File size: 108,685 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 | {
"paper_id": "P09-1004",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T08:53:14.179560Z"
},
"title": "Unsupervised Argument Identification for Semantic Role Labeling",
"authors": [
{
"first": "Omri",
"middle": [],
"last": "Abend",
"suffix": "",
"affiliation": {},
"email": ""
},
{
"first": "Roi",
"middle": [],
"last": "Reichart",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "ICNC Hebrew University of Jerusalem",
"location": {}
},
"email": ""
},
{
"first": "Ari",
"middle": [],
"last": "Rappoport",
"suffix": "",
"affiliation": {},
"email": ""
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "The task of Semantic Role Labeling (SRL) is often divided into two sub-tasks: verb argument identification, and argument classification. Current SRL algorithms show lower results on the identification sub-task. Moreover, most SRL algorithms are supervised, relying on large amounts of manually created data. In this paper we present an unsupervised algorithm for identifying verb arguments, where the only type of annotation required is POS tagging. The algorithm makes use of a fully unsupervised syntactic parser, using its output in order to detect clauses and gather candidate argument collocation statistics. We evaluate our algorithm on PropBank10, achieving a precision of 56%, as opposed to 47% of a strong baseline. We also obtain an 8% increase in precision for a Spanish corpus. This is the first paper that tackles unsupervised verb argument identification without using manually encoded rules or extensive lexical or syntactic resources.",
"pdf_parse": {
"paper_id": "P09-1004",
"_pdf_hash": "",
"abstract": [
{
"text": "The task of Semantic Role Labeling (SRL) is often divided into two sub-tasks: verb argument identification, and argument classification. Current SRL algorithms show lower results on the identification sub-task. Moreover, most SRL algorithms are supervised, relying on large amounts of manually created data. In this paper we present an unsupervised algorithm for identifying verb arguments, where the only type of annotation required is POS tagging. The algorithm makes use of a fully unsupervised syntactic parser, using its output in order to detect clauses and gather candidate argument collocation statistics. We evaluate our algorithm on PropBank10, achieving a precision of 56%, as opposed to 47% of a strong baseline. We also obtain an 8% increase in precision for a Spanish corpus. This is the first paper that tackles unsupervised verb argument identification without using manually encoded rules or extensive lexical or syntactic resources.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "Semantic Role Labeling (SRL) is a major NLP task, providing a shallow sentence-level semantic analysis. SRL aims at identifying the relations between the predicates (usually, verbs) in the sentence and their associated arguments.",
"cite_spans": [
{
"start": 165,
"end": 181,
"text": "(usually, verbs)",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "The SRL task is often viewed as consisting of two parts: argument identification (ARGID) and argument classification. The former aims at identifying the arguments of a given predicate present in the sentence, while the latter determines the type of relation that holds between the identified arguments and their corresponding predicates. The division into two sub-tasks is justified by the fact that they are best addressed using different feature sets (Pradhan et al., 2005) . Performance in the ARGID stage is a serious bottleneck for general SRL performance, since only about 81% of the arguments are identified, while about 95% of the identified arguments are labeled correctly (M\u00e0rquez et al., 2008) . SRL is a complex task, which is reflected by the algorithms used to address it. A standard SRL algorithm requires thousands to dozens of thousands sentences annotated with POS tags, syntactic annotation and SRL annotation. Current algorithms show impressive results but only for languages and domains where plenty of annotated data is available, e.g., English newspaper texts (see Section 2). Results are markedly lower when testing is on a domain wider than the training one, even in English (see the WSJ-Brown results in (Pradhan et al., 2008) ).",
"cite_spans": [
{
"start": 453,
"end": 475,
"text": "(Pradhan et al., 2005)",
"ref_id": "BIBREF29"
},
{
"start": 682,
"end": 704,
"text": "(M\u00e0rquez et al., 2008)",
"ref_id": "BIBREF24"
},
{
"start": 1230,
"end": 1252,
"text": "(Pradhan et al., 2008)",
"ref_id": "BIBREF30"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Only a small number of works that do not require manually labeled SRL training data have been done (Swier and Stevenson, 2004; Swier and Stevenson, 2005; Grenager and Manning, 2006) . These papers have replaced this data with the VerbNet (Kipper et al., 2000) lexical resource or a set of manually written rules and supervised parsers.",
"cite_spans": [
{
"start": 99,
"end": 126,
"text": "(Swier and Stevenson, 2004;",
"ref_id": "BIBREF35"
},
{
"start": 127,
"end": 153,
"text": "Swier and Stevenson, 2005;",
"ref_id": "BIBREF36"
},
{
"start": 154,
"end": 181,
"text": "Grenager and Manning, 2006)",
"ref_id": "BIBREF16"
},
{
"start": 238,
"end": 259,
"text": "(Kipper et al., 2000)",
"ref_id": "BIBREF20"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "A potential answer to the SRL training data bottleneck are unsupervised SRL models that require little to no manual effort for their training. Their output can be used either by itself, or as training material for modern supervised SRL algorithms.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "In this paper we present an algorithm for unsupervised argument identification. The only type of annotation required by our algorithm is POS tag-ging, which needs relatively little manual effort.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "The algorithm consists of two stages. As preprocessing, we use a fully unsupervised parser to parse each sentence. Initially, the set of possible arguments for a given verb consists of all the constituents in the parse tree that do not contain that predicate. The first stage of the algorithm attempts to detect the minimal clause in the sentence that contains the predicate in question. Using this information, it further reduces the possible arguments only to those contained in the minimal clause, and further prunes them according to their position in the parse tree. In the second stage we use pointwise mutual information to estimate the collocation strength between the arguments and the predicate, and use it to filter out instances of weakly collocating predicate argument pairs.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "We use two measures to evaluate the performance of our algorithm, precision and F-score. Precision reflects the algorithm's applicability for creating training data to be used by supervised SRL models, while the standard SRL F-score measures the model's performance when used by itself. The first stage of our algorithm is shown to outperform a strong baseline both in terms of Fscore and of precision. The second stage is shown to increase precision while maintaining a reasonable recall.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "We evaluated our model on sections 2-21 of Propbank. As is customary in unsupervised parsing work (e.g. (Seginer, 2007) ), we bounded sentence length by 10 (excluding punctuation). Our first stage obtained a precision of 52.8%, which is more than 6% improvement over the baseline. Our second stage improved precision to nearly 56%, a 9.3% improvement over the baseline. In addition, we carried out experiments on Spanish (on sentences of length bounded by 15, excluding punctuation), achieving an increase of over 7.5% in precision over the baseline. Our algorithm increases F-score as well, showing an 1.8% improvement over the baseline in English and a 2.2% improvement in Spanish.",
"cite_spans": [
{
"start": 104,
"end": 119,
"text": "(Seginer, 2007)",
"ref_id": "BIBREF33"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Section 2 reviews related work. In Section 3 we detail our algorithm. Sections 4 and 5 describe the experimental setup and results.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "The advance of machine learning based approaches in this field owes to the usage of large scale annotated corpora. English is the most stud-ied language, using the FrameNet (FN) (Baker et al., 1998) and PropBank (PB) (Palmer et al., 2005) resources. PB is a corpus well suited for evaluation, since it annotates every non-auxiliary verb in a real corpus (the WSJ sections of the Penn Treebank). PB is a standard corpus for SRL evaluation and was used in the CoNLL SRL shared tasks of 2004 (Carreras and M\u00e0rquez, 2004) and 2005 (Carreras and M\u00e0rquez, 2005) .",
"cite_spans": [
{
"start": 178,
"end": 198,
"text": "(Baker et al., 1998)",
"ref_id": "BIBREF0"
},
{
"start": 217,
"end": 238,
"text": "(Palmer et al., 2005)",
"ref_id": "BIBREF28"
},
{
"start": 489,
"end": 517,
"text": "(Carreras and M\u00e0rquez, 2004)",
"ref_id": "BIBREF6"
},
{
"start": 527,
"end": 555,
"text": "(Carreras and M\u00e0rquez, 2005)",
"ref_id": "BIBREF7"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "Most work on SRL has been supervised, requiring dozens of thousands of SRL annotated training sentences. In addition, most models assume that a syntactic representation of the sentence is given, commonly in the form of a parse tree, a dependency structure or a shallow parse. Obtaining these is quite costly in terms of required human annotation.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "The first work to tackle SRL as an independent task is (Gildea and Jurafsky, 2002) , which presented a supervised model trained and evaluated on FrameNet. The CoNLL shared tasks of 2004 and 2005 were devoted to SRL, and studied the influence of different syntactic annotations and domain changes on SRL results. Computational Linguistics has recently published a special issue on the task (M\u00e0rquez et al., 2008) , which presents state-of-the-art results and surveys the latest achievements and challenges in the field. Most approaches to the task use a multi-level approach, separating the task to an ARGID and an argument classification sub-tasks. They then use the unlabeled argument structure (without the semantic roles) as training data for the ARGID stage and the entire data (perhaps with other features) for the classification stage. Better performance is achieved on the classification, where stateof-the-art supervised approaches achieve about 81% F-score on the in-domain identification task, of which about 95% are later labeled correctly (M\u00e0rquez et al., 2008) .",
"cite_spans": [
{
"start": 55,
"end": 82,
"text": "(Gildea and Jurafsky, 2002)",
"ref_id": "BIBREF12"
},
{
"start": 389,
"end": 411,
"text": "(M\u00e0rquez et al., 2008)",
"ref_id": "BIBREF24"
},
{
"start": 1051,
"end": 1073,
"text": "(M\u00e0rquez et al., 2008)",
"ref_id": "BIBREF24"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "There have been several exceptions to the standard architecture described in the last paragraph. One suggestion poses the problem of SRL as a sequential tagging of words, training an SVM classifier to determine for each word whether it is inside, outside or in the beginning of an argument (Hacioglu and Ward, 2003) . Other works have integrated argument classification and identification into one step (Collobert and Weston, 2007) , while others went further and combined the former two along with parsing into a single model (Musillo and Merlo, 2006) .",
"cite_spans": [
{
"start": 290,
"end": 315,
"text": "(Hacioglu and Ward, 2003)",
"ref_id": "BIBREF18"
},
{
"start": 403,
"end": 431,
"text": "(Collobert and Weston, 2007)",
"ref_id": "BIBREF9"
},
{
"start": 527,
"end": 552,
"text": "(Musillo and Merlo, 2006)",
"ref_id": "BIBREF27"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "Work on less supervised methods has been scarce. Swier and Stevenson (2004) and Swier and Stevenson (2005) presented the first model that does not use an SRL annotated corpus. However, they utilize the extensive verb lexicon Verb-Net, which lists the possible argument structures allowable for each verb, and supervised syntactic tools. Using VerbNet along with the output of a rule-based chunker (in 2004) and a supervised syntactic parser (in 2005), they spot instances in the corpus that are very similar to the syntactic patterns listed in VerbNet. They then use these as seed for a bootstrapping algorithm, which consequently identifies the verb arguments in the corpus and assigns their semantic roles.",
"cite_spans": [
{
"start": 49,
"end": 75,
"text": "Swier and Stevenson (2004)",
"ref_id": "BIBREF35"
},
{
"start": 80,
"end": 106,
"text": "Swier and Stevenson (2005)",
"ref_id": "BIBREF36"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "Another less supervised work is that of (Grenager and Manning, 2006) , which presents a Bayesian network model for the argument structure of a sentence. They use EM to learn the model's parameters from unannotated data, and use this model to tag a test corpus. However, ARGID was not the task of that work, which dealt solely with argument classification. ARGID was performed by manually-created rules, requiring a supervised or manual syntactic annotation of the corpus to be annotated.",
"cite_spans": [
{
"start": 40,
"end": 68,
"text": "(Grenager and Manning, 2006)",
"ref_id": "BIBREF16"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "The three works above are relevant but incomparable to our work, due to the extensive amount of supervision (namely, VerbNet and a rule-based or supervised syntactic system) they used, both in detecting the syntactic structure and in detecting the arguments.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "Work has been carried out in a few other languages besides English. Chinese has been studied in (Xue, 2008) . Experiments on Catalan and Spanish were done in SemEval 2007 (M\u00e0rquez et al., 2007) with two participating systems. Attempts to compile corpora for German (Burdchardt et al., 2006) and Arabic (Diab et al., 2008) are also underway. The small number of languages for which extensive SRL annotated data exists reflects the considerable human effort required for such endeavors.",
"cite_spans": [
{
"start": 96,
"end": 107,
"text": "(Xue, 2008)",
"ref_id": "BIBREF39"
},
{
"start": 171,
"end": 193,
"text": "(M\u00e0rquez et al., 2007)",
"ref_id": "BIBREF26"
},
{
"start": 265,
"end": 290,
"text": "(Burdchardt et al., 2006)",
"ref_id": null
},
{
"start": 302,
"end": 321,
"text": "(Diab et al., 2008)",
"ref_id": "BIBREF10"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "Some SRL works have tried to use unannotated data to improve the performance of a base supervised model. Methods used include bootstrapping approaches (Gildea and Jurafsky, 2002; Kate and Mooney, 2007) , where large unannotated corpora were tagged with SRL annotation, later to be used to retrain the SRL model. Another ap-proach used similarity measures either between verbs (Gordon and Swanson, 2007) or between nouns (Gildea and Jurafsky, 2002) to overcome lexical sparsity. These measures were estimated using statistics gathered from corpora augmenting the model's training data, and were then utilized to generalize across similar verbs or similar arguments.",
"cite_spans": [
{
"start": 151,
"end": 178,
"text": "(Gildea and Jurafsky, 2002;",
"ref_id": "BIBREF12"
},
{
"start": 179,
"end": 201,
"text": "Kate and Mooney, 2007)",
"ref_id": "BIBREF19"
},
{
"start": 376,
"end": 402,
"text": "(Gordon and Swanson, 2007)",
"ref_id": "BIBREF14"
},
{
"start": 420,
"end": 447,
"text": "(Gildea and Jurafsky, 2002)",
"ref_id": "BIBREF12"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "Attempts to substitute full constituency parsing by other sources of syntactic information have been carried out in the SRL community. Suggestions include posing SRL as a sequence labeling problem or as an edge tagging problem in a dependency representation (Hacioglu, 2004) . Punyakanok et al. (2008) provide a detailed comparison between the impact of using shallow vs. full constituency syntactic information in an English SRL system. Their results clearly demonstrate the advantage of using full annotation.",
"cite_spans": [
{
"start": 258,
"end": 274,
"text": "(Hacioglu, 2004)",
"ref_id": "BIBREF17"
},
{
"start": 277,
"end": 301,
"text": "Punyakanok et al. (2008)",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "The identification of arguments has also been carried out in the context of automatic subcategorization frame acquisition. Notable examples include (Manning, 1993; Briscoe and Carroll, 1997; Korhonen, 2002) who all used statistical hypothesis testing to filter a parser's output for arguments, with the goal of compiling verb subcategorization lexicons. However, these works differ from ours as they attempt to characterize the behavior of a verb type, by collecting statistics from various instances of that verb, and not to determine which are the arguments of specific verb instances.",
"cite_spans": [
{
"start": 148,
"end": 163,
"text": "(Manning, 1993;",
"ref_id": "BIBREF23"
},
{
"start": 164,
"end": 190,
"text": "Briscoe and Carroll, 1997;",
"ref_id": "BIBREF2"
},
{
"start": 191,
"end": 206,
"text": "Korhonen, 2002)",
"ref_id": "BIBREF22"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "The algorithm presented in this paper performs unsupervised clause detection as an intermediate step towards argument identification. Supervised clause detection was also tackled as a separate task, notably in the CoNLL 2001 shared task (Tjong Kim Sang and D\u00e8jean, 2001) . Clause information has been applied to accelerating a syntactic parser (Glaysher and Moldovan, 2006) .",
"cite_spans": [
{
"start": 237,
"end": 270,
"text": "(Tjong Kim Sang and D\u00e8jean, 2001)",
"ref_id": null
},
{
"start": 344,
"end": 373,
"text": "(Glaysher and Moldovan, 2006)",
"ref_id": "BIBREF13"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "In this section we describe our algorithm. It consists of two stages, each of which reduces the set of argument candidates, which a-priori contains all consecutive sequences of words that do not contain the predicate in question.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Algorithm",
"sec_num": "3"
},
{
"text": "As pre-processing, we use an unsupervised parser that generates an unlabeled parse tree for each sen-tence (Seginer, 2007) . This parser is unique in that it is able to induce a bracketing (unlabeled parsing) from raw text (without even using POS tags) achieving state-of-the-art results. Since our algorithm uses millions to tens of millions sentences, we must use very fast tools. The parser's high speed (thousands of words per second) enables us to process these large amounts of data.",
"cite_spans": [
{
"start": 107,
"end": 122,
"text": "(Seginer, 2007)",
"ref_id": "BIBREF33"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Algorithm overview",
"sec_num": "3.1"
},
{
"text": "The only type of supervised annotation we use is POS tagging. We use the taggers MX-POST (Ratnaparkhi, 1996) for English and Tree-Tagger (Schmid, 1994) for Spanish, to obtain POS tags for our model.",
"cite_spans": [
{
"start": 89,
"end": 108,
"text": "(Ratnaparkhi, 1996)",
"ref_id": "BIBREF31"
},
{
"start": 137,
"end": 151,
"text": "(Schmid, 1994)",
"ref_id": "BIBREF32"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Algorithm overview",
"sec_num": "3.1"
},
{
"text": "The first stage of our algorithm uses linguistically motivated considerations to reduce the set of possible arguments. It does so by confining the set of argument candidates only to those constituents which obey the following two restrictions. First, they should be contained in the minimal clause containing the predicate. Second, they should be k-th degree cousins of the predicate in the parse tree. We propose a novel algorithm for clause detection and use its output to determine which of the constituents obey these two restrictions.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Algorithm overview",
"sec_num": "3.1"
},
{
"text": "The second stage of the algorithm uses pointwise mutual information to rule out constituents that appear to be weakly collocating with the predicate in question. Since a predicate greatly restricts the type of arguments with which it may appear (this is often referred to as \"selectional restrictions\"), we expect it to have certain characteristic arguments with which it is likely to collocate.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Algorithm overview",
"sec_num": "3.1"
},
{
"text": "The main idea behind this stage is the observation that most of the arguments of a predicate are contained within the minimal clause that contains the predicate. We tested this on our development data -section 24 of the WSJ PTB, where we saw that 86% of the arguments that are also constituents (in the gold standard parse) were indeed contained in that minimal clause (as defined by the tree label types in the gold standard parse that denote a clause, e.g., S, SBAR). Since we are not provided with clause annotation (or any label), we attempted to detect them in an unsupervised manner. Our algorithm attempts to find sub-trees within the parse tree, whose structure resembles the structure of a full sentence. This approximates the notion of a clause. Figure 1 : An example of an unlabeled POS tagged parse tree. The middle tree is the ST of 'reach' with the root as the encoded ancestor. The bottom one is the ST with its parent as the encoded ancestor.",
"cite_spans": [],
"ref_spans": [
{
"start": 756,
"end": 764,
"text": "Figure 1",
"ref_id": null
}
],
"eq_spans": [],
"section": "Clause detection stage",
"sec_num": "3.2"
},
{
"text": "L L DT The NNS materials L L IN in L DT each NN set L VBP reach L L IN about CD 90 NNS students L L L L L VBP L L VBP L",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Clause detection stage",
"sec_num": "3.2"
},
{
"text": "Statistics gathering. In order to detect which of the verb's ancestors is the minimal clause, we score each of the ancestors and select the one that maximizes the score. We represent each ancestor using its Spinal Tree (ST ). The ST of a given verb's ancestor is obtained by replacing all the constituents that do not contain the verb by a leaf having a label. This effectively encodes all the kth degree cousins of the verb (for every k). The leaf labels are either the word's POS in case the constituent is a leaf, or the generic label \"L\" denoting a non-leaf. See Figure 1 for an example. In this stage we collect statistics of the occurrences of ST s in a large corpus. For every ST in the corpus, we count the number of times it occurs in a form we consider to be a clause (positive examples), and the number of times it appears in other forms (negative examples).",
"cite_spans": [],
"ref_spans": [
{
"start": 567,
"end": 575,
"text": "Figure 1",
"ref_id": null
}
],
"eq_spans": [],
"section": "Clause detection stage",
"sec_num": "3.2"
},
{
"text": "Positive examples are divided into two main types. First, when the ST encodes the root ancestor (as in the middle tree of Figure 1) ; second, when the ancestor complies to a clause lexicosyntactic pattern. In many languages there is a small set of lexico-syntactic patterns that mark a clause, e.g. the English 'that', the German 'dass' and the Spanish 'que'. The patterns which were used in our experiments are shown in Figure 2 .",
"cite_spans": [],
"ref_spans": [
{
"start": 122,
"end": 131,
"text": "Figure 1)",
"ref_id": null
},
{
"start": 421,
"end": 429,
"text": "Figure 2",
"ref_id": "FIGREF0"
}
],
"eq_spans": [],
"section": "Clause detection stage",
"sec_num": "3.2"
},
{
"text": "For each verb instance, we traverse over its an-English TO + VB. The constituent starts with \"to\" followed by a verb in infinitive form.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Clause detection stage",
"sec_num": "3.2"
},
{
"text": "The constituent is preceded by a Wh-pronoun.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "WP.",
"sec_num": null
},
{
"text": "That. The constituent is preceded by a \"that\" marked by an \"IN\" POS tag indicating that it is a subordinating conjunction.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "WP.",
"sec_num": null
},
{
"text": "The constituent is preceded by a word with the POS \"CQUE\" which denotes the word \"que\" as a conjunction.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "CQUE.",
"sec_num": null
},
{
"text": "INT. The constituent is preceded by a word with the POS \"INT\" which denotes an interrogative pronoun.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "CQUE.",
"sec_num": null
},
{
"text": "CSUB. The constituent is preceded by a word with one of the POSs \"CSUBF\", \"CSUBI\" or \"CSUBX\", which denote a subordinating conjunction. cestors from top to bottom. For each of them we update the following counters: sentence(ST ) for the root ancestor's ST , pattern i (ST ) for the ones complying to the i-th lexico-syntactic pattern and negative(ST ) for the other ancestors 1 .",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "CQUE.",
"sec_num": null
},
{
"text": "Clause detection. At test time, when detecting the minimal clause of a verb instance, we use the statistics collected in the previous stage. Denote the ancestors of the verb with A 1 . . . A m . For each of them, we calculate clause(ST A j ) and total(ST A j ). clause(ST A j ) is the sum of sentence(ST A j ) and pattern i (ST A j ) if this ancestor complies to the i-th pattern (if there is no such pattern, clause(ST A j ) is equal to sentence(ST A j )). total(ST A j ) is the sum of clause(ST A j ) and negative(ST A j ).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "CQUE.",
"sec_num": null
},
{
"text": "The selected ancestor is given by:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "CQUE.",
"sec_num": null
},
{
"text": "(1)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "CQUE.",
"sec_num": null
},
{
"text": "A max = argmax A j clause(ST A j ) total(ST A j )",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "CQUE.",
"sec_num": null
},
{
"text": "An ST whose total(ST ) is less than a small threshold 2 is not considered a candidate to be the minimal clause, since its statistics may be unreliable. In case of a tie, we choose the lowest constituent that obtained the maximal score.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "CQUE.",
"sec_num": null
},
{
"text": "If there is only one verb in the sentence 3 or if clause(ST A j ) = 0 for every 1 \u2264 j \u2264 m, we choose the top level constituent by default to be the minimal clause containing the verb. Otherwise, the minimal clause is defined to be the yield of the selected ancestor.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "CQUE.",
"sec_num": null
},
{
"text": "Argument identification. For each predicate in the corpus, its argument candidates are now defined to be the constituents contained in the minimal clause containing the predicate. However, these constituents may be (and are) nested within each other, violating a major restriction on SRL arguments. Hence we now prune our set, by keeping only the siblings of all of the verb's ancestors, as is common in supervised SRL (Xue and Palmer, 2004) .",
"cite_spans": [
{
"start": 419,
"end": 441,
"text": "(Xue and Palmer, 2004)",
"ref_id": "BIBREF38"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "CQUE.",
"sec_num": null
},
{
"text": "We use the following observation to filter out some superfluous argument candidates: since the arguments of a predicate many times bear a semantic connection with that predicate, they consequently tend to collocate with it.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Using collocations",
"sec_num": "3.3"
},
{
"text": "We collect collocation statistics from a large corpus, which we annotate with parse trees and POS tags. We mark arguments using the argument detection algorithm described in the previous two sections, and extract all (predicate, argument) pairs appearing in the corpus. Recall that for each sentence, the arguments are a subset of the constituents in the parse tree.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Using collocations",
"sec_num": "3.3"
},
{
"text": "We use two representations of an argument: one is the POS tag sequence of the terminals contained in the argument, the other is its head word 4 . The predicate is represented as the conjunction of its lemma with its POS tag.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Using collocations",
"sec_num": "3.3"
},
{
"text": "Denote the number of times a predicate x appeared with an argument y by n xy . Denote the total number of (predicate, argument) pairs by N . Using these notations, we define the following quantities: (2) P M I(x, y) = log p(x,y) p(x)\u2022p(y) = log nxy (nx\u2022ny)/N P M I effectively measures the ratio between the number of times x and y appeared together and the number of times they were expected to appear, had they been independent.",
"cite_spans": [
{
"start": 222,
"end": 228,
"text": "p(x,y)",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Using collocations",
"sec_num": "3.3"
},
{
"text": "n x = \u03a3 y n xy , n y = \u03a3 x n xy , p(x) = nx N , p(y) =",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Using collocations",
"sec_num": "3.3"
},
{
"text": "At test time, when an (x, y) pair is observed, we check if P M I(x, y), computed on the large corpus, is lower than a threshold \u03b1 for either of x's representations. If this holds, for at least one representation, we prune all instances of that (x, y) pair. The parameter \u03b1 may be selected differently for each of the argument representations.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Using collocations",
"sec_num": "3.3"
},
{
"text": "In order to avoid using unreliable statistics, we apply this for a given pair only if nx\u2022ny N > r, for some parameter r. That is, we consider P M I(x, y) to be reliable, only if the denominator in equation 2is sufficiently large.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Using collocations",
"sec_num": "3.3"
},
{
"text": "Corpora. We used the PropBank corpus for development and for evaluation on English. Section 24 was used for the development of our model, and sections 2 to 21 were used as our test data. The free parameters of the collocation extraction phase were tuned on the development data. Following the unsupervised parsing literature, multiple brackets and brackets covering a single word are omitted. We exclude punctuation according to the scheme of (Klein, 2005) . As is customary in unsupervised parsing (e.g. (Seginer, 2007) ), we bounded the lengths of the sentences in the corpus to be at most 10 (excluding punctuation). This results in 207 sentences in the development data, containing a total of 132 different verbs and 173 verb instances (of the non-auxiliary verbs in the SRL task, see 'evaluation' below) having 403 arguments. The test data has 6007 sentences containing 1008 different verbs and 5130 verb instances (as above) having 12436 arguments.",
"cite_spans": [
{
"start": 443,
"end": 456,
"text": "(Klein, 2005)",
"ref_id": "BIBREF21"
},
{
"start": 505,
"end": 520,
"text": "(Seginer, 2007)",
"ref_id": "BIBREF33"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Experimental Setup",
"sec_num": "4"
},
{
"text": "Our algorithm requires large amounts of data to gather argument structure and collocation patterns. For the statistics gathering phase of the clause detection algorithm, we used 4.5M sentences of the NANC (Graff, 1995) corpus, bounding their length in the same manner. In order to extract collocations, we used 2M sentences from the British National Corpus (Burnard, 2000) and about 29M sentences from the Dmoz corpus (Gabrilovich and Markovitch, 2005) . Dmoz is a web corpus obtained by crawling and clean-ing the URLs in the Open Directory Project (dmoz.org). All of the above corpora were parsed using Seginer's parser and POS-tagged by MX-POST (Ratnaparkhi, 1996) .",
"cite_spans": [
{
"start": 205,
"end": 218,
"text": "(Graff, 1995)",
"ref_id": "BIBREF15"
},
{
"start": 357,
"end": 372,
"text": "(Burnard, 2000)",
"ref_id": "BIBREF5"
},
{
"start": 418,
"end": 452,
"text": "(Gabrilovich and Markovitch, 2005)",
"ref_id": "BIBREF11"
},
{
"start": 648,
"end": 667,
"text": "(Ratnaparkhi, 1996)",
"ref_id": "BIBREF31"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Experimental Setup",
"sec_num": "4"
},
{
"text": "For our experiments on Spanish, we used 3.3M sentences of length at most 15 (excluding punctuation) extracted from the Spanish Wikipedia. Here we chose to bound the length by 15 due to the smaller size of the available test corpus. The same data was used both for the first and the second stages. Our development and test data were taken from the training data released for the Se-mEval 2007 task on semantic annotation of Spanish (M\u00e0rquez et al., 2007) . This data consisted of 1048 sentences of length up to 15, from which 200 were randomly selected as our development data and 848 as our test data. The development data included 313 verb instances while the test data included 1279. All corpora were parsed using the Seginer parser and tagged by the \"Tree-Tagger\" (Schmid, 1994) .",
"cite_spans": [
{
"start": 431,
"end": 453,
"text": "(M\u00e0rquez et al., 2007)",
"ref_id": "BIBREF26"
},
{
"start": 767,
"end": 781,
"text": "(Schmid, 1994)",
"ref_id": "BIBREF32"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Experimental Setup",
"sec_num": "4"
},
{
"text": "Baselines. Since this is the first paper, to our knowledge, which addresses the problem of unsupervised argument identification, we do not have any previous results to compare to. We instead compare to a baseline which marks all k-th degree cousins of the predicate (for every k) as arguments (this is the second pruning we use in the clause detection stage). We name this baseline the ALL COUSINS baseline. We note that a random baseline would score very poorly since any sequence of terminals which does not contain the predicate is a possible candidate. Therefore, beating this random baseline is trivial.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Experimental Setup",
"sec_num": "4"
},
{
"text": "Evaluation. Evaluation is carried out using standard SRL evaluation software 5 . The algorithm is provided with a list of predicates, whose arguments it needs to annotate. For the task addressed in this paper, non-consecutive parts of arguments are treated as full arguments. A match is considered each time an argument in the gold standard data matches a marked argument in our model's output. An unmatched argument is an argument which appears in the gold standard data, and fails to appear in our model's output, and an excessive argument is an argument which appears in our model's output but does not appear in the gold standard. Precision and recall are defined accordingly. We report an F-score as well (the harmonic mean of precision and recall). We do not attempt to identify multi-word verbs, and therefore do not report the model's performance in identifying verb boundaries.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Experimental Setup",
"sec_num": "4"
},
{
"text": "Since our model detects clauses as an intermediate product, we provide a separate evaluation of this task for the English corpus. We show results on our development data. We use the standard parsing F-score evaluation measure. As a gold standard in this evaluation, we mark for each of the verbs in our development data the minimal clause containing it. A minimal clause is the lowest ancestor of the verb in the parse tree that has a syntactic label of a clause according to the gold standard parse of the PTB. A verb is any terminal marked by one of the POS tags of type verb according to the gold standard POS tags of the PTB.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Experimental Setup",
"sec_num": "4"
},
{
"text": "Our results are shown in Table 1 . The left section presents results on English and the right section presents results on Spanish. The top line lists results of the clause detection stage alone. The next two lines list results of the full algorithm (clause detection + collocations) in two different settings of the collocation stage. The bottom line presents the performance of the ALL COUSINS baseline.",
"cite_spans": [],
"ref_spans": [
{
"start": 25,
"end": 32,
"text": "Table 1",
"ref_id": null
}
],
"eq_spans": [],
"section": "Results",
"sec_num": "5"
},
{
"text": "In the \"Collocation Maximum Precision\" setting the parameters of the collocation stage (\u03b1 and r) were generally tuned such that maximal precision is achieved while preserving a minimal recall level (40% for English, 20% for Spanish on the development data). In the \"Collocation Maximum Fscore\" the collocation parameters were generally tuned such that the maximum possible F-score for the collocation algorithm is achieved.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Results",
"sec_num": "5"
},
{
"text": "The best or close to best F-score is achieved when using the clause detection algorithm alone (59.14% for English, 23.34% for Spanish). Note that for both English and Spanish F-score improvements are achieved via a precision improvement that is more significant than the recall degradation. F-score maximization would be the aim of a system that uses the output of our unsupervised ARGID by itself.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Results",
"sec_num": "5"
},
{
"text": "The \"Collocation Maximum Precision\" achieves the best precision level (55.97% for English, 21.8% for Spanish) but at the expense of the largest recall loss. Still, it maintains a reasonable level of recall. The \"Collocation Maximum F-score\" is an example of a model that provides a precision improvement (over both the baseline and the clause detection stage) with a relatively small recall degradation. In the Spanish experiments its F-score (23.87%) is even a bit higher than that of the clause detection stage (23.34%).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Results",
"sec_num": "5"
},
{
"text": "The full two-stage algorithm (clause detection + collocations) should thus be used when we intend to use the model's output as training data for supervised SRL engines or supervised ARGID algorithms.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Results",
"sec_num": "5"
},
{
"text": "In our algorithm, the initial set of potential arguments consists of constituents in the Seginer parser's parse tree. Consequently the fraction of arguments that are also constituents (81.87% for English and 51.83% for Spanish) poses an upper bound on our algorithm's recall. Note that the recall of the ALL COUSINS baseline is 74.27% (45.75%) for English (Spanish). This score emphasizes the baseline's strength, and justifies the restriction that the arguments should be k-th cousins of the predicate. The difference between these bounds for the two languages provides a partial explanation for the corresponding gap in the algorithm's performance. Figure 3 shows the precision of the collocation model (on development data) as a function of the amount of data it was given. We can see that the algorithm reaches saturation at about 5M sentences. It achieves this precision while maintaining a reasonable recall (an average recall of 43.1% after saturation). The parameters of the collocation model were separately tuned for each corpus size, and the graph displays the maximum which was obtained for each of the corpus sizes.",
"cite_spans": [],
"ref_spans": [
{
"start": 651,
"end": 659,
"text": "Figure 3",
"ref_id": "FIGREF2"
}
],
"eq_spans": [],
"section": "Results",
"sec_num": "5"
},
{
"text": "To better understand our model's performance, we performed experiments on the English corpus to test how well its first stage detects clauses. Clause detection is used by our algorithm as a step towards argument identification, but it can be of potential benefit for other purposes as well (see Section 2). The results are 23.88% recall and 40% precision. As in the ARGID task, a random selection of arguments would have yielded an extremely poor result.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Results",
"sec_num": "5"
},
{
"text": "In this work we presented the first algorithm for argument identification that uses neither supervised syntactic annotation nor SRL tagged data. We have experimented on two languages: English and Spanish. supervised models to different languages is one of their most appealing characteristics. The recent availability of unsupervised syntactic parsers has offered an opportunity to conduct research on SRL, without reliance on supervised syntactic annotation. This work is the first to address the application of unsupervised parses to an SRL related task.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion",
"sec_num": "6"
},
{
"text": "Our model displayed an increase in precision of 9% in English and 8% in Spanish over a strong baseline. Precision is of particular interest in this context, as instances tagged by high quality annotation could be later used as training data for supervised SRL algorithms. In terms of F-score, our model showed an increase of 1.8% in English and of 2.2% in Spanish over the baseline.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion",
"sec_num": "6"
},
{
"text": "Although the quality of unsupervised parses is currently low (compared to that of supervised approaches), using great amounts of data in identifying recurring structures may reduce noise and in addition address sparsity. The techniques presented in this paper are based on this observation, using around 35M sentences in total for English and 3.3M sentences for Spanish.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion",
"sec_num": "6"
},
{
"text": "As this is the first work which addressed unsupervised ARGID, many questions remain to be explored. Interesting issues to address include assessing the utility of the proposed methods when supervised parses are given, comparing our model to systems with no access to unsupervised parses and conducting evaluation using more relaxed measures.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion",
"sec_num": "6"
},
{
"text": "Unsupervised methods for syntactic tasks have matured substantially in the last few years. Notable examples are (Clark, 2003) for unsupervised POS tagging and (Smith and Eisner, 2006) for unsupervised dependency parsing. Adapting our algorithm to use the output of these models, either to reduce the little supervision our algorithm requires (POS tagging) or to provide complementary syntactic information, is an interesting challenge for future work.",
"cite_spans": [
{
"start": 112,
"end": 125,
"text": "(Clark, 2003)",
"ref_id": "BIBREF8"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion",
"sec_num": "6"
},
{
"text": "If while traversing the tree, we encounter an ancestor whose first word is preceded by a coordinating conjunction (marked by the POS tag \"CC\"), we refrain from performing any additional counter updates. Structures containing coordinating conjunctions tend not to obey our lexico-syntactic rules.2 We used 4 per million sentences, derived from development data.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "In this case, every argument in the sentence must be related to that verb.4 Since we do not have syntactic labels, we use an approximate notion. For English we use the Bikel parser default head word rules(Bikel, 2004). For Spanish, we use the leftmost word.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "http://www.lsi.upc.edu/\u223csrlconll/soft.html#software.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
}
],
"back_matter": [],
"bib_entries": {
"BIBREF0": {
"ref_id": "b0",
"title": "The Berkeley FrameNet Project. ACL-COLING '98",
"authors": [
{
"first": "Collin",
"middle": [
"F"
],
"last": "Baker",
"suffix": ""
},
{
"first": "Charles",
"middle": [
"J"
],
"last": "Fillmore",
"suffix": ""
},
{
"first": "John",
"middle": [
"B"
],
"last": "Lowe",
"suffix": ""
}
],
"year": 1998,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Collin F. Baker, Charles J. Fillmore and John B. Lowe, 1998. The Berkeley FrameNet Project. ACL- COLING '98.",
"links": null
},
"BIBREF1": {
"ref_id": "b1",
"title": "Intricacies of Collins' Parsing Model",
"authors": [
{
"first": "M",
"middle": [],
"last": "Daniel",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Bikel",
"suffix": ""
}
],
"year": 2004,
"venue": "Computational Linguistics",
"volume": "30",
"issue": "4",
"pages": "479--511",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Daniel M. Bikel, 2004. Intricacies of Collins' Parsing Model. Computational Linguistics, 30(4):479-511.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "Automatic Extraction of Subcategorization from Corpora",
"authors": [
{
"first": "Ted",
"middle": [],
"last": "Briscoe",
"suffix": ""
},
{
"first": "John",
"middle": [],
"last": "Carroll",
"suffix": ""
}
],
"year": 1997,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ted Briscoe, John Carroll, 1997. Automatic Extraction of Subcategorization from Corpora. Applied NLP 1997.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "German Corpus Resource for Lexical Semantics. LREC '06",
"authors": [
{
"first": "Salsa",
"middle": [],
"last": "The",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Corpus",
"suffix": ""
}
],
"year": null,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "The SALSA Corpus: a German Corpus Resource for Lexical Semantics. LREC '06.",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "User Reference Guide for the British National Corpus",
"authors": [
{
"first": "Lou",
"middle": [],
"last": "Burnard",
"suffix": ""
}
],
"year": 2000,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Lou Burnard, 2000. User Reference Guide for the British National Corpus. Technical report, Oxford University.",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "Introduction to the CoNLL-2004 Shared Task: Semantic Role Labeling",
"authors": [
{
"first": "Xavier",
"middle": [],
"last": "Carreras",
"suffix": ""
},
{
"first": "Llu\u00ecs",
"middle": [],
"last": "M\u00e0rquez",
"suffix": ""
}
],
"year": 2004,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Xavier Carreras and Llu\u00ecs M\u00e0rquez, 2004. Intro- duction to the CoNLL-2004 Shared Task: Semantic Role Labeling. CoNLL '04.",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "Introduction to the CoNLL-2005 Shared Task: Semantic Role Labeling. CoNLL '05",
"authors": [
{
"first": "Xavier",
"middle": [],
"last": "Carreras",
"suffix": ""
},
{
"first": "Llu\u00ecs",
"middle": [],
"last": "M\u00e0rquez",
"suffix": ""
}
],
"year": 2005,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Xavier Carreras and Llu\u00ecs M\u00e0rquez, 2005. Intro- duction to the CoNLL-2005 Shared Task: Semantic Role Labeling. CoNLL '05.",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "Combining Distributional and Morphological Information for Part of Speech Induction",
"authors": [
{
"first": "Alexander",
"middle": [],
"last": "Clark",
"suffix": ""
}
],
"year": 2003,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Alexander Clark, 2003. Combining Distributional and Morphological Information for Part of Speech In- duction. EACL '03.",
"links": null
},
"BIBREF9": {
"ref_id": "b9",
"title": "Fast Semantic Extraction Using a Novel Neural Network Architecture",
"authors": [
{
"first": "Ronan",
"middle": [],
"last": "Collobert",
"suffix": ""
},
{
"first": "Jason",
"middle": [],
"last": "Weston",
"suffix": ""
}
],
"year": 2007,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ronan Collobert and Jason Weston, 2007. Fast Se- mantic Extraction Using a Novel Neural Network Architecture. ACL '07.",
"links": null
},
"BIBREF10": {
"ref_id": "b10",
"title": "Ann Bies and Mohammed Maamouri",
"authors": [
{
"first": "Mona",
"middle": [],
"last": "Diab",
"suffix": ""
},
{
"first": "Aous",
"middle": [],
"last": "Mansouri",
"suffix": ""
},
{
"first": "Martha",
"middle": [],
"last": "Palmer",
"suffix": ""
},
{
"first": "Olga",
"middle": [],
"last": "Babko-Malaya",
"suffix": ""
},
{
"first": "Wajdi",
"middle": [],
"last": "Zaghouani",
"suffix": ""
}
],
"year": 2008,
"venue": "A pilot Arabic Prop-Bank. LREC '08",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Mona Diab, Aous Mansouri, Martha Palmer, Olga Babko-Malaya, Wajdi Zaghouani, Ann Bies and Mohammed Maamouri, 2008. A pilot Arabic Prop- Bank. LREC '08.",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "Feature Generation for Text Categorization using World Knowledge",
"authors": [
{
"first": "Evgeniy",
"middle": [],
"last": "Gabrilovich",
"suffix": ""
},
{
"first": "Shaul",
"middle": [],
"last": "Markovitch",
"suffix": ""
}
],
"year": 2005,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Evgeniy Gabrilovich and Shaul Markovitch, 2005. Feature Generation for Text Categorization using World Knowledge. IJCAI '05.",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"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": "3",
"pages": "245--288",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Daniel Gildea and Daniel Jurafsky, 2002. Automatic Labeling of Semantic Roles. Computational Lin- guistics, 28(3):245-288.",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "Speeding Up Full Syntactic Parsing by Leveraging Partial Parsing Decisions",
"authors": [
{
"first": "Elliot",
"middle": [],
"last": "Glaysher",
"suffix": ""
},
{
"first": "Dan",
"middle": [],
"last": "Moldovan",
"suffix": ""
}
],
"year": 2006,
"venue": "COLING/ACL '06 poster session",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Elliot Glaysher and Dan Moldovan, 2006. Speed- ing Up Full Syntactic Parsing by Leveraging Partial Parsing Decisions. COLING/ACL '06 poster ses- sion.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "Generalizing Semantic Role Annotations across Syntactically Similar Verbs",
"authors": [
{
"first": "Andrew",
"middle": [],
"last": "Gordon",
"suffix": ""
},
{
"first": "Reid",
"middle": [],
"last": "Swanson",
"suffix": ""
}
],
"year": 2007,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Andrew Gordon and Reid Swanson, 2007. Generaliz- ing Semantic Role Annotations across Syntactically Similar Verbs. ACL '07.",
"links": null
},
"BIBREF15": {
"ref_id": "b15",
"title": "North American News Text Corpus. Linguistic Data Consortium",
"authors": [
{
"first": "David",
"middle": [],
"last": "Graff",
"suffix": ""
}
],
"year": 1995,
"venue": "",
"volume": "",
"issue": "",
"pages": "95--116",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "David Graff, 1995. North American News Text Cor- pus. Linguistic Data Consortium. LDC95T21.",
"links": null
},
"BIBREF16": {
"ref_id": "b16",
"title": "Unsupervised Discovery of a Statistical Verb Lexicon",
"authors": [
{
"first": "Trond",
"middle": [],
"last": "Grenager",
"suffix": ""
},
{
"first": "Christopher",
"middle": [
"D"
],
"last": "Manning",
"suffix": ""
}
],
"year": 2006,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Trond Grenager and Christopher D. Manning, 2006. Unsupervised Discovery of a Statistical Verb Lexi- con. EMNLP '06.",
"links": null
},
"BIBREF17": {
"ref_id": "b17",
"title": "Semantic Role Labeling using Dependency Trees",
"authors": [
{
"first": "Kadri",
"middle": [],
"last": "Hacioglu",
"suffix": ""
}
],
"year": 2004,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Kadri Hacioglu, 2004. Semantic Role Labeling using Dependency Trees. COLING '04.",
"links": null
},
"BIBREF18": {
"ref_id": "b18",
"title": "Target Word Detection and Semantic Role Chunking using Support Vector Machines. HLT-NAACL '03",
"authors": [
{
"first": "Kadri",
"middle": [],
"last": "Hacioglu",
"suffix": ""
},
{
"first": "Wayne",
"middle": [],
"last": "Ward",
"suffix": ""
}
],
"year": 2003,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Kadri Hacioglu and Wayne Ward, 2003. Target Word Detection and Semantic Role Chunking using Sup- port Vector Machines. HLT-NAACL '03.",
"links": null
},
"BIBREF19": {
"ref_id": "b19",
"title": "Semi-Supervised Learning for Semantic Parsing using Support Vector Machines",
"authors": [
{
"first": "J",
"middle": [],
"last": "Rohit",
"suffix": ""
},
{
"first": "Raymond",
"middle": [
"J"
],
"last": "Kate",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Mooney",
"suffix": ""
}
],
"year": 2007,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Rohit J. Kate and Raymond J. Mooney, 2007. Semi- Supervised Learning for Semantic Parsing using Support Vector Machines. HLT-NAACL '07.",
"links": null
},
"BIBREF20": {
"ref_id": "b20",
"title": "Class-Based Construction of a Verb Lexicon",
"authors": [
{
"first": "Karin",
"middle": [],
"last": "Kipper",
"suffix": ""
},
{
"first": "Hoa",
"middle": [
"Trang"
],
"last": "Dang",
"suffix": ""
},
{
"first": "Martha",
"middle": [],
"last": "Palmer",
"suffix": ""
}
],
"year": 2000,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Karin Kipper, Hoa Trang Dang and Martha Palmer, 2000. Class-Based Construction of a Verb Lexicon. AAAI '00.",
"links": null
},
"BIBREF21": {
"ref_id": "b21",
"title": "The Unsupervised Learning of Natural Language Structure",
"authors": [
{
"first": "Dan",
"middle": [],
"last": "Klein",
"suffix": ""
}
],
"year": 2005,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Dan Klein, 2005. The Unsupervised Learning of Natu- ral Language Structure. Ph.D. thesis, Stanford Uni- versity.",
"links": null
},
"BIBREF22": {
"ref_id": "b22",
"title": "Subcategorization Acquisition",
"authors": [
{
"first": "Anna",
"middle": [],
"last": "Korhonen",
"suffix": ""
}
],
"year": 2002,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Anna Korhonen, 2002. Subcategorization Acquisition. Ph.D. thesis, University of Cambridge.",
"links": null
},
"BIBREF23": {
"ref_id": "b23",
"title": "Automatic Acquisition of a Large Subcategorization Dictionary",
"authors": [
{
"first": "Christopher",
"middle": [
"D"
],
"last": "Manning",
"suffix": ""
}
],
"year": 1993,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Christopher D. Manning, 1993. Automatic Acquisition of a Large Subcategorization Dictionary. ACL '93.",
"links": null
},
"BIBREF24": {
"ref_id": "b24",
"title": "Semantic Role Labeling: An introdution to the Special Issue",
"authors": [
{
"first": "Llu\u00ecs",
"middle": [],
"last": "M\u00e0rquez",
"suffix": ""
},
{
"first": "Xavier",
"middle": [],
"last": "Carreras",
"suffix": ""
},
{
"first": "Kenneth",
"middle": [
"C"
],
"last": "Littkowski",
"suffix": ""
},
{
"first": "Suzanne",
"middle": [],
"last": "Stevenson",
"suffix": ""
}
],
"year": 2008,
"venue": "Computational Linguistics",
"volume": "34",
"issue": "2",
"pages": "145--159",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Llu\u00ecs M\u00e0rquez, Xavier Carreras, Kenneth C. Lit- tkowski and Suzanne Stevenson, 2008. Semantic Role Labeling: An introdution to the Special Issue. Computational Linguistics, 34(2):145-159",
"links": null
},
"BIBREF25": {
"ref_id": "b25",
"title": "Jesus Gim\u00e8nez Pere Comas and Neus Catal\u00e0",
"authors": [
{
"first": "Llu\u00ecs",
"middle": [],
"last": "M\u00e0rquez",
"suffix": ""
}
],
"year": 2005,
"venue": "Semantic Role Labeling as Sequential Tagging. CoNLL '05",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Llu\u00ecs M\u00e0rquez, Jesus Gim\u00e8nez Pere Comas and Neus Catal\u00e0, 2005. Semantic Role Labeling as Sequential Tagging. CoNLL '05.",
"links": null
},
"BIBREF26": {
"ref_id": "b26",
"title": "Multilevel Semantic Annotation of Catalan and Spanish",
"authors": [
{
"first": "Llu\u00ecs",
"middle": [],
"last": "M\u00e0rquez",
"suffix": ""
},
{
"first": "Lluis",
"middle": [],
"last": "Villarejo",
"suffix": ""
},
{
"first": "M",
"middle": [
"A"
],
"last": "Mart\u00ec",
"suffix": ""
}
],
"year": 2007,
"venue": "The 4th international workshop on Semantic Evaluations (SemEval '07)",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Llu\u00ecs M\u00e0rquez, Lluis Villarejo, M. A. Mart\u00ec and Mar- iona Taul\u00e8, 2007. SemEval-2007 Task 09: Multi- level Semantic Annotation of Catalan and Spanish. The 4th international workshop on Semantic Evalu- ations (SemEval '07).",
"links": null
},
"BIBREF27": {
"ref_id": "b27",
"title": "Accurate Parsing of the proposition bank",
"authors": [
{
"first": "Gabriele",
"middle": [],
"last": "Musillo",
"suffix": ""
},
{
"first": "Paula",
"middle": [],
"last": "Merlo",
"suffix": ""
}
],
"year": 2006,
"venue": "HLT-NAACL '06",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Gabriele Musillo and Paula Merlo, 2006. Accurate Parsing of the proposition bank. HLT-NAACL '06.",
"links": null
},
"BIBREF28": {
"ref_id": "b28",
"title": "The Proposition Bank: A Corpus Annotated with Semantic Roles",
"authors": [
{
"first": "Martha",
"middle": [],
"last": "Palmer",
"suffix": ""
},
{
"first": "Daniel",
"middle": [],
"last": "Gildea",
"suffix": ""
},
{
"first": "Paul",
"middle": [],
"last": "Kingsbury",
"suffix": ""
}
],
"year": 2005,
"venue": "Computational Linguistics",
"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: A Corpus Annotated with Semantic Roles. Computational Linguistics, 31(1):71-106.",
"links": null
},
"BIBREF29": {
"ref_id": "b29",
"title": "Support Vector Learning for Semantic Argument Classification",
"authors": [
{
"first": "Kadri",
"middle": [],
"last": "Sameer Pradhan",
"suffix": ""
},
{
"first": "Valerie",
"middle": [],
"last": "Hacioglu",
"suffix": ""
},
{
"first": "Wayne",
"middle": [],
"last": "Krugler",
"suffix": ""
},
{
"first": "James",
"middle": [
"H"
],
"last": "Ward",
"suffix": ""
},
{
"first": "Daniel",
"middle": [],
"last": "Martin",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Jurafsky",
"suffix": ""
}
],
"year": 2005,
"venue": "Machine Learning",
"volume": "60",
"issue": "",
"pages": "11--39",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Sameer Pradhan, Kadri Hacioglu, Valerie Krugler, Wayne Ward, James H. Martin and Daniel Jurafsky, 2005. Support Vector Learning for Semantic Argu- ment Classification. Machine Learning, 60(1):11- 39.",
"links": null
},
"BIBREF30": {
"ref_id": "b30",
"title": "Towards Robust Semantic Role Labeling",
"authors": [
{
"first": "Sameer",
"middle": [],
"last": "Pradhan",
"suffix": ""
},
{
"first": "Wayne",
"middle": [],
"last": "Ward",
"suffix": ""
},
{
"first": "James",
"middle": [
"H"
],
"last": "Martin",
"suffix": ""
}
],
"year": 2008,
"venue": "Computational Linguistics",
"volume": "34",
"issue": "2",
"pages": "289--310",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Sameer Pradhan, Wayne Ward, James H. Martin, 2008. Towards Robust Semantic Role Labeling. Computa- tional Linguistics, 34(2):289-310.",
"links": null
},
"BIBREF31": {
"ref_id": "b31",
"title": "Maximum Entropy Part-Of-Speech Tagger. EMNLP '96",
"authors": [
{
"first": "Adwait",
"middle": [],
"last": "Ratnaparkhi",
"suffix": ""
}
],
"year": 1996,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Adwait Ratnaparkhi, 1996. Maximum Entropy Part- Of-Speech Tagger. EMNLP '96.",
"links": null
},
"BIBREF32": {
"ref_id": "b32",
"title": "Probabilistic Part-of-Speech Tagging Using Decision Trees International Conference on New Methods in Language Processing",
"authors": [
{
"first": "Helmut",
"middle": [],
"last": "Schmid",
"suffix": ""
}
],
"year": 1994,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Helmut Schmid, 1994. Probabilistic Part-of-Speech Tagging Using Decision Trees International Confer- ence on New Methods in Language Processing.",
"links": null
},
"BIBREF33": {
"ref_id": "b33",
"title": "Fast Unsupervised Incremental Parsing",
"authors": [
{
"first": "Yoav",
"middle": [],
"last": "Seginer",
"suffix": ""
}
],
"year": 2007,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yoav Seginer, 2007. Fast Unsupervised Incremental Parsing. ACL '07.",
"links": null
},
"BIBREF34": {
"ref_id": "b34",
"title": "Annealing Structural Bias in Multilingual Weighted Grammar Induction",
"authors": [
{
"first": "A",
"middle": [],
"last": "Noah",
"suffix": ""
},
{
"first": "Jason",
"middle": [],
"last": "Smith",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Eisner",
"suffix": ""
}
],
"year": 2006,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Noah A. Smith and Jason Eisner, 2006. Annealing Structural Bias in Multilingual Weighted Grammar Induction. ACL '06.",
"links": null
},
"BIBREF35": {
"ref_id": "b35",
"title": "Unsupervised Semantic Role Labeling",
"authors": [
{
"first": "Robert",
"middle": [
"S"
],
"last": "Swier",
"suffix": ""
},
{
"first": "Suzanne",
"middle": [],
"last": "Stevenson",
"suffix": ""
}
],
"year": 2004,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Robert S. Swier and Suzanne Stevenson, 2004. Unsu- pervised Semantic Role Labeling. EMNLP '04.",
"links": null
},
"BIBREF36": {
"ref_id": "b36",
"title": "Exploiting a Verb Lexicon in Automatic Semantic Role Labelling",
"authors": [
{
"first": "Robert",
"middle": [
"S"
],
"last": "Swier",
"suffix": ""
},
{
"first": "Suzanne",
"middle": [],
"last": "Stevenson",
"suffix": ""
}
],
"year": 2005,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Robert S. Swier and Suzanne Stevenson, 2005. Ex- ploiting a Verb Lexicon in Automatic Semantic Role Labelling. EMNLP '05.",
"links": null
},
"BIBREF37": {
"ref_id": "b37",
"title": "Introduction to the CoNLL-2001 Shared Task: Clause Identification",
"authors": [
{
"first": "Erik",
"middle": [
"F"
],
"last": "Tjong",
"suffix": ""
},
{
"first": "Kim",
"middle": [],
"last": "Sang",
"suffix": ""
},
{
"first": "Herv\u00e9",
"middle": [],
"last": "D\u00e9jean",
"suffix": ""
}
],
"year": 2001,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Erik F. Tjong Kim Sang and Herv\u00e9 D\u00e9jean, 2001. In- troduction to the CoNLL-2001 Shared Task: Clause Identification. CoNLL '01.",
"links": null
},
"BIBREF38": {
"ref_id": "b38",
"title": "Calibrating Features for Semantic Role Labeling",
"authors": [
{
"first": "Nianwen",
"middle": [],
"last": "Xue",
"suffix": ""
},
{
"first": "Martha",
"middle": [],
"last": "Palmer",
"suffix": ""
}
],
"year": 2004,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Nianwen Xue and Martha Palmer, 2004. Calibrating Features for Semantic Role Labeling. EMNLP '04.",
"links": null
},
"BIBREF39": {
"ref_id": "b39",
"title": "Labeling Chinese Predicates with Semantic Roles",
"authors": [
{
"first": "Nianwen",
"middle": [],
"last": "Xue",
"suffix": ""
}
],
"year": 2008,
"venue": "Computational Linguistics",
"volume": "34",
"issue": "2",
"pages": "225--255",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Nianwen Xue, 2008. Labeling Chinese Predicates with Semantic Roles. Computational Linguistics, 34(2):225-255.",
"links": null
}
},
"ref_entries": {
"FIGREF0": {
"type_str": "figure",
"text": "The set of lexico-syntactic patterns that mark clauses which were used by our model.",
"uris": null,
"num": null
},
"FIGREF1": {
"type_str": "figure",
"text": "pointwise mutual information of x and y is then given by:",
"uris": null,
"num": null
},
"FIGREF2": {
"type_str": "figure",
"text": "The performance of the second stage on English (squares) vs. corpus size. The precision of the baseline (triangles) and of the first stage (circles) is displayed for reference. The graph indicates the maximum precision obtained for each corpus size. The graph reaches saturation at about 5M sentences. The average recall of the sampled points from there on is 43.1%. Experiments were performed on the English development data.",
"uris": null,
"num": null
},
"TABREF0": {
"num": null,
"type_str": "table",
"content": "<table><tr><td/><td/><td/><td/><td/><td colspan=\"2\">English (Test Data)</td><td>Spanish (Test Data)</td></tr><tr><td/><td/><td/><td/><td/><td colspan=\"2\">Precision Recall F1</td><td>Precision Recall F1</td></tr><tr><td colspan=\"3\">Clause Detection</td><td/><td/><td>52.84</td><td>67.14 59.14 18.00</td><td>33.19 23.34</td></tr><tr><td colspan=\"5\">Collocation Maximum F-score</td><td>54.11</td><td>63.53 58.44 20.22</td><td>29.13 23.87</td></tr><tr><td colspan=\"6\">Collocation Maximum Precision 55.97</td><td>40.02 46.67 21.80</td><td>18.47 20.00</td></tr><tr><td colspan=\"5\">ALL COUSINS baseline</td><td>46.71</td><td>74.27 57.35 14.16</td><td>45.75 21.62</td></tr><tr><td>42 46 48 50 52 Table 1: 0 Precision 44</td><td>2</td><td>4</td><td>6</td><td>8 Second Stage First Stage Baseline</td><td>10</td></tr><tr><td/><td colspan=\"4\">Number of Sentences (Millions)</td><td/></tr><tr><td/><td/><td/><td/><td/><td/><td>The straightforward adaptability of un-</td></tr></table>",
"html": null,
"text": "Precision, Recall and F1 score for the different stages of our algorithm. Results are given for English (PTB, sentences length bounded by 10, left part of the table) and Spanish (SemEval 2007 Spanish SRL task, right part of the table). The results of the collocation (second) stage are given in two configurations, Collocation Maximum F-score and Collocation Maximum Precision (see text). The upper bounds on Recall, obtained by taking all arguments output by our unsupervised parser, are 81.87% for English and 51.83% for Spanish."
}
}
}
} |