File size: 103,570 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 | {
"paper_id": "P13-1008",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T09:33:58.677669Z"
},
"title": "Joint Event Extraction via Structured Prediction with Global Features",
"authors": [
{
"first": "Qi",
"middle": [],
"last": "Li",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "University of New York New York",
"location": {
"postCode": "10016",
"region": "NY",
"country": "USA"
}
},
"email": ""
},
{
"first": "Heng",
"middle": [],
"last": "Ji",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "University of New York New York",
"location": {
"postCode": "10016",
"region": "NY",
"country": "USA"
}
},
"email": ""
},
{
"first": "Liang",
"middle": [],
"last": "Huang",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "University of New York New York",
"location": {
"postCode": "10016",
"region": "NY",
"country": "USA"
}
},
"email": "liang.huang.sh@gmail.com"
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "Traditional approaches to the task of ACE event extraction usually rely on sequential pipelines with multiple stages, which suffer from error propagation since event triggers and arguments are predicted in isolation by independent local classifiers. By contrast, we propose a joint framework based on structured prediction which extracts triggers and arguments together so that the local predictions can be mutually improved. In addition, we propose to incorporate global features which explicitly capture the dependencies of multiple triggers and arguments. Experimental results show that our joint approach with local features outperforms the pipelined baseline, and adding global features further improves the performance significantly. Our approach advances state-ofthe-art sentence-level event extraction, and even outperforms previous argument labeling methods which use external knowledge from other sentences and documents.",
"pdf_parse": {
"paper_id": "P13-1008",
"_pdf_hash": "",
"abstract": [
{
"text": "Traditional approaches to the task of ACE event extraction usually rely on sequential pipelines with multiple stages, which suffer from error propagation since event triggers and arguments are predicted in isolation by independent local classifiers. By contrast, we propose a joint framework based on structured prediction which extracts triggers and arguments together so that the local predictions can be mutually improved. In addition, we propose to incorporate global features which explicitly capture the dependencies of multiple triggers and arguments. Experimental results show that our joint approach with local features outperforms the pipelined baseline, and adding global features further improves the performance significantly. Our approach advances state-ofthe-art sentence-level event extraction, and even outperforms previous argument labeling methods which use external knowledge from other sentences and documents.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "Event extraction is an important and challenging task in Information Extraction (IE), which aims to discover event triggers with specific types and their arguments. Most state-of-the-art approaches (Ji and Grishman, 2008; Liao and Grishman, 2010; Hong et al., 2011) use sequential pipelines as building blocks, which break down the whole task into separate subtasks, such as trigger identification/classification and argument identification/classification. As a common drawback of the staged architecture, errors in upstream component are often compounded and propagated to the downstream classifiers. The downstream components, however, cannot impact earlier deci-sions. For example, consider the following sentences with an ambiguous word \"fired\":",
"cite_spans": [
{
"start": 198,
"end": 221,
"text": "(Ji and Grishman, 2008;",
"ref_id": "BIBREF10"
},
{
"start": 222,
"end": 246,
"text": "Liao and Grishman, 2010;",
"ref_id": "BIBREF13"
},
{
"start": 247,
"end": 265,
"text": "Hong et al., 2011)",
"ref_id": "BIBREF8"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "(1) In Baghdad, a cameraman died when an American tank fired on the Palestine Hotel.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "(2) He has fired his air defense chief .",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "In sentence (1), \"fired\" is a trigger of type Attack.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Because of the ambiguity, a local classifier may miss it or mislabel it as a trigger of End-Position. However, knowing that \"tank\" is very likely to be an Instrument argument of Attack events, the correct event subtype assignment of \"fired\" is obviously Attack. Likewise, in sentence (2), \"air defense chief\" is a job title, hence the argument classifier is likely to label it as an Entity argument for End-Position trigger. In addition, the local classifiers are incapable of capturing inter-dependencies among multiple event triggers and arguments. Consider sentence (1) again. Figure 1 depicts the corresponding event triggers and arguments. The dependency between \"fired\" and \"died\" cannot be captured by the local classifiers, which may fail to attach \"cameraman\" to \"fired\" as a Target argument. By using global features, we can propagate the Victim argument of the Die event to the Target argument of the Attack event. As another example, knowing that an Attack event usually only has one Attacker argument, we could penalize assignments in which one trigger has more than one Attacker. Such global features cannot be easily exploited by a local classifier.",
"cite_spans": [],
"ref_spans": [
{
"start": 580,
"end": 588,
"text": "Figure 1",
"ref_id": null
}
],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Therefore, we take a fresh look at this problem and formulate it, for the first time, as a structured learning problem. We propose a novel joint event extraction algorithm to predict the triggers and arguments simultaneously, and use the structured perceptron (Collins, 2002) to train the joint model. This way we can capture the dependencies between triggers and argument as well as explore",
"cite_spans": [
{
"start": 260,
"end": 275,
"text": "(Collins, 2002)",
"ref_id": "BIBREF5"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "In Baghdad, a cameraman died when an American tank fired on the Palestine Hotel. Figure 1 : Event mentions of example (1). There are two event mentions that share three arguments, namely the Die event mention triggered by \"died\", and the Attack event mention triggered by \"fired\". arbitrary global features over multiple local predictions. However, different from easier tasks such as part-of-speech tagging or noun phrase chunking where efficient dynamic programming decoding is feasible, here exact joint inference is intractable. Therefore we employ beam search in decoding, and train the model using the early-update perceptron variant tailored for beam search (Collins and Roark, 2004; Huang et al., 2012) .",
"cite_spans": [
{
"start": 665,
"end": 690,
"text": "(Collins and Roark, 2004;",
"ref_id": "BIBREF4"
},
{
"start": 691,
"end": 710,
"text": "Huang et al., 2012)",
"ref_id": "BIBREF9"
}
],
"ref_spans": [
{
"start": 81,
"end": 89,
"text": "Figure 1",
"ref_id": null
}
],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "We make the following contributions:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "1. Different from traditional pipeline approach, we present a novel framework for sentencelevel event extraction, which predicts triggers and their arguments jointly (Section 3).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "2. We develop a rich set of features for event extraction which yield promising performance even with the traditional pipeline (Section 3.4.1). In this paper we refer to them as local features.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "3. We introduce various global features to exploit dependencies among multiple triggers and arguments (Section 3.4.2). Experiments show that our approach outperforms the pipelined approach with the same set of local features, and significantly advances the state-of-the-art with the addition of global features which brings a notable further improvement (Section 4).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "In this paper we focus on the event extraction task defined in Automatic Content Extraction (ACE) evaluation. 1 The task defines 8 event types and 33 subtypes such as Attack, End-Position etc. We introduce the terminology of the ACE event extraction that we used in this paper:",
"cite_spans": [
{
"start": 110,
"end": 111,
"text": "1",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Event Extraction Task",
"sec_num": "2"
},
{
"text": "1 http://projects.ldc.upenn.edu/ace/ \u2022 Event mention: an occurrence of an event with a particular type and subtype. \u2022 Event trigger: the word most clearly expresses the event mention. \u2022 Event argument: an entity mention, temporal expression or value (e.g. Job-Title) that serves as a participant or attribute with a specific role in an event mention. \u2022 Event mention: an instance that includes one event trigger and some arguments that appear within the same sentence.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Event Extraction Task",
"sec_num": "2"
},
{
"text": "Given an English text document, an event extraction system should predict event triggers with specific subtypes and their arguments from each sentence. Figure 1 depicts the event triggers and their arguments of sentence (1) in Section 1. The outcome of the entire sentence can be considered a graph in which each argument role is represented as a typed edge from a trigger to its argument.",
"cite_spans": [],
"ref_spans": [
{
"start": 152,
"end": 160,
"text": "Figure 1",
"ref_id": null
}
],
"eq_spans": [],
"section": "Event Extraction Task",
"sec_num": "2"
},
{
"text": "In this work, we assume that argument candidates such as entities are part of the input to the event extraction, and can be from either gold standard or IE system output.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Event Extraction Task",
"sec_num": "2"
},
{
"text": "Based on the hypothesis that facts are interdependent, we propose to use structured perceptron with inexact search to jointly extract triggers and arguments that co-occur in the same sentence. In this section, we will describe the training and decoding algorithms for this model.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Joint Framework for Event Extraction",
"sec_num": "3"
},
{
"text": "Structured perceptron is an extension to the standard linear perceptron for structured prediction, which was proposed in (Collins, 2002) . Given a sentence instance x \u2208 X , which in our case is a sentence with argument candidates, the structured perceptron involves the following decoding prob-lem which finds the best configuration z \u2208 Y according to the current model w:",
"cite_spans": [
{
"start": 121,
"end": 136,
"text": "(Collins, 2002)",
"ref_id": "BIBREF5"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Structured perceptron with beam search",
"sec_num": "3.1"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "z = argmax y \u2208Y(x) w \u2022 f (x, y )",
"eq_num": "(1)"
}
],
"section": "Structured perceptron with beam search",
"sec_num": "3.1"
},
{
"text": "where f (x, y ) represents the feature vector for instance x along with configuration y . The perceptron learns the model w in an online fashion. Let D = {(x (j) , y (j) )} n j=1 be the set of training instances (with j indexing the current training instance). In each iteration, the algorithm finds the best configuration z for x under the current model (Eq. 1). If z is incorrect, the weights are updated as follows:",
"cite_spans": [
{
"start": 158,
"end": 161,
"text": "(j)",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Structured perceptron with beam search",
"sec_num": "3.1"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "w = w + f (x, y) \u2212 f (x, z)",
"eq_num": "(2)"
}
],
"section": "Structured perceptron with beam search",
"sec_num": "3.1"
},
{
"text": "The key step of the training and test is the decoding procedure, which aims to search for the best configuration under the current parameters. In simpler tasks such as part-of-speech tagging and noun phrase chunking, efficient dynamic programming algorithms can be employed to perform exact inference. Unfortunately, it is intractable to perform the exact search in our framework because: (1) by jointly modeling the trigger labeling and argument labeling, the search space becomes much more complex. (2) we propose to make use of arbitrary global features, which makes it infeasible to perform exact inference efficiently.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Structured perceptron with beam search",
"sec_num": "3.1"
},
{
"text": "To address this problem, we apply beam-search along with early-update strategy to perform inexact decoding. Collins and Roark (2004) proposed the early-update idea, and Huang et al. (2012) later proved its convergence and formalized a general framework which includes it as a special case. Figure 2 describes the skeleton of perceptron training algorithm with beam search. In each step of the beam search, if the prefix of oracle assignment y falls out from the beam, then the top result in the beam is returned for early update. One could also use the standard-update for inference, however, with highly inexact search the standardupdate generally does not work very well because of \"invalid updates\", i.e., updates that do not fix a violation (Huang et al., 2012) . In Section 4.5 we will show that the standard perceptron introduces many invalid updates especially with smaller beam sizes, also observed by Huang et al. (2012) .",
"cite_spans": [
{
"start": 108,
"end": 132,
"text": "Collins and Roark (2004)",
"ref_id": "BIBREF4"
},
{
"start": 169,
"end": 188,
"text": "Huang et al. (2012)",
"ref_id": "BIBREF9"
},
{
"start": 745,
"end": 765,
"text": "(Huang et al., 2012)",
"ref_id": "BIBREF9"
},
{
"start": 910,
"end": 929,
"text": "Huang et al. (2012)",
"ref_id": "BIBREF9"
}
],
"ref_spans": [
{
"start": 290,
"end": 296,
"text": "Figure",
"ref_id": null
}
],
"eq_spans": [],
"section": "Structured perceptron with beam search",
"sec_num": "3.1"
},
{
"text": "To reduce overfitting, we used averaged parameters after training to decode test instances in our experiments. The resulting model is called averaged perceptron (Collins, 2002) .",
"cite_spans": [
{
"start": 161,
"end": 176,
"text": "(Collins, 2002)",
"ref_id": "BIBREF5"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Structured perceptron with beam search",
"sec_num": "3.1"
},
{
"text": "Input: Training set D = {(x (j) , y (j) )} n i=1 , maximum iteration number T Output: Model parameters w 1 Initialization: Set w = 0; 2 for t \u2190 1...T do 3 foreach (x, y) \u2208 D do 4 z \u2190 beamSearch (x, y, w) 5 if z = y then 6 w \u2190 w + f (x, y [1:|z|] ) \u2212 f (x, z)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Structured perceptron with beam search",
"sec_num": "3.1"
},
{
"text": "Figure 2: Perceptron training with beamsearch (Huang et al., 2012) . Here y [1:i] denotes the prefix of y that has length i, e.g., y [1:3] = (y 1 , y 2 , y 3 ).",
"cite_spans": [
{
"start": 46,
"end": 66,
"text": "(Huang et al., 2012)",
"ref_id": "BIBREF9"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Structured perceptron with beam search",
"sec_num": "3.1"
},
{
"text": "Here we introduce the label sets for trigger and argument in the model. We use L \u222a {\u22a5} to denote the trigger label alphabet, where L represents the 33 event subtypes, and \u22a5 indicates that the token is not a trigger. Similarly, R \u222a {\u22a5} denotes the argument label sets, where R is the set of possible argument roles, and \u22a5 means that the argument candidate is not an argument for the current trigger. It is worth to note that the set R of each particular event subtype is subject to the entity type constraints defined in the official ACE annotation guideline 2 . For example, the Attacker argument for an Attack event can only be one of PER, ORG and GPE (Geo-political Entity).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Label sets",
"sec_num": "3.2"
},
{
"text": "Let x = (x 1 , x 2 , ..., x s )",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Decoding",
"sec_num": "3.3"
},
{
"text": ", E denote the sentence instance, where x i represents the i-th token in the sentence and E = {e k } m k=1 is the set of argument candidates. We use",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Decoding",
"sec_num": "3.3"
},
{
"text": "y = (t 1 , a 1,1 , . . . , a 1,m , . . . , t s , a s,1 , . . . , a s,m )",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Decoding",
"sec_num": "3.3"
},
{
"text": "to denote the corresponding gold standard structure, where t i represents the trigger assignment for the token x i , and a i,k represents the argument role label for the edge between x i and argument candidate e k . For simplicity, throughout this paper we use y g(i) and y h(i,k) to represent t i and a i,k , respectively. Figure 3 demonstrates the notation with s = 3 and m = 2. The variables for the toy sentence \"Jobs founded Apple\" are as follows:",
"cite_spans": [],
"ref_spans": [
{
"start": 324,
"end": 332,
"text": "Figure 3",
"ref_id": "FIGREF1"
}
],
"eq_spans": [],
"section": "Decoding",
"sec_num": "3.3"
},
{
"text": "y = (t 1 , a 1,1 , a 1,2 , t 2 , a 2,1 , a 2,2 , | {z } arguments for x 2 t 3 , a 3,1 , a 3,2 ) g(1) g(2) h(2, 1) h(3, 2)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Decoding",
"sec_num": "3.3"
},
{
"text": "x = (Jobs, x 2 founded, Apple), E {Jobs PER , Apple ORG } y = (\u22a5, \u22a5, \u22a5, Start Org t 2 , Agent, Org args for founded , \u22a5, \u22a5, \u22a5)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Decoding",
"sec_num": "3.3"
},
{
"text": "Figure 4 describes the beam-search procedure with early-update for event extraction. During each step with token i, there are two sub-steps:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Decoding",
"sec_num": "3.3"
},
{
"text": "\u2022 Trigger labeling We enumerate all possible trigger labels for the current token. The linear model defined in Eq. 1is used to score each partial configuration. Then the K-best partial configurations are selected to the beam, assuming the beam size is K. \u2022 Argument labeling After the trigger labeling step, we traverse all configurations in the beam. Once a trigger label for x i is found in the beam, the decoder searches through the argument candidates E to label the edges between each argument candidate and the trigger. After labeling each argument candidate, we again score each partial assignment and select the K-best results to the beam.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Decoding",
"sec_num": "3.3"
},
{
"text": "After the second step, the rank of different trigger assignments can be changed because of the argument edges. Likewise, the decision on later argument candidates may be affected by earlier argument assignments. The overall time complexity for decoding is O(K \u2022 s \u2022 m).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Decoding",
"sec_num": "3.3"
},
{
"text": "In this framework, we define two types of features, namely local features and global features. We first introduce the definition of local and global features in this paper, and then describe the implementation details later. Recall that in the linear model defined in Eq. (1), f (x, y) denotes the features extracted from the input instance x along Input: Instance x = (x 1 , x 2 , ..., x s ), E and the oracle output y if for training. K: Beam size. L \u222a {\u22a5}: trigger label alphabet. R \u222a {\u22a5}: argument label alphabet.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Features",
"sec_num": "3.4"
},
{
"text": "Output: 1-best prediction z for x 1 Set beam B \u2190 [ ] /*empty configuration*/ 2 for i \u2190 1...s do 3 buf \u2190 {z \u2022 l | z \u2208 B, l \u2208 L \u222a {\u22a5}} B \u2190K-best(buf ) 4 if y [1:g(i)] \u2208 B then 5 return B[0] /*for early-update*/ 6",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Features",
"sec_num": "3.4"
},
{
"text": "for e k \u2208 E do /*search for arguments*/ with configuration y. In general, each feature instance f in f is a function f : X \u00d7 Y \u2192 R, which maps x and y to a feature value. Local features are only related to predictions on individual trigger or argument. In the case of unigram tagging for trigger labeling, each local feature takes the form of f (x, i, y g(i) ), where i denotes the index of the current token, and y g(i) is its trigger label. In practice, it is convenient to define the local feature function as an indicator function, for example:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Features",
"sec_num": "3.4"
},
{
"text": "7 buf \u2190 \u2205 8 for z \u2208 B do 9 buf \u2190 buf \u222a {z \u2022 \u22a5} 10 if z g(i) = \u22a5 then /*x i is a trigger*/ 11 buf \u2190 buf \u222a {z \u2022 r | r \u2208 R} 12 B \u2190K-best(buf ) 13 if y [1:h(i,k)] \u2208 B then 14 return B[0] /*for early-update*/ 15 return B[0]",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Features",
"sec_num": "3.4"
},
{
"text": "f 1 (x, i, y g(i) ) = 1 if y g(i) = Attack and xi = \"fire\" 0 otherwise",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Features",
"sec_num": "3.4"
},
{
"text": "The global features, by contrast, involve longer range of the output structure. Formally, each global feature function takes the form of f (x, i, k, y), where i and k denote the indices of the current token and argument candidate in decoding, respectively. The following indicator function is a simple example of global features: . unigrams/bigrams of the current and context words within the window of size 2 2. unigrams/bigrams of part-of-speech tags of the current and context words within the window of size 2 3. lemma and synonyms of the current token 4. base form of the current token extracted from Nomlex (Macleod et al., 1998) 5. Brown clusters that are learned from ACE English corpus (Brown et al., 1992; Miller et al., 2004; Sun et al., 2011) . We used the clusters with prefixes of length 13, 16 and 20 for each token.",
"cite_spans": [
{
"start": 613,
"end": 635,
"text": "(Macleod et al., 1998)",
"ref_id": "BIBREF14"
},
{
"start": 695,
"end": 715,
"text": "(Brown et al., 1992;",
"ref_id": "BIBREF1"
},
{
"start": 716,
"end": 736,
"text": "Miller et al., 2004;",
"ref_id": "BIBREF16"
},
{
"start": 737,
"end": 754,
"text": "Sun et al., 2011)",
"ref_id": "BIBREF21"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Features",
"sec_num": "3.4"
},
{
"text": "f 101 (x, i, k, y) = \uf8f1 \uf8f4 \uf8f2 \uf8f4 \uf8f3 1 if y g(i) =",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Features",
"sec_num": "3.4"
},
{
"text": "6. dependent and governor words of the current token 7. dependency types associated the current token 8. whether the current token is a modifier of job title 9. whether the current token is a non-referential pronoun Entity Information 10. unigrams/bigrams normalized by entity types 11. dependency features normalized by entity types 12. nearest entity type and string in the sentence/clause Argument Basic 1. context words of the entity mention 2. trigger word and subtype 3. entity type, subtype and entity role if it is a geo-political entity mention 4. entity mention head, and head of any other name mention from co-reference chain 5. lexical distance between the argument candidate and the trigger 6. the relative position between the argument candidate and the trigger: {before, after, overlap, or separated by punctuation} 7. whether it is the nearest argument candidate with the same type 8. whether it is the only mention of the same entity type in the sentence Syntactic 9. dependency path between the argument candidate and the trigger 10. path from the argument candidate and the trigger in constituent parse tree 11. length of the path between the argument candidate and the trigger in dependency graph 12. common root node and its depth of the argument candidate and parse tree 13. whether the argument candidate and the trigger appear in the same clause ",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Syntactic",
"sec_num": null
},
{
"text": "In general there are two kinds of local features:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Local features",
"sec_num": "3.4.1"
},
{
"text": "Trigger features The local feature function for trigger labeling can be factorized as",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Local features",
"sec_num": "3.4.1"
},
{
"text": "f (x, i, y g(i) ) = p(x, i) \u2022 q(y g(i) ), where p(x, i)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Local features",
"sec_num": "3.4.1"
},
{
"text": "is a predicate about the input, which we call text feature, and q(y g(i) ) is a predicate on the trigger label. In practice, we define two versions of q(y g(i) ):",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Local features",
"sec_num": "3.4.1"
},
{
"text": "q 0 (y g(i) ) = y g(i) (event subtype) q 1 (y g(i) ) = event type of y g(i) q 1 (y g(i)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Local features",
"sec_num": "3.4.1"
},
{
"text": ") is a backoff version of the standard unigram feature. Some text features for the same event type may share a certain distributional similarity regardless of the subtypes. For example, if the nearest entity mention is \"Company\", the current token is likely to be Personnel no matter whether it is End-Postion or Start-Position.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Local features",
"sec_num": "3.4.1"
},
{
"text": "Argument features Similarly, the local feature function for argument labeling can be rep-",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Local features",
"sec_num": "3.4.1"
},
{
"text": "resented as f (x, i, k, y g(i) , y h(i,k) ) = p(x, i, k) \u2022 q(y g(i) , y h(i,k) ),",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Local features",
"sec_num": "3.4.1"
},
{
"text": "where y h(i,k) denotes the argument assignment for the edge between trigger word i and argument candidate e k . We define two versions of q(y g(i) , y h(i,k) ):",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Local features",
"sec_num": "3.4.1"
},
{
"text": "q 0 (y g(i) , y h(i,k) ) = \uf8f1 \uf8f4 \uf8f2 \uf8f4 \uf8f3 y h(i,k) if y h(i,k) is Place, Time or None y g(i) \u2022 y h(i,k) otherwise q 1 (y g(i) , y h(i,k) ) = 1 if y h(i,k) =None 0 otherwise",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Local features",
"sec_num": "3.4.1"
},
{
"text": "It is notable that Place and Time arguments are applicable and behave similarly to all event subtypes. Therefore features for these arguments are not conjuncted with trigger labels. q 1 (y h(i,k) ) can be considered as a backoff version of q 0 (y h(i,k)) , which does not discriminate different argument roles but only focuses on argument identification. Table 1 summarizes the text features about the input for trigger and argument labeling. In our experiments, we used the Stanford parser (De Marneffe et al., 2006) to create dependency parses. ",
"cite_spans": [
{
"start": 495,
"end": 517,
"text": "Marneffe et al., 2006)",
"ref_id": "BIBREF6"
}
],
"ref_spans": [
{
"start": 355,
"end": 362,
"text": "Table 1",
"ref_id": "TABREF0"
}
],
"eq_spans": [],
"section": "Local features",
"sec_num": "3.4.1"
},
{
"text": "Feature Description Trigger",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Global features",
"sec_num": "3.4.2"
},
{
"text": "1. bigram of trigger types occur in the same sentence or the same clause 2. binary feature indicating whether synonyms in the same sentence have the same trigger label 3. context and dependency paths between two triggers conjuncted with their types Argument 4. context and dependency features about two argument candidates which share the same role within the same event mention 5. features about one argument candidate which plays as arguments in two event mentions in the same sentence 6. features about two arguments of an event mention which are overlapping 7. the number of arguments with each role type of an event mention conjuncted with the event subtype 8. the pairs of time arguments within an event mention conjuncted with the event subtype Trigger global feature This type of feature captures the dependencies between two triggers within the same sentence. For instance: feature (1) captures the co-occurrence of trigger types. This kind of feature is motivated by the fact that two event mentions in the same sentence tend to be semantically coherent. As an example, from Table 3 we can see that Attack event often co-occur with Die event in the same sentence, but rarely co-occur with Start-Position event. Feature (2) encourages synonyms or identical tokens to have the same label. Feature (3) exploits the lexical and syntactic relation between two triggers. A simple example is whether an Attack trigger and a Die trigger are linked by the dependency relation conj and.",
"cite_spans": [],
"ref_spans": [
{
"start": 1085,
"end": 1092,
"text": "Table 3",
"ref_id": "TABREF4"
}
],
"eq_spans": [],
"section": "Global features",
"sec_num": "3.4.2"
},
{
"text": "Argument global feature This type of feature is defined over multiple arguments for the same or different triggers. Consider the following sentence:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Global features",
"sec_num": "3.4.2"
},
{
"text": "(3) Trains running to southern Sudan were used to transport abducted women and children.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Global features",
"sec_num": "3.4.2"
},
{
"text": "The Transport event mention \"transport\" has two Artifact arguments, \"women\" and \"children\".",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Global features",
"sec_num": "3.4.2"
},
{
"text": "The dependency edge conj and between \"women\" and \"children\" indicates that they should play the same role in the event mention. The triangle structure in Figure 5(a) is an example of feature (4) for the above example. This feature encourages entities that are linked by dependency relation conj and to play the same role Artifact in any Transport event.",
"cite_spans": [],
"ref_spans": [
{
"start": 154,
"end": 165,
"text": "Figure 5(a)",
"ref_id": "FIGREF4"
}
],
"eq_spans": [],
"section": "Global features",
"sec_num": "3.4.2"
},
{
"text": "Similarly, Figure 5 (b) depicts an example of feature (5) for sentence (1) in Section 1. In this example, an entity mention is Victim argument to Die event and Target argument to Attack event, and the two event triggers are connected by the typed dependency advcl. Here advcl means that the word \"fired\" is an adverbial clause modier of \"died\". The job title \"co-chief executive of Vivendi Universal Entertainment\" overlaps with the Organization mention \"Vivendi Universal Entertainment\". The feature in the triangle shape can be considered as a soft constraint such that if a Job-Title mention is a Position argument to an End-Position trigger, then the Organization mention which appears at the end of it should be labeled as Entity argument for the same trigger. Feature (7-8) are based on the statistics about different arguments for the same trigger. For instance, in many cases, a trigger can only have one Place argument. If a partial configuration mistakenly classifies more than one entity mention as Place arguments for the same trigger, then it will be penalized.",
"cite_spans": [],
"ref_spans": [
{
"start": 11,
"end": 19,
"text": "Figure 5",
"ref_id": "FIGREF4"
}
],
"eq_spans": [],
"section": "Global features",
"sec_num": "3.4.2"
},
{
"text": "We utilized the ACE 2005 corpus as our testbed. For comparison, we used the same test set with 40 newswire articles (672 sentences) as in (Ji and Grishman, 2008; Liao and Grishman, 2010) for the experiments, and randomly selected 30 other documents (863 sentences) from different genres as the development set. The rest 529 documents (14, 840 sentences) are used for training.",
"cite_spans": [
{
"start": 138,
"end": 161,
"text": "(Ji and Grishman, 2008;",
"ref_id": "BIBREF10"
},
{
"start": 162,
"end": 186,
"text": "Liao and Grishman, 2010)",
"ref_id": "BIBREF13"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Data set and evaluation metric",
"sec_num": "4.1"
},
{
"text": "Following previous work (Ji and Grishman, 2008; Liao and Grishman, 2010; Hong et al., 2011) , we use the following criteria to determine the correctness of an predicted event mention:",
"cite_spans": [
{
"start": 24,
"end": 47,
"text": "(Ji and Grishman, 2008;",
"ref_id": "BIBREF10"
},
{
"start": 48,
"end": 72,
"text": "Liao and Grishman, 2010;",
"ref_id": "BIBREF13"
},
{
"start": 73,
"end": 91,
"text": "Hong et al., 2011)",
"ref_id": "BIBREF8"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Data set and evaluation metric",
"sec_num": "4.1"
},
{
"text": "\u2022 A trigger is correct if its event subtype and offsets match those of a reference trigger. \u2022 An argument is correctly identified if its event subtype and offsets match those of any of the reference argument mentions. \u2022 An argument is correctly identified and classified if its event subtype, offsets and argument role match those of any of the reference argument mentions.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Data set and evaluation metric",
"sec_num": "4.1"
},
{
"text": "Finally we use Precision (P), Recall (R) and Fmeasure (F 1 ) to evaluate the overall performance.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Data set and evaluation metric",
"sec_num": "4.1"
},
{
"text": "Chen and Ng (2012) have proven that performing identification and classification in one step is better than two steps. To compare our proposed method with the previous pipelined approaches, we implemented two Maximum Entropy (Max-Ent) classifiers for trigger labeling and argument labeling respectively. To make a fair comparison, the feature sets in the baseline are identical to the local text features we developed in our framework (see Figure 1 ).",
"cite_spans": [],
"ref_spans": [
{
"start": 440,
"end": 448,
"text": "Figure 1",
"ref_id": null
}
],
"eq_spans": [],
"section": "Baseline system",
"sec_num": "4.2"
},
{
"text": "We use the harmonic mean of the trigger's F 1 measure and argument's F 1 measure to measure the performance on the development set. Figure 6 shows the training curves of the averaged perceptron with respect to the performance on the development set when the beam size is 4. As we can see both curves converge around iteration 20 and the global features improve the overall performance, compared to its counterpart with only local features. Therefore we set the number of iterations as 20 in the remaining experiments.",
"cite_spans": [],
"ref_spans": [
{
"start": 132,
"end": 140,
"text": "Figure 6",
"ref_id": "FIGREF6"
}
],
"eq_spans": [],
"section": "Training curves",
"sec_num": "4.3"
},
{
"text": "The beam size is an important hyper parameter in both training and test. Larger beam size will increase the computational cost while smaller beam size may reduce the performance. Table 4 shows the performance on the development set with several different beam sizes. When beam size = 4, the algorithm achieved the highest performance on the development set with trigger F 1 = 67.9, argument F 1 = 51.5, and harmonic mean = 58.6. When the size is increased to 32, the accuracy was not improved. Based on this observation, we chose beam size as 4 for the remaining experiments. -update vs. standard-update Huang et al. (2012) define \"invalid update\" to be an update that does not fix a violation (and instead reinforces the error), and show that it strongly (anti-)correlates with search quality and learning quality. Figure 7 depicts the percentage of invalid updates in standard-update with and without global features, respectively. With global features, there are numerous invalid updates when the Figure 7 : Percentage of the so-called \"invalid updates\" (Huang et al., 2012) 67.9 51.5 67.5 52.7 Table 5 : Comparison between the performance (%) of standard-update and early-update with global features. Here b stands for beam size.",
"cite_spans": [
{
"start": 576,
"end": 623,
"text": "-update vs. standard-update Huang et al. (2012)",
"ref_id": null
},
{
"start": 1057,
"end": 1077,
"text": "(Huang et al., 2012)",
"ref_id": "BIBREF9"
}
],
"ref_spans": [
{
"start": 179,
"end": 186,
"text": "Table 4",
"ref_id": null
},
{
"start": 816,
"end": 824,
"text": "Figure 7",
"ref_id": null
},
{
"start": 1000,
"end": 1008,
"text": "Figure 7",
"ref_id": null
},
{
"start": 1098,
"end": 1105,
"text": "Table 5",
"ref_id": null
}
],
"eq_spans": [],
"section": "Impact of beam size",
"sec_num": "4.4"
},
{
"text": "beam size is small. The ratio decreases monotonically as beam size increases. The model with only local features made much smaller numbers of invalid updates, which suggests that the use of global features makes the search problem much harder. This observation justify the application of early-update in this work. To further investigate the difference between early-update and standardupdate, we tested the performance of both strategies, which is summarized in Table 5 . As we can see the performance of standard-update is generally worse than early-update. When the beam size is increased (b = 4), the gap becomes smaller as the ratio of invalid updates is reduced. Table 6 shows the overall performance on the blind test set. In addition to our baseline, we compare against the sentence-level system reported in Hong et al. (2011) , which, to the best of our knowledge, is the best-reported system in the literature based on gold standard argument candidates. The proposed joint framework with local features achieves comparable performance for triggers and outperforms the staged baseline especially on arguments. By adding global features, the overall performance is further improved significantly. Compared to the staged baseline, it gains 1.6% improvement on trigger's F-measure and 8.8% improvement on argument's F-measure. Remarkably, compared to the cross-entity approach reported in (Hong et al., 2011) , which attained 68.3% F 1 for triggers and 48.3% for arguments, our approach with global features achieves even better performance on argument labeling although we only used sentencelevel information. We also tested the performance with argument candidates automatically extracted by a highperforming name tagger (Li et al., 2012b ) and an IE system (Grishman et al., 2005) . The results are summarized in Table 7 . The joint approach with global features significantly outperforms the baseline and the model with only local features. We also show that it outperforms the sentencelevel baseline reported in (Ji and Grishman, 2008; Liao and Grishman, 2010) , both of which attained 59.7% F 1 for triggers and 36.6% for arguments. Our approach aims to tackle the problem of sentence-level event extraction, thereby only used intra-sentential evidence. Nevertheless, the performance of our approach is still comparable with the best-reported methods based on cross-document and cross-event inference (Ji and Grishman, 2008; Liao and Grishman, 2010) .",
"cite_spans": [
{
"start": 816,
"end": 834,
"text": "Hong et al. (2011)",
"ref_id": "BIBREF8"
},
{
"start": 1395,
"end": 1414,
"text": "(Hong et al., 2011)",
"ref_id": "BIBREF8"
},
{
"start": 1729,
"end": 1746,
"text": "(Li et al., 2012b",
"ref_id": "BIBREF12"
},
{
"start": 1766,
"end": 1789,
"text": "(Grishman et al., 2005)",
"ref_id": "BIBREF7"
},
{
"start": 2023,
"end": 2046,
"text": "(Ji and Grishman, 2008;",
"ref_id": "BIBREF10"
},
{
"start": 2047,
"end": 2071,
"text": "Liao and Grishman, 2010)",
"ref_id": "BIBREF13"
},
{
"start": 2413,
"end": 2436,
"text": "(Ji and Grishman, 2008;",
"ref_id": "BIBREF10"
},
{
"start": 2437,
"end": 2461,
"text": "Liao and Grishman, 2010)",
"ref_id": "BIBREF13"
}
],
"ref_spans": [
{
"start": 463,
"end": 470,
"text": "Table 5",
"ref_id": null
},
{
"start": 669,
"end": 676,
"text": "Table 6",
"ref_id": "TABREF8"
},
{
"start": 1822,
"end": 1829,
"text": "Table 7",
"ref_id": null
}
],
"eq_spans": [],
"section": "Early",
"sec_num": "4.5"
},
{
"text": "Most recent studies about ACE event extraction rely on staged pipeline which consists of separate local classifiers for trigger labeling and argument labeling (Grishman et al., 2005; Ahn, 2006; Ji and Grishman, 2008; Chen and Ji, 2009; Liao and Grishman, 2010; Hong et al., 2011; Li et al., 2012a; Chen and Ng, 2012) . To the best of our knowledge, our work is the first attempt to jointly model these two ACE event subtasks. Argument Role (%)",
"cite_spans": [
{
"start": 159,
"end": 182,
"text": "(Grishman et al., 2005;",
"ref_id": "BIBREF7"
},
{
"start": 183,
"end": 193,
"text": "Ahn, 2006;",
"ref_id": "BIBREF0"
},
{
"start": 194,
"end": 216,
"text": "Ji and Grishman, 2008;",
"ref_id": "BIBREF10"
},
{
"start": 217,
"end": 235,
"text": "Chen and Ji, 2009;",
"ref_id": "BIBREF2"
},
{
"start": 236,
"end": 260,
"text": "Liao and Grishman, 2010;",
"ref_id": "BIBREF13"
},
{
"start": 261,
"end": 279,
"text": "Hong et al., 2011;",
"ref_id": "BIBREF8"
},
{
"start": 280,
"end": 297,
"text": "Li et al., 2012a;",
"ref_id": "BIBREF11"
},
{
"start": 298,
"end": 316,
"text": "Chen and Ng, 2012)",
"ref_id": "BIBREF3"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "5"
},
{
"text": "P R F 1 P R F 1 P R F 1 P R F 1",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "5"
},
{
"text": "Sentence-level in Hong et al. (2011) Joint w/ local + global 65.6 (\u21931.9) 41.8 (\u219310.9) Table 7 : Overall performance (%) with predicted entities, timex, and values. \u2193 indicates the performance drop from experiments with gold-standard argument candidates (see Table 6 ).",
"cite_spans": [
{
"start": 18,
"end": 36,
"text": "Hong et al. (2011)",
"ref_id": "BIBREF8"
}
],
"ref_spans": [
{
"start": 86,
"end": 93,
"text": "Table 7",
"ref_id": null
},
{
"start": 258,
"end": 265,
"text": "Table 6",
"ref_id": "TABREF8"
}
],
"eq_spans": [],
"section": "Related Work",
"sec_num": "5"
},
{
"text": "For the Message Understanding Conference (MUC) and FAS Program for Monitoring Emerging Diseases (ProMED) event extraction tasks, Patwardhan and Riloff (2009) proposed a probabilistic framework to extract event role fillers conditioned on the sentential event occurrence. Besides having different task definitions, the key difference from our approach is that their role filler recognizer and sentential event recognizer are trained independently but combined in the test stage. Our experiments, however, have demonstrated that it is more advantageous to do both training and testing with joint inference.",
"cite_spans": [
{
"start": 129,
"end": 157,
"text": "Patwardhan and Riloff (2009)",
"ref_id": "BIBREF17"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "5"
},
{
"text": "There has been some previous work on joint modeling for biomedical events (Riedel and Mc-Callum, 2011a; Riedel et al., 2009; McClosky et al., 2011; Riedel and McCallum, 2011b) . (Mc-Closky et al., 2011) is most closely related to our approach. They casted the problem of biomedical event extraction as a dependency parsing problem. The key assumption that event structure can be considered as trees is incompatible with ACE event extraction. In addition, they used a separate classifier to predict the event triggers before applying the parser, while we extract the triggers and argument jointly. Finally, the features in the parser are edge-factorized. To exploit global features, they applied a MaxEnt-based global re-ranker. In comparison, our approach is a unified framework based on beam search, which allows us to exploit arbitrary global features efficiently.",
"cite_spans": [
{
"start": 74,
"end": 103,
"text": "(Riedel and Mc-Callum, 2011a;",
"ref_id": null
},
{
"start": 104,
"end": 124,
"text": "Riedel et al., 2009;",
"ref_id": "BIBREF20"
},
{
"start": 125,
"end": 147,
"text": "McClosky et al., 2011;",
"ref_id": "BIBREF15"
},
{
"start": 148,
"end": 175,
"text": "Riedel and McCallum, 2011b)",
"ref_id": "BIBREF19"
},
{
"start": 178,
"end": 202,
"text": "(Mc-Closky et al., 2011)",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "5"
},
{
"text": "We presented a joint framework for ACE event extraction based on structured perceptron with inexact search. As opposed to traditional pipelined approaches, we re-defined the task as a structured prediction problem. The experiments proved that the perceptron with local features outperforms the staged baseline and the global features further improve the performance significantly, surpassing the current state-of-the-art by a large margin.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusions and Future Work",
"sec_num": "6"
},
{
"text": "As shown in Table 7 , the overall performance drops substantially when using predicted argument candidates. To improve the accuracy of endto-end IE system, we plan to develop a complete joint framework to recognize entities together with event mentions for future work. Also we are interested in applying this framework to other IE tasks such as relation extraction.",
"cite_spans": [],
"ref_spans": [
{
"start": 12,
"end": 19,
"text": "Table 7",
"ref_id": null
}
],
"eq_spans": [],
"section": "Conclusions and Future Work",
"sec_num": "6"
},
{
"text": "http://projects.ldc.upenn.edu/ace/docs/English-Events-Guidelines v5.4.3.pdf",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
}
],
"back_matter": [],
"bib_entries": {
"BIBREF0": {
"ref_id": "b0",
"title": "The stages of event extraction",
"authors": [
{
"first": "David",
"middle": [],
"last": "Ahn",
"suffix": ""
}
],
"year": 2006,
"venue": "Proceedings of the Workshop on Annotating and Reasoning about Time and Events",
"volume": "",
"issue": "",
"pages": "1--8",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "David Ahn. 2006. The stages of event extraction. In Proceedings of the Workshop on Annotating and Reasoning about Time and Events, pages 1-8.",
"links": null
},
"BIBREF1": {
"ref_id": "b1",
"title": "Class-based n-gram models of natural language",
"authors": [
{
"first": "",
"middle": [],
"last": "Peter F Brown",
"suffix": ""
},
{
"first": "V",
"middle": [],
"last": "Peter",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Desouza",
"suffix": ""
},
{
"first": "L",
"middle": [],
"last": "Robert",
"suffix": ""
},
{
"first": "Vincent J Della",
"middle": [],
"last": "Mercer",
"suffix": ""
},
{
"first": "Jenifer C",
"middle": [],
"last": "Pietra",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Lai",
"suffix": ""
}
],
"year": 1992,
"venue": "Computational linguistics",
"volume": "18",
"issue": "4",
"pages": "467--479",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Peter F Brown, Peter V Desouza, Robert L Mercer, Vincent J Della Pietra, and Jenifer C Lai. 1992. Class-based n-gram models of natural language. Computational linguistics, 18(4):467-479.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "Language specific issue and feature exploration in chinese event extraction",
"authors": [
{
"first": "Zheng",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Heng",
"middle": [],
"last": "Ji",
"suffix": ""
}
],
"year": 2009,
"venue": "The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers",
"volume": "",
"issue": "",
"pages": "209--212",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Zheng Chen and Heng Ji. 2009. Language specific issue and feature exploration in chinese event ex- traction. In Proceedings of Human Language Tech- nologies: The 2009 Annual Conference of the North American Chapter of the Association for Computa- tional Linguistics, Companion Volume: Short Pa- pers, pages 209-212.",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "Joint modeling for chinese event extraction with rich linguistic features",
"authors": [
{
"first": "Chen",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Vincent",
"middle": [],
"last": "Ng",
"suffix": ""
}
],
"year": 2012,
"venue": "COLING",
"volume": "",
"issue": "",
"pages": "529--544",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Chen Chen and Vincent Ng. 2012. Joint modeling for chinese event extraction with rich linguistic features. In COLING, pages 529-544.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "Incremental parsing with the perceptron algorithm",
"authors": [
{
"first": "Michael",
"middle": [],
"last": "Collins",
"suffix": ""
},
{
"first": "Brian",
"middle": [],
"last": "Roark",
"suffix": ""
}
],
"year": 2004,
"venue": "Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Michael Collins and Brian Roark. 2004. Incremental parsing with the perceptron algorithm. In Proceed- ings of the 42nd Annual Meeting on Association for Computational Linguistics, page 111.",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "Discriminative training methods for hidden markov models: Theory and experiments with perceptron algorithms",
"authors": [
{
"first": "Michael",
"middle": [],
"last": "Collins",
"suffix": ""
}
],
"year": 2002,
"venue": "Proceedings of the ACL-02 conference on Empirical methods in natural language processing",
"volume": "10",
"issue": "",
"pages": "1--8",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Michael Collins. 2002. Discriminative training meth- ods for hidden markov models: Theory and exper- iments with perceptron algorithms. In Proceedings of the ACL-02 conference on Empirical methods in natural language processing-Volume 10, pages 1-8.",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "Generating typed dependency parses from phrase structure parses",
"authors": [
{
"first": "Marie-Catherine De",
"middle": [],
"last": "Marneffe",
"suffix": ""
},
{
"first": "Bill",
"middle": [],
"last": "Maccartney",
"suffix": ""
},
{
"first": "Christopher D",
"middle": [],
"last": "Manning",
"suffix": ""
}
],
"year": 2006,
"venue": "Proceedings of LREC",
"volume": "6",
"issue": "",
"pages": "449--454",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Marie-Catherine De Marneffe, Bill MacCartney, and Christopher D Manning. 2006. Generating typed dependency parses from phrase structure parses. In Proceedings of LREC, volume 6, pages 449-454.",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "Nyu's english ace 2005 system description",
"authors": [
{
"first": "Ralph",
"middle": [],
"last": "Grishman",
"suffix": ""
},
{
"first": "David",
"middle": [],
"last": "Westbrook",
"suffix": ""
},
{
"first": "Adam",
"middle": [],
"last": "Meyers",
"suffix": ""
}
],
"year": 2005,
"venue": "Proceedings of ACE 2005 Evaluation Workshop",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ralph Grishman, David Westbrook, and Adam Meyers. 2005. Nyu's english ace 2005 system description. In Proceedings of ACE 2005 Evaluation Workshop. Washington.",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "Using cross-entity inference to improve event extraction",
"authors": [
{
"first": "Yu",
"middle": [],
"last": "Hong",
"suffix": ""
},
{
"first": "Jianfeng",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Bin",
"middle": [],
"last": "Ma",
"suffix": ""
},
{
"first": "Jian-Min",
"middle": [],
"last": "Yao",
"suffix": ""
},
{
"first": "Guodong",
"middle": [],
"last": "Zhou",
"suffix": ""
},
{
"first": "Qiaoming",
"middle": [],
"last": "Zhu",
"suffix": ""
}
],
"year": 2011,
"venue": "Proceedings of ACL",
"volume": "",
"issue": "",
"pages": "1127--1136",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yu Hong, Jianfeng Zhang, Bin Ma, Jian-Min Yao, Guodong Zhou, and Qiaoming Zhu. 2011. Using cross-entity inference to improve event extraction. In Proceedings of ACL, pages 1127-1136.",
"links": null
},
"BIBREF9": {
"ref_id": "b9",
"title": "Structured perceptron with inexact search",
"authors": [
{
"first": "Liang",
"middle": [],
"last": "Huang",
"suffix": ""
},
{
"first": "Suphan",
"middle": [],
"last": "Fayong",
"suffix": ""
},
{
"first": "Yang",
"middle": [],
"last": "Guo",
"suffix": ""
}
],
"year": 2012,
"venue": "Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
"volume": "",
"issue": "",
"pages": "142--151",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Liang Huang, Suphan Fayong, and Yang Guo. 2012. Structured perceptron with inexact search. In Pro- ceedings of the 2012 Conference of the North Amer- ican Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 142-151.",
"links": null
},
"BIBREF10": {
"ref_id": "b10",
"title": "Refining event extraction through cross-document inference",
"authors": [
{
"first": "Heng",
"middle": [],
"last": "Ji",
"suffix": ""
},
{
"first": "Ralph",
"middle": [],
"last": "Grishman",
"suffix": ""
}
],
"year": 2008,
"venue": "Proceedings of ACL",
"volume": "",
"issue": "",
"pages": "254--262",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Heng Ji and Ralph Grishman. 2008. Refining event ex- traction through cross-document inference. In Pro- ceedings of ACL, pages 254-262.",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "Employing compositional semantics and discourse consistency in chinese event extraction",
"authors": [
{
"first": "Peifeng",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Guodong",
"middle": [],
"last": "Zhou",
"suffix": ""
},
{
"first": "Qiaoming",
"middle": [],
"last": "Zhu",
"suffix": ""
},
{
"first": "Libin",
"middle": [],
"last": "Hou",
"suffix": ""
}
],
"year": 2012,
"venue": "Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning",
"volume": "",
"issue": "",
"pages": "1006--1016",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Peifeng Li, Guodong Zhou, Qiaoming Zhu, and Li- bin Hou. 2012a. Employing compositional seman- tics and discourse consistency in chinese event ex- traction. In Proceedings of the 2012 Joint Confer- ence on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 1006-1016.",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "Joint bilingual name tagging for parallel corpora",
"authors": [
{
"first": "Qi",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Haibo",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Heng",
"middle": [],
"last": "Ji",
"suffix": ""
},
{
"first": "Wen",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Jing",
"middle": [],
"last": "Zheng",
"suffix": ""
},
{
"first": "Fei",
"middle": [],
"last": "Huang",
"suffix": ""
}
],
"year": 2012,
"venue": "Proceedings of the 21st ACM international conference on Information and knowledge management",
"volume": "",
"issue": "",
"pages": "1727--1731",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Qi Li, Haibo Li, Heng Ji, Wen Wang, Jing Zheng, and Fei Huang. 2012b. Joint bilingual name tagging for parallel corpora. In Proceedings of the 21st ACM international conference on Information and knowl- edge management, pages 1727-1731.",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "Using document level cross-event inference to improve event extraction",
"authors": [
{
"first": "Shasha",
"middle": [],
"last": "Liao",
"suffix": ""
},
{
"first": "Ralph",
"middle": [],
"last": "Grishman",
"suffix": ""
}
],
"year": 2010,
"venue": "Proceedings of ACL",
"volume": "",
"issue": "",
"pages": "789--797",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Shasha Liao and Ralph Grishman. 2010. Using doc- ument level cross-event inference to improve event extraction. In Proceedings of ACL, pages 789-797.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "Nomlex: A lexicon of nominalizations",
"authors": [
{
"first": "Catherine",
"middle": [],
"last": "Macleod",
"suffix": ""
},
{
"first": "Ralph",
"middle": [],
"last": "Grishman",
"suffix": ""
},
{
"first": "Adam",
"middle": [],
"last": "Meyers",
"suffix": ""
},
{
"first": "Leslie",
"middle": [],
"last": "Barrett",
"suffix": ""
},
{
"first": "Ruth",
"middle": [],
"last": "Reeves",
"suffix": ""
}
],
"year": 1998,
"venue": "Proceedings of EU-RALEX",
"volume": "98",
"issue": "",
"pages": "187--193",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Catherine Macleod, Ralph Grishman, Adam Meyers, Leslie Barrett, and Ruth Reeves. 1998. Nomlex: A lexicon of nominalizations. In Proceedings of EU- RALEX, volume 98, pages 187-193.",
"links": null
},
"BIBREF15": {
"ref_id": "b15",
"title": "Event extraction as dependency parsing",
"authors": [
{
"first": "David",
"middle": [],
"last": "Mcclosky",
"suffix": ""
},
{
"first": "Mihai",
"middle": [],
"last": "Surdeanu",
"suffix": ""
},
{
"first": "Christopher",
"middle": [
"D"
],
"last": "Manning",
"suffix": ""
}
],
"year": 2011,
"venue": "Proceedings of ACL",
"volume": "",
"issue": "",
"pages": "1626--1635",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "David McClosky, Mihai Surdeanu, and Christopher D. Manning. 2011. Event extraction as dependency parsing. In Proceedings of ACL, pages 1626-1635.",
"links": null
},
"BIBREF16": {
"ref_id": "b16",
"title": "Name tagging with word clusters and discriminative training",
"authors": [
{
"first": "Scott",
"middle": [],
"last": "Miller",
"suffix": ""
},
{
"first": "Jethran",
"middle": [],
"last": "Guinness",
"suffix": ""
},
{
"first": "Alex",
"middle": [],
"last": "Zamanian",
"suffix": ""
}
],
"year": 2004,
"venue": "Proceedings of HLT-NAACL",
"volume": "4",
"issue": "",
"pages": "337--342",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Scott Miller, Jethran Guinness, and Alex Zamanian. 2004. Name tagging with word clusters and discrim- inative training. In Proceedings of HLT-NAACL, volume 4, pages 337-342.",
"links": null
},
"BIBREF17": {
"ref_id": "b17",
"title": "A unified model of phrasal and sentential evidence for information extraction",
"authors": [
{
"first": "Siddharth",
"middle": [],
"last": "Patwardhan",
"suffix": ""
},
{
"first": "Ellen",
"middle": [],
"last": "Riloff",
"suffix": ""
}
],
"year": 2009,
"venue": "Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing",
"volume": "1",
"issue": "",
"pages": "151--160",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Siddharth Patwardhan and Ellen Riloff. 2009. A uni- fied model of phrasal and sentential evidence for in- formation extraction. In Proceedings of the 2009 Conference on Empirical Methods in Natural Lan- guage Processing: Volume 1-Volume 1, pages 151- 160.",
"links": null
},
"BIBREF18": {
"ref_id": "b18",
"title": "Fast and robust joint models for biomedical event extraction",
"authors": [
{
"first": "Sebastian",
"middle": [],
"last": "Riedel",
"suffix": ""
},
{
"first": "Andrew",
"middle": [],
"last": "Mccallum",
"suffix": ""
}
],
"year": 2011,
"venue": "Proceedings of the Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "1--12",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Sebastian Riedel and Andrew McCallum. 2011a. Fast and robust joint models for biomedical event extrac- tion. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 1- 12.",
"links": null
},
"BIBREF19": {
"ref_id": "b19",
"title": "Robust biomedical event extraction with dual decomposition and minimal domain adaptation",
"authors": [
{
"first": "Sebastian",
"middle": [],
"last": "Riedel",
"suffix": ""
},
{
"first": "Andrew",
"middle": [],
"last": "Mccallum",
"suffix": ""
}
],
"year": 2011,
"venue": "Proceedings of the BioNLP Shared Task 2011 Workshop",
"volume": "",
"issue": "",
"pages": "46--50",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Sebastian Riedel and Andrew McCallum. 2011b. Ro- bust biomedical event extraction with dual decom- position and minimal domain adaptation. In Pro- ceedings of the BioNLP Shared Task 2011 Work- shop, pages 46-50.",
"links": null
},
"BIBREF20": {
"ref_id": "b20",
"title": "A markov logic approach to bio-molecular event extraction",
"authors": [
{
"first": "Sebastian",
"middle": [],
"last": "Riedel",
"suffix": ""
},
{
"first": "Hong-Woo",
"middle": [],
"last": "Chun",
"suffix": ""
},
{
"first": "Toshihisa",
"middle": [],
"last": "Takagi",
"suffix": ""
},
{
"first": "Jun'ichi",
"middle": [],
"last": "Tsujii",
"suffix": ""
}
],
"year": 2009,
"venue": "Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing: Shared Task",
"volume": "",
"issue": "",
"pages": "41--49",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Sebastian Riedel, Hong-Woo Chun, Toshihisa Takagi, and Jun'ichi Tsujii. 2009. A markov logic approach to bio-molecular event extraction. In Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing: Shared Task, pages 41-49.",
"links": null
},
"BIBREF21": {
"ref_id": "b21",
"title": "Semi-supervised relation extraction with large-scale word clustering",
"authors": [
{
"first": "Ang",
"middle": [],
"last": "Sun",
"suffix": ""
},
{
"first": "Ralph",
"middle": [],
"last": "Grishman",
"suffix": ""
},
{
"first": "Satoshi",
"middle": [],
"last": "Sekine",
"suffix": ""
}
],
"year": 2011,
"venue": "Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies",
"volume": "",
"issue": "",
"pages": "521--529",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ang Sun, Ralph Grishman, and Satoshi Sekine. 2011. Semi-supervised relation extraction with large-scale word clustering. In Proceedings of the 49th An- nual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 521-529.",
"links": null
}
},
"ref_entries": {
"FIGREF1": {
"num": null,
"uris": null,
"text": "Example notation with s = 3, m = 2.",
"type_str": "figure"
},
"FIGREF2": {
"num": null,
"uris": null,
"text": "Decoding algorithm for event extraction. z \u2022l means appending label l to the end of z. During test, lines 4-5 & 13-14 are omitted.",
"type_str": "figure"
},
"FIGREF3": {
"num": null,
"uris": null,
"text": "Attack and y has only one \"Attacker\"",
"type_str": "figure"
},
"FIGREF4": {
"num": null,
"uris": null,
"text": "Illustration of global features (4-6) in",
"type_str": "figure"
},
"FIGREF5": {
"num": null,
"uris": null,
"text": "(c) shows an example of feature (6) for the following sentence:(4) Barry Diller resigned as co-chief executive of Vivendi Universal Entertainment.",
"type_str": "figure"
},
"FIGREF6": {
"num": null,
"uris": null,
"text": "Training curves on dev set.",
"type_str": "figure"
},
"TABREF0": {
"num": null,
"type_str": "table",
"content": "<table/>",
"html": null,
"text": "Local features."
},
"TABREF1": {
"num": null,
"type_str": "table",
"content": "<table/>",
"html": null,
"text": ""
},
"TABREF2": {
"num": null,
"type_str": "table",
"content": "<table/>",
"html": null,
"text": "Global features."
},
"TABREF3": {
"num": null,
"type_str": "table",
"content": "<table><tr><td>Event</td><td>Probability</td></tr><tr><td>Attack</td><td>0.34</td></tr><tr><td>Die</td><td>0.14</td></tr><tr><td>Transport</td><td>0.08</td></tr><tr><td>Injure</td><td>0.04</td></tr><tr><td>Meet</td><td>0.02</td></tr></table>",
"html": null,
"text": ""
},
"TABREF4": {
"num": null,
"type_str": "table",
"content": "<table/>",
"html": null,
"text": "Top 5 event subtypes that co-occur with Attack event in the same sentence."
},
"TABREF5": {
"num": null,
"type_str": "table",
"content": "<table><tr><td/><td>Beam size</td><td/><td>1</td><td>2</td><td>4</td><td>8</td><td>16</td><td>32</td></tr><tr><td/><td colspan=\"7\">Training time (sec) 993 2,034 3,982 8,036 15,878 33,026</td></tr><tr><td/><td colspan=\"2\">Harmonic mean</td><td colspan=\"2\">57.6 57.7</td><td>58.6</td><td>58.0</td><td>57.8</td><td>57.8</td></tr><tr><td>% of invalid updates</td><td>2 Table 4: 1 4 beam size 8 0.00 0.05 0.10 0.15 0.20 0.25 0.30 0.35 0.40 0.45</td><td colspan=\"2\">16 local+global local</td><td>32</td><td/><td/></tr></table>",
"html": null,
"text": "Comparison of training time and accuracy on the dev set."
},
"TABREF8": {
"num": null,
"type_str": "table",
"content": "<table><tr><td>Methods</td><td>Trigger F 1</td><td>Arg F 1</td></tr><tr><td>Ji and Grishman (2008)</td><td>67.3</td><td>42.6</td></tr><tr><td>cross-doc Inference</td><td/><td/></tr><tr><td>Ji and Grishman (2008)</td><td>59.7</td><td>36.6</td></tr><tr><td>sentence-level</td><td/><td/></tr><tr><td>MaxEnt classifiers</td><td colspan=\"2\">64.7 (\u21931.2) 33.7 (\u219310.2)</td></tr><tr><td>Joint w/ local</td><td colspan=\"2\">63.7 (\u21932.0) 35.8 (\u219310.7)</td></tr></table>",
"html": null,
"text": "Overall performance with gold-standard entities, timex, and values. \u2020 beyond sentence level."
}
}
}
} |