superiority of sense based features, different variants of a travel review domain corpus were generated by using automatic/manual sense disambiguation techniques. Thereafter, accuracies of classifiers based on different sense-based and word-based features were compared. The results suggested that WordNet synset based features performed better than word-based features.",
"cite_spans": [
{
"start": 149,
"end": 173,
"text": "Balamurali et al. (2011)",
"ref_id": "BIBREF0"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Approach 1: Clustering based on WordNet Sense",
"sec_num": "3.1"
},
{
"text": "In this study, synset identifiers are extracted from manually/automatically sense annotated corpora and used as features for creating sentiment classifiers. The classifier thus build is used as a baseline. Apart from this, another baseline employing word based features are used for a comprehensive comparison.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Approach 1: Clustering based on WordNet Sense",
"sec_num": "3.1"
},
{
"text": "For this particular study, a co-occurrence based algorithm is used to create word clusters. As the algorithm is based on co-occurrence, one can extract the classes that have the flavour of syntagmatic grouping, depending on the nature of underlying statistics. Agglomerative clustering algorithm by Brown et al. 1992is used for this purpose. It is a hard clustering algorithm i.e., each word belongs to one cluster only. Formally, as mentioned in Brown et al. 1992, let C be a hard clustering function which maps vocabulary V to one of the K clusters. Then, the likelihood (L()) of a sequence of word tokens,",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Approach 2: Syntagmatic Property based Clustering",
"sec_num": "3.2"
},
{
"text": "w = [w j ] m j=1 , with w j \u2208 V , can be factored as, L(w; C) = m j=1 p(w j |C(w j ))p(C(w j )|C(w j\u22121 )))",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Approach 2: Syntagmatic Property based Clustering",
"sec_num": "3.2"
},
{
"text": "(1) Words are assigned to clusters such that the above quantity is maximized. For the purpose of sentiment classification, cluster identifiers representing words in the document are used as features for training.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Approach 2: Syntagmatic Property based Clustering",
"sec_num": "3.2"
},
{
"text": "Existing approaches for CLSA depend on an intermediary machine translation system to bridge the language gap (Hiroshi et al., 2004; Banea et al., 2008) . Machine translation is very resource intensive. If a language is truly resource scarce, it is mostly unlikely to have an MT system. Given that sentiment analysis is a less resource intensive task compared to machine translation, the use of an MT system is hard to justify for performing CLSA. As a viable alternative, cluster linkages could be learned from a bilingual parallel corpus and these linkages can be used to bridge the language gap for CLSA. In this section, three approaches using clusters as features for CLSA are compared. The language whose annotated data is used for training is called the source language (S), while the language whose documents are to be sentiment classified is referred to as the target language (T ).",
"cite_spans": [
{
"start": 109,
"end": 131,
"text": "(Hiroshi et al., 2004;",
"ref_id": "BIBREF16"
},
{
"start": 132,
"end": 151,
"text": "Banea et al., 2008)",
"ref_id": "BIBREF3"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Clustering for Cross Lingual Sentiment Analysis",
"sec_num": "4"
},
{
"text": "In this approach, a Multidict is used to bridge the language gap for SA. A Multidict is an instance of WordNet where the same sense from different languages are linked (Mohanty et al., 2008) . An entry in the multidict will have a WordNet sense identifier from S and the corresponding WordNet sense identifier from T . The approach of projection based on sense is explained in Algorithm 1. Note that after the Sense Mark operation, each document will be represented as a vector of WordNet sense identifiers.",
"cite_spans": [
{
"start": 168,
"end": 190,
"text": "(Mohanty et al., 2008)",
"ref_id": "BIBREF29"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Approach 1: Projection based on Sense (PS)",
"sec_num": "4.1"
},
{
"text": "Algorithm 1 Projection based on sense Input: Polarity labeled data in source language (S) and data in target language (T ) to be labeled Output: Classified documents 1: Sense mark the polarity labeled data from S 2: Project the sense marked corpora from S to T using a Multidict 3: Model the sentiment classifier using the data obtained in step-2 4: Sense mark the unlabelled data from T 5: Test the sentiment classifier on data obtained in step-4 using model obtained in step-3 Sense identifiers are the features for the classifier. For those sense identifiers which do not have a corresponding entry in the Multidict, no projection is performed.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Approach 1: Projection based on Sense (PS)",
"sec_num": "4.1"
},
{
"text": "Given a parallel bilingual corpus, word clusters in S can be aligned to clusters in T . Word alignments are created using parallel corpora. Given two aligned word sequences",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Approach 2: Direct Cluster Linking (DCL)",
"sec_num": "4.2"
},
{
"text": "w S = [w S j ] m j=1 and w T = [w T k ] n k=1",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Approach 2: Direct Cluster Linking (DCL)",
"sec_num": "4.2"
},
{
"text": ", let \u03b1 T |S be a set of scored alignments from the source language to the target language. Here, an alignment from the a k th source word to the k th target word, with score s k,a k > \u03b5 is represented as (w T k , w S a k , s k,a k ) \u2208 \u03b1 T |S . To simplify, k \u2208 \u03b1 T |S is used to denote those target words w T k that are aligned to some source word w S a k . The source and the target side clusters are linked using the Equation 2.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Approach 2: Direct Cluster Linking (DCL)",
"sec_num": "4.2"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "LC(l) = argmax t k\u2208\u03b1 T |S \u222a \u03b1 S|T s.t.C T (w T k )=t C S (w S a k )=l s k,a k",
"eq_num": "(2)"
}
],
"section": "Approach 2: Direct Cluster Linking (DCL)",
"sec_num": "4.2"
},
{
"text": "Here, a target side cluster t \u2208 C T is linked to a source side cluster l \u2208 C S such that the total alignment score between words in l and words in t is maximum. C S and C T stands for source and target side cluster list respectively. LC(l) gives the target side cluster t to which l is linked.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Approach 2: Direct Cluster Linking (DCL)",
"sec_num": "4.2"
},
{
"text": "Direct cluster linking approach suffers from the size of alignment dataset in the form of parallel corpora. The size of the alignment dataset is typically smaller than the monolingual dataset.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Approach 3: Cross-Lingual Clustering (XC)",
"sec_num": "4.3"
},
{
"text": "To circumvent this problem, T\u00e4ckstr\u00f6m et al. (2012) introduced cross-lingual clustering. In cross-lingual clustering, the objective function maximizes the joint likelihood of monolingual and cross-lingual factors. Given a list of words and clusters it belongs to, a clustering algorithm tries to obtain word-cluster association which maximizes the joint likelihood of words and clusters.",
"cite_spans": [
{
"start": 28,
"end": 51,
"text": "T\u00e4ckstr\u00f6m et al. (2012)",
"ref_id": "BIBREF40"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Approach 3: Cross-Lingual Clustering (XC)",
"sec_num": "4.3"
},
{
"text": "Whereas in case of crosslingual clustering, the same clustering can be explained in terms of maximizing the likelihood of monolingual word-cluster pairs of the source, the target and alignments between them.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Approach 3: Cross-Lingual Clustering (XC)",
"sec_num": "4.3"
},
{
"text": "Formally, as stated in T\u00e4ckstr\u00f6m et al. (2012) , Using the model of Uszkoreit and Brants (2008) , the likelihood of a sequence of word tokens, w = [w j ] m j=1 , with w j \u2208 V , can be factored as, 1), where C(w j ) was conditioned on C(w j\u22121 ). This makes the computation easier as suggested in the original paper. The Equation (3) in a cross lingual setting will be transformed as given below:",
"cite_spans": [
{
"start": 23,
"end": 46,
"text": "T\u00e4ckstr\u00f6m et al. (2012)",
"ref_id": "BIBREF40"
},
{
"start": 68,
"end": 95,
"text": "Uszkoreit and Brants (2008)",
"ref_id": "BIBREF45"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Approach 3: Cross-Lingual Clustering (XC)",
"sec_num": "4.3"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "L(w; C) = m j=1 p(w j |C(w j ))p(C(w j )|w j\u22121 ))",
"eq_num": "(3)"
}
],
"section": "Approach 3: Cross-Lingual Clustering (XC)",
"sec_num": "4.3"
},
{
"text": "L S,T (w S , w T ; \u03b1 T |S , \u03b1 S|T , C S , C T ) = L S (...).L T (...).L T |S (...).L S|T (...) (4)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Approach 3: Cross-Lingual Clustering (XC)",
"sec_num": "4.3"
},
{
"text": "Here, L T |S (...) and L S|T (...) are factors based on word alignments, which can be represented as:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Approach 3: Cross-Lingual Clustering (XC)",
"sec_num": "4.3"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "L T |S (w T ; \u03b1 T |S , C T , C S ) = k\u2208\u03b1 T |S p(w T k |C T (w T k ))p(C T (w T k )|C S (w S a k )))",
"eq_num": "(5)"
}
],
"section": "Approach 3: Cross-Lingual Clustering (XC)",
"sec_num": "4.3"
},
{
"text": "Based on the optimization objective in Equation 4, a pseudo algorithm is defined in Algorithm 2. For more information, readers are requested to refer T\u00e4ckstr\u00f6m et al. (2012) .",
"cite_spans": [
{
"start": 150,
"end": 173,
"text": "T\u00e4ckstr\u00f6m et al. (2012)",
"ref_id": "BIBREF40"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Approach 3: Cross-Lingual Clustering (XC)",
"sec_num": "4.3"
},
{
"text": "Input: Source and target language corpus Output:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Algorithm 2 Cross-lingual Clustering (XC)",
"sec_num": null
},
{
"text": "Cross-lingual clusters 1: ## C S , C T randomly initialized 2: for i \u2190 1 to N do 3: Find C S * \u2248 argmax C S L S (w S ; C S ) 4: Project C S * to C T 5: Find C T * \u2248 argmax C T L T (w T ; C T ) 6:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Algorithm 2 Cross-lingual Clustering (XC)",
"sec_num": null
},
{
"text": "Project C T * to C S 7: end for An MT based CLSA approach is used as the baseline. Training data from S is translated to T and classification model is learned using unigram based features. Thereafter, the classifier is directly tested on data from T .",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Algorithm 2 Cross-lingual Clustering (XC)",
"sec_num": null
},
{
"text": "Analysis was performed on three languages, viz., English (En), Hindi (Hi) and Marathi (Mar). CLSA was performed on two language pairs, English-Hindi and English-Marathi. For clustering the words, monolingual data of Indian Languages Corpora Initiative (ILCI) 2 was used. It should also be noted that sentiment annotated data was also included in the data used for the word clusterings process. For Brown clustering, an implementation by Liang (2005) was used. Cross-lingual clustering for CLSA 2 http://sanskrit.jnu.ac.in/ilci/index. jsp was implemented as directed in T\u00e4ckstr\u00f6m et al. (2012) .",
"cite_spans": [
{
"start": 437,
"end": 449,
"text": "Liang (2005)",
"ref_id": "BIBREF20"
},
{
"start": 569,
"end": 592,
"text": "T\u00e4ckstr\u00f6m et al. (2012)",
"ref_id": "BIBREF40"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Experimental Setup",
"sec_num": "5"
},
{
"text": "Monolingual SA: For experiments in English, two polarity datasets were used.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Experimental Setup",
"sec_num": "5"
},
{
"text": "The first one (En-TD) by Ye et al. (2009) contains userwritten reviews on travel destinations. The dataset consists of approximately 600 positive and 591 negative reviews. Reviews were also manually sense annotated using WordNet 2.1. The sense annotation was performed by two annotators with an inter-annotation agreement of 93%. The second dataset (En-PD) 3 on product reviews (music instruments) from Amazon by Blitzer et al. (2007) contains 1000 positive and 1000 negative reviews. This dataset was sense annotated using an automatic WSD engine which was trained on tourism domain (Khapra et al., 2010) . Experiments using this dataset were done to study the effect of domain on CLSA. For experiments in Hindi and Marathi, polarity datasets by Balamurali et al. (2012) were used. 4 These are reviews collected from various Hindi and Marathi blogs and Sunday editorials. Hindi dataset consist of 98 positive and 100 negative reviews. Whereas Marathi dataset contains 75 positive and 75 negative reviews. Apart from being marked with polarity labels at document level, they are also manually sense annotated using Hindi and Marathi WordNet respectively.",
"cite_spans": [
{
"start": 25,
"end": 41,
"text": "Ye et al. (2009)",
"ref_id": "BIBREF48"
},
{
"start": 413,
"end": 434,
"text": "Blitzer et al. (2007)",
"ref_id": "BIBREF5"
},
{
"start": 584,
"end": 605,
"text": "(Khapra et al., 2010)",
"ref_id": "BIBREF18"
},
{
"start": 747,
"end": 771,
"text": "Balamurali et al. (2012)",
"ref_id": "BIBREF1"
},
{
"start": 783,
"end": 784,
"text": "4",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Experimental Setup",
"sec_num": "5"
},
{
"text": "The same datasets used in SA are also used for CLSA. Three approaches (as described in section 4) were tested for English-Hindi and English-Marathi language pairs. To create alignments, English-Hindi and English-Marathi parallel corpora from ILCI were used. English-Hindi parallel corpus contains 45992 sentences and English-Marathi parallel corpus contains 47881 sentences. To create alignments, GIZA++ 5 was used (Och and Ney, 2003) .",
"cite_spans": [
{
"start": 415,
"end": 434,
"text": "(Och and Ney, 2003)",
"ref_id": "BIBREF33"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "CLSA:",
"sec_num": null
},
{
"text": "As a preprocessing step, all stop words were removed. Stemming was performed on English and Hindi whereas for Marathi data, Morphological Analyzer was used to reduce the words to their respective lemmas.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "CLSA:",
"sec_num": null
},
{
"text": "All experiments were performed using C-SVM Table 2 : Classification accuracy (in %) versus cluster size (number of clusters to be used).",
"cite_spans": [],
"ref_spans": [
{
"start": 43,
"end": 50,
"text": "Table 2",
"ref_id": null
}
],
"eq_spans": [],
"section": "CLSA:",
"sec_num": null
},
{
"text": "(linear kernel with parameter optimized over training set using 5 fold cross validation) available as a part of LibSVM package 6 . SVM was used since it is known to perform well for sentiment classification (Pang et al., 2002) . Results reported are based on the average of ten-fold crossvalidation accuracies. Standard text metrics are used for reporting the experimental results.",
"cite_spans": [
{
"start": 207,
"end": 226,
"text": "(Pang et al., 2002)",
"ref_id": "BIBREF34"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "CLSA:",
"sec_num": null
},
{
"text": "Monolingual classification results are shown in Table 7 1. Table shows accuracies of SA systems developed on feature set based on words, senses and clusters. It must be noted that accuracies reported for cluster based features are with respect to the best accuracy based on different cluster sizes. The improvements in results of cluster features based approach is found to be statistically significant over the word features based approach and sense features based approach at 95% confidence level when tested using a paired t-test (except for Hindi cluster features based approach). But in general, their accuracies do not significantly vary after cluster size crosses 1500. Table 2 shows the classification accuracy variation when cluster size is altered.",
"cite_spans": [],
"ref_spans": [
{
"start": 48,
"end": 55,
"text": "Table 7",
"ref_id": null
},
{
"start": 59,
"end": 70,
"text": "Table shows",
"ref_id": null
},
{
"start": 677,
"end": 684,
"text": "Table 2",
"ref_id": null
}
],
"eq_spans": [],
"section": "Results",
"sec_num": "6"
},
{
"text": "For, En-TD and En-PD experiments, the cluster size was varied between 200-3000 with an interval of 500 (after a size of 500). In the En-TD experiment, the best accuracy is achieved for cluster size 500, which is lesser than the number of unique-words/unique-senses (6435/6004) present in the data. Similarly, for the En-PD experiment, 6 http://www.csie.ntu.edu.tw/\u02dccjlin/ libsvm 7 All results reported here are based on 10-fold except for Marathi (2-fold-5-repeats), as it had comparatively lesser data samples.",
"cite_spans": [
{
"start": 335,
"end": 336,
"text": "6",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Results",
"sec_num": "6"
},
{
"text": "the optimal cluster size of 2500 is also lesser than the number of unique-words/unique-senses (30468/4735) present in the data.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Results",
"sec_num": "6"
},
{
"text": "To see the effect of training data size variation for different SA approaches in the En-TD experiment, the training data size is varied between 50 to 500. For this, a test set consisting of 100 positive and 100 negative documents is fixed. The training data size is varied by selecting different number of documents from rest of the dataset (\u223c500 negative and \u223c500 positive) as a training set. For each training data set 10 repeats are performed, e.g., for training data size of 50, 50 negative and 50 positive documents are randomly selected from the training data pool of \u223c500 negative and \u223c500 positive. This was repeated 10 times (with replacement). The results of this experiment are presented in Figure 1 . Cross-lingual SA accuracies are presented in Table 3 . As in monolingual case, the reported accuracies are for features based on the best cluster size. Table 3 : Cross-Lingual SA accuracy (%) on T =Hi and T =Mar with S=En for different approaches (MT=Machine Translation, PS=Projection based on Sense, DCL=Direct Cluster Linking , XC=Cross-Lingual Clustering. There is no MT system available for (S=En, T =Mar).",
"cite_spans": [],
"ref_spans": [
{
"start": 702,
"end": 710,
"text": "Figure 1",
"ref_id": "FIGREF2"
},
{
"start": 758,
"end": 765,
"text": "Table 3",
"ref_id": null
},
{
"start": 865,
"end": 872,
"text": "Table 3",
"ref_id": null
}
],
"eq_spans": [],
"section": "Results",
"sec_num": "6"
},
{
"text": "In this section, some important observations from the results are discussed. 1. Syntagmatic analysis may be used in lieu of paradigmatic analysis for SA: The results suggest that word cluster based features using syntagmatic analysis is comparatively better than cluster (sense) based features using paradigmatic analysis. For two datasets in English and for the one in Marathi this holds true. For English, the gap between classification accuracy based on sense features and cluster features is around 10%. A state-of-art accuracy is obtained for the public dataset on travel domain (En-TD).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Discussions",
"sec_num": "7"
},
{
"text": "The difference in accuracy reduces as the language gets morphologically rich.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Discussions",
"sec_num": "7"
},
{
"text": "In a morphologically rich language, morphology encompasses syntactical information, limiting the context it can provide for clustering. This can be seen from the classification results on Marathi. However for Hindi, classifier built on features based on syntagmatic analysis trails the one based on paradigmatic analysis.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Discussions",
"sec_num": "7"
},
{
"text": "Compared to Marathi, Hindi is a less morphologically rich language, hence, a better result was expected. However, a contrary result was obtained. In Hindi, the subject and the object of the sentence are linked using a case marker. Upon error analysis, it was found that there was a lot of irregular compounding based on case markers. Case markers were compounded with the succeeding word. This is a deviation from the real scenario which would have resulted in incorrect clustering leading to an unexpected result.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Discussions",
"sec_num": "7"
},
{
"text": "However, the same would not have occurred for a classifier developed on sense based features as it was manually sense tagged.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Discussions",
"sec_num": "7"
},
{
"text": "Clustering induces a reduction in the data sparsity. For example, on En-PD, percentage of features present in the test set and not present in the training set to those present in the test set are 34. 17%, 11.24%, 0.31% for words, synsets and cluster based features respectively. The improvement in the performance of classifiers may be attributed to this feature size reduction. However, it must be noted that clustering based on unlabelled corpora is less taxing than manually creating paradigmatic property based clusters like WordNet synsets.",
"cite_spans": [
{
"start": 200,
"end": 237,
"text": "17%, 11.24%, 0.31% for words, synsets",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Discussions",
"sec_num": "7"
},
{
"text": "Barring one instance, both cluster based features outperform word based features. The reason for the drop in the accuracy of approach based on sense features for En-PD dataset is the domain specific nature of sentiment analysis (Blitzer et al., 2007) , which is explained in the next point.",
"cite_spans": [
{
"start": 228,
"end": 250,
"text": "(Blitzer et al., 2007)",
"ref_id": "BIBREF5"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Discussions",
"sec_num": "7"
},
{
"text": "For En-PD, the classifier developed using sense features based on paradigmatic analysis performs inferior to word based features. Compared to other datasets used for analysis, this dataset was sense annotated using an automatic WSD engine. This engine was trained on a travel domain corpus and as WSD is also domain specific, the final classification performance suffered. Additionally, as the target domain was on products, the automatic WSD engine employed had an in-domain accuracy of 78%. The sense disambiguation accuracy of the same would have lowered in a cross-domain setting. This might have had a degrading effect on the SA accuracy.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Domain issues are resolved while using cluster based features:",
"sec_num": "2."
},
{
"text": "However, it was seen that classifier developed on cluster features based on syntagmatic analysis do not suffer from this.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Domain issues are resolved while using cluster based features:",
"sec_num": "2."
},
{
"text": "Such clusters obliterate domain relates issues. In addition, as more unlabelled data is included for clustering, the classification accuracy improves. 8 Thus, clustering may be employed to tackle other specific domain related issues in SA.",
"cite_spans": [
{
"start": 151,
"end": 152,
"text": "8",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Domain issues are resolved while using cluster based features:",
"sec_num": "2."
},
{
"text": "Cluster based features drastically reduces the dimension of the feature vector. For instance, the size of sense based features for En-TD dataset was 1/6 th of the size of word based features. This reduces the perplexity of the classification model. The reduction in the perplexity leads to the reduction of training documents to attain the same classification accuracy without any dimensionality reduction. This is evident from Figure 1 where accuracy of the cluster features based on unlabelled corpora are higher even with lesser training data.",
"cite_spans": [],
"ref_spans": [
{
"start": 428,
"end": 436,
"text": "Figure 1",
"ref_id": "FIGREF2"
}
],
"eq_spans": [],
"section": "Cluster based features using syntagmatic analysis requires lesser training data:",
"sec_num": "3."
},
{
"text": "The cluster size (number of clusters employed) has an implication on the purity of each cluster with respect to the application. The system performance improved upon increasing the cluster size and converged after attaining a certain level of accuracy. In general, it was found that the best classification accuracy was obtained for a cluster size between 1000 and 2500. As evident from Table 2, once the optimal accuracy is obtained, no significant changes were observed by increasing the cluster size.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Effect of cluster size:",
"sec_num": "4."
},
{
"text": "Clustering based CLSA is effective: For target language as Hindi, CLSA accuracy based on cross-lingual clustering (syntagmatic) outperforms the one based on MT (refer to Table 3 ).",
"cite_spans": [],
"ref_spans": [
{
"start": 170,
"end": 177,
"text": "Table 3",
"ref_id": null
}
],
"eq_spans": [],
"section": "5.",
"sec_num": null
},
{
"text": "This was true for the constraint clustering approach based on cross-lingual clustering. Whereas, sentiment classifier using sense (PS) or direct cluster linking (DCL) is not very effective. In case of PS approach, the coverage of the multidict was a problem. The number of a linkages between sense from English to Hindi is only around 1/3 rd the size of Princeton WordNet (Fellbaum, 1998) . Similarly in case of DCL approach, monolingual likelihood is different from the cross-lingual likelihood in terms of the linkages.",
"cite_spans": [
{
"start": 372,
"end": 388,
"text": "(Fellbaum, 1998)",
"ref_id": "BIBREF13"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "5.",
"sec_num": null
},
{
"text": "Note that there is no publicly available MT system for English to Marathi. Moreover, the digital content in Marathi language does not have a standard encoding format. This impedes the automatic crawling of the web for corpora creation for SA. Much manual effort has to be put to collect enough corpora for analysis. However, even in these languages, unlabelled corpora is easy to obtain. Marathi was chosen to depict a truly resource scarce SA scenario. Cluster features based classifier comparatively performed well with 60% classification accuracy. An MT based system would have suffered in this case as Marathi, as stated earlier, is a morphologically rich language and as compared to English, has a different word ordering. This could degrade the accuracy of the machine translation itself, limiting the performance of an MT based CLSA system. All this is obliterated by the use of a cluster based CLSA approach. Moreover, as more monolingual copora is added for clustering, the cross lingual cluster linkages could be refined. This can further boost the CLSA accuracy.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "A note on CLSA for truly resource scarce languages:",
"sec_num": "6."
},
{
"text": "This paper explored feasibility of using word cluster based features in lieu of features based on WordNet senses for sentiment analysis to alleviate the problem of data sparsity. Abstractly, the motivation was to see if highly effective features based on paradigmatic property based clustering could be replaced with the inexpensive ones based on syntagmatic property for SA.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion and Future Work",
"sec_num": "8"
},
{
"text": "The study was performed for both monolingual SA and cross-lingual SA. It was found that cluster features based on syntagmatic analysis are better than the WordNet sense features based on paradigmatic analysis for SA. Invesitgation revealed that a considerable decrease in the training data could be achieved while using such class based features. Moreover, as syntagma based word clusters are homogenous, it was able to address domain specific nature of SA as well.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion and Future Work",
"sec_num": "8"
},
{
"text": "For CLSA, clusters linked together using unlabelled parallel corpora do away with the need of translating labelled corpora from one language to another using an intermediary MT system or bilingual dictionary. Such a method outperforms an MT based CLSA approach. Further, this approach was found to be useful in cases where there are no MT systems to perform CLSA and the language of analysis is truly resource scarce. Thus, wider implication of this study is that many widely spoken yet resource scare languages like Pashto, Sundanese, Hausa, Gujarati and Punjabi which do not have an MT system could now be analysed for sentiment. The approach presented here for CLSA will still require a parallel corpora. However, the size of the parallel corpora required for CLSA can considerably be much lesser than the size of the parallel corpora required to train an MT system.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion and Future Work",
"sec_num": "8"
},
{
"text": "A naive cluster linkage algorithm based on word alignments was used to perform CLSA. As a result, there were many erroneous linkages which lowered the final SA accuracy. Better clusterlinking approaches could be explored to alleviate this problem. There are many applications which use WordNet like IR, IE etc. It would be interesting to see if these could be replaced by clusters based on the syntagmatic property.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion and Future Work",
"sec_num": "8"
},
{
"text": "Hindi and Marathi belong to the Indo-Aryan subgroup of the Indo-European language family and are two widely spoken Indian languages with a speaker population of 450 million and 72 million respectively.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "http://www.cs.jhu.edu/\u02dcmdredze/ datasets/sentiment/ 4 http://www.cfilt.iitb.ac. in/resources/senti/MPLC_tour_ downloaderInfo.php 5 http://www-i6.informatik.rwth-aachen. de/Colleagues/och/software/GIZA++.html",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "It was observed that adding 0.1 million unlabelled documents, SA accuracy improved by 1%. This was observed in the case of English for which there is abundant unlabelled corpus.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
}
],
"back_matter": [],
"bib_entries": {
"BIBREF0": {
"ref_id": "b0",
"title": "Harnessing wordnet senses for supervised sentiment classification",
"authors": [
{
"first": "A",
"middle": [
"R"
],
"last": "Balamurali",
"suffix": ""
},
{
"first": "Aditya",
"middle": [],
"last": "Joshi",
"suffix": ""
},
{
"first": "Pushpak",
"middle": [],
"last": "Bhattacharyya",
"suffix": ""
}
],
"year": 2011,
"venue": "Proceedings of EMNLP 2011",
"volume": "",
"issue": "",
"pages": "1081--1091",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "A. R. Balamurali, Aditya Joshi, and Pushpak Bhat- tacharyya. 2011. Harnessing wordnet senses for su- pervised sentiment classification. In Proceedings of EMNLP 2011, pages 1081-1091, Stroudsburg, PA, USA.",
"links": null
},
"BIBREF1": {
"ref_id": "b1",
"title": "Cross-lingual sentiment analysis for Indian languages using linked wordnets",
"authors": [
{
"first": "A",
"middle": [
"R"
],
"last": "Balamurali",
"suffix": ""
},
{
"first": "Aditya",
"middle": [],
"last": "Joshi",
"suffix": ""
},
{
"first": "Pushpak",
"middle": [],
"last": "Bhattacharyya",
"suffix": ""
}
],
"year": 2012,
"venue": "Proceedings of COLING 2012",
"volume": "",
"issue": "",
"pages": "73--82",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "A. R. Balamurali, Aditya Joshi, and Pushpak Bhat- tacharyya. 2012. Cross-lingual sentiment analysis for Indian languages using linked wordnets. In Pro- ceedings of COLING 2012, pages 73-82, Mumbai, India.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "Lost in translation: viability of machine translation for cross language sentiment analysis",
"authors": [
{
"first": "A",
"middle": [
"R"
],
"last": "Balamurali",
"suffix": ""
},
{
"first": "M",
"middle": [],
"last": "Mitesh",
"suffix": ""
},
{
"first": "Pushpak",
"middle": [],
"last": "Khapra",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Bhattacharyya",
"suffix": ""
}
],
"year": 2013,
"venue": "Proceedings of CICLing 2013",
"volume": "",
"issue": "",
"pages": "38--49",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "A. R. Balamurali, Mitesh M. Khapra, and Pushpak Bhattacharyya. 2013. Lost in translation: viability of machine translation for cross language sentiment analysis. In Proceedings of CICLing 2013, pages 38-49, Berlin, Heidelberg.",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "Multilingual subjectivity analysis using machine translation",
"authors": [
{
"first": "Carmen",
"middle": [],
"last": "Banea",
"suffix": ""
},
{
"first": "Rada",
"middle": [],
"last": "Mihalcea",
"suffix": ""
},
{
"first": "Janyce",
"middle": [],
"last": "Wiebe",
"suffix": ""
},
{
"first": "Samer",
"middle": [],
"last": "Hassan",
"suffix": ""
}
],
"year": 2008,
"venue": "Proceedings of EMNLP 2008",
"volume": "",
"issue": "",
"pages": "127--135",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Carmen Banea, Rada Mihalcea, Janyce Wiebe, and Samer Hassan. 2008. Multilingual subjectivity analysis using machine translation. In Proceedings of EMNLP 2008, pages 127-135, Honolulu, Hawaii.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "Sentiment analysis: Adjectives and adverbs are better than adjectives alone",
"authors": [
{
"first": "Farah",
"middle": [],
"last": "Benamara",
"suffix": ""
},
{
"first": "Sabatier",
"middle": [],
"last": "Irit",
"suffix": ""
},
{
"first": "Carmine",
"middle": [],
"last": "Cesarano",
"suffix": ""
},
{
"first": "Napoli",
"middle": [],
"last": "Federico",
"suffix": ""
},
{
"first": "Diego",
"middle": [],
"last": "Reforgiato",
"suffix": ""
}
],
"year": 2007,
"venue": "Proceedings of the International Conference on Weblogs and Social Media",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Farah Benamara, Sabatier Irit, Carmine Cesarano, Napoli Federico, and Diego Reforgiato. 2007. Sen- timent analysis: Adjectives and adverbs are better than adjectives alone. In Proceedings of the Inter- national Conference on Weblogs and Social Media.",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classification",
"authors": [
{
"first": "John",
"middle": [],
"last": "Blitzer",
"suffix": ""
},
{
"first": "Mark",
"middle": [],
"last": "Dredze",
"suffix": ""
},
{
"first": "Fernando",
"middle": [],
"last": "Pereira",
"suffix": ""
}
],
"year": 2007,
"venue": "Proceedings of ACL 2007",
"volume": "",
"issue": "",
"pages": "440--447",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "John Blitzer, Mark Dredze, and Fernando Pereira. 2007. Biographies, bollywood, boom-boxes and blenders: Domain adaptation for sentiment classi- fication. In Proceedings of ACL 2007, pages 440- 447, Prague, Czech Republic.",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "Cross-linguistic sentiment analysis: From english to spanish",
"authors": [
{
"first": "Julian",
"middle": [],
"last": "Brooke",
"suffix": ""
},
{
"first": "Milan",
"middle": [],
"last": "Tofiloski",
"suffix": ""
},
{
"first": "Maite",
"middle": [],
"last": "Taboada",
"suffix": ""
}
],
"year": 2009,
"venue": "Proceedings of the International Conference RANLP-2009",
"volume": "",
"issue": "",
"pages": "50--54",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Julian Brooke, Milan Tofiloski, and Maite Taboada. 2009. Cross-linguistic sentiment analysis: From en- glish to spanish. In Proceedings of the International Conference RANLP-2009, pages 50-54, Borovets, Bulgaria.",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "Class-based n-gram models of natural language",
"authors": [
{
"first": "F",
"middle": [],
"last": "Peter",
"suffix": ""
},
{
"first": "Peter",
"middle": [
"V"
],
"last": "Brown",
"suffix": ""
},
{
"first": "Robert",
"middle": [
"L"
],
"last": "Desouza",
"suffix": ""
},
{
"first": "Vincent",
"middle": [
"J"
],
"last": "Mercer",
"suffix": ""
},
{
"first": "Jenifer",
"middle": [
"C"
],
"last": "Della Pietra",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Lai",
"suffix": ""
}
],
"year": 1992,
"venue": "Computational Linguistics",
"volume": "",
"issue": "",
"pages": "467--479",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Peter F. Brown, Peter V. deSouza, Robert L. Mer- cer, Vincent J. Della Pietra, and Jenifer C. Lai. 1992. Class-based n-gram models of natural lan- guage. Computational Linguistics, pages 467-479, December.",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "Semiotics for beginners",
"authors": [
{
"first": "D",
"middle": [],
"last": "Chandler",
"suffix": ""
}
],
"year": 2012,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "D. Chandler. 2012. Semiotics for begin- ners. http://users.aber.ac.uk/dgc/ Documents/S4B/sem01.html. Online, ac- cessed 20-February-2013.",
"links": null
},
"BIBREF9": {
"ref_id": "b9",
"title": "Lexical Semantics",
"authors": [
{
"first": "D",
"middle": [
"A"
],
"last": "Cruse",
"suffix": ""
}
],
"year": 1986,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "D. A. Cruse. 1986. Lexical Semantics. Cambridge University Press.",
"links": null
},
"BIBREF10": {
"ref_id": "b10",
"title": "Mining the peanut gallery: opinion extraction and semantic classification of product reviews",
"authors": [
{
"first": "Kushal",
"middle": [],
"last": "Dave",
"suffix": ""
},
{
"first": "Steve",
"middle": [],
"last": "Lawrence",
"suffix": ""
},
{
"first": "David",
"middle": [
"M"
],
"last": "Pennock",
"suffix": ""
}
],
"year": 2003,
"venue": "Proceedings of WWW 2003",
"volume": "",
"issue": "",
"pages": "519--528",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Kushal Dave, Steve Lawrence, and David M. Pennock. 2003. Mining the peanut gallery: opinion extraction and semantic classification of product reviews. In Proceedings of WWW 2003, pages 519-528, New York, NY, USA.",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "Is machine translation ripe for cross-lingual sentiment classification?",
"authors": [
{
"first": "Kevin",
"middle": [],
"last": "Duh",
"suffix": ""
},
{
"first": "Akinori",
"middle": [],
"last": "Fujino",
"suffix": ""
},
{
"first": "Masaaki",
"middle": [],
"last": "Nagata",
"suffix": ""
}
],
"year": 2011,
"venue": "Proceedings of ACL-HLT 2011",
"volume": "",
"issue": "",
"pages": "429--433",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Kevin Duh, Akinori Fujino, and Masaaki Nagata. 2011. Is machine translation ripe for cross-lingual sentiment classification? In Proceedings of ACL- HLT 2011, pages 429-433, Stroudsburg, PA, USA.",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "Training and Evaluating a German Named Entity Recognizer with Semantic Generalization",
"authors": [
{
"first": "Manaal",
"middle": [],
"last": "Faruqui",
"suffix": ""
},
{
"first": "Sebastian",
"middle": [],
"last": "Pad\u00f3",
"suffix": ""
}
],
"year": 2010,
"venue": "Proceedings of KONVENS 2010",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Manaal Faruqui and Sebastian Pad\u00f3. 2010. Training and Evaluating a German Named Entity Recognizer with Semantic Generalization. In Proceedings of KONVENS 2010, Saarbr\u00fccken, Germany.",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "WordNet: An Electronic Lexical Database",
"authors": [
{
"first": "Christiane",
"middle": [],
"last": "Fellbaum",
"suffix": ""
}
],
"year": 1998,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Christiane Fellbaum. 1998. WordNet: An Electronic Lexical Database. Bradford Books.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "Further experiments in sentiment analysis of french movie reviews",
"authors": [
{
"first": "Hatem",
"middle": [],
"last": "Ghorbel",
"suffix": ""
},
{
"first": "David",
"middle": [],
"last": "Jacot",
"suffix": ""
}
],
"year": 2011,
"venue": "Proceedings of AWIC 2011",
"volume": "",
"issue": "",
"pages": "19--28",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Hatem Ghorbel and David Jacot. 2011. Further ex- periments in sentiment analysis of french movie re- views. In Proceedings of AWIC 2011, pages 19-28, Fribourg, Switzerland.",
"links": null
},
"BIBREF15": {
"ref_id": "b15",
"title": "An ensemble model that combines syntactic and semantic clustering for discriminative dependency parsing",
"authors": [
{
"first": "Gholamreza",
"middle": [],
"last": "Haffari",
"suffix": ""
},
{
"first": "Marzieh",
"middle": [],
"last": "Razavi",
"suffix": ""
},
{
"first": "Anoop",
"middle": [],
"last": "Sarkar",
"suffix": ""
}
],
"year": 2011,
"venue": "Proceedings of ACL-HLT 2011",
"volume": "",
"issue": "",
"pages": "710--714",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Gholamreza Haffari, Marzieh Razavi, and Anoop Sarkar. 2011. An ensemble model that combines syntactic and semantic clustering for discriminative dependency parsing. In Proceedings of ACL-HLT 2011, pages 710-714, Stroudsburg, PA, USA.",
"links": null
},
"BIBREF16": {
"ref_id": "b16",
"title": "Deeper sentiment analysis using machine translation technology",
"authors": [
{
"first": "Kanayama",
"middle": [],
"last": "Hiroshi",
"suffix": ""
},
{
"first": "Nasukawa",
"middle": [],
"last": "Tetsuya",
"suffix": ""
},
{
"first": "Watanabe",
"middle": [],
"last": "Hideo",
"suffix": ""
}
],
"year": 2004,
"venue": "Proceedings of COLING 2004",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Kanayama Hiroshi, Nasukawa Tetsuya, and Watanabe Hideo. 2004. Deeper sentiment analysis using machine translation technology. In Proceedings of COLING 2004, Stroudsburg, PA, USA.",
"links": null
},
"BIBREF17": {
"ref_id": "b17",
"title": "Learning to shift the polarity of words for sentiment classification",
"authors": [
{
"first": "Daisuke",
"middle": [],
"last": "Ikeda",
"suffix": ""
},
{
"first": "Hiroya",
"middle": [],
"last": "Takamura",
"suffix": ""
},
{
"first": "Manabu",
"middle": [],
"last": "Lev Arie Ratinov",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Okumura",
"suffix": ""
}
],
"year": 2008,
"venue": "Proceedings of the Third International Joint Conference on Natural Language Processing",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Daisuke Ikeda, Hiroya Takamura, Lev arie Ratinov, and Manabu Okumura. 2008. Learning to shift the po- larity of words for sentiment classification. In Pro- ceedings of the Third International Joint Conference on Natural Language Processing.",
"links": null
},
"BIBREF18": {
"ref_id": "b18",
"title": "Domain-specific word sense disambiguation combining corpus based and wordnet based parameters",
"authors": [
{
"first": "Mitesh",
"middle": [],
"last": "Khapra",
"suffix": ""
},
{
"first": "Sapan",
"middle": [],
"last": "Shah",
"suffix": ""
}
],
"year": 2010,
"venue": "Proceedings of Global Wordnet Conference",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Mitesh Khapra, Sapan Shah, Piyush Kedia, and Push- pak Bhattacharyya. 2010. Domain-specific word sense disambiguation combining corpus based and wordnet based parameters. In Proceedings of Global Wordnet Conference.",
"links": null
},
"BIBREF19": {
"ref_id": "b19",
"title": "Simple semi-supervised dependency parsing",
"authors": [
{
"first": "Terry",
"middle": [],
"last": "Koo",
"suffix": ""
},
{
"first": "Xavier",
"middle": [],
"last": "Carreras",
"suffix": ""
},
{
"first": "Michael",
"middle": [],
"last": "Collins",
"suffix": ""
}
],
"year": 2008,
"venue": "Proceedings of ACL-HLT 2008",
"volume": "",
"issue": "",
"pages": "595--603",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Terry Koo, Xavier Carreras, and Michael Collins. 2008. Simple semi-supervised dependency parsing. In Proceedings of ACL-HLT 2008, pages 595-603, Columbus, Ohio.",
"links": null
},
"BIBREF20": {
"ref_id": "b20",
"title": "Semi-supervised learning for natural language",
"authors": [
{
"first": "Percy",
"middle": [],
"last": "Liang",
"suffix": ""
}
],
"year": 2005,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Percy Liang. 2005. Semi-supervised learning for natu- ral language. M. eng. thesis, Massachusetts Institute of Technology.",
"links": null
},
"BIBREF21": {
"ref_id": "b21",
"title": "Joint sentiment/topic model for sentiment analysis",
"authors": [
{
"first": "Chenghua",
"middle": [],
"last": "Lin",
"suffix": ""
},
{
"first": "Yulan",
"middle": [],
"last": "He",
"suffix": ""
}
],
"year": 2009,
"venue": "Proceedings of CIKM 2009",
"volume": "",
"issue": "",
"pages": "375--384",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Chenghua Lin and Yulan He. 2009. Joint senti- ment/topic model for sentiment analysis. In Pro- ceedings of CIKM 2009, pages 375-384, New York, NY, USA.",
"links": null
},
"BIBREF22": {
"ref_id": "b22",
"title": "Joint bilingual sentiment classification with unlabeled parallel corpora",
"authors": [
{
"first": "Bin",
"middle": [],
"last": "Lu",
"suffix": ""
},
{
"first": "Chenhao",
"middle": [],
"last": "Tan",
"suffix": ""
},
{
"first": "Claire",
"middle": [],
"last": "Cardie",
"suffix": ""
},
{
"first": "Benjamin",
"middle": [
"K"
],
"last": "Tsou",
"suffix": ""
}
],
"year": 2011,
"venue": "Proceedings of ACL-HLT 2011",
"volume": "",
"issue": "",
"pages": "320--330",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Bin Lu, Chenhao Tan, Claire Cardie, and Benjamin K. Tsou. 2011. Joint bilingual sentiment classification with unlabeled parallel corpora. In Proceedings of ACL-HLT 2011, pages 320-330, Stroudsburg, PA, USA.",
"links": null
},
"BIBREF23": {
"ref_id": "b23",
"title": "Algorithms for bigram and trigram word clustering",
"authors": [
{
"first": "Sven",
"middle": [],
"last": "Martin",
"suffix": ""
},
{
"first": "Jrg",
"middle": [],
"last": "Liermann",
"suffix": ""
},
{
"first": "Hermann",
"middle": [],
"last": "Ney",
"suffix": ""
}
],
"year": 1995,
"venue": "Speech Communication",
"volume": "",
"issue": "",
"pages": "1253--1256",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Sven Martin, Jrg Liermann, and Hermann Ney. 1995. Algorithms for bigram and trigram word clustering. In Speech Communication, pages 1253-1256.",
"links": null
},
"BIBREF24": {
"ref_id": "b24",
"title": "Delta TFIDF: An improved feature space for sentiment analysis",
"authors": [
{
"first": "Justin",
"middle": [],
"last": "Martineau",
"suffix": ""
},
{
"first": "Tim",
"middle": [],
"last": "Finin",
"suffix": ""
}
],
"year": 2009,
"venue": "Proceedings of ICWSM",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Justin Martineau and Tim Finin. 2009. Delta TFIDF: An improved feature space for sentiment analysis. In Proceedings of ICWSM.",
"links": null
},
"BIBREF25": {
"ref_id": "b25",
"title": "Sentiment classification using word sub-sequences and dependency sub-trees",
"authors": [
{
"first": "Shotaro",
"middle": [],
"last": "Matsumoto",
"suffix": ""
},
{
"first": "Hiroya",
"middle": [],
"last": "Takamura",
"suffix": ""
},
{
"first": "Manabu",
"middle": [],
"last": "Okumura",
"suffix": ""
}
],
"year": 2005,
"venue": "Advances in Knowledge Discovery and Data Mining",
"volume": "",
"issue": "",
"pages": "301--311",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Shotaro Matsumoto, Hiroya Takamura, and Manabu Okumura. 2005. Sentiment classification using word sub-sequences and dependency sub-trees. In Advances in Knowledge Discovery and Data Min- ing, Lecture Notes in Computer Science, pages 301- 311.",
"links": null
},
"BIBREF26": {
"ref_id": "b26",
"title": "Topic sentiment mixture: modeling facets and opinions in weblogs",
"authors": [
{
"first": "Qiaozhu",
"middle": [],
"last": "Mei",
"suffix": ""
},
{
"first": "Xu",
"middle": [],
"last": "Ling",
"suffix": ""
},
{
"first": "Matthew",
"middle": [],
"last": "Wondra",
"suffix": ""
},
{
"first": "Hang",
"middle": [],
"last": "Su",
"suffix": ""
},
{
"first": "Chengxiang",
"middle": [],
"last": "Zhai",
"suffix": ""
}
],
"year": 2007,
"venue": "Proceedings of WWW 2007",
"volume": "",
"issue": "",
"pages": "171--180",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Qiaozhu Mei, Xu Ling, Matthew Wondra, Hang Su, and ChengXiang Zhai. 2007. Topic sentiment mix- ture: modeling facets and opinions in weblogs. In Proceedings of WWW 2007, pages 171-180, New York, NY, USA.",
"links": null
},
"BIBREF27": {
"ref_id": "b27",
"title": "Name tagging with word clusters and discriminative training",
"authors": [
{
"first": "Scott",
"middle": [],
"last": "Miller",
"suffix": ""
},
{
"first": "Jethran",
"middle": [],
"last": "Guinness",
"suffix": ""
},
{
"first": "Alex",
"middle": [],
"last": "Zamanian",
"suffix": ""
}
],
"year": 2004,
"venue": "Proceedings of HLT-NAACL 2004: Main Proceedings",
"volume": "",
"issue": "",
"pages": "337--342",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Scott Miller, Jethran Guinness, and Alex Zamanian. 2004. Name tagging with word clusters and dis- criminative training. In Proceedings of HLT-NAACL 2004: Main Proceedings, pages 337-342, Boston, Massachusetts, USA.",
"links": null
},
"BIBREF28": {
"ref_id": "b28",
"title": "Generating complex morphology for machine translation",
"authors": [
{
"first": "Einat",
"middle": [],
"last": "Minkov",
"suffix": ""
},
{
"first": "Kristina",
"middle": [],
"last": "Toutanova",
"suffix": ""
},
{
"first": "Hisami",
"middle": [],
"last": "Suzuki",
"suffix": ""
}
],
"year": 2007,
"venue": "Proceedings of ACL 2007",
"volume": "",
"issue": "",
"pages": "128--135",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Einat Minkov, Kristina Toutanova, and Hisami Suzuki. 2007. Generating complex morphology for machine translation. In Proceedings of ACL 2007, pages 128-135, Prague, Czech Republic.",
"links": null
},
"BIBREF29": {
"ref_id": "b29",
"title": "Synset based multilingual dictionary: Insights, applications and challenges",
"authors": [
{
"first": "Rajat",
"middle": [],
"last": "Mohanty",
"suffix": ""
},
{
"first": "Pushpak",
"middle": [],
"last": "Bhattacharyya",
"suffix": ""
},
{
"first": "Prabhakar",
"middle": [],
"last": "Pande",
"suffix": ""
},
{
"first": "Shraddha",
"middle": [],
"last": "Kalele",
"suffix": ""
},
{
"first": "Mitesh",
"middle": [],
"last": "Khapra",
"suffix": ""
},
{
"first": "Aditya",
"middle": [],
"last": "Sharma",
"suffix": ""
}
],
"year": 2008,
"venue": "Proceedings of Global Wordnet Conference",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Rajat Mohanty, Pushpak Bhattacharyya, Prabhakar Pande, Shraddha Kalele, Mitesh Khapra, and Aditya Sharma. 2008. Synset based multilingual dictio- nary: Insights, applications and challenges. In Pro- ceedings of Global Wordnet Conference.",
"links": null
},
"BIBREF30": {
"ref_id": "b30",
"title": "Sentiment analysis using support vector machines with diverse information sources",
"authors": [
{
"first": "Tony",
"middle": [],
"last": "Mullen",
"suffix": ""
},
{
"first": "Nigel",
"middle": [],
"last": "Collier",
"suffix": ""
}
],
"year": 2004,
"venue": "Proceedings of EMNLP 2004",
"volume": "",
"issue": "",
"pages": "412--418",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Tony Mullen and Nigel Collier. 2004. Sentiment anal- ysis using support vector machines with diverse in- formation sources. In Proceedings of EMNLP 2004, pages 412-418, Barcelona, Spain.",
"links": null
},
"BIBREF31": {
"ref_id": "b31",
"title": "Dependency tree-based sentiment classification using crfs with hidden variables",
"authors": [
{
"first": "Tetsuji",
"middle": [],
"last": "Nakagawa",
"suffix": ""
},
{
"first": "Kentaro",
"middle": [],
"last": "Inui",
"suffix": ""
},
{
"first": "Sadao",
"middle": [],
"last": "Kurohashi",
"suffix": ""
}
],
"year": 2010,
"venue": "Proceedings of HLT-NAACL 2010",
"volume": "",
"issue": "",
"pages": "786--794",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Tetsuji Nakagawa, Kentaro Inui, and Sadao Kurohashi. 2010. Dependency tree-based sentiment classifica- tion using crfs with hidden variables. In Proceed- ings of HLT-NAACL 2010, pages 786-794, Strouds- burg, PA, USA.",
"links": null
},
"BIBREF32": {
"ref_id": "b32",
"title": "Examining the role of linguistic knowledge sources in the automatic identification and classification of reviews",
"authors": [
{
"first": "Vincent",
"middle": [],
"last": "Ng",
"suffix": ""
},
{
"first": "Sajib",
"middle": [],
"last": "Dasgupta",
"suffix": ""
},
{
"first": "S",
"middle": [
"M"
],
"last": "",
"suffix": ""
}
],
"year": 2006,
"venue": "Proceedings of the COLING 2006",
"volume": "",
"issue": "",
"pages": "611--618",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Vincent Ng, Sajib Dasgupta, and S. M. Niaz Arifin. 2006. Examining the role of linguistic knowledge sources in the automatic identification and classifi- cation of reviews. In Proceedings of the COLING 2006, pages 611-618, Stroudsburg, PA, USA.",
"links": null
},
"BIBREF33": {
"ref_id": "b33",
"title": "A systematic comparison of various statistical alignment models",
"authors": [
{
"first": "Josef",
"middle": [],
"last": "Franz",
"suffix": ""
},
{
"first": "Hermann",
"middle": [],
"last": "Och",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Ney",
"suffix": ""
}
],
"year": 2003,
"venue": "Computational Linguistics",
"volume": "29",
"issue": "1",
"pages": "19--51",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Franz Josef Och and Hermann Ney. 2003. A sys- tematic comparison of various statistical alignment models. Computational Linguistics, 29(1):19-51, March.",
"links": null
},
"BIBREF34": {
"ref_id": "b34",
"title": "Thumbs up? sentiment classification using machine learning techniques",
"authors": [
{
"first": "Bo",
"middle": [],
"last": "Pang",
"suffix": ""
},
{
"first": "Lillian",
"middle": [],
"last": "Lee",
"suffix": ""
}
],
"year": 2002,
"venue": "Proceedings of EMNLP 2002",
"volume": "",
"issue": "",
"pages": "79--86",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Bo Pang and Lillian Lee. 2002. Thumbs up? sen- timent classification using machine learning tech- niques. In Proceedings of EMNLP 2002, pages 79- 86, Stroudsburg, PA, USA.",
"links": null
},
"BIBREF35": {
"ref_id": "b35",
"title": "Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval",
"authors": [
{
"first": "Bo",
"middle": [],
"last": "Pang",
"suffix": ""
},
{
"first": "Lillian",
"middle": [],
"last": "Lee",
"suffix": ""
}
],
"year": 2008,
"venue": "",
"volume": "2",
"issue": "",
"pages": "1--135",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Bo Pang and Lillian Lee. 2008. Opinion mining and sentiment analysis. Foundations and Trends in In- formation Retrieval, 2(1-2):1-135, January.",
"links": null
},
"BIBREF36": {
"ref_id": "b36",
"title": "Sentiment analysis of figurative language using a word sense disambiguation approach",
"authors": [
{
"first": "Vassiliki",
"middle": [],
"last": "Rentoumi",
"suffix": ""
},
{
"first": "George",
"middle": [],
"last": "Giannakopoulos",
"suffix": ""
},
{
"first": "Vangelis",
"middle": [],
"last": "Karkaletsis",
"suffix": ""
},
{
"first": "George",
"middle": [
"A"
],
"last": "Vouros",
"suffix": ""
}
],
"year": 2009,
"venue": "Proceedings of RANLP 2009",
"volume": "",
"issue": "",
"pages": "370--375",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Vassiliki Rentoumi, George Giannakopoulos, Vangelis Karkaletsis, and George A. Vouros. 2009. Senti- ment analysis of figurative language using a word sense disambiguation approach. In Proceedings of RANLP 2009, pages 370-375, Borovets, Bulgaria, September.",
"links": null
},
"BIBREF37": {
"ref_id": "b37",
"title": "An exploration into the use of contextual document clustering for cluster sentiment analysis",
"authors": [
{
"first": "Niall",
"middle": [],
"last": "Rooney",
"suffix": ""
},
{
"first": "Hui",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Fiona",
"middle": [],
"last": "Browne",
"suffix": ""
},
{
"first": "Fergal",
"middle": [],
"last": "Monaghan",
"suffix": ""
},
{
"first": "Jann",
"middle": [],
"last": "Mller",
"suffix": ""
},
{
"first": "Alan",
"middle": [],
"last": "Sergeant",
"suffix": ""
},
{
"first": "Zhiwei",
"middle": [],
"last": "Lin",
"suffix": ""
},
{
"first": "Philip",
"middle": [],
"last": "Taylor",
"suffix": ""
},
{
"first": "Vladimir",
"middle": [],
"last": "Dobrynin",
"suffix": ""
}
],
"year": 2011,
"venue": "Proceedings of RANLP 2011",
"volume": "",
"issue": "",
"pages": "140--145",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Niall Rooney, Hui Wang, Fiona Browne, Fergal Mon- aghan, Jann Mller, Alan Sergeant, Zhiwei Lin, Philip Taylor, and Vladimir Dobrynin. 2011. An ex- ploration into the use of contextual document clus- tering for cluster sentiment analysis. In Proceedings of RANLP 2011, pages 140-145, Hissar, Bulgaria.",
"links": null
},
"BIBREF38": {
"ref_id": "b38",
"title": "A class-based language model for large-vocabulary speech recognition extracted from part-of-speech statistics",
"authors": [
{
"first": "C",
"middle": [],
"last": "Samuelsson",
"suffix": ""
},
{
"first": "W",
"middle": [],
"last": "Reichl",
"suffix": ""
}
],
"year": 1999,
"venue": "Proceedings of ICASSP 1999",
"volume": "",
"issue": "",
"pages": "537--540",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "C. Samuelsson and W. Reichl. 1999. A class-based language model for large-vocabulary speech recog- nition extracted from part-of-speech statistics. In Proceedings of ICASSP 1999, pages 537-540.",
"links": null
},
"BIBREF39": {
"ref_id": "b39",
"title": "Clustered word classes for preordering in statistical machine translation",
"authors": [
{
"first": "Sara",
"middle": [],
"last": "Stymne",
"suffix": ""
}
],
"year": 2012,
"venue": "Proceedings of the Joint Workshop on Unsupervised and Semi-Supervised Learning in NLP",
"volume": "",
"issue": "",
"pages": "28--34",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Sara Stymne. 2012. Clustered word classes for pre- ordering in statistical machine translation. In Pro- ceedings of the Joint Workshop on Unsupervised and Semi-Supervised Learning in NLP, pages 28-34.",
"links": null
},
"BIBREF40": {
"ref_id": "b40",
"title": "Cross-lingual Word Clusters for Direct Transfer of Linguistic Structure",
"authors": [
{
"first": "Oscar",
"middle": [],
"last": "T\u00e4ckstr\u00f6m",
"suffix": ""
},
{
"first": "Ryan",
"middle": [],
"last": "Mcdonald",
"suffix": ""
},
{
"first": "Jakob",
"middle": [],
"last": "Uszkoreit",
"suffix": ""
}
],
"year": 2012,
"venue": "Proceedings of NAACL-HLT 2012",
"volume": "",
"issue": "",
"pages": "477--487",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Oscar T\u00e4ckstr\u00f6m, Ryan McDonald, and Jakob Uszkor- eit. 2012. Cross-lingual Word Clusters for Direct Transfer of Linguistic Structure. In Proceedings of NAACL-HLT 2012, pages 477-487, Montr\u00e9al, Canada.",
"links": null
},
"BIBREF41": {
"ref_id": "b41",
"title": "Word sense disambiguation in opinion mining: Pros and cons",
"authors": [
{
"first": "Martin",
"middle": [],
"last": "Tamara",
"suffix": ""
},
{
"first": "Alexandra",
"middle": [],
"last": "Balahur",
"suffix": ""
}
],
"year": 2010,
"venue": "Journal Research in Computing Science",
"volume": "46",
"issue": "",
"pages": "119--130",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Martin Tamara, Balahur Alexandra, and Montoyo An- dres. 2010. Word sense disambiguation in opinion mining: Pros and cons. Journal Research in Com- puting Science, 46:119-130.",
"links": null
},
"BIBREF42": {
"ref_id": "b42",
"title": "A fast, accurate, non-projective, semantically-enriched parser",
"authors": [
{
"first": "Stephen",
"middle": [],
"last": "Tratz",
"suffix": ""
},
{
"first": "Eduard",
"middle": [],
"last": "Hovy",
"suffix": ""
}
],
"year": 2011,
"venue": "Proceedings of EMNLP 2011",
"volume": "",
"issue": "",
"pages": "1257--1268",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Stephen Tratz and Eduard Hovy. 2011. A fast, ac- curate, non-projective, semantically-enriched parser. In Proceedings of EMNLP 2011, pages 1257-1268, Stroudsburg, PA, USA.",
"links": null
},
"BIBREF43": {
"ref_id": "b43",
"title": "Word representations: a simple and general method for semi-supervised learning",
"authors": [
{
"first": "Joseph",
"middle": [],
"last": "Turian",
"suffix": ""
},
{
"first": "Lev",
"middle": [],
"last": "Ratinov",
"suffix": ""
},
{
"first": "Yoshua",
"middle": [],
"last": "Bengio",
"suffix": ""
}
],
"year": 2010,
"venue": "Proceedings of ACL 2010",
"volume": "",
"issue": "",
"pages": "384--394",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Joseph Turian, Lev Ratinov, and Yoshua Bengio. 2010. Word representations: a simple and general method for semi-supervised learning. In Proceedings of ACL 2010, pages 384-394, Stroudsburg, PA, USA.",
"links": null
},
"BIBREF44": {
"ref_id": "b44",
"title": "Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews",
"authors": [
{
"first": "D",
"middle": [],
"last": "Peter",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Turney",
"suffix": ""
}
],
"year": 2002,
"venue": "Proceedings of ACL 2002",
"volume": "",
"issue": "",
"pages": "417--424",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Peter D. Turney. 2002. Thumbs up or thumbs down?: semantic orientation applied to unsupervised classi- fication of reviews. In Proceedings of ACL 2002, pages 417-424, Stroudsburg, PA, USA.",
"links": null
},
"BIBREF45": {
"ref_id": "b45",
"title": "Distributed word clustering for large scale class-based language modeling in machine translation",
"authors": [
{
"first": "Jakob",
"middle": [],
"last": "Uszkoreit",
"suffix": ""
},
{
"first": "Thorsten",
"middle": [],
"last": "Brants",
"suffix": ""
}
],
"year": 2008,
"venue": "Proceedings of ACL-HLT 2008",
"volume": "",
"issue": "",
"pages": "755--762",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jakob Uszkoreit and Thorsten Brants. 2008. Dis- tributed word clustering for large scale class-based language modeling in machine translation. In Pro- ceedings of ACL-HLT 2008, pages 755-762, Colum- bus, Ohio.",
"links": null
},
"BIBREF46": {
"ref_id": "b46",
"title": "Co-training for cross-lingual sentiment classification",
"authors": [
{
"first": "Xiaojun",
"middle": [],
"last": "Wan",
"suffix": ""
}
],
"year": 2009,
"venue": "Proceedings of ACL 2009",
"volume": "",
"issue": "",
"pages": "235--243",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Xiaojun Wan. 2009. Co-training for cross-lingual sen- timent classification. In Proceedings of ACL 2009, pages 235-243, Stroudsburg, PA, USA.",
"links": null
},
"BIBREF47": {
"ref_id": "b47",
"title": "Scalable collaborative filtering using cluster-based smoothing",
"authors": [
{
"first": "Gui-Rong",
"middle": [],
"last": "Xue",
"suffix": ""
},
{
"first": "Chenxi",
"middle": [],
"last": "Lin",
"suffix": ""
},
{
"first": "Qiang",
"middle": [],
"last": "Yang",
"suffix": ""
},
{
"first": "Wensi",
"middle": [],
"last": "Xi",
"suffix": ""
},
{
"first": "Hua-Jun",
"middle": [],
"last": "Zeng",
"suffix": ""
},
{
"first": "Yong",
"middle": [],
"last": "Yu",
"suffix": ""
},
{
"first": "Zheng",
"middle": [],
"last": "Chen",
"suffix": ""
}
],
"year": 2005,
"venue": "Proceedings of SIGIR 2005",
"volume": "",
"issue": "",
"pages": "114--121",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Gui-Rong Xue, Chenxi Lin, Qiang Yang, WenSi Xi, Hua-Jun Zeng, Yong Yu, and Zheng Chen. 2005. Scalable collaborative filtering using cluster-based smoothing. In Proceedings of SIGIR 2005, pages 114-121, New York, NY, USA.",
"links": null
},
"BIBREF48": {
"ref_id": "b48",
"title": "Sentiment classification of online reviews to travel destinations by supervised machine learning approaches",
"authors": [
{
"first": "Qiang",
"middle": [],
"last": "Ye",
"suffix": ""
},
{
"first": "Ziqiong",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Rob",
"middle": [],
"last": "Law",
"suffix": ""
}
],
"year": 2009,
"venue": "Expert Systems with Applications",
"volume": "36",
"issue": "3",
"pages": "6527--6535",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Qiang Ye, Ziqiong Zhang, and Rob Law. 2009. Sentiment classification of online reviews to travel destinations by supervised machine learning ap- proaches. Expert Systems with Applications, 36(3, Part 2):6527-6535.",
"links": null
},
"BIBREF49": {
"ref_id": "b49",
"title": "Clustering product features for opinion mining",
"authors": [
{
"first": "Zhongwu",
"middle": [],
"last": "Zhai",
"suffix": ""
},
{
"first": "Bing",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "Hua",
"middle": [],
"last": "Xu",
"suffix": ""
},
{
"first": "Peifa",
"middle": [],
"last": "Jia",
"suffix": ""
}
],
"year": 2011,
"venue": "Proceedings of WSDM 2011",
"volume": "",
"issue": "",
"pages": "347--354",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Zhongwu Zhai, Bing Liu, Hua Xu, and Peifa Jia. 2011. Clustering product features for opinion mining. In Proceedings of WSDM 2011, pages 347-354, New York, NY, USA.",
"links": null
},
"BIBREF50": {
"ref_id": "b50",
"title": "Transitionbased dependency parsing with rich non-local features",
"authors": [
{
"first": "Yue",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Joakim",
"middle": [],
"last": "Nivre",
"suffix": ""
}
],
"year": 2011,
"venue": "Proceedings of ACL-HLT 2011",
"volume": "",
"issue": "",
"pages": "188--193",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yue Zhang and Joakim Nivre. 2011. Transition- based dependency parsing with rich non-local fea- tures. In Proceedings of ACL-HLT 2011, pages 188- 193, Stroudsburg, PA, USA.",
"links": null
}
},
"ref_entries": {
"FIGREF0": {
"text": "Note this is different from the likelihood estimation of Brown et al. (1992) (Equation",
"uris": null,
"num": null,
"type_str": "figure"
},
"FIGREF2": {
"text": "Training data variation on En-TD dataset.",
"uris": null,
"num": null,
"type_str": "figure"
},
"TABREF0": {
"html": null,
"num": null,
"type_str": "table",
"content": "<table><tr><td/><td colspan=\"2\">Features</td><td/><td colspan=\"2\">En-TD En-PD</td><td>Hi</td><td>Mar</td><td/></tr><tr><td/><td colspan=\"2\">Words</td><td/><td>87.02</td><td>77.60</td><td>77.36</td><td>92.28</td><td/></tr><tr><td/><td colspan=\"4\">WordNet Sense (Paradigmatic) 89.13</td><td>74.50</td><td>85.80</td><td>96.88</td><td/></tr><tr><td/><td colspan=\"3\">Clusters (Syntagmatic)</td><td>97.45</td><td colspan=\"2\">87.80 83.50</td><td>98.66</td><td/></tr><tr><td colspan=\"9\">Features Words Clust-200 Clust-500 Clust-1000 Clust-1500 Clust-2000 Clust-2500 Clust-3000</td></tr><tr><td>En-TD</td><td>87.02</td><td>97.37</td><td>97.45</td><td>96.94</td><td>96.94</td><td>96.52</td><td>96.52</td><td>96.52</td></tr><tr><td>En-PD</td><td>77.60</td><td>73.20</td><td>82.30</td><td>84.30</td><td>86.35</td><td>86.45</td><td>87.80</td><td>87.40</td></tr></table>",
"text": "Classification accuracy for monolingual sentiment analysis. For English, results are reported on two publicly available datasets based on Travel Domain (TD) and Product Domain (PD)."
}
}
}
} |