File size: 140,507 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 | {
"paper_id": "P19-1014",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T08:21:59.365807Z"
},
"title": "A Joint Named-Entity Recognizer for Heterogeneous Tag-sets Using a Tag Hierarchy",
"authors": [
{
"first": "Genady",
"middle": [],
"last": "Beryozkin",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Google Research Tel Aviv",
"location": {
"country": "Israel"
}
},
"email": "genady@google.com"
},
{
"first": "Yoel",
"middle": [],
"last": "Drori",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Google Research Tel Aviv",
"location": {
"country": "Israel"
}
},
"email": ""
},
{
"first": "Oren",
"middle": [],
"last": "Gilon",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Google Research Tel Aviv",
"location": {
"country": "Israel"
}
},
"email": "ogilon@google.com"
},
{
"first": "Tzvika",
"middle": [],
"last": "Hartman",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Google Research Tel Aviv",
"location": {
"country": "Israel"
}
},
"email": "tzvika@google.com"
},
{
"first": "Idan",
"middle": [],
"last": "Szpektor",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Google Research Tel Aviv",
"location": {
"country": "Israel"
}
},
"email": "szpektor@google.com"
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "We study a variant of domain adaptation for named-entity recognition where multiple, heterogeneously tagged training sets are available. Furthermore, the test tag-set is not identical to any individual training tag-set. Yet, the relations between all tags are provided in a tag hierarchy, covering the test tags as a combination of training tags. This setting occurs when various datasets are created using different annotation schemes. This is also the case of extending a tag-set with a new tag by annotating only the new tag in a new dataset. We propose to use the given tag hierarchy to jointly learn a neural network that shares its tagging layer among all tag-sets. We compare this model to combining independent models and to a model based on the multitasking approach. Our experiments show the benefit of the tag-hierarchy model, especially when facing non-trivial consolidation of tag-sets.",
"pdf_parse": {
"paper_id": "P19-1014",
"_pdf_hash": "",
"abstract": [
{
"text": "We study a variant of domain adaptation for named-entity recognition where multiple, heterogeneously tagged training sets are available. Furthermore, the test tag-set is not identical to any individual training tag-set. Yet, the relations between all tags are provided in a tag hierarchy, covering the test tags as a combination of training tags. This setting occurs when various datasets are created using different annotation schemes. This is also the case of extending a tag-set with a new tag by annotating only the new tag in a new dataset. We propose to use the given tag hierarchy to jointly learn a neural network that shares its tagging layer among all tag-sets. We compare this model to combining independent models and to a model based on the multitasking approach. Our experiments show the benefit of the tag-hierarchy model, especially when facing non-trivial consolidation of tag-sets.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "Named Entity Recognition (NER) has seen significant progress in the last couple of years with the application of Neural Networks to the task. Such models achieve state-of-the-art performance with little or no manual feature engineering (Collobert et al., 2011; Huang et al., 2015; Lample et al., 2016; Ma and Hovy, 2016; Dernoncourt et al., 2017) . Following this success, more complex NER setups are approached with neural models, among them domain adaptation (Qu et al., 2016; He and Sun, 2017; Dong et al., 2017) .",
"cite_spans": [
{
"start": 236,
"end": 260,
"text": "(Collobert et al., 2011;",
"ref_id": "BIBREF7"
},
{
"start": 261,
"end": 280,
"text": "Huang et al., 2015;",
"ref_id": "BIBREF16"
},
{
"start": 281,
"end": 301,
"text": "Lample et al., 2016;",
"ref_id": "BIBREF19"
},
{
"start": 302,
"end": 320,
"text": "Ma and Hovy, 2016;",
"ref_id": "BIBREF25"
},
{
"start": 321,
"end": 346,
"text": "Dernoncourt et al., 2017)",
"ref_id": "BIBREF8"
},
{
"start": 461,
"end": 478,
"text": "(Qu et al., 2016;",
"ref_id": "BIBREF29"
},
{
"start": 479,
"end": 496,
"text": "He and Sun, 2017;",
"ref_id": "BIBREF14"
},
{
"start": 497,
"end": 515,
"text": "Dong et al., 2017)",
"ref_id": "BIBREF9"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "In this work we study one type of domain adaptation for NER, denoted here heterogeneous tagsets. In this variant, samples from the test set are not available at training time. Furthermore, the test tag-set differs from each training tag-set. However every test tag can be represented either as a single training tag or as a combination of several training tags. This information is given in the form of a hypernym hierarchy over all tags, training and test (see Fig. 1 ).",
"cite_spans": [],
"ref_spans": [
{
"start": 462,
"end": 468,
"text": "Fig. 1",
"ref_id": null
}
],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "This setting arises when different schemes are used for annotating multiple datasets for the same task. This often occurs in the medical domain, where healthcare providers use customized tagsets to create their own private test sets (Shickel et al., 2017; Lee et al., 2018) . Another scenario is selective annotation, as in the case of extending an existing tag-set, e.g. {'Name', 'Location'}, with another tag, e.g. 'Date'. To save annotation effort, new training data is labeled only with the new tag. This case of disjoint tag-sets is also discussed in the work of Greenberg et al. (2018) . A similar case is extending a training-set with new examples in which only rare tags are annotated. In domains where training data is scarce, out-ofdomain datasets annotated with infrequent tags may be very valuable.",
"cite_spans": [
{
"start": 233,
"end": 255,
"text": "(Shickel et al., 2017;",
"ref_id": "BIBREF32"
},
{
"start": 256,
"end": 273,
"text": "Lee et al., 2018)",
"ref_id": "BIBREF20"
},
{
"start": 568,
"end": 591,
"text": "Greenberg et al. (2018)",
"ref_id": "BIBREF12"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "A naive approach concatenates all trainingsets, ignoring the differences between the tagging schemes in each example. A different approach would be to learn to tag with multiple training tagsets. Then, in a post-processing step, the predictions from the different tag-sets need to be consolidated into a single test tag sequence, resolving tagging differences along the way. We study two such models. The first model learns an independent NER model for each training tag-set. The second model applies the multitasking (MTL) (Collobert et al., 2011; Ruder, 2017) paradigm, in which a shared latent representation of the input text is fed into separate tagging layers.",
"cite_spans": [
{
"start": 524,
"end": 548,
"text": "(Collobert et al., 2011;",
"ref_id": "BIBREF7"
},
{
"start": 549,
"end": 561,
"text": "Ruder, 2017)",
"ref_id": "BIBREF30"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "The above models require heuristic postprocessing to consolidate the different predicted tag sequences. To overcome this limitation, we propose a model that incorporates the given tag hierarchy within the neural NER model. Specifically, this model learns to predict a tag sequence only over the fine-grained tags in the hierarchy.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Tag-set 1 (T 1 ): Name, Street, City, Hospital, Age>90 Tag-set 2 (T 2 ): First Name, Last Name, Address, Age Tag-set 3 (T 3 ) Figure 1 : A tag hierarchy for three tag-sets.",
"cite_spans": [
{
"start": 24,
"end": 31,
"text": "Street,",
"ref_id": null
},
{
"start": 32,
"end": 37,
"text": "City,",
"ref_id": null
},
{
"start": 38,
"end": 47,
"text": "Hospital,",
"ref_id": null
},
{
"start": 48,
"end": 54,
"text": "Age>90",
"ref_id": null
}
],
"ref_spans": [
{
"start": 109,
"end": 125,
"text": "Tag-set 3 (T 3 )",
"ref_id": "TABREF5"
},
{
"start": 126,
"end": 134,
"text": "Figure 1",
"ref_id": null
}
],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "At training time, gradients on each dataset-specific labeled examples are propagated as gradients on plausible fine-grained tags. At inference time the model predicts a single sequence of fine-grained tags, which are then mapped to the test tag-set by traversing the tag hierarchy. Importantly, all tagging decisions are performed in the model without the need for a post-processing consolidation step.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "We conducted two experiments. The first evaluated the extension of a tag-set with a new tag via selective annotation of a new dataset with only the extending tag, using datasets from the medical and news domains. In the second experiment we integrated two full tag-sets from the medical domain with their training data while evaluating on a third test tag-set. The results show that the model which incorporates the tag-hierarchy is more robust compared to a combination of independent models or MTL, and typically outperforms them. This is especially evident when many tagging collisions need to be settled at post-processing. In these cases, the performance gap in favor of the tag-hierarchy model is large.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "The goal in the heterogeneous tag-sets domain adaptation task is to learn an NER model M that given an input token sequence",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Task Definition",
"sec_num": "2.1"
},
{
"text": "x = {x i } n 1 infers a tag sequence y = {y i } n 1 = M (x) over a test tag-set T s , \u2200 i y i \u2208T s . To learn the model, K train- ing datasets {DS r k } K k=1",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Task Definition",
"sec_num": "2.1"
},
{
"text": "are provided, each labeled with its own tag-set T r k . Superscripts 's' and 'r' stand for 'test' and 'training', respectively. In this task, no training tag-set is identical to the test tagset T s by itself. However, all tags in T s can be covered by combining the training tag-sets {T r k } K k=1 . This information is provided in the form of a directed acyclic graph (DAG) representing hyper- nymy relations between all training and test tags. Fig. 1 illustrates such a hierarchy.",
"cite_spans": [],
"ref_spans": [
{
"start": 447,
"end": 453,
"text": "Fig. 1",
"ref_id": null
}
],
"eq_spans": [],
"section": "Task Definition",
"sec_num": "2.1"
},
{
"text": "As mentioned above, an example scenario is selective annotation, in which an original tag-set is extended with a new tag t, each with its own training data, and the test tag-set is their union. But, some setups require combinations other than a simple union, e.g. covering the test tag 'Address' with the finer training tags 'Street' and 'City', each from a different tag-set.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Task Definition",
"sec_num": "2.1"
},
{
"text": "This task is different from inductive domain adaptation (Pan and Yang, 2010; Ruder, 2017) , in which the tag-sets are different but the tasks differ as well (e.g. NER and parsing), with no need to map the outcomes to a single tag-set at test time.",
"cite_spans": [
{
"start": 56,
"end": 76,
"text": "(Pan and Yang, 2010;",
"ref_id": "BIBREF26"
},
{
"start": 77,
"end": 89,
"text": "Ruder, 2017)",
"ref_id": "BIBREF30"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Task Definition",
"sec_num": "2.1"
},
{
"text": "As the underlying architecture shared by all models in this paper, we follow the neural network proposed by Lample et al. (2016) , which achieved state-of-the-art results on NER. In this model, depicted in Fig. 2 , each input token x i is represented as a combination of: (a) a one-hot vector x w i , mapping the input to a fixed word vocabulary, and (b) a sequence of one-hot vectors {x c i,j } n i j=1 , representing the input word's character sequence.",
"cite_spans": [
{
"start": 108,
"end": 128,
"text": "Lample et al. (2016)",
"ref_id": "BIBREF19"
}
],
"ref_spans": [
{
"start": 206,
"end": 212,
"text": "Fig. 2",
"ref_id": "FIGREF0"
}
],
"eq_spans": [],
"section": "Neural network for NER",
"sec_num": "2.2"
},
{
"text": "Each input token x i is first embedded in latent space by applying both a word-embedding matrix, we i = E x w i , and a character-based embedding layer ce i = CharBiRNN({x c i,j }) (Ling et al., 2015) . This output of this step is e i = ce i \u2295 we i , where \u2295 stands for vector concatenation. Then, the embedding vector sequence {e i } n is re-encoded in context using a bidirectional RNN layer {r i } n 1 = BiRNN({e i } n 1 ) (Schuster and Paliwal, 1997) . The sequence {r i } n 1 constitutes the latent representation of the input text.",
"cite_spans": [
{
"start": 181,
"end": 200,
"text": "(Ling et al., 2015)",
"ref_id": "BIBREF22"
},
{
"start": 426,
"end": 454,
"text": "(Schuster and Paliwal, 1997)",
"ref_id": "BIBREF31"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Neural network for NER",
"sec_num": "2.2"
},
{
"text": "Finally, each re-encoded vector r i is projected to tag space for the target tag-set T ,",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Neural network for NER",
"sec_num": "2.2"
},
{
"text": "t i = P r i , where |t i | = |T |. The sequence {t i } n",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Neural network for NER",
"sec_num": "2.2"
},
{
"text": "1 is then taken as input to a CRF layer (Lafferty et al., 2001 ), which maintains a global tag transition matrix. At inference time, the model output is y = M (x), the most probable CRF tag sequence for input x.",
"cite_spans": [
{
"start": 40,
"end": 62,
"text": "(Lafferty et al., 2001",
"ref_id": "BIBREF18"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Neural network for NER",
"sec_num": "2.2"
},
{
"text": "One way to learn a model for the heterogeneous tag-sets setting is to train a base NER (Sec. 2.2) on the concatenation of all training-sets, predicting tags from the union of all training tag-sets. In our experiments, this model under performed, due to the fact that it treats each training example as fully tagged despite being tagged only with the tags belonging to the training-set from which the example is taken (see Sec. 6).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Models for Multiple Tagging Layers",
"sec_num": "3"
},
{
"text": "We next present two models that instead learn to tag each training tag-set separately. In the first model the outputs from independent base models, each trained on a different tag-set, are merged. The second model utilizes the the multitasking approach to train separate tagging layers that share a single text representation layer.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Models for Multiple Tagging Layers",
"sec_num": "3"
},
{
"text": "In this model, we train a separate NER model for each training set, resulting in K models {M k } K k=1 . At test time, each model predicts a sequence y k = M k (x) over the corresponding tag-set T r k . The sequences {y k } K k=1 are consolidated into a single sequence y s over the test tag-set T s .",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Combining independent models",
"sec_num": "3.1"
},
{
"text": "We perform this consolidation in a postprocessing step. First, each predicted tag y k,i is mapped to the test tag-set as y s k,i . We employ the provided tag hierarchy for this mapping by traversing it starting from y k,i until a test tag is reached. Then, for every token x i , we consider the test tags predicted at position i by the different models M (x i ) = {y s k,i |y s k,i = 'Other'}. Cases where M (x i ) contains more than one tag are called collisions. Models must consolidate collisions, selecting a single predicted tag for x i .",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Combining independent models",
"sec_num": "3.1"
},
{
"text": "We introduce three different consolidation methods. The first is to randomly select a tag from M (x i ). The second chooses the tag that originates from the tag sequence y k with the highest CRF probability score. The third computes the marginal CRF tag probability for each tag and selects the one with the highest probability.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Combining independent models",
"sec_num": "3.1"
},
{
"text": "Lately, several works explored using multitasking (MTL) for inductive transfer learning within a neural architecture (Collobert and Weston, 2008; Chen et al., 2016; Peng and Dredze, 2017) . Such algorithms jointly train a single model to solve different NLP tasks, such as NER, sentiment analysis and text classification. The various tasks share the same text representation layer in the model but maintain a separate tagging layer per task.",
"cite_spans": [
{
"start": 117,
"end": 145,
"text": "(Collobert and Weston, 2008;",
"ref_id": "BIBREF6"
},
{
"start": 146,
"end": 164,
"text": "Chen et al., 2016;",
"ref_id": "BIBREF4"
},
{
"start": 165,
"end": 187,
"text": "Peng and Dredze, 2017)",
"ref_id": "BIBREF27"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Multitasking for heterogeneous tag-sets",
"sec_num": "3.2"
},
{
"text": "We adapt multitasking to heterogeneous tagsets by considering each training dataset, which has a different tag-set T r k , as a separate NER task. Thus, a single model is trained, in which the latent text representation {r i } n 1 (see Sec. 2.2) is shared between NER tasks. As mentioned above, the tagging layers (projection and CRF) are kept separate for each tag-set. Fig. 3 illustrates this architecture.",
"cite_spans": [],
"ref_spans": [
{
"start": 371,
"end": 377,
"text": "Fig. 3",
"ref_id": "FIGREF1"
}
],
"eq_spans": [],
"section": "Multitasking for heterogeneous tag-sets",
"sec_num": "3.2"
},
{
"text": "We emphasize that the output of the MTL model still consists of {y k } K k=1 different tag sequence predictions. They are consolidated into a final single sequence y s using the same post-processing step described in Sec. 3.1.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Multitasking for heterogeneous tag-sets",
"sec_num": "3.2"
},
{
"text": "The models introduced in Sec. 3.1 and 3.2 learn to predict a tag sequence for each training tagset separately and they do not share parameters between tagging layers. In addition, they require Figure 4 : The tag hierarchy in Fig. 1 for three tag-sets after closure extension. Green nodes and edges were automatically added in this process. Fine-grained tags are surrounded by a dotted box.",
"cite_spans": [],
"ref_spans": [
{
"start": 193,
"end": 201,
"text": "Figure 4",
"ref_id": null
},
{
"start": 225,
"end": 231,
"text": "Fig. 1",
"ref_id": null
}
],
"eq_spans": [],
"section": "Tag Hierarchy Model",
"sec_num": "4"
},
{
"text": "Tag-set 1 (T 1 ): Name, Street, City, Hospital, Age>90, T1-Other Tag-set 2 (T 2 ): First Name, Last Name, Address, Age, T 2 -Other Tag-set 3 (T 3 ): Name, Location,",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Tag Hierarchy Model",
"sec_num": "4"
},
{
"text": "a post-processing step, outside of the model, for merging the tag sequences inferred for the different tag-sets. A simple concatenation of all training data is also not enough to accommodate the differences between the tag-sets within the model (see Sec. 3). Moreover, none of these models utilizes the relations between tags, which are provided as input in the form of a tag hierarchy.",
"cite_spans": [],
"ref_spans": [
{
"start": 250,
"end": 254,
"text": "Sec.",
"ref_id": null
}
],
"eq_spans": [],
"section": "Tag Hierarchy Model",
"sec_num": "4"
},
{
"text": "In this section, we propose a model that addresses these limitations. This model utilizes the given tag hierarchy at training time to learn a single, shared tagging layer that predicts only finegrained tags. The hierarchy is then used during inference to map fine-grained tags onto a target tag-set. Consequently, all tagging decisions are made in the model, without the need for a postprocessing step.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Tag Hierarchy Model",
"sec_num": "4"
},
{
"text": "In the input hierarchy DAG, each node represents some semantic role of words in sentences, (e.g. 'Name'). A directed edge c \u2192 d implies that c is a hyponym of d, meaning c captures a subset of the semantics of d. Examples include 'LastName' \u2192 'Name', and 'Street' \u2192 'Location' in Fig. 1 . We denote the set of all tags that capture some subset of semantics of d by If a node d has no hyponyms (Sem(d) = {d}), it represents some fine-grained tag semantics. We denote the set of all fine-grained tags by T F G . We also denote all fine-grained tags that are hyponyms of d by",
"cite_spans": [],
"ref_spans": [
{
"start": 280,
"end": 286,
"text": "Fig. 1",
"ref_id": null
}
],
"eq_spans": [],
"section": "Notations",
"sec_num": "4.1"
},
{
"text": "Sem(d) = {d} \u222a {c|c R \u2212 \u2192 d}, where R \u2212 \u2192 in- dicates that",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Notations",
"sec_num": "4.1"
},
{
"text": "Fine(d) = T F G \u2229 Sem(d), e.g.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Notations",
"sec_num": "4.1"
},
{
"text": "Fine(N ame) = {LastN ame, F irstN ame}. As mentioned above, our hierarchical model predicts tag sequences only from T F G and then maps them onto a target tag-set.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Notations",
"sec_num": "4.1"
},
{
"text": "For each tag d we would like the semantics captured by the union of semantics of all tags in Fine(d) to be exactly the semantics of d, making sure we will not miss any aspect of d when predicting only over T F G . Yet, this semantics-equality property does not hold in general. One such example in Fig. 4 is 'Age> 90'\u2192'Age', because there may be age mentions below 90 annotated in T 2 's dataset.",
"cite_spans": [],
"ref_spans": [
{
"start": 298,
"end": 304,
"text": "Fig. 4",
"ref_id": null
}
],
"eq_spans": [],
"section": "Hierarchy extension with 'Other' tags",
"sec_num": "4.2"
},
{
"text": "To fix the semantics-equality above, we use the notion of the 'Other' tag in NER, which has the semantics of \"all the rest\". Specifically, for every d / \u2208 T F G , a fine-grained tag 'd-Other' \u2208 T F G and an edge 'd-Other'\u2192'd' are automatically added to the graph, hence 'd-Other'\u2208 Fine(d). For instance, 'Age-Other'\u2192'Age'. These new tags represent the aspects of d not captured by the other tags in Fine(d).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Hierarchy extension with 'Other' tags",
"sec_num": "4.2"
},
{
"text": "Next a tag 'T i -Other' is automatically added to each tag-set T i , explicitly representing the \"all the rest\" semantics of T i . The labels for 'T i -Other' are induced automatically from unlabeled tokens in the original DS r i dataset. To make sure that the semantics-equality property above also holds for 'T i -Other', a fine-grained tag 'FG-Other' is also added, which captures the \"all the rest\" semantics at the fine-grained level. Then, each 'T i -Other' is connected to all fine-grained tags that do not capture some semantics of the tags in T i , defining:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Hierarchy extension with 'Other' tags",
"sec_num": "4.2"
},
{
"text": "Fine(T i -Other) = T F G \\ d\u2208T i {T i -Other} Sem(d)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Hierarchy extension with 'Other' tags",
"sec_num": "4.2"
},
{
"text": "This mapping is important at training time, where 'T i -Other' labels are used as distant supervision over their related fine-grained tags (Sec. 4.3). Fig. 4 depicts our hierarchy example after this step. We emphasize that all extensions in this step are done automatically as part of the model's algorithm.",
"cite_spans": [],
"ref_spans": [
{
"start": 151,
"end": 158,
"text": "Fig. 4",
"ref_id": null
}
],
"eq_spans": [],
"section": "Hierarchy extension with 'Other' tags",
"sec_num": "4.2"
},
{
"text": "One outcome of the extension step is that the set of fine-grained tags T F G covers all distinct finegrained semantics across all tag-sets. In the following, we train a single NER model (Sec. 2.2) that predicts sequences of tags from the T F G tagset. As there is only one tagging layer, model parameters are shared across all training examples.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "NER model with tag hierarchy",
"sec_num": "4.3"
},
{
"text": "At inference time, this model predicts the most likely fine-grained tag sequence y f g for the input",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "NER model with tag hierarchy",
"sec_num": "4.3"
},
{
"text": "x. As the model outputs only a single sequence, post-processing consolidation is not needed. The tag hierarchy is used to map each predicated finegrained tag y f g i to a tag in a test tag-set T s by traversing the out-edges of y f g i until a tag in T s is reached. This procedure is also used in the baseline models (see Sec. 3.1) for mapping their predictions onto the test tag-set. However, unlike the baselines, which end with multiple candidate predictions in the test tag-set and need to consolidate between them, here, only a single fine-grained tag sequence is mapped, so no further consolidation is needed.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "NER model with tag hierarchy",
"sec_num": "4.3"
},
{
"text": "At training time, each example x that belongs to some training dataset DS r i is labeled with a gold-standard tag sequence y where the tags are taken only from the corresponding tag-set T r i . This means that tags {y i } are not necessarily finegrained tags, so there is no direct supervision for predicting fine-grained tag sequences. However, each gold label y i provides distant supervision over its related fine-grained tags, Fine(y i ). It indicates that one of them is the correct fine-grained label without explicitly stating which one, so we consider all possibilities in a probabilistic manner.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "NER model with tag hierarchy",
"sec_num": "4.3"
},
{
"text": "Henceforth, we say that a fine-grained tag se- We denote all fine-grained tag sequences that agree with y by AgreeWith(y).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "NER model with tag hierarchy",
"sec_num": "4.3"
},
{
"text": "quence y f g agrees with y if \u2200 i y f g i \u2208 Fine(y i ), i.e. y f",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "NER model with tag hierarchy",
"sec_num": "4.3"
},
{
"text": "Using this definition, the tag-hierarchy model is trained with the loss function:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "NER model with tag hierarchy",
"sec_num": "4.3"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "loss(y) = \u2212log( Z y Z ) (1) Z y = y f g \u2208AgreeWith(y) \u03c6(y f g )",
"eq_num": "(2)"
}
],
"section": "NER model with tag hierarchy",
"sec_num": "4.3"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "Z = y f g \u03c6(y f g )",
"eq_num": "(3)"
}
],
"section": "NER model with tag hierarchy",
"sec_num": "4.3"
},
{
"text": "where \u03c6(y) stands for the model's score for sequence y, viewed as unnormalized probability. Z is the standard CRF partition function over all possible fine-grained tag sequences. Z y , on the other hand, accumulates scores only of fine-grained tag sequences that agree with y. Thus, this loss function aims at increasing the summed probability of all fine-grained sequences agreeing with y. Both Z y and Z can be computed efficiently using the Forward-Backward algorithm (Lafferty et al., 2001) . We note that we also considered finding the most likely tag sequence over a test tag-set at inference time by summing the probabilities of all finegrained tag sequences that agree with each candidate sequence y: max y y f g \u2208AgreeWith(y) \u03c6(y f g ). However, this problem is NP-hard (Lyngs\u00f8 and Pedersen, 2002) . We plan to explore other alternatives in future work.",
"cite_spans": [
{
"start": 471,
"end": 494,
"text": "(Lafferty et al., 2001)",
"ref_id": "BIBREF18"
},
{
"start": 779,
"end": 806,
"text": "(Lyngs\u00f8 and Pedersen, 2002)",
"ref_id": "BIBREF24"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "NER model with tag hierarchy",
"sec_num": "4.3"
},
{
"text": "To test the tag-hierarchy model under heterogeneous tag-set scenarios, we conducted experiments using datasets from two domains. We next describe these datasets as well as implementation details for the tested models. Sec. 6 then details the experiments and their results.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Experimental Settings",
"sec_num": "5"
},
{
"text": "Five datasets from two domains, medical and news, were used in our experiments. Table 1 summarizes their main statistics.",
"cite_spans": [],
"ref_spans": [
{
"start": 80,
"end": 87,
"text": "Table 1",
"ref_id": null
}
],
"eq_spans": [],
"section": "Datasets",
"sec_num": "5.1"
},
{
"text": "For the medical domain we used the datasets I2B2-2006 (denoted I2B2'06) (Uzuner et al., 2007) , I2B2-2014 (denoted I2B2'14) (Stubbs and Uzuner, 2015) and the PhysioNet golden set (denoted Physio) (Goldberger et al., 2000) . These datasets are all annotated for the NER task of deidentification (a.k.a text anonymization) (Dernoncourt et al., 2017). Still, as seen in Table 1 , each dataset is annotated with a different tag-set. Both I2B2'06 and I2B2'14 include train and test sets, while Physio contains only a test set.",
"cite_spans": [
{
"start": 72,
"end": 93,
"text": "(Uzuner et al., 2007)",
"ref_id": "BIBREF38"
},
{
"start": 124,
"end": 149,
"text": "(Stubbs and Uzuner, 2015)",
"ref_id": "BIBREF36"
},
{
"start": 196,
"end": 221,
"text": "(Goldberger et al., 2000)",
"ref_id": "BIBREF10"
}
],
"ref_spans": [
{
"start": 367,
"end": 374,
"text": "Table 1",
"ref_id": null
}
],
"eq_spans": [],
"section": "Datasets",
"sec_num": "5.1"
},
{
"text": "For the news domain we used the English part of CONLL-2003 (denoted Conll) (Tjong Kim Sang and De Meulder, 2003) and OntoNotes-v5 (denoted Onto) (Weischedel et al., 2013) , both with train and test sets. We note that I2B2'14, Conll and Onto also contain a dev-set, which is used for hyper-param tuning (see below).",
"cite_spans": [
{
"start": 82,
"end": 112,
"text": "Kim Sang and De Meulder, 2003)",
"ref_id": "BIBREF37"
},
{
"start": 145,
"end": 170,
"text": "(Weischedel et al., 2013)",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Datasets",
"sec_num": "5.1"
},
{
"text": "In all experiments, each example is a full document. Each document is split into tokens on whitespaces and punctuation. A tag-hierarchy covering the 57 tags from all five datasets was given as input to all models in all experiments. We constructed this hierarchy manually. The only non-trivial tag was 'Location', which in I2B2'14 is split into finer tags ('City', 'Street' etc.) and includes also hospital mentions in Conll and Onto. We resolved these relations similarly to the graph in Figure 1 .",
"cite_spans": [],
"ref_spans": [
{
"start": 489,
"end": 497,
"text": "Figure 1",
"ref_id": null
}
],
"eq_spans": [],
"section": "Datasets",
"sec_num": "5.1"
},
{
"text": "Four models were compared in our experiments:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Compared Models",
"sec_num": "5.2"
},
{
"text": "M Concat A single NER model on the concatenation of datasets and tag-sets (Sec. 3).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Compared Models",
"sec_num": "5.2"
},
{
"text": "M Indep Combining predictions of independent NER models, one per tag-set (Sec. 3.1).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Compared Models",
"sec_num": "5.2"
},
{
"text": "M MTL Multitasking over training tag-sets (Sec. 3.2).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Compared Models",
"sec_num": "5.2"
},
{
"text": "M Hier A tag hierarchy employed within a single base model (Sec. 4).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Compared Models",
"sec_num": "5.2"
},
{
"text": "All models are based on the neural network described in Sec. 2.2. We tuned the hyper-params in the base model to achieve state-of-the-art results for a single NER model on Conll and I2B2'14 when trained and tested on the same dataset (Strubell et al., 2017; Dernoncourt et al., 2017) (see Table 2 ). This is done to maintain a constant baseline, and is also due to the fact that I2B2'06 does not have a standard dev-set.",
"cite_spans": [
{
"start": 234,
"end": 257,
"text": "(Strubell et al., 2017;",
"ref_id": "BIBREF35"
},
{
"start": 258,
"end": 283,
"text": "Dernoncourt et al., 2017)",
"ref_id": "BIBREF8"
}
],
"ref_spans": [
{
"start": 289,
"end": 296,
"text": "Table 2",
"ref_id": "TABREF4"
}
],
"eq_spans": [],
"section": "Compared Models",
"sec_num": "5.2"
},
{
"text": "We tuned hyper-params over the dev-sets of Conll and I2B2'14. For character-based embedding we used a single bidirectional LSTM (Hochreiter and Schmidhuber, 1997) with hidden state size of 25. For word embeddings we used pre-trained GloVe embeddings 1 (Pennington et al., 2014) , without further training. For token recoding we used a two-level stacked bidirectional LSTM (Graves et al., 2013) with both output and hidden state of size 100.",
"cite_spans": [
{
"start": 128,
"end": 162,
"text": "(Hochreiter and Schmidhuber, 1997)",
"ref_id": "BIBREF15"
},
{
"start": 252,
"end": 277,
"text": "(Pennington et al., 2014)",
"ref_id": "BIBREF28"
},
{
"start": 372,
"end": 393,
"text": "(Graves et al., 2013)",
"ref_id": "BIBREF11"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Compared Models",
"sec_num": "5.2"
},
{
"text": "Once these hyper-params were set, no further tuning was made in our experiments, which means all models for heterogeneous tag-sets were tested under the above fixed hyper-param set. In each experiment, each model was trained until convergence on the respective training set. ",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Compared Models",
"sec_num": "5.2"
},
{
"text": "We performed two experiments. The first refers to selective annotation, in which an existing tag-set is extended with a new tag by annotating a new dataset only with the new tag. The second experiment tests the ability of each model to integrate two full tag-sets.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Experiments and Results",
"sec_num": "6"
},
{
"text": "In all experiments we assess model performance via micro-averaged tag F1, in accordance with CoNLL evaluation (Tjong Kim Sang and De Meulder, 2003) . Statistical significance was computed using the Wilcoxon two-sided signed ranks test at p = 0.01 (Wilcoxon, 1945) . We next detail each experiment and its results.",
"cite_spans": [
{
"start": 117,
"end": 147,
"text": "Kim Sang and De Meulder, 2003)",
"ref_id": "BIBREF37"
},
{
"start": 247,
"end": 263,
"text": "(Wilcoxon, 1945)",
"ref_id": "BIBREF40"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Experiments and Results",
"sec_num": "6"
},
{
"text": "In all our experiments, we found the performance of the different consolidation methods (Sec. 3.1) to be on par. One reason that using model scores does not beat random selection may be due to the overconfidence of the tagging models -their prediction probabilities are close to 0 or 1. We report figures for random selection as representative of all consolidation methods.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Experiments and Results",
"sec_num": "6"
},
{
"text": "In this experiment, we considered the 4 most frequent tags that occur in at least two of our datasets: 'Name', 'Date', 'Location' and 'Hospital' (Table 3 summarizes their statistics). For each frequent tag t and an ordered pair of datasets in which t occurs, we constructed new training sets by removing t from the first training set (termed base dataset) and remove all tags but t from the second training set (termed extending dataset). For example, for the triplet of { 'Name', I2B2'14, I2B2'06}, we constructed a version of I2B2'14 without 'Name' annotations and a version of I2B2'06 containing only annotations for 'Name'. This process yielded 32 such triplets. For every triplet, we train all tested models on the two modified training sets and test them on the test-set of the base dataset (I2B2'14 in the example above). Each test-set was not altered and contains all tags of the base tag-set, including t.",
"cite_spans": [],
"ref_spans": [
{
"start": 145,
"end": 153,
"text": "(Table 3",
"ref_id": "TABREF5"
}
],
"eq_spans": [],
"section": "Tag-set extension experiment",
"sec_num": "6.1"
},
{
"text": "M Concat performed poorly in this experiment. For example, on the dataset extending I2B2'14 with 'Name' from I2B2'06, M Concat tagged only one 'Name' out of over 4000 'Name' mentions in the test set. Given this, we do not provide further details of the results of M Concat in this experiment.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Tag-set extension experiment",
"sec_num": "6.1"
},
{
"text": "For the three models tested, this experiment yields 96 results. The main results 2 of this experiment are shown in Table 4 . Surprisingly, in more tests M Indep outperformed M MTL than vice versa, adding to prior observations that multitasking can hurt performance instead of improving it (Bingel and S\u00f8gaard, 2017; Alonso and Plank, 2017; Bjerva, 2017) . But, applying a shared tagging layer on top of a shared text representation boosts the model's capability and stability. Indeed, overall, M Hier outperforms the other models in most tests, and in the rest it is similar to the best performing model.",
"cite_spans": [
{
"start": 289,
"end": 315,
"text": "(Bingel and S\u00f8gaard, 2017;",
"ref_id": "BIBREF2"
},
{
"start": 316,
"end": 339,
"text": "Alonso and Plank, 2017;",
"ref_id": "BIBREF0"
},
{
"start": 340,
"end": 353,
"text": "Bjerva, 2017)",
"ref_id": "BIBREF3"
}
],
"ref_spans": [
{
"start": 115,
"end": 122,
"text": "Table 4",
"ref_id": "TABREF7"
}
],
"eq_spans": [],
"section": "Tag-set extension experiment",
"sec_num": "6.1"
},
{
"text": "Analyzing the results, we noticed that the gap between model performance increases when more collisions are encountered for M MTL and M Indep at post-processing time (see Sec. 3.1). The amount of collisions may be viewed as a predictor for the baselines' difficulty to handle a specific heterogeneous tag-sets setting. Table 5 presents the tests in which more than 100 collisions were detected for either M Indep or M MTL , constituting 66% of all 2 Detailed results for all 96 tests are given in the Appendix. test triplets. In these tests, M Hier is a clear winner, outperforming the compared models in all but two comparisons, often by a significant margin. Finally, we compared the models trained with selective annotation to an \"upper-bound\" of training and testing a single NER model on the same dataset with all tags annotated (Table 2) . As expected, performance is usually lower with selective annotation. But, the drop intensifies when the base and extending datasets are from different domains -medical and news. In these cases, we observed that M Hier is more robust. Its drop compared to combining datasets from the same domain is the least in almost all such combinations. Table 6 provides some illustrative examples.",
"cite_spans": [
{
"start": 448,
"end": 449,
"text": "2",
"ref_id": null
}
],
"ref_spans": [
{
"start": 319,
"end": 326,
"text": "Table 5",
"ref_id": "TABREF9"
},
{
"start": 834,
"end": 843,
"text": "(Table 2)",
"ref_id": "TABREF4"
}
],
"eq_spans": [],
"section": "Tag-set extension experiment",
"sec_num": "6.1"
},
{
"text": "A scenario distinct from selective annotation is the integration of full tag-sets. On one hand, more training data is available for similar tags. On the other hand, more tags need to be consolidated among the tag-sets. To test this scenario, we trained the tested model types on the training sets of I2B2'06 and I2B2'14, which have different tag-sets. The models were evaluated both on the test sets of these datasets and on Physio, an unseen test-set that requires the combination of the two training tag-sets for full coverage of its tag-set. We also compared the models to single models trained on each of the training sets alone. Table 7 displays the results.",
"cite_spans": [],
"ref_spans": [
{
"start": 634,
"end": 641,
"text": "Table 7",
"ref_id": "TABREF12"
}
],
"eq_spans": [],
"section": "Full tag-set integration experiment",
"sec_num": "6.2"
},
{
"text": "As expected, single models do well on the testset companion of their training-set but they underperform on the other test-sets. This is expected because the tag-set on which they were trained does not cover well the tag-sets in the other test-sets.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Full tag-set integration experiment",
"sec_num": "6.2"
},
{
"text": "When compared with the best-performing single model, using M Concat shows reduced results on all 3 test sets. This can be attributed to reduced performance for types that are semantically different between datasets (e.g. 'Date'), while performance on similar tags (e.g. 'Name') does not drop.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Full tag-set integration experiment",
"sec_num": "6.2"
},
{
"text": "Combining the two training sets using either M Indep or M MTL leads to substantial performance drop in 5 out of 6 test-sets compared to the bestperforming single model. This is strongly correlated with the number of collisions encountered (see Table 7 ). Indeed, the only competitive result, M MTL tested on Physio, had less than 100 collisions. This demonstrates the non triviality in realworld tag-set integration, and the difficulty of resolving tagging decisions across tag-sets.",
"cite_spans": [],
"ref_spans": [
{
"start": 244,
"end": 251,
"text": "Table 7",
"ref_id": "TABREF12"
}
],
"eq_spans": [],
"section": "Full tag-set integration experiment",
"sec_num": "6.2"
},
{
"text": "By contrast, M Hier has no performance drop compared to the single models trained and tested on the same dataset. Moreover, it is the best performing model on the unseen Physio test-set, with 6% relative improvement in F1 over the best single model. This experiment points up the robustness of the tag hierarchy approach when applied to this heterogeneous tag-set scenario. Collobert et al. (2011) introduced the first competitive NN-based NER that required little or no feature engineering. Huang et al. (2015) combined LSTM with CRF, showing performance similar to non-NN models. Lample et al. (2016) extended this model with character-based embeddings in addition to word embedding, achieving state-of-theart results. Similar architectures, such as combinations of convolutional networks as replacements of RNNs were shown to out-perform previous NER models (Ma and Hovy, 2016; Chiu and Nichols, 2016; Strubell et al., 2017) . Dernoncourt et al. (2017) and Liu et al. (2017) showed that the LSTM-CRF model achieves stateof-the-art results also for de-identification in the medical domain. Lee et al. (2018) demonstrated how performance drops significantly when the LSTM-CRF model is tested under transfer learning within the same domain in this task. Collobert and Weston (2008) introduced MTL for NN, and other works followed, showing it helps in various NLP tasks (Chen et al., 2016; Peng and Dredze, 2017) . S\u00f8gaard and Goldberg (2016) and Hashimoto et al. (2017) argue that cascading architectures can improve MTL performance. Several works have explored conditions for successful application of MTL (Bingel and S\u00f8gaard, 2017; Bjerva, 2017; Alonso and Plank, 2017) .",
"cite_spans": [
{
"start": 374,
"end": 397,
"text": "Collobert et al. (2011)",
"ref_id": "BIBREF7"
},
{
"start": 492,
"end": 511,
"text": "Huang et al. (2015)",
"ref_id": "BIBREF16"
},
{
"start": 582,
"end": 602,
"text": "Lample et al. (2016)",
"ref_id": "BIBREF19"
},
{
"start": 861,
"end": 880,
"text": "(Ma and Hovy, 2016;",
"ref_id": "BIBREF25"
},
{
"start": 881,
"end": 904,
"text": "Chiu and Nichols, 2016;",
"ref_id": "BIBREF5"
},
{
"start": 905,
"end": 927,
"text": "Strubell et al., 2017)",
"ref_id": "BIBREF35"
},
{
"start": 930,
"end": 955,
"text": "Dernoncourt et al. (2017)",
"ref_id": "BIBREF8"
},
{
"start": 960,
"end": 977,
"text": "Liu et al. (2017)",
"ref_id": "BIBREF23"
},
{
"start": 1092,
"end": 1109,
"text": "Lee et al. (2018)",
"ref_id": "BIBREF20"
},
{
"start": 1254,
"end": 1281,
"text": "Collobert and Weston (2008)",
"ref_id": "BIBREF6"
},
{
"start": 1369,
"end": 1388,
"text": "(Chen et al., 2016;",
"ref_id": "BIBREF4"
},
{
"start": 1389,
"end": 1411,
"text": "Peng and Dredze, 2017)",
"ref_id": "BIBREF27"
},
{
"start": 1414,
"end": 1441,
"text": "S\u00f8gaard and Goldberg (2016)",
"ref_id": "BIBREF34"
},
{
"start": 1446,
"end": 1469,
"text": "Hashimoto et al. (2017)",
"ref_id": "BIBREF13"
},
{
"start": 1607,
"end": 1633,
"text": "(Bingel and S\u00f8gaard, 2017;",
"ref_id": "BIBREF2"
},
{
"start": 1634,
"end": 1647,
"text": "Bjerva, 2017;",
"ref_id": "BIBREF3"
},
{
"start": 1648,
"end": 1671,
"text": "Alonso and Plank, 2017)",
"ref_id": "BIBREF0"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Full tag-set integration experiment",
"sec_num": "6.2"
},
{
"text": "Few works attempt to share information across datasets at the tagging level. Greenberg et al. (2018) proposed a single CRF model for tagging with heterogeneous tag-sets but without a hierarchy. They show the utility of this method for indomain datasets with a balanced tag distribution. Our model can be viewed as an extension of theirs for tag hierarchies. Augenstein et al. (2018) use tag embeddings in MTL to further propagate information between tasks. Li et al. (2017) propose to use a tag-set made of cross-product of two different POS tag-sets and train a model for it. Given the explosion in tag-set size, they introduce automatic pruning of cross-product tags. Kim et al. (2015) and Qu et al. (2016) automatically learn correlations between tag-sets, given training data for both tag-sets. They rely on similar contexts for related source and target tags, such as 'professor' and 'student'.",
"cite_spans": [
{
"start": 77,
"end": 100,
"text": "Greenberg et al. (2018)",
"ref_id": "BIBREF12"
},
{
"start": 358,
"end": 382,
"text": "Augenstein et al. (2018)",
"ref_id": "BIBREF1"
},
{
"start": 457,
"end": 473,
"text": "Li et al. (2017)",
"ref_id": "BIBREF21"
},
{
"start": 670,
"end": 687,
"text": "Kim et al. (2015)",
"ref_id": "BIBREF17"
},
{
"start": 692,
"end": 708,
"text": "Qu et al. (2016)",
"ref_id": "BIBREF29"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "7"
},
{
"text": "Our tag-hierarchy model was inspired by recent work on hierarchical multi-label classification (Silla and Freitas, 2011; Zhang and Zhou, 2014) , and can be viewed as an extension of this direction onto sequences tagging.",
"cite_spans": [
{
"start": 95,
"end": 120,
"text": "(Silla and Freitas, 2011;",
"ref_id": "BIBREF33"
},
{
"start": 121,
"end": 142,
"text": "Zhang and Zhou, 2014)",
"ref_id": "BIBREF41"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "7"
},
{
"text": "We proposed a tag-hierarchy model for the heterogeneous tag-sets NER setting, which does not require a consolidation post-processing stage. In the conducted experiments, the proposed model consistently outperformed the baselines in difficult tagging cases and showed robustness when applying a single trained model to varied test sets.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusions",
"sec_num": "8"
},
{
"text": "In the case of integrating datasets from the news and medical domains we found the blending task to be difficult. In future work, we'd like to improve this integration in order to gain from training on examples from different domains for tags like 'Name' and 'Location'. ",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusions",
"sec_num": "8"
}
],
"back_matter": [
{
"text": "The authors would like to thank Yossi Matias, Katherine Chou, Greg Corrado, Avinatan Hassidim, Rony Amira, Itay Laish and Amit Markel for their help in creating this work.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Acknowledgments",
"sec_num": null
}
],
"bib_entries": {
"BIBREF0": {
"ref_id": "b0",
"title": "When is multitask learning effective? semantic sequence prediction under varying data conditions",
"authors": [
{
"first": "Hector",
"middle": [],
"last": "Martinez",
"suffix": ""
},
{
"first": "Alonso",
"middle": [],
"last": "",
"suffix": ""
},
{
"first": "Barbara",
"middle": [],
"last": "Plank",
"suffix": ""
}
],
"year": 2017,
"venue": "EACL 2017-15th Conference of the European Chapter of the Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "1--10",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Hector Martinez Alonso and Barbara Plank. 2017. When is multitask learning effective? semantic se- quence prediction under varying data conditions. In EACL 2017-15th Conference of the European Chap- ter of the Association for Computational Linguistics, pages 1-10.",
"links": null
},
"BIBREF1": {
"ref_id": "b1",
"title": "Multi-task learning of pairwise sequence classification tasks over disparate label spaces",
"authors": [
{
"first": "Isabelle",
"middle": [],
"last": "Augenstein",
"suffix": ""
},
{
"first": "Sebastian",
"middle": [],
"last": "Ruder",
"suffix": ""
},
{
"first": "Anders",
"middle": [],
"last": "S\u00f8gaard",
"suffix": ""
}
],
"year": 2018,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1802.09913v2"
]
},
"num": null,
"urls": [],
"raw_text": "Isabelle Augenstein, Sebastian Ruder, and Anders S\u00f8gaard. 2018. Multi-task learning of pairwise sequence classification tasks over disparate label spaces. arXiv:1802.09913v2.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "Identifying beneficial task relations for multi-task learning in deep neural networks. In ACL",
"authors": [
{
"first": "Joachim",
"middle": [],
"last": "Bingel",
"suffix": ""
},
{
"first": "Anders",
"middle": [],
"last": "S\u00f8gaard",
"suffix": ""
}
],
"year": 2017,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Joachim Bingel and Anders S\u00f8gaard. 2017. Identify- ing beneficial task relations for multi-task learning in deep neural networks. In ACL.",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "Will my auxiliary tagging task help? estimating auxiliary tasks effectivity in multi-task learning",
"authors": [
{
"first": "Johannes",
"middle": [],
"last": "Bjerva",
"suffix": ""
}
],
"year": 2017,
"venue": "Proceedings of the 21st Nordic Conference on Computational Linguistics",
"volume": "131",
"issue": "",
"pages": "216--220",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Johannes Bjerva. 2017. Will my auxiliary tagging task help? estimating auxiliary tasks effectivity in multi-task learning. In Proceedings of the 21st Nordic Conference on Computational Linguistics, NoDaLiDa, 22-24 May 2017, Gothenburg, Sweden, 131, pages 216-220. Link\u00f6ping University Elec- tronic Press.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "Neural network for heterogeneous annotations",
"authors": [
{
"first": "Hongshen",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Yue",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Qun",
"middle": [],
"last": "Liu",
"suffix": ""
}
],
"year": 2016,
"venue": "EMNLP",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Hongshen Chen, Yue Zhang, and Qun Liu. 2016. Neural network for heterogeneous annotations. In EMNLP.",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "Named entity recognition with bidirectional lstm-cnns",
"authors": [
{
"first": "Jason",
"middle": [],
"last": "Chiu",
"suffix": ""
},
{
"first": "Eric",
"middle": [],
"last": "Nichols",
"suffix": ""
}
],
"year": 2016,
"venue": "TACL",
"volume": "4",
"issue": "1",
"pages": "357--370",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jason Chiu and Eric Nichols. 2016. Named entity recognition with bidirectional lstm-cnns. TACL, 4(1):357-370.",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "A unified architecture for natural language processing: Deep neural networks with multitask learning",
"authors": [
{
"first": "Ronan",
"middle": [],
"last": "Collobert",
"suffix": ""
},
{
"first": "Jason",
"middle": [],
"last": "Weston",
"suffix": ""
}
],
"year": 2008,
"venue": "ICML",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ronan Collobert and Jason Weston. 2008. A unified architecture for natural language processing: Deep neural networks with multitask learning. In ICML.",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "Natural language processing (almost) from scratch",
"authors": [
{
"first": "Ronan",
"middle": [],
"last": "Collobert",
"suffix": ""
},
{
"first": "Jason",
"middle": [],
"last": "Weston",
"suffix": ""
},
{
"first": "L\u00e9on",
"middle": [],
"last": "Bottou",
"suffix": ""
},
{
"first": "Michael",
"middle": [],
"last": "Karlen",
"suffix": ""
},
{
"first": "Koray",
"middle": [],
"last": "Kavukcuoglu",
"suffix": ""
},
{
"first": "Pavel",
"middle": [],
"last": "Kuksa",
"suffix": ""
}
],
"year": 2011,
"venue": "JMLR",
"volume": "12",
"issue": "",
"pages": "2493--2537",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ronan Collobert, Jason Weston, L\u00e9on Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. 2011. Natural language processing (almost) from scratch. JMLR, 12(Aug):2493-2537.",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "De-identification of patient notes with recurrent neural networks",
"authors": [
{
"first": "Franck",
"middle": [],
"last": "Dernoncourt",
"suffix": ""
},
{
"first": "Ji",
"middle": [
"Young"
],
"last": "Lee",
"suffix": ""
},
{
"first": "Ozlem",
"middle": [],
"last": "Uzuner",
"suffix": ""
},
{
"first": "Peter",
"middle": [],
"last": "Szolovits",
"suffix": ""
}
],
"year": 2017,
"venue": "J. Am Med Inform Assoc",
"volume": "24",
"issue": "3",
"pages": "596--606",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Franck Dernoncourt, Ji Young Lee, Ozlem Uzuner, and Peter Szolovits. 2017. De-identification of patient notes with recurrent neural networks. J. Am Med Inform Assoc, 24(3):596-606.",
"links": null
},
"BIBREF9": {
"ref_id": "b9",
"title": "Multichannel lstm-crf for named entity recognition in chinese social media",
"authors": [
{
"first": "Chuanhai",
"middle": [],
"last": "Dong",
"suffix": ""
},
{
"first": "Huijia",
"middle": [],
"last": "Wu",
"suffix": ""
},
{
"first": "Jiajun",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Chengqing",
"middle": [],
"last": "Zong",
"suffix": ""
}
],
"year": 2017,
"venue": "CCL/NLP-NABD",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Chuanhai Dong, Huijia Wu, Jiajun Zhang, and Chengqing Zong. 2017. Multichannel lstm-crf for named entity recognition in chinese social media. In CCL/NLP-NABD. Springer.",
"links": null
},
"BIBREF10": {
"ref_id": "b10",
"title": "Physiobank, physiotoolkit, and physionet",
"authors": [
{
"first": "L",
"middle": [],
"last": "Ary",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Goldberger",
"suffix": ""
},
{
"first": "A",
"middle": [
"N"
],
"last": "Luis",
"suffix": ""
},
{
"first": "Leon",
"middle": [],
"last": "Amaral",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Glass",
"suffix": ""
},
{
"first": "M",
"middle": [],
"last": "Jeffrey",
"suffix": ""
},
{
"first": "Plamen",
"middle": [
"Ch"
],
"last": "Hausdorff",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Ivanov",
"suffix": ""
},
{
"first": "G",
"middle": [],
"last": "Roger",
"suffix": ""
},
{
"first": "Joseph",
"middle": [
"E"
],
"last": "Mark",
"suffix": ""
},
{
"first": "George",
"middle": [
"B"
],
"last": "Mietus",
"suffix": ""
},
{
"first": "Chung-Kang",
"middle": [],
"last": "Moody",
"suffix": ""
},
{
"first": "H Eugene",
"middle": [],
"last": "Peng",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Stanley",
"suffix": ""
}
],
"year": 2000,
"venue": "",
"volume": "101",
"issue": "",
"pages": "215--220",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ary L Goldberger, Luis AN Amaral, Leon Glass, Jef- frey M Hausdorff, Plamen Ch Ivanov, Roger G Mark, Joseph E Mietus, George B Moody, Chung- Kang Peng, and H Eugene Stanley. 2000. Phys- iobank, physiotoolkit, and physionet. Circulation, 101(23):215-220.",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "Speech recognition with deep recurrent neural networks",
"authors": [
{
"first": "Alex",
"middle": [],
"last": "Graves",
"suffix": ""
},
{
"first": "Mohamed",
"middle": [],
"last": "Abdel-Rahman",
"suffix": ""
},
{
"first": "Geoffrey",
"middle": [],
"last": "Hinton",
"suffix": ""
}
],
"year": 2013,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Alex Graves, Abdel-rahman Mohamed, and Geoffrey Hinton. 2013. Speech recognition with deep recur- rent neural networks. In ICASSP.",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "Marginal likelihood training of bilstm-crf for biomedical named entity recognition from disjoint label sets",
"authors": [
{
"first": "Nathan",
"middle": [],
"last": "Greenberg",
"suffix": ""
},
{
"first": "Trapit",
"middle": [],
"last": "Bansal",
"suffix": ""
},
{
"first": "Patrick",
"middle": [],
"last": "Verga",
"suffix": ""
},
{
"first": "Andrew",
"middle": [],
"last": "Mccallum",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "2824--2829",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Nathan Greenberg, Trapit Bansal, Patrick Verga, and Andrew McCallum. 2018. Marginal likelihood training of bilstm-crf for biomedical named entity recognition from disjoint label sets. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 2824-2829.",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "A joint many-task model: Growing a neural network for multiple nlp tasks",
"authors": [
{
"first": "Kazuma",
"middle": [],
"last": "Hashimoto",
"suffix": ""
},
{
"first": "Yoshimasa",
"middle": [],
"last": "Tsuruoka",
"suffix": ""
},
{
"first": "Richard",
"middle": [],
"last": "Socher",
"suffix": ""
}
],
"year": 2017,
"venue": "EMNLP",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Kazuma Hashimoto, Yoshimasa Tsuruoka, Richard Socher, et al. 2017. A joint many-task model: Grow- ing a neural network for multiple nlp tasks. In EMNLP.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "A unified model for cross-domain and semi-supervised named entity recognition in chinese social media",
"authors": [
{
"first": "Hangfeng",
"middle": [],
"last": "He",
"suffix": ""
},
{
"first": "Xu",
"middle": [],
"last": "Sun",
"suffix": ""
}
],
"year": 2017,
"venue": "AAAI",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Hangfeng He and Xu Sun. 2017. A unified model for cross-domain and semi-supervised named entity recognition in chinese social media. In AAAI.",
"links": null
},
"BIBREF15": {
"ref_id": "b15",
"title": "Long short-term memory",
"authors": [
{
"first": "Sepp",
"middle": [],
"last": "Hochreiter",
"suffix": ""
},
{
"first": "J\u00fcrgen",
"middle": [],
"last": "Schmidhuber",
"suffix": ""
}
],
"year": 1997,
"venue": "Neural computation",
"volume": "9",
"issue": "8",
"pages": "1735--1780",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long short-term memory. Neural computation, 9(8):1735-1780.",
"links": null
},
"BIBREF16": {
"ref_id": "b16",
"title": "Bidirectional lstm-crf models for sequence tagging",
"authors": [
{
"first": "Zhiheng",
"middle": [],
"last": "Huang",
"suffix": ""
},
{
"first": "Wei",
"middle": [],
"last": "Xu",
"suffix": ""
},
{
"first": "Kai",
"middle": [],
"last": "Yu",
"suffix": ""
}
],
"year": 2015,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1508.01991"
]
},
"num": null,
"urls": [],
"raw_text": "Zhiheng Huang, Wei Xu, and Kai Yu. 2015. Bidi- rectional lstm-crf models for sequence tagging. arXiv:1508.01991.",
"links": null
},
"BIBREF17": {
"ref_id": "b17",
"title": "New transfer learning techniques for disparate label sets",
"authors": [
{
"first": "Young-Bum",
"middle": [],
"last": "Kim",
"suffix": ""
},
{
"first": "Karl",
"middle": [],
"last": "Stratos",
"suffix": ""
},
{
"first": "Ruhi",
"middle": [],
"last": "Sarikaya",
"suffix": ""
},
{
"first": "Minwoo",
"middle": [],
"last": "Jeong",
"suffix": ""
}
],
"year": 2015,
"venue": "ACL",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Young-Bum Kim, Karl Stratos, Ruhi Sarikaya, and Minwoo Jeong. 2015. New transfer learning tech- niques for disparate label sets. In ACL.",
"links": null
},
"BIBREF18": {
"ref_id": "b18",
"title": "Conditional random fields: Probabilistic models for segmenting and labeling sequence data",
"authors": [
{
"first": "John",
"middle": [
"D"
],
"last": "Lafferty",
"suffix": ""
},
{
"first": "Andrew",
"middle": [],
"last": "Mccallum",
"suffix": ""
},
{
"first": "Fernando",
"middle": [
"C N"
],
"last": "Pereira",
"suffix": ""
}
],
"year": 2001,
"venue": "ICML",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "John D. Lafferty, Andrew McCallum, and Fernando C. N. Pereira. 2001. Conditional random fields: Probabilistic models for segmenting and labeling se- quence data. In ICML.",
"links": null
},
"BIBREF19": {
"ref_id": "b19",
"title": "Neural architectures for named entity recognition",
"authors": [
{
"first": "Guillaume",
"middle": [],
"last": "Lample",
"suffix": ""
},
{
"first": "Miguel",
"middle": [],
"last": "Ballesteros",
"suffix": ""
},
{
"first": "Sandeep",
"middle": [],
"last": "Subramanian",
"suffix": ""
},
{
"first": "Kazuya",
"middle": [],
"last": "Kawakami",
"suffix": ""
},
{
"first": "Chris",
"middle": [],
"last": "Dyer",
"suffix": ""
}
],
"year": 2016,
"venue": "ACL",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Guillaume Lample, Miguel Ballesteros, Sandeep Sub- ramanian, Kazuya Kawakami, and Chris Dyer. 2016. Neural architectures for named entity recognition. In ACL.",
"links": null
},
"BIBREF20": {
"ref_id": "b20",
"title": "Transfer learning for named-entity recognition with neural networks",
"authors": [
{
"first": "Ji",
"middle": [
"Young"
],
"last": "Lee",
"suffix": ""
},
{
"first": "Franck",
"middle": [],
"last": "Dernoncourt",
"suffix": ""
},
{
"first": "Peter",
"middle": [],
"last": "Szolovits",
"suffix": ""
}
],
"year": 2018,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ji Young Lee, Franck Dernoncourt, and Peter Szolovits. 2018. Transfer learning for named-entity recogni- tion with neural networks.",
"links": null
},
"BIBREF21": {
"ref_id": "b21",
"title": "Coupled pos tagging on heterogeneous annotations",
"authors": [
{
"first": "Zhenghua",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Jiayuan",
"middle": [],
"last": "Chao",
"suffix": ""
},
{
"first": "Min",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Wenliang",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Meishan",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Guohong",
"middle": [],
"last": "Fu",
"suffix": ""
},
{
"first": "Zhenghua",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Jiayuan",
"middle": [],
"last": "Chao",
"suffix": ""
},
{
"first": "Min",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Wenliang",
"middle": [],
"last": "Chen",
"suffix": ""
}
],
"year": 2017,
"venue": "TASLP",
"volume": "25",
"issue": "3",
"pages": "557--571",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Zhenghua Li, Jiayuan Chao, Min Zhang, Wenliang Chen, Meishan Zhang, Guohong Fu, Zhenghua Li, Jiayuan Chao, Min Zhang, Wenliang Chen, et al. 2017. Coupled pos tagging on heterogeneous an- notations. TASLP, 25(3):557-571.",
"links": null
},
"BIBREF22": {
"ref_id": "b22",
"title": "Finding function in form: Compositional character models for open vocabulary word representation",
"authors": [
{
"first": "Wang",
"middle": [],
"last": "Ling",
"suffix": ""
},
{
"first": "Chris",
"middle": [],
"last": "Dyer",
"suffix": ""
},
{
"first": "Alan",
"middle": [
"W"
],
"last": "Black",
"suffix": ""
},
{
"first": "Isabel",
"middle": [],
"last": "Trancoso",
"suffix": ""
},
{
"first": "Ramon",
"middle": [],
"last": "Fernandez",
"suffix": ""
},
{
"first": "Silvio",
"middle": [],
"last": "Amir",
"suffix": ""
},
{
"first": "Luis",
"middle": [],
"last": "Marujo",
"suffix": ""
},
{
"first": "Tiago",
"middle": [],
"last": "Luis",
"suffix": ""
}
],
"year": 2015,
"venue": "EMNLP",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Wang Ling, Chris Dyer, Alan W Black, Isabel Tran- coso, Ramon Fernandez, Silvio Amir, Luis Marujo, and Tiago Luis. 2015. Finding function in form: Compositional character models for open vocabu- lary word representation. In EMNLP.",
"links": null
},
"BIBREF23": {
"ref_id": "b23",
"title": "De-identification of clinical notes via recurrent neural network and conditional random field",
"authors": [
{
"first": "Zengjian",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "Buzhou",
"middle": [],
"last": "Tang",
"suffix": ""
},
{
"first": "Xiaolong",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Qingcai",
"middle": [],
"last": "Chen",
"suffix": ""
}
],
"year": 2017,
"venue": "J. Biomed. Inf",
"volume": "75",
"issue": "",
"pages": "34--42",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Zengjian Liu, Buzhou Tang, Xiaolong Wang, and Qingcai Chen. 2017. De-identification of clinical notes via recurrent neural network and conditional random field. J. Biomed. Inf., 75:34-42.",
"links": null
},
"BIBREF24": {
"ref_id": "b24",
"title": "The consensus string problem and the complexity of comparing hidden markov models",
"authors": [
{
"first": "B",
"middle": [],
"last": "Rune",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Lyngs\u00f8",
"suffix": ""
},
{
"first": "N",
"middle": [
"S"
],
"last": "Christian",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Pedersen",
"suffix": ""
}
],
"year": 2002,
"venue": "Journal of Computer and System Sciences",
"volume": "65",
"issue": "3",
"pages": "545--569",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Rune B Lyngs\u00f8 and Christian NS Pedersen. 2002. The consensus string problem and the complexity of comparing hidden markov models. Journal of Com- puter and System Sciences, 65(3):545-569.",
"links": null
},
"BIBREF25": {
"ref_id": "b25",
"title": "End-to-end sequence labeling via bi-directional lstm-cnns-crf",
"authors": [
{
"first": "Xuezhe",
"middle": [],
"last": "Ma",
"suffix": ""
},
{
"first": "Eduard",
"middle": [],
"last": "Hovy",
"suffix": ""
}
],
"year": 2016,
"venue": "ACL",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Xuezhe Ma and Eduard Hovy. 2016. End-to-end se- quence labeling via bi-directional lstm-cnns-crf. In ACL.",
"links": null
},
"BIBREF26": {
"ref_id": "b26",
"title": "A survey on transfer learning",
"authors": [
{
"first": "Qiang",
"middle": [],
"last": "Sinno Jialin Pan",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Yang",
"suffix": ""
}
],
"year": 2010,
"venue": "IEEE Transactions on knowledge and data engineering",
"volume": "22",
"issue": "10",
"pages": "1345--1359",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Sinno Jialin Pan and Qiang Yang. 2010. A survey on transfer learning. IEEE Transactions on knowledge and data engineering, 22(10):1345-1359.",
"links": null
},
"BIBREF27": {
"ref_id": "b27",
"title": "Multi-task domain adaptation for sequence tagging",
"authors": [
{
"first": "Nanyun",
"middle": [],
"last": "Peng",
"suffix": ""
},
{
"first": "Mark",
"middle": [],
"last": "Dredze",
"suffix": ""
}
],
"year": 2017,
"venue": "Proceedings of the 2nd Workshop on Representation Learning for NLP",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Nanyun Peng and Mark Dredze. 2017. Multi-task do- main adaptation for sequence tagging. In Proceed- ings of the 2nd Workshop on Representation Learn- ing for NLP.",
"links": null
},
"BIBREF28": {
"ref_id": "b28",
"title": "Glove: Global vectors for word representation",
"authors": [
{
"first": "Jeffrey",
"middle": [],
"last": "Pennington",
"suffix": ""
},
{
"first": "Richard",
"middle": [],
"last": "Socher",
"suffix": ""
},
{
"first": "Christopher",
"middle": [],
"last": "Manning",
"suffix": ""
}
],
"year": 2014,
"venue": "EMNLP",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014. Glove: Global vectors for word rep- resentation. In EMNLP.",
"links": null
},
"BIBREF29": {
"ref_id": "b29",
"title": "Named entity recognition for novel types by transfer learning",
"authors": [
{
"first": "Lizhen",
"middle": [],
"last": "Qu",
"suffix": ""
},
{
"first": "Gabriela",
"middle": [],
"last": "Ferraro",
"suffix": ""
},
{
"first": "Liyuan",
"middle": [],
"last": "Zhou",
"suffix": ""
},
{
"first": "Weiwei",
"middle": [],
"last": "Hou",
"suffix": ""
},
{
"first": "Timothy",
"middle": [],
"last": "Baldwin",
"suffix": ""
}
],
"year": 2016,
"venue": "EMNLP",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Lizhen Qu, Gabriela Ferraro, Liyuan Zhou, Weiwei Hou, and Timothy Baldwin. 2016. Named entity recognition for novel types by transfer learning. In EMNLP.",
"links": null
},
"BIBREF30": {
"ref_id": "b30",
"title": "An overview of multi-task learning in",
"authors": [
{
"first": "Sebastian",
"middle": [],
"last": "Ruder",
"suffix": ""
}
],
"year": 2017,
"venue": "deep neural networks",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1706.05098"
]
},
"num": null,
"urls": [],
"raw_text": "Sebastian Ruder. 2017. An overview of multi-task learning in deep neural networks. arXiv:1706.05098.",
"links": null
},
"BIBREF31": {
"ref_id": "b31",
"title": "Bidirectional recurrent neural networks",
"authors": [
{
"first": "Mike",
"middle": [],
"last": "Schuster",
"suffix": ""
},
{
"first": "K",
"middle": [],
"last": "Kuldip",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Paliwal",
"suffix": ""
}
],
"year": 1997,
"venue": "IEEE Transactions on Signal Processing",
"volume": "45",
"issue": "11",
"pages": "2673--2681",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Mike Schuster and Kuldip K Paliwal. 1997. Bidirec- tional recurrent neural networks. IEEE Transactions on Signal Processing, 45(11):2673-2681.",
"links": null
},
"BIBREF32": {
"ref_id": "b32",
"title": "Deep ehr: A survey of recent advances in deep learning techniques for electronic health record (ehr) analysis",
"authors": [
{
"first": "Benjamin",
"middle": [],
"last": "Shickel",
"suffix": ""
},
{
"first": "Patrick",
"middle": [
"James"
],
"last": "Tighe",
"suffix": ""
},
{
"first": "Azra",
"middle": [],
"last": "Bihorac",
"suffix": ""
},
{
"first": "Parisa",
"middle": [],
"last": "Rashidi",
"suffix": ""
}
],
"year": 2017,
"venue": "IEEE Journal of Biomedical and Health Informatics",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Benjamin Shickel, Patrick James Tighe, Azra Bihorac, and Parisa Rashidi. 2017. Deep ehr: A survey of re- cent advances in deep learning techniques for elec- tronic health record (ehr) analysis. IEEE Journal of Biomedical and Health Informatics.",
"links": null
},
"BIBREF33": {
"ref_id": "b33",
"title": "A survey of hierarchical classification across different application domains. Data Mining and Knowledge Discovery",
"authors": [
{
"first": "N",
"middle": [],
"last": "Carlos",
"suffix": ""
},
{
"first": "Alex",
"middle": [
"A"
],
"last": "Silla",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Freitas",
"suffix": ""
}
],
"year": 2011,
"venue": "",
"volume": "22",
"issue": "",
"pages": "31--72",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Carlos N Silla and Alex A Freitas. 2011. A survey of hierarchical classification across different appli- cation domains. Data Mining and Knowledge Dis- covery, 22(1-2):31-72.",
"links": null
},
"BIBREF34": {
"ref_id": "b34",
"title": "Deep multi-task learning with low level tasks supervised at lower layers",
"authors": [
{
"first": "Anders",
"middle": [],
"last": "S\u00f8gaard",
"suffix": ""
},
{
"first": "Yoav",
"middle": [],
"last": "Goldberg",
"suffix": ""
}
],
"year": 2016,
"venue": "ACL",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Anders S\u00f8gaard and Yoav Goldberg. 2016. Deep multi-task learning with low level tasks supervised at lower layers. In ACL.",
"links": null
},
"BIBREF35": {
"ref_id": "b35",
"title": "Fast and accurate entity recognition with iterated dilated convolutions",
"authors": [
{
"first": "Emma",
"middle": [],
"last": "Strubell",
"suffix": ""
},
{
"first": "Patrick",
"middle": [],
"last": "Verga",
"suffix": ""
},
{
"first": "David",
"middle": [],
"last": "Belanger",
"suffix": ""
},
{
"first": "Andrew",
"middle": [],
"last": "Mccallum",
"suffix": ""
}
],
"year": 2017,
"venue": "ENNLP",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Emma Strubell, Patrick Verga, David Belanger, and Andrew McCallum. 2017. Fast and accurate entity recognition with iterated dilated convolutions. In ENNLP.",
"links": null
},
"BIBREF36": {
"ref_id": "b36",
"title": "Annotating longitudinal clinical narratives for de-identification: The 2014 i2b2/uthealth corpus",
"authors": [
{
"first": "Amber",
"middle": [],
"last": "Stubbs",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Uzuner",
"suffix": ""
}
],
"year": 2015,
"venue": "J. Biomed. Inf",
"volume": "58",
"issue": "",
"pages": "20--29",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Amber Stubbs and\u00d6zlem Uzuner. 2015. Annotating longitudinal clinical narratives for de-identification: The 2014 i2b2/uthealth corpus. J. Biomed. Inf., 58:20-29.",
"links": null
},
"BIBREF37": {
"ref_id": "b37",
"title": "Introduction to the conll-2003 shared task: Language-independent named entity recognition",
"authors": [
{
"first": "Erik F Tjong Kim",
"middle": [],
"last": "Sang",
"suffix": ""
},
{
"first": "Fien",
"middle": [],
"last": "De Meulder",
"suffix": ""
}
],
"year": 2003,
"venue": "NAACL",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Erik F Tjong Kim Sang and Fien De Meulder. 2003. Introduction to the conll-2003 shared task: Language-independent named entity recognition. In NAACL.",
"links": null
},
"BIBREF38": {
"ref_id": "b38",
"title": "Evaluating the state-of-the-art in automatic deidentification",
"authors": [
{
"first": "Ozlem",
"middle": [],
"last": "Uzuner",
"suffix": ""
},
{
"first": "Yuan",
"middle": [],
"last": "Luo",
"suffix": ""
},
{
"first": "Peter",
"middle": [],
"last": "Szolovits",
"suffix": ""
}
],
"year": 2007,
"venue": "J. Am Med Inform Assoc",
"volume": "14",
"issue": "5",
"pages": "550--563",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ozlem Uzuner, Yuan Luo, and Peter Szolovits. 2007. Evaluating the state-of-the-art in automatic de- identification. J. Am Med Inform Assoc, 14(5):550- 563.",
"links": null
},
"BIBREF40": {
"ref_id": "b40",
"title": "Individual comparisons by ranking methods",
"authors": [
{
"first": "Frank",
"middle": [],
"last": "Wilcoxon",
"suffix": ""
}
],
"year": 1945,
"venue": "Biometrics bulletin",
"volume": "1",
"issue": "6",
"pages": "80--83",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Frank Wilcoxon. 1945. Individual comparisons by ranking methods. Biometrics bulletin, 1(6):80-83.",
"links": null
},
"BIBREF41": {
"ref_id": "b41",
"title": "A review on multi-label learning algorithms",
"authors": [
{
"first": "Min-Ling",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Zhi-Hua",
"middle": [],
"last": "Zhou",
"suffix": ""
}
],
"year": 2014,
"venue": "IEEE transactions on knowledge and data engineering",
"volume": "26",
"issue": "8",
"pages": "1819--1837",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Min-Ling Zhang and Zhi-Hua Zhou. 2014. A re- view on multi-label learning algorithms. IEEE transactions on knowledge and data engineering, 26(8):1819-1837.",
"links": null
}
},
"ref_entries": {
"FIGREF0": {
"type_str": "figure",
"num": null,
"uris": null,
"text": "Neural architecture for NER."
},
"FIGREF1": {
"type_str": "figure",
"num": null,
"uris": null,
"text": "NER multitasking architecture for 3 tag-sets."
},
"FIGREF2": {
"type_str": "figure",
"num": null,
"uris": null,
"text": "there is a directed path from c to d in the graph. For example, Sem(N ame) = {N ame, LastN ame, F irstN ame}."
},
"FIGREF3": {
"type_str": "figure",
"num": null,
"uris": null,
"text": "g is a plausible interpretation for y at the fine-grained tag level. For example, following Fig. 4, sequences ['Hospital', 'City'] and ['Street', 'City'] agree with ['Location', 'Location'], unlike ['City', 'Last Name']."
},
"TABREF3": {
"type_str": "table",
"html": null,
"num": null,
"content": "<table><tr><td/><td>I2B2'06</td><td>I2B2'14</td><td>Conll</td><td>Onto</td></tr><tr><td>Micro avg. F1</td><td>0.894</td><td>0.960</td><td>0.926</td><td>0.896</td></tr></table>",
"text": "nlp.stanford.edu/data/glove.6B.zip"
},
"TABREF4": {
"type_str": "table",
"html": null,
"num": null,
"content": "<table><tr><td/><td colspan=\"4\">Tag Frequency in training / test (%)</td></tr><tr><td/><td>I2B2'06</td><td>I2B2'14</td><td>Conll</td><td>Onto</td></tr><tr><td>Name</td><td>1.4 / 1.3</td><td>1.0 / 1.0</td><td>4.3 / 4.9</td><td>3.1 / 2.9</td></tr><tr><td>Date</td><td>1.7 / 1.5</td><td>2.4 / 2.5</td><td>0 / 0</td><td>2.7 / 3.1</td></tr><tr><td>Location</td><td>0.1 / 0.1</td><td>0.2 / 0.3</td><td>3.2 / 3.4</td><td>2.7 / 3.2</td></tr><tr><td>Hospital</td><td>0.6 / 0.7</td><td>0.3 / 0.3</td><td>0 / 0</td><td>0 / 0</td></tr></table>",
"text": "F1 for training and testing a single base NER model on the same dataset."
},
"TABREF5": {
"type_str": "table",
"html": null,
"num": null,
"content": "<table/>",
"text": "Occurrence statistics for tags used in the tagset extension experiment, reported as % out of all tokens in the training and test sets of each dataset."
},
"TABREF7": {
"type_str": "table",
"html": null,
"num": null,
"content": "<table/>",
"text": "F1 in the tag-set extension experiment, averaged over extending datasets for every base dataset."
},
"TABREF9": {
"type_str": "table",
"html": null,
"num": null,
"content": "<table><tr><td colspan=\"6\">: F1 for tag-set extensions with more than 100</td></tr><tr><td colspan=\"6\">collisions. Blank entries indicate fewer than 100 colli-</td></tr><tr><td colspan=\"6\">sions. (*) indicates all results that are statistically sig-</td></tr><tr><td colspan=\"4\">nificantly better than others in that row.</td><td/><td/></tr><tr><td>F1</td><td/><td/><td colspan=\"2\">Model</td><td/></tr><tr><td>Tag</td><td>Base</td><td>Extending</td><td>Hier</td><td>Indep</td><td>MTL</td></tr><tr><td>Location</td><td>I2B2'14</td><td>I2B2'06 Onto</td><td>0.953 0.954</td><td>0.919 0.899</td><td>0.919 0.887</td></tr><tr><td>Name</td><td>Conll</td><td>I2B2'06 Onto</td><td>0.846 0.895</td><td>0.827 0.888</td><td>0.809 0.890</td></tr></table>",
"text": ""
},
"TABREF10": {
"type_str": "table",
"html": null,
"num": null,
"content": "<table><tr><td>: Examples for performance differences when</td></tr><tr><td>base datasets are extended with an in-domain dataset</td></tr><tr><td>compared to an out-of-domain dataset.</td></tr></table>",
"text": ""
},
"TABREF12": {
"type_str": "table",
"html": null,
"num": null,
"content": "<table><tr><td>: F1 for combining I2B2'06 and I2B2'14. The</td></tr><tr><td>top two models were trained only on a single dataset.</td></tr><tr><td>The lower table part holds the number of collisions at</td></tr><tr><td>post-processing. (*) indicates results that are statisti-</td></tr><tr><td>cally significantly better than others in that column.</td></tr></table>",
"text": ""
},
"TABREF13": {
"type_str": "table",
"html": null,
"num": null,
"content": "<table><tr><td>F1</td><td/><td/><td/><td>Model</td><td/></tr><tr><td>Tag</td><td>Base</td><td>Extending</td><td>Hier</td><td>Indep</td><td>MTL</td></tr><tr><td>Date</td><td>I2B2'14</td><td>I2B2'06</td><td>0.899</td><td>0.904</td><td>0.903</td></tr><tr><td/><td/><td>Onto</td><td>0.713</td><td>0.686</td><td>0.671</td></tr><tr><td/><td>I2B2'06</td><td>I2B2'14</td><td>0.871</td><td>0.840</td><td>0.875</td></tr><tr><td/><td/><td>Onto</td><td>0.641</td><td>0.681</td><td>0.698</td></tr><tr><td/><td>Onto</td><td>I2B2'14</td><td>0.837</td><td>0.830</td><td>0.831</td></tr><tr><td/><td/><td>I2B2'06</td><td>0.834</td><td>0.826</td><td>0.807</td></tr><tr><td>Hospital</td><td>I2B2'14</td><td>I2B2'06</td><td>0.931</td><td>0.941</td><td>0.918</td></tr><tr><td/><td>I2B2'06</td><td>I2B2'14</td><td>0.867</td><td>0.866</td><td>0.853</td></tr><tr><td>Location</td><td>Conll</td><td>I2B2'14</td><td>0.818</td><td>0.783</td><td>0.812</td></tr><tr><td/><td/><td>I2B2'06</td><td>0.748</td><td>0.739</td><td>0.730</td></tr><tr><td/><td/><td>Onto</td><td>0.836</td><td>0.830</td><td>0.836</td></tr><tr><td/><td>I2B2'14</td><td>Conll</td><td>0.954</td><td>0.899</td><td>0.887</td></tr><tr><td/><td/><td>I2B2'06</td><td>0.953</td><td>0.919</td><td>0.919</td></tr><tr><td/><td/><td>Onto</td><td>0.951</td><td>0.921</td><td>0.907</td></tr><tr><td/><td>I2B2'06</td><td>Conll</td><td>0.876</td><td>0.816</td><td>0.760</td></tr><tr><td/><td/><td>I2B2'14</td><td>0.886</td><td>0.883</td><td>0.888</td></tr><tr><td/><td/><td>Onto</td><td>0.869</td><td>0.847</td><td>0.812</td></tr><tr><td/><td>Onto</td><td>Conll</td><td>0.747</td><td>0.701</td><td>0.703</td></tr><tr><td/><td/><td>I2B2'14</td><td>0.793</td><td>0.691</td><td>0.707</td></tr><tr><td/><td/><td>I2B2'06</td><td>0.814</td><td>0.691</td><td>0.666</td></tr><tr><td>Name</td><td>Conll</td><td>I2B2'14</td><td>0.855</td><td>0.771</td><td>0.690</td></tr><tr><td/><td/><td>I2B2'06</td><td>0.827</td><td>0.666</td><td>0.631</td></tr><tr><td/><td/><td>Onto</td><td>0.860</td><td>0.841</td><td>0.867</td></tr><tr><td/><td>I2B2'14</td><td>Conll</td><td>0.900</td><td>0.863</td><td>0.890</td></tr><tr><td/><td/><td>I2B2'06</td><td>0.943</td><td>0.893</td><td>0.927</td></tr><tr><td/><td/><td>Onto</td><td>0.911</td><td>0.882</td><td>0.891</td></tr><tr><td/><td>I2B2'06</td><td>Conll</td><td>0.662</td><td>0.679</td><td>0.653</td></tr><tr><td/><td/><td>I2B2'14</td><td>0.834</td><td>0.824</td><td>0.808</td></tr><tr><td/><td/><td>Onto</td><td>0.726</td><td>0.726</td><td>0.727</td></tr><tr><td/><td>Onto</td><td>Conll</td><td>0.895</td><td>0.888</td><td>0.890</td></tr><tr><td/><td/><td>I2B2'14</td><td>0.892</td><td>0.872</td><td>0.886</td></tr><tr><td/><td/><td>I2B2'06</td><td>0.846</td><td>0.827</td><td>0.809</td></tr></table>",
"text": "Full experiment results for Section 6.1"
}
}
}
} |