File size: 115,932 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 | {
"paper_id": "D09-1012",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T16:39:01.764541Z"
},
"title": "Reverse Engineering of Tree Kernel Feature Spaces",
"authors": [
{
"first": "Daniele",
"middle": [],
"last": "Pighin Fbk-Irst",
"suffix": "",
"affiliation": {},
"email": ""
},
{
"first": "Alessandro",
"middle": [],
"last": "Moschitti",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "University of Trento",
"location": {
"addrLine": "DISI Via di Sommarive, 14 I",
"postCode": "38100",
"settlement": "Povo (TN)",
"country": "Italy"
}
},
"email": "moschitti@disi.unitn.it"
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "We present a framework to extract the most important features (tree fragments) from a Tree Kernel (TK) space according to their importance in the target kernelbased machine, e.g. Support Vector Machines (SVMs). In particular, our mining algorithm selects the most relevant features based on SVM estimated weights and uses this information to automatically infer an explicit representation of the input data. The explicit features (a) improve our knowledge on the target problem domain and (b) make large-scale learning practical, improving training and test time, while yielding accuracy in line with traditional TK classifiers. Experiments on semantic role labeling and question classification illustrate the above claims.",
"pdf_parse": {
"paper_id": "D09-1012",
"_pdf_hash": "",
"abstract": [
{
"text": "We present a framework to extract the most important features (tree fragments) from a Tree Kernel (TK) space according to their importance in the target kernelbased machine, e.g. Support Vector Machines (SVMs). In particular, our mining algorithm selects the most relevant features based on SVM estimated weights and uses this information to automatically infer an explicit representation of the input data. The explicit features (a) improve our knowledge on the target problem domain and (b) make large-scale learning practical, improving training and test time, while yielding accuracy in line with traditional TK classifiers. Experiments on semantic role labeling and question classification illustrate the above claims.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "The last decade has seen a massive use of Support Vector Machines (SVMs) for carrying out NLP tasks. Indeed, their appealing properties such as 1) solid theoretical foundations, 2) robustness to irrelevant features and 3) outperforming accuracy have been exploited to design state-of-the-art language applications.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "More recently, kernel functions, which implicitly represent data in some high dimensional space, have been employed to study and further improve many natural language systems, e.g. (Collins and Duffy, 2002) , (Kudo and Matsumoto, 2003) , (Cumby and Roth, 2003) , (Cancedda et al., 2003) , (Culotta and Sorensen, 2004) , (Toutanova et al., 2004) , (Kazama and Torisawa, 2005) , (Shen et al., 2003) , (Gliozzo et al., 2005) , (Kudo et al., 2005) , , (Diab et al., 2008) . Unfortunately, the benefit to easily and effectively model the target linguistic phenomena is reduced by the the implicit nature of the kernel space, which prevents to directly observe the most relevant features. As a consequence, even very accurate models generally fail in providing useful feedback for improving our understanding of the problems at study. Moreover, the computational burden induced by high dimensional kernels makes the application of SVMs to large corpora still more problematic.",
"cite_spans": [
{
"start": 181,
"end": 206,
"text": "(Collins and Duffy, 2002)",
"ref_id": "BIBREF6"
},
{
"start": 209,
"end": 235,
"text": "(Kudo and Matsumoto, 2003)",
"ref_id": "BIBREF20"
},
{
"start": 238,
"end": 260,
"text": "(Cumby and Roth, 2003)",
"ref_id": "BIBREF8"
},
{
"start": 263,
"end": 286,
"text": "(Cancedda et al., 2003)",
"ref_id": "BIBREF3"
},
{
"start": 289,
"end": 317,
"text": "(Culotta and Sorensen, 2004)",
"ref_id": "BIBREF7"
},
{
"start": 320,
"end": 344,
"text": "(Toutanova et al., 2004)",
"ref_id": "BIBREF34"
},
{
"start": 347,
"end": 374,
"text": "(Kazama and Torisawa, 2005)",
"ref_id": "BIBREF18"
},
{
"start": 377,
"end": 396,
"text": "(Shen et al., 2003)",
"ref_id": "BIBREF32"
},
{
"start": 399,
"end": 421,
"text": "(Gliozzo et al., 2005)",
"ref_id": "BIBREF12"
},
{
"start": 424,
"end": 443,
"text": "(Kudo et al., 2005)",
"ref_id": "BIBREF21"
},
{
"start": 448,
"end": 467,
"text": "(Diab et al., 2008)",
"ref_id": "BIBREF9"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "In (Pighin and Moschitti, 2009) , we proposed a feature extraction algorithm for Tree Kernel (TK) spaces, which selects the most relevant features (tree fragments) according to the gradient components (weight vector) of the hyperplane learnt by an SVM, in line with current research, e.g. (Rakotomamonjy, 2003; Weston et al., 2003; Kudo and Matsumoto, 2003) . In particular, we provided algorithmic solutions to deal with the huge dimensionality and, consequently, high computational complexity of the fragment space. Our experimental results showed that our approach reduces learning and classification processing time leaving the accuracy unchanged.",
"cite_spans": [
{
"start": 3,
"end": 31,
"text": "(Pighin and Moschitti, 2009)",
"ref_id": "BIBREF30"
},
{
"start": 289,
"end": 310,
"text": "(Rakotomamonjy, 2003;",
"ref_id": "BIBREF31"
},
{
"start": 311,
"end": 331,
"text": "Weston et al., 2003;",
"ref_id": "BIBREF38"
},
{
"start": 332,
"end": 357,
"text": "Kudo and Matsumoto, 2003)",
"ref_id": "BIBREF20"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "In this paper, we present a new version of such algorithm which, under the same parameterization, is almost three times as fast while producing the same results. Most importantly, we explored tree fragment spaces for two interesting natural language tasks: Semantic Role Labeling (SRL) and Question Classification (QC). The results show that: (a) on large data sets, our approach can improve training and test time while yielding almost unaffected classification accuracy, and (b) our framework can effectively exploit the ability of TKs and SVMs to, respectively, generate and recognize relevant structured features. In particular, we (i) study in more detail the relevant fragments identfied for the boundary classification task of SRL, (ii) closely observe the most relevant fragments for each QC class and (iii) look at the diverse syntactic patterns characterizing each ques-tion category.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "The rest of the paper is structured as follows: Section 2 will briefly review SVMs and TK functions; Section 3 will detail our proposal for the linearization of a TK feature space; Section 4 will review previous work on related subjects; Section 5 will detail the outcome of our experiments, and Section 6 will discuss some relevant aspects of the evaluation; finally, in Section 7 we will draw our conclusions.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "The decision function of an SVM is:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Tree Kernel Functions",
"sec_num": "2"
},
{
"text": "f ( x) = w \u2022 x + b = n i=1 \u03b1 i y i x i \u2022 x + b (1)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Tree Kernel Functions",
"sec_num": "2"
},
{
"text": "where x is a classifying example and w and b are the separating hyperplane's gradient and its bias, respectively. The gradient is a linear combination of the training points x i , their labels y i and their weights \u03b1 i . Applying the so-called kernel trick it is possible to replace the scalar product with a kernel function defined over pairs of objects:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Tree Kernel Functions",
"sec_num": "2"
},
{
"text": "f (o) = n i=1 \u03b1 i y i k(o i , o) + b",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Tree Kernel Functions",
"sec_num": "2"
},
{
"text": "with the advantage that we do not need to provide an explicit mapping \u03c6(\u2022) of our examples in a vector space. A Tree Kernel function is a convolution kernel (Haussler, 1999) defined over pairs of trees. Practically speaking, the kernel between two trees evaluates the number of substructures (or fragments) they have in common, i.e. it is a measure of their overlap. The function can be computed recursively in closed form, and quite efficient implementations are available (Moschitti, 2006) . Different TK functions are characterized by alternative fragment definitions, e.g. (Collins and Duffy, 2002) and (Kashima and Koyanagi, 2002) . In the context of this paper we will be focusing on the SubSet Tree (SST) kernel described in (Collins and Duffy, 2002) , which relies on a fragment definition that does not allow to break production rules (i.e. if any child of a node is included in a fragment, then also all the other children have to). As such, it is especially indicated for tasks involving constituency parsed texts.",
"cite_spans": [
{
"start": 157,
"end": 173,
"text": "(Haussler, 1999)",
"ref_id": "BIBREF15"
},
{
"start": 474,
"end": 491,
"text": "(Moschitti, 2006)",
"ref_id": "BIBREF26"
},
{
"start": 577,
"end": 602,
"text": "(Collins and Duffy, 2002)",
"ref_id": "BIBREF6"
},
{
"start": 607,
"end": 635,
"text": "(Kashima and Koyanagi, 2002)",
"ref_id": "BIBREF17"
},
{
"start": 732,
"end": 757,
"text": "(Collins and Duffy, 2002)",
"ref_id": "BIBREF6"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Tree Kernel Functions",
"sec_num": "2"
},
{
"text": "Implicitly, a TK function establishes a correspondence between distinct fragments and dimensions in some fragment space, i.e. the space of all",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Tree Kernel Functions",
"sec_num": "2"
},
{
"text": "A B A A B A B A A B A C A B A B A C A C D B A D B A C 1 2 3 4 5 6 7 T1 A B A B A C T2 D B A C \u03c6(T 1) = [2, 1, 1, 1, 1, 0, 0] \u03c6(T 2) = [0, 0, 0, 0, 1, 1, 1] K(T 1, T 2) = \u03c6(T 1), \u03c6(T 2) = 1",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Fragment space",
"sec_num": null
},
{
"text": "Figure 1: Esemplification of a fragment space and the kernel product between two trees.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Fragment space",
"sec_num": null
},
{
"text": "the possible fragments. To simplify, a tree t can be represented as a vector whose attributes count the occurrences of each fragment within the tree. The kernel between two trees is then equivalent to the scalar product between pairs of such vectors, as exemplified in Figure 1 .",
"cite_spans": [],
"ref_spans": [
{
"start": 269,
"end": 277,
"text": "Figure 1",
"ref_id": null
}
],
"eq_spans": [],
"section": "Fragment space",
"sec_num": null
},
{
"text": "Our objective is to efficiently mine the most relevant fragments from the huge fragment space, so that we can explicitly represent our input trees in terms of these fragments and learn fast and accurate linear classifiers. The framework defines five distinct activities, detailed in the following paragraphs.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Linearization of a TK function",
"sec_num": "3"
},
{
"text": "The first step involves the generation of an approximation of the whole fragment space, i.e. we can consider only the trees that encode the most relevant fragments. To this end, we can partition our training data into S smaller sets, and use the SVM and the SST kernel to learn S models. We will only consider the fragments encoded by the support vectors of the S models. In the next stage, we will use the SVM estimated weights to drive our feature selection process.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Kernel Space Learning (KSL)",
"sec_num": "3.1"
},
{
"text": "Since time complexity of SVM training is approximately quadratic in the number of examples, by breaking training data into smaller sets we can considerably accelerate the process of filtering trees and estimating support vector weights. According to statistical learning theory, being trained on smaller subsets of the available data these models will be less robust with respect to the minimization of the empirical risk (Vapnik, 1998) .",
"cite_spans": [
{
"start": 422,
"end": 436,
"text": "(Vapnik, 1998)",
"ref_id": "BIBREF35"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Kernel Space Learning (KSL)",
"sec_num": "3.1"
},
{
"text": "Algorithm 3.1: MINE MODEL(M, L, \u03bb) global maxexp prev \u2190 \u2205 ; CLEAR INDEX() for each \u03b1y, t \u2208 M do \uf8f1 \uf8f4 \uf8f4 \uf8f2 \uf8f4 \uf8f4 \uf8f3 Ti \u2190 \u03b1 \u2022 y/ t for each n \u2208 Nt do f \u2190 FRAG(n) ; rel = \u03bb \u2022 Ti prev \u2190 prev \u222a {f, rel} PUT(f, rel) best pr \u2190 BEST(L) ; while true do \uf8f1 \uf8f4 \uf8f4 \uf8f4 \uf8f4 \uf8f4 \uf8f4 \uf8f4 \uf8f4 \uf8f4 \uf8f4 \uf8f4 \uf8f4 \uf8f4 \uf8f4 \uf8f4 \uf8f2 \uf8f4 \uf8f4 \uf8f4 \uf8f4 \uf8f4 \uf8f4 \uf8f4 \uf8f4 \uf8f4 \uf8f4 \uf8f4 \uf8f4 \uf8f4 \uf8f4 \uf8f4 \uf8f3 next \u2190 \u2205 for each f, rel \u2208 prev if f \u2208 best pr do \uf8f1 \uf8f4 \uf8f4 \uf8f4 \uf8f4 \uf8f2 \uf8f4 \uf8f4 \uf8f4 \uf8f4 \uf8f3 X = EXPAND(f, maxexp) rel exp \u2190 \u03bb \u2022 rel for each f rag \u2208 X do temp = {f rag, rel exp} next \u2190 next \u222a temp PUT(f rag, rel exp) best \u2190 BEST(L) if not CHANGED() then break best pr \u2190 best prev \u2190 next FL \u2190 best pr return (FL)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Kernel Space Learning (KSL)",
"sec_num": "3.1"
},
{
"text": "Nonetheless, since we do not need to employ them for classification (but just to direct our feature selection process, as we will describe shortly), we can accept to rely on sub-optimal weights. Furthermore, research results in the field of SVM parallelization using cascades of SVMs (Graf et al., 2004) suggest that support vectors collected from locally learnt models can encode many of the relevant features retained by models learnt globally. Henceforth, let M s be the model associated with the s-th split, and F s the fragment space that can describe all the trees in M s .",
"cite_spans": [
{
"start": 279,
"end": 303,
"text": "SVMs (Graf et al., 2004)",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Kernel Space Learning (KSL)",
"sec_num": "3.1"
},
{
"text": "In Equation 1 it is possible to isolate the gradient",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Fragment Mining and Indexing (FMI)",
"sec_num": "3.2"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "w = n i=1 \u03b1 i y i x i , with x i = [x (1) i , . . . , x",
"eq_num": "(N )"
}
],
"section": "Fragment Mining and Indexing (FMI)",
"sec_num": "3.2"
},
{
"text": "i ], N being the dimensionality of the feature space. For a tree kernel function, we can rewrite x (j) i as:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Fragment Mining and Indexing (FMI)",
"sec_num": "3.2"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "x (j) i = t i,j \u03bb \u2113(f j ) t i = t i,j \u03bb \u2113(f j ) N k=1 (t i,k \u03bb \u2113(f k ) ) 2",
"eq_num": "(2)"
}
],
"section": "Fragment Mining and Indexing (FMI)",
"sec_num": "3.2"
},
{
"text": "where: t i,j is the number of occurrences of the fragment f j , associated with the j-th dimension of the feature space, in the tree t i ; \u03bb is the kernel decay factor; and \u2113(f j ) is the depth of the fragment.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Fragment Mining and Indexing (FMI)",
"sec_num": "3.2"
},
{
"text": "The relevance |w (j) | of the fragment f j can be measured as:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Fragment Mining and Indexing (FMI)",
"sec_num": "3.2"
},
{
"text": "|w (j) | = n i=1 \u03b1 i y i x (j) i = n i=1 \u03b1 i y i t i,j \u03bb \u2113(f j ) t i .",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Fragment Mining and Indexing (FMI)",
"sec_num": "3.2"
},
{
"text": "(3) We fix a threshold L and from each model M s (learnt during KSL) we select the L most relevant fragments, i.e. we build the set F s,L = \u222a k {f k } so that:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Fragment Mining and Indexing (FMI)",
"sec_num": "3.2"
},
{
"text": "|F s,L | = L and |w (k) | \u2265 |w (i) |\u2200f i \u2208 F \\ F s,L .",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Fragment Mining and Indexing (FMI)",
"sec_num": "3.2"
},
{
"text": "To generate all the fragments encoded in a model, we adopt the greedy strategy described in Algorithm 3.1. Its arguments are: an SVM model M represented as \u03b1y, t pairs, where t is a tree structure; the threshold value L; and the kernel decay factor \u03bb.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Fragment Mining and Indexing (FMI)",
"sec_num": "3.2"
},
{
"text": "The function FRAG(n) generates the smallest fragment rooted in node n (i.e. for an SST kernel, the fragment consisting of n and its direct children). We call such fragment a base fragment. The function EXPAND(f, maxexp) generates all the fragments that can be derived from the fragment f by expanding, i.e. including in the fragment the direct children of some of its nodes. These fragments are derived from f . The parameter maxexp limits fragment proliferation by setting the maximum number of nodes which can be expanded in a fragment expansion operation. For example, if there are 10 nodes which can be expanded in fragment f , then only the fragments where at most 3 of the 10 nodes are expanded will be generated by a call to EXPAND(f, 3).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Fragment Mining and Indexing (FMI)",
"sec_num": "3.2"
},
{
"text": "Every time we generate a fragment f , the function PUT(f, rel) saves the fragment along with its relevance rel in an index. The index keeps track of the cumulative relevance of a fragment, and its implementation has been optimized for fast insertions and spatial compactness.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Fragment Mining and Indexing (FMI)",
"sec_num": "3.2"
},
{
"text": "A whole cycle of expansions is considered as an iteration of the mining process: we take into account all the fragments that have undergone k expansions and produce all the fragments that result from a further expansion, i.e. all the fragments expanded k + 1 times.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Fragment Mining and Indexing (FMI)",
"sec_num": "3.2"
},
{
"text": "We keep iterating until we reach a stop criterion, which we base on the threshold value L, i.e. the limit on the number of fragments that we are interested in mining from a model. During each iteration k+1, we only expand the best L fragments identified during the previous iteration k. When the iteration is complete we re-evaluate the set of L best fragments in the index, and we stop only if the worst of them, i.e. the L-th ranked fragment at the step k + 1, and its score are the same as at the end of the previous iteration. That is, we assume that if none of the fragments mined during the (k + 1)-th iteration managed to affect the bottom of the pool of the L most relevant fragments, then none of their expansions is likely to succeed. In the algorithm, N t is the set of nodes of the tree t; BEST(L) returns the L highest ranked fragments in the index; CHANGED() verifies whether the bottom of the L-best set has been affected by the last iteration or not.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Fragment Mining and Indexing (FMI)",
"sec_num": "3.2"
},
{
"text": "We call MINE MODEL(\u2022) on each of the models M s that we learnt from the S initial splits. For each model, the function returns the set of L-best fragments in the model. The union of all the fragments harvested from each model is then saved into a dictionary D L which will be used by the next stage.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Fragment Mining and Indexing (FMI)",
"sec_num": "3.2"
},
{
"text": "With respect to the algorithm presented in (Pighin and Moschitti, 2009) , the one presented here has the following advantages:",
"cite_spans": [
{
"start": 43,
"end": 71,
"text": "(Pighin and Moschitti, 2009)",
"ref_id": "BIBREF30"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Discussion on FMI algorithm",
"sec_num": "3.2.1"
},
{
"text": "\u2022 the process of building fragments is strictly small-to-large: fragments that span n + 1 levels of the tree may be generated only after all those spanning n levels;",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Discussion on FMI algorithm",
"sec_num": "3.2.1"
},
{
"text": "\u2022 the threshold value L is a parameter of the mining process, and it is used to prevent the algorithm from generating more fragments than necessary, thus making it more efficient;",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Discussion on FMI algorithm",
"sec_num": "3.2.1"
},
{
"text": "\u2022 it has one less parameter (maxdepth) which was used to force fragments to span at-most a given number of levels. The new algorithm does not need it since the maximum number of iterations is implicitly set via L.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Discussion on FMI algorithm",
"sec_num": "3.2.1"
},
{
"text": "These differences result in improved efficiency for the FMI stage. For example, on the data for the boundary classification task (see Section 5), using comparable parameters the old algorithm required 85 minutes to mine the most relevant fragments, whereas the new one only takes 31, i.e. it is 2.74 times as fast.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Discussion on FMI algorithm",
"sec_num": "3.2.1"
},
{
"text": "During this phase we actually linearize our data: a file encoding label-tree pairs y i , t i is trans-formed to encode label-vector pairs y i , v i . To do so, we generate the fragment space of t i , using a variant of the mining algorithm described in Algorithm 3.1, and encode in v i all and only the fragments t i,j so that t i,j \u2208 D L . The algorithm exploits labels and production rules found in the fragments listed in the dictionary to generate only the fragments that may be in the dictionary. For example, if the dictionary does not contain a fragment whose root is labeled N , then if a node N is encountered during TFX neither its base fragment nor its expansions are generated. The process is applied to the whole training (TFX-train) and test (TFX-test) sets. The fragment space is now explicit, as there is a mapping between the input vectors and the fragments they encode.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Tree Fragment Extraction (TFX)",
"sec_num": "3.3"
},
{
"text": "Linearized training data is used to learn a very fast model by using all the available data and a linear kernel.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Explicit Space Learning (ESL)",
"sec_num": "3.4"
},
{
"text": "The linear model is used to classify linearized test data and evaluate the accuracy of the resulting classifier.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Explicit Space Classification (ESC)",
"sec_num": "3.5"
},
{
"text": "A rather comprehensive overview of feature selection techniques is carried out in (Guyon and Elisseeff, 2003) . Non-filter approaches for SVMs and kernel machines are often concerned with polynomial and Gaussian kernels, e.g. (Weston et al., 2001) and (Neumann et al., 2005) . Weston et al. (2003) use the \u2113 0 norm in the SVM optimizer to stress the feature selection capabilities of the learning algorithm. In (Kudo and Matsumoto, 2003) , an extension of the PrefixSpan algorithm (Pei et al., 2001 ) is used to efficiently mine the features in a low degree polynomial kernel space. The authors discuss an approximation of their method that allows them to handle high degree polynomial kernels. present an embedded approach to feature selection for convolution kernels based on \u03c7 2 -driven relevance assessment. To our knowledge, this is the only published work clearly focusing on feature selection for tree kernel functions, and indeed has been one of the major sources of inspiration for our methodology. With respect to their work, the difference in our approach is that we want to exploit the SVM optimizer to select the most relevant features instead of a relevance assessment measure that moves from different statistical assumptions than the learning algorithm.",
"cite_spans": [
{
"start": 82,
"end": 109,
"text": "(Guyon and Elisseeff, 2003)",
"ref_id": "BIBREF14"
},
{
"start": 226,
"end": 247,
"text": "(Weston et al., 2001)",
"ref_id": "BIBREF37"
},
{
"start": 252,
"end": 274,
"text": "(Neumann et al., 2005)",
"ref_id": "BIBREF27"
},
{
"start": 277,
"end": 297,
"text": "Weston et al. (2003)",
"ref_id": "BIBREF38"
},
{
"start": 411,
"end": 437,
"text": "(Kudo and Matsumoto, 2003)",
"ref_id": "BIBREF20"
},
{
"start": 481,
"end": 498,
"text": "(Pei et al., 2001",
"ref_id": "BIBREF29"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Previous work",
"sec_num": "4"
},
{
"text": "In (Graf et al., 2004) , an approach to SVM parallelization is presented which is based on a divide-et-impera strategy to reduce optimization time. The idea of using a compact graph representation to represent the support vectors of a TK function is explored in (Aiolli et al., 2006) , where a Direct Acyclic Graph (DAG) is employed. In (Moschitti, 2006; Bloehdorn and Moschitti, 2007a; Bloehdorn and Moschitti, 2007b; , the SST kernel along with other tree and combined kernels are employed for question classification and semantic role labeling with interesting results.",
"cite_spans": [
{
"start": 3,
"end": 22,
"text": "(Graf et al., 2004)",
"ref_id": "BIBREF13"
},
{
"start": 262,
"end": 283,
"text": "(Aiolli et al., 2006)",
"ref_id": "BIBREF0"
},
{
"start": 337,
"end": 354,
"text": "(Moschitti, 2006;",
"ref_id": "BIBREF26"
},
{
"start": 355,
"end": 386,
"text": "Bloehdorn and Moschitti, 2007a;",
"ref_id": "BIBREF1"
},
{
"start": 387,
"end": 418,
"text": "Bloehdorn and Moschitti, 2007b;",
"ref_id": "BIBREF2"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Previous work",
"sec_num": "4"
},
{
"text": "We evaluated the capability of our model to extract relevant features on two data sets: the CoNLL 2005 shared task on Semantic Role Labeling (SRL) (Carreras and M\u00e0rquez, 2005) , and the Question Classification (QC) task based on data from the TREC 10 QA competition (Voorhees, 2001 ). The next sections will detail the setup and outcome of the two sets of experiments.",
"cite_spans": [
{
"start": 147,
"end": 175,
"text": "(Carreras and M\u00e0rquez, 2005)",
"ref_id": "BIBREF4"
},
{
"start": 266,
"end": 281,
"text": "(Voorhees, 2001",
"ref_id": "BIBREF36"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Experiments",
"sec_num": "5"
},
{
"text": "All the experiments were run on a machine equipped with 4 Intel R Xeon R CPUs clocked at 1.6 GHz and 4 GB of RAM. As a supervised learning framework we used SVM-Light-TK 1 , which extends the SVM-Light optimizer (Joachims, 2000) with tree kernel support. For each classification task, we compare the accuracy of a vanilla SST classifier against the corresponding linearized SST classifier (SST \u2113 ). For KSL and SST training we used the default decay factor \u03bb = 0.4. For ESL, we use a non-normalized, linear kernel. No further parametrization of the learning algorithms is carried out. Indeed, our focus is on showing that, under the same conditions, our linearized tree kernel can be as accurate as the original kernel, and choosing of parameters may just bias such test.",
"cite_spans": [
{
"start": 212,
"end": 228,
"text": "(Joachims, 2000)",
"ref_id": "BIBREF16"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Experiments",
"sec_num": "5"
},
{
"text": "For our experiments on semantic role labeling we used PropBank annotations (Palmer et al., 2005) 1 http://disi.unitn.it/\u02dcmoschitt/ Tree-Kernel.htm and automatic Charniak parse trees (Charniak, 2000) as provided for the CoNLL 2005 evaluation campaign (Carreras and M\u00e0rquez, 2005) . SRL can be decomposed into two tasks: boundary detection, where the word sequences that are arguments of a predicate word w are identified, and role classification, where each argument is assigned the proper role. The former task requires a binary Boundary Classifier (BC), whereas the second involves a Role Multi-class Classifier (RM).",
"cite_spans": [
{
"start": 75,
"end": 96,
"text": "(Palmer et al., 2005)",
"ref_id": "BIBREF28"
},
{
"start": 182,
"end": 198,
"text": "(Charniak, 2000)",
"ref_id": "BIBREF5"
},
{
"start": 250,
"end": 278,
"text": "(Carreras and M\u00e0rquez, 2005)",
"ref_id": "BIBREF4"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Semantic Role Labeling",
"sec_num": "5.1"
},
{
"text": "If the constituency parse tree t of a sentence s is available, we can look at all the pairs p, n i , where n i is any node in the tree and p is the node dominating w, and decide whether n i is an argument node or not, i.e. whether it exactly dominates all and only the words encoding any of w's arguments. The objects that we classify are subsets of the input parse tree that encompass both p and n i . Namely, we use the AST m structure defined in , which is the minimal tree that covers all and only the words of p and n i . In the AST m , p and n i are marked so that they can be distinguished from the other nodes. An AST m is regarded as a positive example for BC if n i is an argument node, otherwise it is considered a negative example. Positive BC examples can be used to train an efficient RM: for each role r we can train a classifier whose positive examples are argument nodes whose label is exactly r, whereas negative examples are argument nodes labeled r \u2032 = r. Two AST m s extracted from an example parse tree are shown in Figure 2 : the first structure is a negative example for BC and is not part of the data set of RM, whereas the second is a positive instance for BC and A1.",
"cite_spans": [],
"ref_spans": [
{
"start": 1038,
"end": 1046,
"text": "Figure 2",
"ref_id": "FIGREF0"
}
],
"eq_spans": [],
"section": "Setup",
"sec_num": "5.1.1"
},
{
"text": "To train BC we used PropBank sections 1 through 6, extracting AST m structures out of the first 1 million p, n i pairs from the corresponding parse trees. As a test set we used the 149,140 instance collected from the annotations in Section 24. There are 61,062 positive examples in the training set (i.e. 6.1%) and 8,515 in the test set (i.e. 5.7%).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Setup",
"sec_num": "5.1.1"
},
{
"text": "For RM we considered all the argument nodes of any of the six PropBank core roles (i.e. A0, . . . , A5) from all the available training sections, i.e. 2 through 21, for a total of 179,091 training instances. Similarly, we collected 5,928 test instances from the annotations of Section 24. Columns Tr + and Te + of Table 1 show the number of positive training and test examples, respectively, for BC and the role classifiers.",
"cite_spans": [],
"ref_spans": [
{
"start": 314,
"end": 321,
"text": "Table 1",
"ref_id": "TABREF1"
}
],
"eq_spans": [],
"section": "Setup",
"sec_num": "5.1.1"
},
{
"text": "For all the linearized classifiers, we used 50 splits for the FMI stage and we set the threshold value L = 50k and maxexp = 1 during FMI and TFX. We did not validate these parameters, which we know to be sub-optimal. These values were selected during the development of the software because, on a very small test bed, they resulted in a responsive and accurate system.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Setup",
"sec_num": "5.1.1"
},
{
"text": "We should point out that other experiments have shown that linearization is very robust with respect to parametrization: due to the huge number and variety of fragments in the TK space, different choices of the parameters result in different explicit spaces and more or less efficient solutions, but in most cases the final accuracy of the linearized classifiers is affected only marginally. For example, it could be expected that reducing the number of splits during KSL would improve the final accuracy of a linearized classifier, as the weights used for FMI would then converge to the global optimum. Instead, we have observed that increasing the number of splits does not necessarily decrease the accuracy of the linearized classifier.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Setup",
"sec_num": "5.1.1"
},
{
"text": "The evaluation on the whole SRL task using the official CoNLL'05 evaluator was not carried out because producing complete annotations requires several steps (e.g. overlap resolution, OvA or Pairwise combination of individual role classifiers) that would shade off the actual impact of the methodology on classification.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Setup",
"sec_num": "5.1.1"
},
{
"text": "The left side of Table 1 shows the distribution of positive data points in the training and test sets of each classifier. Columns SST and SST \u2113 compare side by side the F 1 measure of the non-linearized and linearized classifier for each class. The accuracy of the RM classifier is the percentage of correct class assignments.",
"cite_spans": [],
"ref_spans": [
{
"start": 17,
"end": 24,
"text": "Table 1",
"ref_id": "TABREF1"
}
],
"eq_spans": [],
"section": "Results",
"sec_num": "5.1.2"
},
{
"text": "We can see that the accuracy of linearized classifiers is always in line with vanilla SST, even if the selected linearization parameters generate a very rough approximation of the original fragment space, generally consisting of billions of fragments. BC \u2113 (i.e. the linearized BC) has an F 1 of 81.3, just 0.5% less than BC, i.e. 81.8. Concerning RM \u2113 , its accuracy is the same as the non linearized classifier, i.e. 87.8. We should consider that the linearization framework can drastically improve the efficiency of learning and classification when dealing with large amounts of data. For a linearized classifier, we consider training time to be the overall time required to carry out the following activities: KSL, FMI, TFX on training data and ESL. Similarly, we consider test time the time necessary to perform TFX on test data and ESC. Training BC took more than two days of CPU time and testing about 4 hours, while training and testing the linearized boundary classifier required only 381 and 25 minutes, respectively. That is, on the same amount of data we can train a linearized classifier about 8 times as fast, and test it in about 1 tenth of the time. Concerning RM, sequential training of the 6 models took 2,596 minutes, while testing took 27 minutes. The linearized role multi classifier required 448 and 24 minutes for training and testing, respectively, i.e. training is about 5 times as fast while testing time is about the same. If compared with the boundary classifier, the improvement in efficiency is less evident: indeed, the relatively small size of the role classifiers data sets limits the positive effect of splitting training data into smaller chunks. SRL fragment space. Table 3 lists the best fragments identified for the Boundary Classifier. We should remember that we are using AST m struc-tures as input to our classifiers: nodes whose label end with \"-P\" are predicate nodes, while nodes whose label ends with \"-B\" are candidate argument nodes.",
"cite_spans": [],
"ref_spans": [
{
"start": 1702,
"end": 1709,
"text": "Table 3",
"ref_id": null
}
],
"eq_spans": [],
"section": "Results",
"sec_num": "5.1.2"
},
{
"text": "All the most relevant fragments encode the minimum sub-tree encompassing the predicate and the argument node. This kind of structured feature subsumes several features traditionally employed for explicit SRL models: the Path (i.e. the sequence of nodes connecting the predicate and the candidate argument node), Phrase Type (i.e. the label of the candidate argument node), Predicate POS (i.e. the POS of the predicate word), Position (i.e. whether the argument is to the left or to the right of the predicate) and Governing Category (i.e. the label of the common ancestor) defined in (Gildea and Jurafsky, 2002) .",
"cite_spans": [
{
"start": 584,
"end": 611,
"text": "(Gildea and Jurafsky, 2002)",
"ref_id": "BIBREF10"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Results",
"sec_num": "5.1.2"
},
{
"text": "The linearized model for BC contains about 160 thousand fragments. Of these, about 70 and 33 thousand encompass the candidate argument or the predicate node, respectively. About 16 thousand fragments contain both.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Results",
"sec_num": "5.1.2"
},
{
"text": "For question classification we used the data set from the TREC 10 QA evaluation campaign 2 , consisting of 5,500 training and 500 test questions.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Question Classification",
"sec_num": "5.2"
},
{
"text": "Given a question, the QC task consists in selecting the most appropriate expected answer type from a given set of possibilities. We adopted the question taxonomy known as coarse grained, which has been described in (Zhang and Lee, 2003) and (Li and Roth, 2006) , consisting of six non overlapping classes: Abbreviations (ABBR), Descriptions (DESC, e.g. definitions or explanations), Entity (ENTY, e.g. animal, body or color), Human (HUM, e.g. group or individual), Location (LOC, e.g. cities or countries) and Numeric (NUM, e.g. amounts or dates).",
"cite_spans": [
{
"start": 215,
"end": 236,
"text": "(Zhang and Lee, 2003)",
"ref_id": "BIBREF39"
},
{
"start": 241,
"end": 260,
"text": "(Li and Roth, 2006)",
"ref_id": "BIBREF22"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Setup",
"sec_num": "5.2.1"
},
{
"text": "For each question, we generate the full parse of the sentence and use it to train SST and (linearized) SST \u2113 models. The automatic parses are obtained with the Stanford parser 3 (Klein and Manning, 2003) . We actually have only 5,483 sentences in our training set, due to parsing issues with a few of them. The classifiers are arranged in a one-vs.-all (OvA) configuration, where each sentence is a positive example for one of the six classes, and negative for the other five. Given the very small size of the data set, we used S = 1 during KSL for the linearized classifier (i.e. we didn't partition training data). We carried out no validation of the parameters, and we used maxexp = 4 and L = 50k in order to generate a rich fragment space. Table 2 shows the number of positive examples in the training and test set of each individual binary classifiers. Columns SST and SST \u2113 compare the F 1 measure of the vanilla and linearized classifiers on the individual classes, and the accuracy of the complete QC task (Row Overall) in terms of percentage of correct class assignments. Also in this case, we can notice that the accuracy of the linearized classifiers is always in line with nonlinearized ones, e.g. 86.6 vs. 86.2 for the multiclassifiers. These results are lower than those derived in (Moschitti, 2006; , i.e. 88.2 and 90.4, respectively, where the parameters for each classifier were carefully optimized.",
"cite_spans": [
{
"start": 178,
"end": 203,
"text": "(Klein and Manning, 2003)",
"ref_id": "BIBREF19"
},
{
"start": 1296,
"end": 1313,
"text": "(Moschitti, 2006;",
"ref_id": "BIBREF26"
}
],
"ref_spans": [
{
"start": 744,
"end": 751,
"text": "Table 2",
"ref_id": "TABREF3"
}
],
"eq_spans": [],
"section": "Setup",
"sec_num": "5.2.1"
},
{
"text": "Tables from 4 to 9 list the top fragments identified for each class 4 .",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "QC Fragment space.",
"sec_num": null
},
{
"text": "As expected, for all the categories the domain lexical information is very relevant. For example, film, color, book, novel and sport for ENTY or city, country, state and capital for LOC. Of the six classes, ENTY (Table 6 ) is mostly characterized by lexical features. Interestingly, function words, which would have been eliminated by a pure Information Retrieval approach (i.e. by means of standard stop-list), are in the top positions, e.g.: why and how for DESC, what for ENTY, who for HUM, where for LOC and when for NUM. For the latter, also how seems to be important suggesting that features may strongly characterize more than one given class.",
"cite_spans": [],
"ref_spans": [
{
"start": 212,
"end": 220,
"text": "(Table 6",
"ref_id": null
}
],
"eq_spans": [],
"section": "QC Fragment space.",
"sec_num": null
},
{
"text": "Characteristic syntactic features appear in the top positions for each class, for example: (VP (VB (stand)) (PP)), which suggests that stand should be followed by a prepositional phrase to characterize ABBR; or (NP (NP (DT) (NN (abbreviation) )) (PP)), which suggests that, to be in a relevant pattern, abbreviation should be preceded by an article and followed by a PP. Also, the syntactic structure is useful to differentiate the use of the same important words, e.g. (SBARQ (WHADVP (WRB (How))) (SQ) (.)) for DESC better characterizes the use of how with respect to NUM, in which a relevant use is (WHADJP (WRB (How)) (JJ)).",
"cite_spans": [
{
"start": 202,
"end": 223,
"text": "ABBR; or (NP (NP (DT)",
"ref_id": null
}
],
"ref_spans": [
{
"start": 224,
"end": 242,
"text": "(NN (abbreviation)",
"ref_id": null
}
],
"eq_spans": [],
"section": "QC Fragment space.",
"sec_num": null
},
{
"text": "In it was shown that the use of TK improves QC of 1.2 percent points, i.e. from 90.6 to 91.8: further analysis of these fragments may help us to device compact, less sparse syntactic features and design more accurate models for the task. whose SVM weights are the lowest, i.e. those that are (almost) irrelevant for the SVM. Therefore, the chance of this resulting in an improvement is rather low.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "QC Fragment space.",
"sec_num": null
},
{
"text": "With respect to cases where our model is less accurate than a standard SST, we should consider that our choice of parameters is sub-optimal and we adopt a very aggressive feature selection strategy, that only retains a few thousand features from a space where there are hundreds of millions of different features.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "QC Fragment space.",
"sec_num": null
},
{
"text": "We introduced a novel framework for support vector classification that combines advantages of convolution kernels, i.e. the generation of a very high dimensional structure space, with the efficiency and clarity of explicit representations in a linear space.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusions",
"sec_num": "7"
},
{
"text": "For this paper, we focused on the SubSet Tree kernel and verified the potential of the proposed solution on two NLP tasks, i.e. semantic role labeling and question classification. The experiments show that our framework drastically reduces processing time, e.g. boundary classification for SRL, while preserving the accuracy.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusions",
"sec_num": "7"
},
{
"text": "We presented a selection of the most relevant fragments identified for the SRL boundary classifier as well as for each class of the coarse grained QC task. Our analysis shows that our framework can discover state-of-the-art features, e.g. the Path feature for SRL. We believe that sharing these fragments with the NLP community and studying them in more depth will be useful to identify new, relevant features for the characterization of several learning problems. For this purpose, we made available the fragment spaces at http://danielepighin.net and we will keep them updated with new set of experiments on new tasks, e.g. SRL based on FrameNet and VerbNet, e.g. (Giuglea and Moschitti, 2004) .",
"cite_spans": [
{
"start": 666,
"end": 695,
"text": "(Giuglea and Moschitti, 2004)",
"ref_id": "BIBREF11"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusions",
"sec_num": "7"
},
{
"text": "In our future work, we plan to widen the list of covered tasks and to extend our algorithm to cope with different kernel families, such as the partial tree kernel and kernels defined over pairs of trees, e.g. the ones used for textual entailment in (Moschitti and Zanzotto, 2007) . We also plan to move from mining fragments to mining classes of fragments, i.e. to identify prototypical fragments in the fragment space that generalize topological sub-classes of the most relevant fragments.",
"cite_spans": [
{
"start": 249,
"end": 279,
"text": "(Moschitti and Zanzotto, 2007)",
"ref_id": "BIBREF23"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusions",
"sec_num": "7"
},
{
"text": "http://l2r.cs.uiuc.edu/cogcomp/Data/ QA/QC/ 3 http://nlp.stanford.edu/software/ lex-parser.shtml",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "Some categories show meaningful syntactic fragments after the first 10, so for them we report more subtrees.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
}
],
"back_matter": [
{
"text": "The fact that our model doesn't always improve the accuracy of a standard SST model might be related to the process of splitting training data and employing locally estimated weights during FMI.Concerning the experiments presented in this paper, this objection might apply to the results on SRL, where we used 50 splits to identify the most relevant fragments, but not to those on QC, where given the limited size of the data set we decided not to split training data at all as explained in Section 5.2. Furthermore, as we already discussed, we have evidence that there is no direct correlation between the number of splits used for KSL and the accuracy of the resulting classifier. After all, the optimization carried out during ESL is global, and we can assume that, if we mined enough fragments during FMI, than those actually retained by the global linear model would be by and large the same, regardless of the split configuration.More in general, feature selection may give an improvement to some learning algorithm but if it can help SVMs is debatable, since its related theory show that they are robust to irrelevant features. In our specific case, we remove features (WRB(Why)) (WHADVP(WRB(Why))) (WHADVP(WRB(How))) (WHADVP(WRB)) (VB(mean)) (VBZ(causes)) (VB(do)) (ROOT(SBARQ(WHADVP(WRB(How)))(SQ)(.))) (ROOT(SBARQ(WHADVP(WRB(How)))(SQ)(.(?)))) (SBARQ(WHADVP(WRB(How)))(SQ)) (WRB(How)) (SBARQ(WHADVP(WRB(How)))(SQ)(.)) (SBARQ(WHADVP(WRB(How)))(SQ)(.(?))) (SBARQ(WHADVP(WRB(Why)))(SQ)) (ROOT(SBARQ(WHADVP(WRB(Why)))(SQ))) (SBARQ(WHADVP(WRB))(SQ)) (WRB(How)) (WHADVP(WRB(When))) (WRB(When)) (JJ(many)) (NN(year)) (WHADJP(WRB)(JJ)) (NP(NN(year))) (WHADJP(WRB(How))(JJ)) (NN(date)) (SBARQ(WHADVP(WRB(When)))(SQ)(.(?))) (SBARQ(WHADVP(WRB(When)))(SQ)(.)) (NN(day)) (NN(population)) (ROOT(SBARQ(WHADVP(WRB(When)))(SQ)(.))) (ROOT(SBARQ(WHADVP(WRB(When)))(SQ)(.(?)))) (JJ(average)) (NN(number))",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Discussion",
"sec_num": "6"
}
],
"bib_entries": {
"BIBREF0": {
"ref_id": "b0",
"title": "Fast on-line kernel learning for trees",
"authors": [
{
"first": "Fabio",
"middle": [],
"last": "Aiolli",
"suffix": ""
},
{
"first": "Giovanni",
"middle": [],
"last": "Da San",
"suffix": ""
},
{
"first": "Alessandro",
"middle": [],
"last": "Martino",
"suffix": ""
},
{
"first": "Alessandro",
"middle": [],
"last": "Sperduti",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Moschitti",
"suffix": ""
}
],
"year": 2006,
"venue": "Proceedings of ICDM'06",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Fabio Aiolli, Giovanni Da San Martino, Alessandro Sper- duti, and Alessandro Moschitti. 2006. Fast on-line kernel learning for trees. In Proceedings of ICDM'06.",
"links": null
},
"BIBREF1": {
"ref_id": "b1",
"title": "Combined syntactic and semantic kernels for text classification",
"authors": [
{
"first": "Stephan",
"middle": [],
"last": "Bloehdorn",
"suffix": ""
},
{
"first": "Alessandro",
"middle": [],
"last": "Moschitti",
"suffix": ""
}
],
"year": 2007,
"venue": "Proceedings of ECIR 2007",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Stephan Bloehdorn and Alessandro Moschitti. 2007a. Com- bined syntactic and semantic kernels for text classification. In Proceedings of ECIR 2007, Rome, Italy.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "Structure and semantics for expressive text kernels",
"authors": [
{
"first": "Stephan",
"middle": [],
"last": "Bloehdorn",
"suffix": ""
},
{
"first": "Alessandro",
"middle": [],
"last": "Moschitti",
"suffix": ""
}
],
"year": 2007,
"venue": "Proceedings of CIKM '07",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Stephan Bloehdorn and Alessandro Moschitti. 2007b. Struc- ture and semantics for expressive text kernels. In In Pro- ceedings of CIKM '07.",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "Word sequence kernels",
"authors": [
{
"first": "Nicola",
"middle": [],
"last": "Cancedda",
"suffix": ""
},
{
"first": "Eric",
"middle": [],
"last": "Gaussier",
"suffix": ""
},
{
"first": "Cyril",
"middle": [],
"last": "Goutte",
"suffix": ""
},
{
"first": "Jean",
"middle": [
"Michel"
],
"last": "Renders",
"suffix": ""
}
],
"year": 2003,
"venue": "Journal of Machine Learning Research",
"volume": "3",
"issue": "",
"pages": "1059--1082",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Nicola Cancedda, Eric Gaussier, Cyril Goutte, and Jean Michel Renders. 2003. Word sequence kernels. Journal of Machine Learning Research, 3:1059-1082.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "Introduction to the CoNLL-2005 Shared Task: Semantic Role Labeling",
"authors": [
{
"first": "Xavier",
"middle": [],
"last": "Carreras",
"suffix": ""
},
{
"first": "Llu\u00eds",
"middle": [],
"last": "M\u00e0rquez",
"suffix": ""
}
],
"year": 2005,
"venue": "Proceedings of CoNLL'05",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Xavier Carreras and Llu\u00eds M\u00e0rquez. 2005. Introduction to the CoNLL-2005 Shared Task: Semantic Role Labeling. In Proceedings of CoNLL'05.",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "A maximum-entropy-inspired parser",
"authors": [
{
"first": "Eugene",
"middle": [],
"last": "Charniak",
"suffix": ""
}
],
"year": 2000,
"venue": "Proceedings of NAACL'00",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Eugene Charniak. 2000. A maximum-entropy-inspired parser. In Proceedings of NAACL'00.",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "New Ranking Algorithms for Parsing and Tagging: Kernels over Discrete Structures, and the Voted Perceptron",
"authors": [
{
"first": "Michael",
"middle": [],
"last": "Collins",
"suffix": ""
},
{
"first": "Nigel",
"middle": [],
"last": "Duffy",
"suffix": ""
}
],
"year": 2002,
"venue": "Proceedings of ACL'02",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Michael Collins and Nigel Duffy. 2002. New Ranking Al- gorithms for Parsing and Tagging: Kernels over Discrete Structures, and the Voted Perceptron. In Proceedings of ACL'02.",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "Dependency Tree Kernels for Relation Extraction",
"authors": [
{
"first": "Aron",
"middle": [],
"last": "Culotta",
"suffix": ""
},
{
"first": "Jeffrey",
"middle": [],
"last": "Sorensen",
"suffix": ""
}
],
"year": 2004,
"venue": "Proceedings of ACL'04",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Aron Culotta and Jeffrey Sorensen. 2004. Dependency Tree Kernels for Relation Extraction. In Proceedings of ACL'04.",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "Kernel Methods for Relational Learning",
"authors": [
{
"first": "Chad",
"middle": [],
"last": "Cumby",
"suffix": ""
},
{
"first": "Dan",
"middle": [],
"last": "Roth",
"suffix": ""
}
],
"year": 2003,
"venue": "Proceedings of ICML",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Chad Cumby and Dan Roth. 2003. Kernel Methods for Re- lational Learning. In Proceedings of ICML 2003.",
"links": null
},
"BIBREF9": {
"ref_id": "b9",
"title": "Semantic role labeling systems for Arabic using kernel methods",
"authors": [
{
"first": "Mona",
"middle": [],
"last": "Diab",
"suffix": ""
},
{
"first": "Alessandro",
"middle": [],
"last": "Moschitti",
"suffix": ""
},
{
"first": "Daniele",
"middle": [],
"last": "Pighin",
"suffix": ""
}
],
"year": 2008,
"venue": "Proceedings of ACL-08: HLT",
"volume": "",
"issue": "",
"pages": "798--806",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Mona Diab, Alessandro Moschitti, and Daniele Pighin. 2008. Semantic role labeling systems for Arabic using kernel methods. In Proceedings of ACL-08: HLT, pages 798- 806.",
"links": null
},
"BIBREF10": {
"ref_id": "b10",
"title": "Automatic labeling of semantic roles",
"authors": [
{
"first": "Daniel",
"middle": [],
"last": "Gildea",
"suffix": ""
},
{
"first": "Daniel",
"middle": [],
"last": "Jurafsky",
"suffix": ""
}
],
"year": 2002,
"venue": "Computational Linguistics",
"volume": "28",
"issue": "",
"pages": "245--288",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Daniel Gildea and Daniel Jurafsky. 2002. Automatic label- ing of semantic roles. Computational Linguistics, 28:245- 288.",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "Knowledge discovery using framenet, verbnet and propbank",
"authors": [
{
"first": "Ana-Maria",
"middle": [],
"last": "Giuglea",
"suffix": ""
},
{
"first": "Alessandro",
"middle": [],
"last": "Moschitti",
"suffix": ""
}
],
"year": 2004,
"venue": "Workshop on Ontology and Knowledge Discovering at ECML",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ana-Maria Giuglea and Alessandro Moschitti. 2004. Knowledge discovery using framenet, verbnet and prop- bank. In A. Meyers, editor, Workshop on Ontology and Knowledge Discovering at ECML 2004, Pisa, Italy.",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "Domain kernels for word sense disambiguation",
"authors": [
{
"first": "Alfio",
"middle": [],
"last": "Gliozzo",
"suffix": ""
},
{
"first": "Claudio",
"middle": [],
"last": "Giuliano",
"suffix": ""
},
{
"first": "Carlo",
"middle": [],
"last": "Strapparava",
"suffix": ""
}
],
"year": 2005,
"venue": "Proceedings of ACL'05",
"volume": "",
"issue": "",
"pages": "403--410",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Alfio Gliozzo, Claudio Giuliano, and Carlo Strapparava. 2005. Domain kernels for word sense disambiguation. In Proceedings of ACL'05, pages 403-410.",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "Parallel support vector machines: The cascade svm",
"authors": [
{
"first": "P",
"middle": [],
"last": "Hans",
"suffix": ""
},
{
"first": "Eric",
"middle": [],
"last": "Graf",
"suffix": ""
},
{
"first": "Leon",
"middle": [],
"last": "Cosatto",
"suffix": ""
},
{
"first": "Igor",
"middle": [],
"last": "Bottou",
"suffix": ""
},
{
"first": "Vladimir",
"middle": [],
"last": "Durdanovic",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Vapnik",
"suffix": ""
}
],
"year": 2004,
"venue": "Neural Information Processing Systems",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Hans P. Graf, Eric Cosatto, Leon Bottou, Igor Durdanovic, and Vladimir Vapnik. 2004. Parallel support vector ma- chines: The cascade svm. In Neural Information Process- ing Systems.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "An introduction to variable and feature selection",
"authors": [
{
"first": "Isabelle",
"middle": [],
"last": "Guyon",
"suffix": ""
},
{
"first": "Andr\u00e9",
"middle": [],
"last": "Elisseeff",
"suffix": ""
}
],
"year": 2003,
"venue": "Journal of Machine Learning Research",
"volume": "3",
"issue": "",
"pages": "1157--1182",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Isabelle Guyon and Andr\u00e9 Elisseeff. 2003. An introduc- tion to variable and feature selection. Journal of Machine Learning Research, 3:1157-1182.",
"links": null
},
"BIBREF15": {
"ref_id": "b15",
"title": "Convolution kernels on discrete structures",
"authors": [
{
"first": "David",
"middle": [],
"last": "Haussler",
"suffix": ""
}
],
"year": 1999,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "David Haussler. 1999. Convolution kernels on discrete struc- tures. Technical report, Dept. of Computer Science, Uni- versity of California at Santa Cruz.",
"links": null
},
"BIBREF16": {
"ref_id": "b16",
"title": "Estimating the generalization performance of a SVM efficiently",
"authors": [
{
"first": "T",
"middle": [],
"last": "Joachims",
"suffix": ""
}
],
"year": 2000,
"venue": "Proceedings of ICML'00",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "T. Joachims. 2000. Estimating the generalization perfor- mance of a SVM efficiently. In Proceedings of ICML'00.",
"links": null
},
"BIBREF17": {
"ref_id": "b17",
"title": "Kernels for semi-structured data",
"authors": [
{
"first": "Hisashi",
"middle": [],
"last": "Kashima",
"suffix": ""
},
{
"first": "Teruo",
"middle": [],
"last": "Koyanagi",
"suffix": ""
}
],
"year": 2002,
"venue": "Proceedings of ICML'02",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Hisashi Kashima and Teruo Koyanagi. 2002. Kernels for semi-structured data. In Proceedings of ICML'02.",
"links": null
},
"BIBREF18": {
"ref_id": "b18",
"title": "Speeding up training with tree kernels for node relation labeling",
"authors": [
{
"first": "Kentaro",
"middle": [],
"last": "Jun'ichi Kazama",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Torisawa",
"suffix": ""
}
],
"year": 2005,
"venue": "Proceedings of HLT-EMNLP'05",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jun'ichi Kazama and Kentaro Torisawa. 2005. Speeding up training with tree kernels for node relation labeling. In Proceedings of HLT-EMNLP'05.",
"links": null
},
"BIBREF19": {
"ref_id": "b19",
"title": "Accurate unlexicalized parsing",
"authors": [
{
"first": "Dan",
"middle": [],
"last": "Klein",
"suffix": ""
},
{
"first": "Christopher",
"middle": [
"D"
],
"last": "Manning",
"suffix": ""
}
],
"year": 2003,
"venue": "Proceedings of ACL'03",
"volume": "",
"issue": "",
"pages": "423--430",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Dan Klein and Christopher D. Manning. 2003. Accurate unlexicalized parsing. In Proceedings of ACL'03, pages 423-430.",
"links": null
},
"BIBREF20": {
"ref_id": "b20",
"title": "Fast methods for kernel-based text analysis",
"authors": [
{
"first": "Taku",
"middle": [],
"last": "Kudo",
"suffix": ""
},
{
"first": "Yuji",
"middle": [],
"last": "Matsumoto",
"suffix": ""
}
],
"year": 2003,
"venue": "Proceedings of ACL'03",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Taku Kudo and Yuji Matsumoto. 2003. Fast methods for kernel-based text analysis. In Proceedings of ACL'03.",
"links": null
},
"BIBREF21": {
"ref_id": "b21",
"title": "Boostingbased parse reranking with subtree features",
"authors": [
{
"first": "Taku",
"middle": [],
"last": "Kudo",
"suffix": ""
},
{
"first": "Jun",
"middle": [],
"last": "Suzuki",
"suffix": ""
},
{
"first": "Hideki",
"middle": [],
"last": "Isozaki",
"suffix": ""
}
],
"year": 2005,
"venue": "Proceedings of ACL'05",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Taku Kudo, Jun Suzuki, and Hideki Isozaki. 2005. Boosting- based parse reranking with subtree features. In Proceed- ings of ACL'05.",
"links": null
},
"BIBREF22": {
"ref_id": "b22",
"title": "Learning question classifiers: the role of semantic information",
"authors": [
{
"first": "Xin",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Dan",
"middle": [],
"last": "Roth",
"suffix": ""
}
],
"year": 2006,
"venue": "Natural Language Engineering",
"volume": "12",
"issue": "3",
"pages": "229--249",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Xin Li and Dan Roth. 2006. Learning question classifiers: the role of semantic information. Natural Language En- gineering, 12(3):229-249.",
"links": null
},
"BIBREF23": {
"ref_id": "b23",
"title": "Fast and effective kernels for relational learning from texts",
"authors": [
{
"first": "Alessandro",
"middle": [],
"last": "Moschitti",
"suffix": ""
},
{
"first": "Fabio",
"middle": [
"Massimo"
],
"last": "Zanzotto",
"suffix": ""
}
],
"year": 2007,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Alessandro Moschitti and Fabio Massimo Zanzotto. 2007. Fast and effective kernels for relational learning from texts. In ICML'07.",
"links": null
},
"BIBREF24": {
"ref_id": "b24",
"title": "Exploiting syntactic and shallow semantic kernels for question/answer classification",
"authors": [
{
"first": "Alessandro",
"middle": [],
"last": "Moschitti",
"suffix": ""
},
{
"first": "Silvia",
"middle": [],
"last": "Quarteroni",
"suffix": ""
},
{
"first": "Roberto",
"middle": [],
"last": "Basili",
"suffix": ""
},
{
"first": "Suresh",
"middle": [],
"last": "Manandhar",
"suffix": ""
}
],
"year": 2007,
"venue": "Proceedings of ACL'07",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Alessandro Moschitti, Silvia Quarteroni, Roberto Basili, and Suresh Manandhar. 2007. Exploiting syntactic and shal- low semantic kernels for question/answer classification. In Proceedings of ACL'07.",
"links": null
},
"BIBREF25": {
"ref_id": "b25",
"title": "Tree kernels for semantic role labeling",
"authors": [
{
"first": "Alessandro",
"middle": [],
"last": "Moschitti",
"suffix": ""
},
{
"first": "Daniele",
"middle": [],
"last": "Pighin",
"suffix": ""
},
{
"first": "Roberto",
"middle": [],
"last": "Basili",
"suffix": ""
}
],
"year": 2008,
"venue": "Computational Linguistics",
"volume": "34",
"issue": "2",
"pages": "193--224",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Alessandro Moschitti, Daniele Pighin, and Roberto Basili. 2008. Tree kernels for semantic role labeling. Compu- tational Linguistics, 34(2):193-224.",
"links": null
},
"BIBREF26": {
"ref_id": "b26",
"title": "Efficient convolution kernels for dependency and constituent syntactic trees",
"authors": [
{
"first": "Alessandro",
"middle": [],
"last": "Moschitti",
"suffix": ""
}
],
"year": 2006,
"venue": "Proceedings of ECML'06",
"volume": "",
"issue": "",
"pages": "318--329",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Alessandro Moschitti. 2006. Efficient convolution kernels for dependency and constituent syntactic trees. In Pro- ceedings of ECML'06, pages 318-329.",
"links": null
},
"BIBREF27": {
"ref_id": "b27",
"title": "Combined SVM-Based Feature Selection and Classification",
"authors": [
{
"first": "Julia",
"middle": [],
"last": "Neumann",
"suffix": ""
},
{
"first": "Christoph",
"middle": [],
"last": "Schnorr",
"suffix": ""
},
{
"first": "Gabriele",
"middle": [],
"last": "Steidl",
"suffix": ""
}
],
"year": 2005,
"venue": "Machine Learning",
"volume": "",
"issue": "",
"pages": "129--150",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Julia Neumann, Christoph Schnorr, and Gabriele Steidl. 2005. Combined SVM-Based Feature Selection and Clas- sification. Machine Learning, 61(1-3):129-150.",
"links": null
},
"BIBREF28": {
"ref_id": "b28",
"title": "The proposition bank: An annotated corpus of semantic roles",
"authors": [
{
"first": "Martha",
"middle": [],
"last": "Palmer",
"suffix": ""
},
{
"first": "Daniel",
"middle": [],
"last": "Gildea",
"suffix": ""
},
{
"first": "Paul",
"middle": [],
"last": "Kingsbury",
"suffix": ""
}
],
"year": 2005,
"venue": "Comput. Linguist",
"volume": "31",
"issue": "1",
"pages": "71--106",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Martha Palmer, Daniel Gildea, and Paul Kingsbury. 2005. The proposition bank: An annotated corpus of semantic roles. Comput. Linguist., 31(1):71-106.",
"links": null
},
"BIBREF29": {
"ref_id": "b29",
"title": "PrefixSpan Mining Sequential Patterns Efficiently by Prefix Projected Pattern Growth",
"authors": [
{
"first": "J",
"middle": [],
"last": "Pei",
"suffix": ""
},
{
"first": "J",
"middle": [],
"last": "Han",
"suffix": ""
},
{
"first": "Mortazavi",
"middle": [
"B"
],
"last": "Asl",
"suffix": ""
},
{
"first": "H",
"middle": [],
"last": "Pinto",
"suffix": ""
},
{
"first": "Q",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "U",
"middle": [],
"last": "Dayal",
"suffix": ""
},
{
"first": "M",
"middle": [
"C"
],
"last": "Hsu",
"suffix": ""
}
],
"year": 2001,
"venue": "Proceedings of ICDE'01",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "J. Pei, J. Han, Mortazavi B. Asl, H. Pinto, Q. Chen, U. Dayal, and M. C. Hsu. 2001. PrefixSpan Mining Sequential Pat- terns Efficiently by Prefix Projected Pattern Growth. In Proceedings of ICDE'01.",
"links": null
},
"BIBREF30": {
"ref_id": "b30",
"title": "Efficient linearization of tree kernel functions",
"authors": [
{
"first": "Daniele",
"middle": [],
"last": "Pighin",
"suffix": ""
},
{
"first": "Alessandro",
"middle": [],
"last": "Moschitti",
"suffix": ""
}
],
"year": 2009,
"venue": "Proceedings of CoNLL'09",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Daniele Pighin and Alessandro Moschitti. 2009. Efficient linearization of tree kernel functions. In Proceedings of CoNLL'09.",
"links": null
},
"BIBREF31": {
"ref_id": "b31",
"title": "Variable selection using SVM based criteria",
"authors": [
{
"first": "Alain",
"middle": [],
"last": "Rakotomamonjy",
"suffix": ""
}
],
"year": 2003,
"venue": "Journal of Machine Learning Research",
"volume": "3",
"issue": "",
"pages": "1357--1370",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Alain Rakotomamonjy. 2003. Variable selection using SVM based criteria. Journal of Machine Learning Research, 3:1357-1370.",
"links": null
},
"BIBREF32": {
"ref_id": "b32",
"title": "Using LTAG Based Features in Parse Reranking",
"authors": [
{
"first": "Libin",
"middle": [],
"last": "Shen",
"suffix": ""
},
{
"first": "Anoop",
"middle": [],
"last": "Sarkar",
"suffix": ""
},
{
"first": "Aravind",
"middle": [
"K"
],
"last": "Joshi",
"suffix": ""
}
],
"year": 2003,
"venue": "Proceedings of EMNLP'06",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Libin Shen, Anoop Sarkar, and Aravind k. Joshi. 2003. Us- ing LTAG Based Features in Parse Reranking. In Proceed- ings of EMNLP'06.",
"links": null
},
"BIBREF33": {
"ref_id": "b33",
"title": "Sequence and Tree Kernels with Statistical Feature Mining",
"authors": [
{
"first": "Jun",
"middle": [],
"last": "Suzuki",
"suffix": ""
},
{
"first": "Hideki",
"middle": [],
"last": "Isozaki",
"suffix": ""
}
],
"year": 2005,
"venue": "Proceedings of NIPS'05",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jun Suzuki and Hideki Isozaki. 2005. Sequence and Tree Kernels with Statistical Feature Mining. In Proceedings of NIPS'05.",
"links": null
},
"BIBREF34": {
"ref_id": "b34",
"title": "The Leaf Path Projection View of Parse Trees: Exploring String Kernels for HPSG Parse Selection",
"authors": [
{
"first": "Kristina",
"middle": [],
"last": "Toutanova",
"suffix": ""
},
{
"first": "Penka",
"middle": [],
"last": "Markova",
"suffix": ""
},
{
"first": "Christopher",
"middle": [],
"last": "Manning",
"suffix": ""
}
],
"year": 2004,
"venue": "Proceedings of EMNLP",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Kristina Toutanova, Penka Markova, and Christopher Man- ning. 2004. The Leaf Path Projection View of Parse Trees: Exploring String Kernels for HPSG Parse Selec- tion. In Proceedings of EMNLP 2004.",
"links": null
},
"BIBREF35": {
"ref_id": "b35",
"title": "Statistical Learning Theory",
"authors": [
{
"first": "Vladimir",
"middle": [
"N"
],
"last": "Vapnik",
"suffix": ""
}
],
"year": 1998,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Vladimir N. Vapnik. 1998. Statistical Learning Theory. Wiley-Interscience.",
"links": null
},
"BIBREF36": {
"ref_id": "b36",
"title": "Overview of the trec 2001 question answering track",
"authors": [
{
"first": "Ellen",
"middle": [
"M"
],
"last": "Voorhees",
"suffix": ""
}
],
"year": 2001,
"venue": "Proceedings of the Tenth Text REtrieval Conference (TREC",
"volume": "",
"issue": "",
"pages": "42--51",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ellen M. Voorhees. 2001. Overview of the trec 2001 ques- tion answering track. In In Proceedings of the Tenth Text REtrieval Conference (TREC, pages 42-51.",
"links": null
},
"BIBREF37": {
"ref_id": "b37",
"title": "Feature Selection for SVMs",
"authors": [
{
"first": "Jason",
"middle": [],
"last": "Weston",
"suffix": ""
},
{
"first": "Sayan",
"middle": [],
"last": "Mukherjee",
"suffix": ""
},
{
"first": "Olivier",
"middle": [],
"last": "Chapelle",
"suffix": ""
},
{
"first": "Massimiliano",
"middle": [],
"last": "Pontil",
"suffix": ""
},
{
"first": "Tomaso",
"middle": [],
"last": "Poggio",
"suffix": ""
},
{
"first": "Vladimir",
"middle": [],
"last": "Vapnik",
"suffix": ""
}
],
"year": 2001,
"venue": "Proceedings of NIPS'01",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jason Weston, Sayan Mukherjee, Olivier Chapelle, Massimil- iano Pontil, Tomaso Poggio, and Vladimir Vapnik. 2001. Feature Selection for SVMs. In Proceedings of NIPS'01.",
"links": null
},
"BIBREF38": {
"ref_id": "b38",
"title": "Use of the zero norm with linear models and kernel methods",
"authors": [
{
"first": "Jason",
"middle": [],
"last": "Weston",
"suffix": ""
},
{
"first": "Andr\u00e9",
"middle": [],
"last": "Elisseeff",
"suffix": ""
},
{
"first": "Bernhard",
"middle": [],
"last": "Sch\u00f6lkopf",
"suffix": ""
},
{
"first": "Mike",
"middle": [],
"last": "Tipping",
"suffix": ""
}
],
"year": 2003,
"venue": "J. Mach. Learn. Res",
"volume": "3",
"issue": "",
"pages": "1439--1461",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jason Weston, Andr\u00e9 Elisseeff, Bernhard Sch\u00f6lkopf, and Mike Tipping. 2003. Use of the zero norm with lin- ear models and kernel methods. J. Mach. Learn. Res., 3:1439-1461.",
"links": null
},
"BIBREF39": {
"ref_id": "b39",
"title": "Question classification using support vector machines",
"authors": [
{
"first": "Dell",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Wee",
"middle": [],
"last": "Sun Lee",
"suffix": ""
}
],
"year": 2003,
"venue": "Proceedings of SI-GIR'03",
"volume": "",
"issue": "",
"pages": "26--32",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Dell Zhang and Wee Sun Lee. 2003. Question classifica- tion using support vector machines. In Proceedings of SI- GIR'03, pages 26-32.",
"links": null
}
},
"ref_entries": {
"FIGREF0": {
"text": "Examples of AST m structured features.",
"uris": null,
"num": null,
"type_str": "figure"
},
"TABREF1": {
"html": null,
"text": "",
"type_str": "table",
"num": null,
"content": "<table><tr><td>: Number of positive training (Tr + ) and test</td></tr><tr><td>(Te + ) examples in the SRL dataset. Accuracy of</td></tr><tr><td>the non-linearized (SST) and linearized (SST \u2113 ) bi-</td></tr><tr><td>nary classifiers (i.e. BC, A0, . . . A5) is F 1 measure.</td></tr><tr><td>Accuracy of RM is the percentage of correct class</td></tr><tr><td>assignments.</td></tr></table>"
},
"TABREF3": {
"html": null,
"text": "Number of positive training (Tr + ) and test (Te + ) examples in the QA dataset. Accuracy of the non-linearized (SST) and linearized (SST \u2113 ) binary classifiers is F 1 measure. Overall accuracy is the percentage of correct class assignments.",
"type_str": "table",
"num": null,
"content": "<table/>"
},
"TABREF4": {
"html": null,
"text": "Best fragments for the NUM class.",
"type_str": "table",
"num": null,
"content": "<table/>"
}
}
}
} |