File size: 107,800 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 | {
"paper_id": "P15-1017",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T09:09:00.818577Z"
},
"title": "Event Extraction via Dynamic Multi-Pooling Convolutional Neural Networks",
"authors": [
{
"first": "Yubo",
"middle": [],
"last": "Chen",
"suffix": "",
"affiliation": {
"laboratory": "National Laboratory of Pattern Recognition",
"institution": "Chinese Academy of Sciences",
"location": {
"postCode": "100190",
"settlement": "Beijing",
"country": "China"
}
},
"email": "yubo.chen@nlpr.ia.ac.cn"
},
{
"first": "Liheng",
"middle": [],
"last": "Xu",
"suffix": "",
"affiliation": {
"laboratory": "National Laboratory of Pattern Recognition",
"institution": "Chinese Academy of Sciences",
"location": {
"postCode": "100190",
"settlement": "Beijing",
"country": "China"
}
},
"email": "lhxu@nlpr.ia.ac.cn"
},
{
"first": "Kang",
"middle": [],
"last": "Liu",
"suffix": "",
"affiliation": {
"laboratory": "National Laboratory of Pattern Recognition",
"institution": "Chinese Academy of Sciences",
"location": {
"postCode": "100190",
"settlement": "Beijing",
"country": "China"
}
},
"email": "kliu@nlpr.ia.ac.cn"
},
{
"first": "Daojian",
"middle": [],
"last": "Zeng",
"suffix": "",
"affiliation": {
"laboratory": "National Laboratory of Pattern Recognition",
"institution": "Chinese Academy of Sciences",
"location": {
"postCode": "100190",
"settlement": "Beijing",
"country": "China"
}
},
"email": "djzeng@nlpr.ia.ac.cn"
},
{
"first": "Jun",
"middle": [],
"last": "Zhao",
"suffix": "",
"affiliation": {
"laboratory": "National Laboratory of Pattern Recognition",
"institution": "Chinese Academy of Sciences",
"location": {
"postCode": "100190",
"settlement": "Beijing",
"country": "China"
}
},
"email": "jzhao@nlpr.ia.ac.cn"
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "Traditional approaches to the task of ACE event extraction primarily rely on elaborately designed features and complicated natural language processing (NLP) tools. These traditional approaches lack generalization, take a large amount of human effort and are prone to error propagation and data sparsity problems. This paper proposes a novel event-extraction method, which aims to automatically extract lexical-level and sentence-level features without using complicated NLP tools. We introduce a word-representation model to capture meaningful semantic regularities for words and adopt a framework based on a convolutional neural network (CNN) to capture sentence-level clues. However, CNN can only capture the most important information in a sentence and may miss valuable facts when considering multiple-event sentences. We propose a dynamic multi-pooling convolutional neural network (DMCNN), which uses a dynamic multi-pooling layer according to event triggers and arguments, to reserve more crucial information. The experimental results show that our approach significantly outperforms other state-of-the-art methods.",
"pdf_parse": {
"paper_id": "P15-1017",
"_pdf_hash": "",
"abstract": [
{
"text": "Traditional approaches to the task of ACE event extraction primarily rely on elaborately designed features and complicated natural language processing (NLP) tools. These traditional approaches lack generalization, take a large amount of human effort and are prone to error propagation and data sparsity problems. This paper proposes a novel event-extraction method, which aims to automatically extract lexical-level and sentence-level features without using complicated NLP tools. We introduce a word-representation model to capture meaningful semantic regularities for words and adopt a framework based on a convolutional neural network (CNN) to capture sentence-level clues. However, CNN can only capture the most important information in a sentence and may miss valuable facts when considering multiple-event sentences. We propose a dynamic multi-pooling convolutional neural network (DMCNN), which uses a dynamic multi-pooling layer according to event triggers and arguments, to reserve more crucial information. The experimental results show that our approach significantly outperforms other state-of-the-art methods.",
"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. Current state-of-the-art methods (Li et al., 2014; Li et al., 2013; Hong et al., 2011; Liao and Grishman, 2010; Ji and Grishman, 2008) often use a set of elaborately designed features that are extracted by textual analysis and linguistic knowledge. In general, we can divide the features into two categories: lexical features and contextual features.",
"cite_spans": [
{
"start": 198,
"end": 215,
"text": "(Li et al., 2014;",
"ref_id": "BIBREF15"
},
{
"start": 216,
"end": 232,
"text": "Li et al., 2013;",
"ref_id": "BIBREF14"
},
{
"start": 233,
"end": 251,
"text": "Hong et al., 2011;",
"ref_id": "BIBREF8"
},
{
"start": 252,
"end": 276,
"text": "Liao and Grishman, 2010;",
"ref_id": "BIBREF16"
},
{
"start": 277,
"end": 299,
"text": "Ji and Grishman, 2008)",
"ref_id": "BIBREF9"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Lexical features contain part-of-speech tags (POS), entity information, and morphology features (e.g., token, lemma, etc.), which aim to capture semantics or the background knowledge of words. For example, consider the following sentence with an ambiguous word beats: S1: Obama beats McCain. S2: Tyson beats his opponent .",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "In S1, beats is a trigger of type Elect. However, in S2, beats is a trigger of type Attack, which is more common than type Elect. Because of the ambiguity, a traditional approach may mislabel beats in S1 as a trigger of Attack. However, if we have the priori knowledge that Obama and McCain are presidential contenders, we have ample evidence to predict that beats is a trigger of type Elect. We call these knowledge lexical-level clues. To represent such features, the existing methods (Hong et al., 2011) often rely on human ingenuity, which is a time-consuming process and lacks generalization. Furthermore, traditional lexical features in previous methods are a one-hot representation, which may suffer from the data sparsity problem and may not be able to adequately capture the semantics of the words (Turian et al., 2010) .",
"cite_spans": [
{
"start": 487,
"end": 506,
"text": "(Hong et al., 2011)",
"ref_id": "BIBREF8"
},
{
"start": 807,
"end": 828,
"text": "(Turian et al., 2010)",
"ref_id": "BIBREF21"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "To identify events and arguments more precisely, previous methods often captured contextual features, such as syntactic features, which aim to understand how facts are tied together from a larger field of view. For example, in S3, there are two events that share three arguments as shown in Figure 1 . From the dependency relation of nsubj between the argument cameraman and trigger died, we can induce a Victim role to cameraman in the Die event. We call such information sentence-level clues. However, the argument word cameraman and its trigger word fired are in different clauses, and there is no direct de- Figure 1: Event mentions and syntactic parser results of S3. The upper side shows two event mentions that share three arguments: the Die event mention, triggered by \"died\", and the Attack event mention, triggered by \"fired\". The lower side shows the collapsed dependency results.",
"cite_spans": [],
"ref_spans": [
{
"start": 291,
"end": 299,
"text": "Figure 1",
"ref_id": null
}
],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "pendency path between them. Thus it is difficult to find the Target role between them using traditional dependency features. In addition, extracting such features depends heavily on the performance of pre-existing NLP systems, which could suffer from error propagation. S3: 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": "To correctly attach cameraman to fired as a Target argument, we must exploit internal semantics over the entire sentence such that the Attack event results in Die event. Recent improvements of convolutional neural networks (CNNs) have been proven to be efficient for capturing syntactic and semantics between words within a sentence (Collobert et al., 2011; Kalchbrenner and Blunsom, 2013; Zeng et al., 2014) for NLP tasks. CNNs typically use a max-pooling layer, which applies a max operation over the representation of an entire sentence to capture the most useful information. However, in event extraction, one sentence may contain two or more events, and these events may share the argument with different roles. For example, there are two events in S3, namely, the Die event and Attack event. If we use a traditional max-pooling layer and only keep the most important information to represent the sentence, we may obtain the information that depicts \"a cameraman died\" but miss the information about \"American tank fired on the Palestine Hotel\", which is important for predicting the Attack event and valuable for attaching cameraman to fired as an Target argument. In our experiments, we found that such multiple-event sentences comprise 27.3% of our dataset, which is a phenomenon we cannot ignore.",
"cite_spans": [
{
"start": 333,
"end": 357,
"text": "(Collobert et al., 2011;",
"ref_id": "BIBREF4"
},
{
"start": 358,
"end": 389,
"text": "Kalchbrenner and Blunsom, 2013;",
"ref_id": "BIBREF10"
},
{
"start": 390,
"end": 408,
"text": "Zeng et al., 2014)",
"ref_id": "BIBREF24"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "In this paper, we propose a dynamic multipooling convolutional neural network (DMCNN) to address the problems stated above. To capture lexical-level clues and reduce human effort, we introduce a word-representation model (Mikolov et al., 2013b) , which has been shown to be able to capture the meaningful semantic regularities of words (Bengio et al., 2003; Erhan et al., 2010; Mikolov et al., 2013a ). To capture sentence-level clues without using complicated NLP tools, and to reserve information more comprehensively, we devise a dynamic multi-pooling layer for CNN, which returns the maximum value in each part of the sentence according to event triggers and arguments. In summary, the contributions of this paper are as follows:",
"cite_spans": [
{
"start": 221,
"end": 244,
"text": "(Mikolov et al., 2013b)",
"ref_id": "BIBREF19"
},
{
"start": 336,
"end": 357,
"text": "(Bengio et al., 2003;",
"ref_id": "BIBREF2"
},
{
"start": 358,
"end": 377,
"text": "Erhan et al., 2010;",
"ref_id": "BIBREF5"
},
{
"start": 378,
"end": 399,
"text": "Mikolov et al., 2013a",
"ref_id": "BIBREF18"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "\u2022 We present a novel framework for event extraction, which can automatically induce lexical-level and sentence-level features from plain texts without complicated NLP preprocessing.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "\u2022 We devise a dynamic multi-pooling convolutional neural network (DMCNN), which aims to capture more valuable information within a sentence for event extraction.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "\u2022 We conduct experiments on a widely used ACE2005 event extraction dataset, and the experimental results show that our approach outperforms other state-of-the-art methods.",
"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 1 (ACE) evaluation, where an event is defined as a specific occurrence involving participants. First, we introduce some ACE terminology to understand this task more easily:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Event Extraction Task",
"sec_num": "2"
},
{
"text": "\u2022 Event mention: a phrase or sentence within which an event is described, including a trigger and arguments.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Event Extraction Task",
"sec_num": "2"
},
{
"text": "\u2022 Event trigger: the main word that most clearly expresses the occurrence of an event (An ACE event trigger is typically a verb or a noun).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Event Extraction Task",
"sec_num": "2"
},
{
"text": "\u2022 Event argument: an entity mention, temporal expression or value (e.g. Job-Title) that is involved in an event (viz., participants).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Event Extraction Task",
"sec_num": "2"
},
{
"text": "\u2022 Argument role: the relationship between an argument to the event in which it participates.",
"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 for each sentence. The upper side of figure 1 depicts the event triggers and their arguments for S3 in Section 1. ACE defines 8 event types and 33 subtypes, such as Attack or Elect.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Event Extraction Task",
"sec_num": "2"
},
{
"text": "Although event extraction depends on name identification and entity mention co-reference, it is another difficult task in ACE evaluation and not the focus in the event extraction task. Thus, in this paper, we directly leverage the entity label provided by the ACE, following most previous works (Hong et al., 2011; Liao and Grishman, 2010; Ji and Grishman, 2008) .",
"cite_spans": [
{
"start": 295,
"end": 314,
"text": "(Hong et al., 2011;",
"ref_id": "BIBREF8"
},
{
"start": 315,
"end": 339,
"text": "Liao and Grishman, 2010;",
"ref_id": "BIBREF16"
},
{
"start": 340,
"end": 362,
"text": "Ji and Grishman, 2008)",
"ref_id": "BIBREF9"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Event Extraction Task",
"sec_num": "2"
},
{
"text": "In this paper, event extraction is formulated as a two-stage, multi-class classification via dynamic multi-pooling convolutional neural networks with the automatically learned features. The first stage is called trigger classification, in which we use a DMCNN to classify each word in a sentence to identify trigger words. If one sentence has triggers, the second stage is conducted, which applies a similar DMCNN to assign arguments to triggers and align the roles of the arguments. We call this argument classification. Because the second stage is more complicated, we first describe the methodology of argument classification in Section 3.1\u223c3.4 and then illustrate the difference between the DMCNNs that are used for trigger classification and those used for argument classification in Section 3.5. Figure 2 describes the architecture of argument classification, which primarily involves the following four components: (i) word-embedding learning, which reveals the embedding vectors of words in an unsupervised manner; (ii) lexical-level feature representation, which directly uses embedding vectors of words to capture lexical clues; (iii) sentence-level feature extraction, which proposes a DMCNN to learn the compositional semantic features of sentences; and (iv) argument classifier output, which calculates a confidence score for each argument role candidate.",
"cite_spans": [],
"ref_spans": [
{
"start": 802,
"end": 810,
"text": "Figure 2",
"ref_id": "FIGREF1"
}
],
"eq_spans": [],
"section": "Methodology",
"sec_num": "3"
},
{
"text": "Lexical-level features serve as important clues for event extraction (Hong et al., 2011; Li et al., 2013) . Traditional lexical-level features primarily include lemma, synonyms and POS tag of the candidate words. The quality of such features depends strongly on the results of existing NLP tools and human ingenuity. Additionally, the traditional features remain unsatisfactory for capturing the semantics of words, which are important in event extraction, as showed in S1 and S2. As Erhan et al. 2010reported, word embeddings learned from a significant amount of unlabeled data are more powerful for capturing the meaningful semantic regularities of words. This paper uses unsupervised pre-trained word embedding as the source of base features. We select the word embeddings of candidate words (candidate trigger, candidate argument) and the context tokens (left and right tokens of the candidate words). Then, all of these word embeddings are concatenated into the lexical-level features vector L to represent the lexical-level features in argument classification.",
"cite_spans": [
{
"start": 69,
"end": 88,
"text": "(Hong et al., 2011;",
"ref_id": "BIBREF8"
},
{
"start": 89,
"end": 105,
"text": "Li et al., 2013)",
"ref_id": "BIBREF14"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Word Embedding Learning and Lexical-Level Feature Representation",
"sec_num": "3.1"
},
{
"text": "In this work, we use the Skip-gram model to pre-train the word embedding. This model is the state-of-the-art model in many NLP tasks (Baroni et al., 2014) . The Skip-gram model trains the embeddings of words w 1 , w 2 ...w m by maximizing the average log probability,",
"cite_spans": [
{
"start": 133,
"end": 154,
"text": "(Baroni et al., 2014)",
"ref_id": "BIBREF1"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Word Embedding Learning and Lexical-Level Feature Representation",
"sec_num": "3.1"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "1 m m t=1 \u2212c\u2264j\u2264c,j =0 log p(w t+j |w t )",
"eq_num": "(1)"
}
],
"section": "Word Embedding Learning and Lexical-Level Feature Representation",
"sec_num": "3.1"
},
{
"text": "where c is the size of the training window. Basically, p(w t+j |w t ) is defined as,",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Word Embedding Learning and Lexical-Level Feature Representation",
"sec_num": "3.1"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "p(w t+j |w t ) = exp(e T t+j e t ) m w=1 exp(e T w e t )",
"eq_num": "(2)"
}
],
"section": "Word Embedding Learning and Lexical-Level Feature Representation",
"sec_num": "3.1"
},
{
"text": "where m is the vocabulary of the unlabeled text. e i is another embedding for e i , see Morin and Bengio (2005) ",
"cite_spans": [
{
"start": 88,
"end": 111,
"text": "Morin and Bengio (2005)",
"ref_id": "BIBREF20"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Word Embedding Learning and Lexical-Level Feature Representation",
"sec_num": "3.1"
},
{
"text": "The CNN, with max-pooling layers, is a good choice to capture the semantics of long-distance words within a sentence (Collobert et al., 2011) . However, as noted in the section 1, traditional CNN is incapable of addressing the event extraction problem. Because a sentence may contain more than one event, using only the most important information to represent a sentence, as in the traditional CNN, will miss valuable clues. To resolve this problem, we propose a DMCNN to extract the sentence-level features. The DMCNN uses a dynamic multi-pooling layer to obtain a maximum value for each part of a sentence, which is split by event triggers and event arguments. Thus, the DMCNN is expected to capture more valuable clues compared to traditional CNN methods.",
"cite_spans": [
{
"start": 117,
"end": 141,
"text": "(Collobert et al., 2011)",
"ref_id": "BIBREF4"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Extracting Sentence-Level Features Using a DMCNN",
"sec_num": "3.2"
},
{
"text": "This subsection illustrates the input needed for a DMCNN to extract sentence-level features. The semantic interactions between the predicted trigger words and argument candidates are crucial for argument classification. Therefore, we propose three types of input that the DMCNN uses to capture these important clues:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Input",
"sec_num": "3.2.1"
},
{
"text": "\u2022 Context-word feature (CWF): Similar to Kalchbrenner et al. (2014) and Collobert et al. (2011) , we take all the words of the whole sentence as the context. CWF is the vector of each word token transformed by looking up word embeddings.",
"cite_spans": [
{
"start": 41,
"end": 67,
"text": "Kalchbrenner et al. (2014)",
"ref_id": "BIBREF11"
},
{
"start": 72,
"end": 95,
"text": "Collobert et al. (2011)",
"ref_id": "BIBREF4"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Input",
"sec_num": "3.2.1"
},
{
"text": "\u2022 Position feature (PF): It is necessary to spec-ify which words are the predicted trigger or candidate argument in the argument classification. Thus, we proposed the PF, which is defined as the relative distance of the current word to the predicted trigger or candidate argument. For example, in S3, the relative distances of tank to the candidate argument cameraman is 5. To encode the position feature, each distance value is also represented by an embedding vector. Similar to word embedding, Distance Values are randomly initialized and optimized through back propagation.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Input",
"sec_num": "3.2.1"
},
{
"text": "\u2022 Event-type feature (EF): The event type of a current trigger is valuable for argument classification (Ahn, 2006; Hong et al., 2011; Liao and Grishman, 2010; Li et al., 2013 ), so we encode event type predicted in the trigger classification stage as an important clue for the DMCNN, as in the PF. Figure 2 assumes that word embedding has size d w = 4, position embedding has size d p = 1 and event-type embedding has size d e = 1. Let x i \u2208 R d be the d-dimensional vector representation corresponding to the i-th word in the sentence, where",
"cite_spans": [
{
"start": 103,
"end": 114,
"text": "(Ahn, 2006;",
"ref_id": "BIBREF0"
},
{
"start": 115,
"end": 133,
"text": "Hong et al., 2011;",
"ref_id": "BIBREF8"
},
{
"start": 134,
"end": 158,
"text": "Liao and Grishman, 2010;",
"ref_id": "BIBREF16"
},
{
"start": 159,
"end": 174,
"text": "Li et al., 2013",
"ref_id": "BIBREF14"
}
],
"ref_spans": [
{
"start": 298,
"end": 306,
"text": "Figure 2",
"ref_id": "FIGREF1"
}
],
"eq_spans": [],
"section": "Input",
"sec_num": "3.2.1"
},
{
"text": "d = d w + d p * 2 + d e .",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Input",
"sec_num": "3.2.1"
},
{
"text": "A sentence of length n is represented as follows:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Input",
"sec_num": "3.2.1"
},
{
"text": "x 1:n = x 1 \u2295 x 2 \u2295 ... \u2295 x n (3)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Input",
"sec_num": "3.2.1"
},
{
"text": "where \u2295 is the concatenation operator. Thus, combined word embedding, position embedding and event-type embedding transform an instance as a matrix X \u2208 R n\u00d7d . Then, X is fed into the convolution part.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Input",
"sec_num": "3.2.1"
},
{
"text": "The convolution layer aims to capture the compositional semantics of a entire sentence and compress these valuable semantics into feature maps. In general, let x i:i+j refer to the concatenation of words x i , x i+1 , ..., x i+j . A convolution operation involves a filter w \u2208 R h\u00d7d , which is applied to a window of h words to produce a new feature. For example, a feature c i is generated from a window of words x i:i+h\u22121 by the following operator,",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Convolution",
"sec_num": "3.2.2"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "c i = f (w \u2022 x i:i+h\u22121 + b)",
"eq_num": "(4)"
}
],
"section": "Convolution",
"sec_num": "3.2.2"
},
{
"text": "where b \u2208 R is a bias term and f is a non-linear function such as the hyperbolic tangent. This filter is applied to each possible window of words in the sentence x 1:h , x 2:h+1 , ..., x n\u2212h+1:n to produce a feature map c i where the index i ranges from 1 to n \u2212 h + 1.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Convolution",
"sec_num": "3.2.2"
},
{
"text": "We have described the process of how one feature map is extracted from one filter. To capture different features, it usually use multiple filters in the convolution. Assuming that we use m filters W = w 1 , w 2 , ..., w m , the convolution operation can be expressed as:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Convolution",
"sec_num": "3.2.2"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "c ji = f (w j \u2022 x i:i+h\u22121 + b j )",
"eq_num": "(5)"
}
],
"section": "Convolution",
"sec_num": "3.2.2"
},
{
"text": "where j ranges from 1 to m. The convolution result is a matrix C \u2208 R m\u00d7(n\u2212h+1) .",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Convolution",
"sec_num": "3.2.2"
},
{
"text": "To extract the most important features (max value) within each feature map, traditional CNNs (Collobert et al., 2011; Kim, 2014; Zeng et al., 2014) take one feature map as a pool and only get one max value for each feature map. However, single max-pooling is not sufficient for event extraction. Because in the task of this paper, one sentence may contain two or more events, and one argument candidate may play a different role with a different trigger. To make an accurate prediction, it is necessary to capture the most valuable information with regard to the change of the candidate words. Thus, we split each feature map into three parts according to the candidate argument and predicted trigger in the argument classification stage. Instead of using one max value for an entire feature map to represent the sentence, we keep the max value of each split part and call it dynamic multi-pooling. Compared to traditional max-pooling, dynamic multi-pooling can reserve more valuable information without missing the max-pooling value. As shown in Figure 2 , the feature map output c j is divided into three sections c j1 , c j2 , c j3 by \"cameraman\" and \"fired\". The dynamic multi-pooling can be expressed as formula 6,where 1 \u2264 j \u2264 m and 1 \u2264 i \u2264 3.",
"cite_spans": [
{
"start": 93,
"end": 117,
"text": "(Collobert et al., 2011;",
"ref_id": "BIBREF4"
},
{
"start": 118,
"end": 128,
"text": "Kim, 2014;",
"ref_id": "BIBREF12"
},
{
"start": 129,
"end": 147,
"text": "Zeng et al., 2014)",
"ref_id": "BIBREF24"
}
],
"ref_spans": [
{
"start": 1047,
"end": 1055,
"text": "Figure 2",
"ref_id": "FIGREF1"
}
],
"eq_spans": [],
"section": "Dynamic Multi-Pooling",
"sec_num": "3.2.3"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "p ji = max(c ji )",
"eq_num": "(6)"
}
],
"section": "Dynamic Multi-Pooling",
"sec_num": "3.2.3"
},
{
"text": "Through the dynamic multi-pooling layer, we obtain the p ji for each feature map. Then, we concatenate all p ji to form a vector P \u2208 R 3m , which can be considered as higher-level features (sentence-level features).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Dynamic Multi-Pooling",
"sec_num": "3.2.3"
},
{
"text": "The automatically learned lexical and sentencelevel features mentioned above are concatenated into a single vector F = [L, P ]. To compute the confidence of each argument role, the feature vector F \u2208 R 3m+d l , where m is the number of the feature map and d l is the dimension of the lexicallevel features, is fed into a classifier.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Output",
"sec_num": "3.3"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "O = W s F + b s",
"eq_num": "(7)"
}
],
"section": "Output",
"sec_num": "3.3"
},
{
"text": "W s \u2208 R n 1 \u00d7(3m+d l ) is the transformation matrix and O \u2208 R n 1 is the final output of the network, where n 1 is equal to the number of the argument role including the \"None role\" label for the candidate argument which don't play any role in the event. For regularization, we also employ dropout(Hinton et al., 2012) on the penultimate layer, which can prevent the co-adaptation of hidden units by randomly dropping out a proportion p of the hidden units during forward and backpropagation.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Output",
"sec_num": "3.3"
},
{
"text": "We define all of the parameters for the stage of argument classification to be trained as \u03b8 = (E, P F 1 , P F 2 , EF, W, b, W S , b s ). Specifically, E is the word embedding, P F 1 and P F 2 are the position embedding, EF is the embedding of the event type, W and b are the parameter of the filter, W s and b s are all of the parameters of the output layer.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Training",
"sec_num": "3.4"
},
{
"text": "Given an input example s, the network with parameter \u03b8 outputs the vector O, where the i-th component O i contains the score for argument role i. To obtain the conditional probability p(i|x, \u03b8), we apply a softmax operation over all argument role types:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Training",
"sec_num": "3.4"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "p(i|x, \u03b8) = e o i n 1 k=1 e o k",
"eq_num": "(8)"
}
],
"section": "Training",
"sec_num": "3.4"
},
{
"text": "Given all of our (suppose T) training examples (x i ; y i ), we can then define the objective function as follows:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Training",
"sec_num": "3.4"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "J (\u03b8) = T i=1 log p(y (i) |x (i) , \u03b8)",
"eq_num": "(9)"
}
],
"section": "Training",
"sec_num": "3.4"
},
{
"text": "To compute the network parameter \u03b8, we maximize the log likelihood J (\u03b8) through stochastic gradient descent over shuffled mini-batches with the Adadelta (Zeiler, 2012) update rule.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Training",
"sec_num": "3.4"
},
{
"text": "In the above sections, we presented our model and features for argument classification. The method proposed above is also suitable for trigger classification, but the task only need to find triggers in the sentence, which is less complicated than argument classification. Thus we can used a simplified version of DMCNN.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Model for Trigger Classification",
"sec_num": "3.5"
},
{
"text": "In the trigger classification, we only use the candidate trigger and its left and right tokens in the lexical-level feature representation. In the feature representation of the sentence level, we use the same CWF as does in argument classification, but we only use the position of the candidate trigger to embed the position feature. Furthermore, instead of splitting the sentence into three parts, the sentence is split into two parts by a candidate trigger. Except for the above change in the features and model, we classify a trigger as the classification of an argument. Both stages form the framework of the event extraction.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Model for Trigger Classification",
"sec_num": "3.5"
},
{
"text": "We utilized the ACE 2005 corpus as our dataset. For comparison, as the same as Li et al. (2013) , Hong et al. (2011) and Liao and Grishman (2010) , we used the same test set with 40 newswire articles and the same development set with 30 other documents randomly selected from different genres and the rest 529 documents are used for training. Similar to previous work (Li et al., 2013; Hong et al., 2011; Liao and Grishman, 2010; Ji and Grishman, 2008) , we use the following criteria to judge the correctness of each predicted event mention:",
"cite_spans": [
{
"start": 79,
"end": 95,
"text": "Li et al. (2013)",
"ref_id": "BIBREF14"
},
{
"start": 98,
"end": 116,
"text": "Hong et al. (2011)",
"ref_id": "BIBREF8"
},
{
"start": 121,
"end": 145,
"text": "Liao and Grishman (2010)",
"ref_id": "BIBREF16"
},
{
"start": 368,
"end": 385,
"text": "(Li et al., 2013;",
"ref_id": "BIBREF14"
},
{
"start": 386,
"end": 404,
"text": "Hong et al., 2011;",
"ref_id": "BIBREF8"
},
{
"start": 405,
"end": 429,
"text": "Liao and Grishman, 2010;",
"ref_id": "BIBREF16"
},
{
"start": 430,
"end": 452,
"text": "Ji and Grishman, 2008)",
"ref_id": "BIBREF9"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Dataset 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.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Dataset and Evaluation Metric",
"sec_num": "4.1"
},
{
"text": "\u2022 An argument is correctly identified if its event subtype and offsets match those of any of the reference argument mentions.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Dataset and Evaluation Metric",
"sec_num": "4.1"
},
{
"text": "\u2022 An argument is correctly 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": "Dataset and Evaluation Metric",
"sec_num": "4.1"
},
{
"text": "Finally we use Precision (P ), Recall (R) and F measure (F 1 ) as the evaluation metrics.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Dataset and Evaluation Metric",
"sec_num": "4.1"
},
{
"text": "We select the following state-of-the-art methods for comparison. 1) Li's baseline is the feature-based system proposed by Li et al. (2013) , which only employs human-designed lexical features, basic features and syntactic features.",
"cite_spans": [
{
"start": 122,
"end": 138,
"text": "Li et al. (2013)",
"ref_id": "BIBREF14"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Our Method vs. State-of-the-art Methods",
"sec_num": "4.2"
},
{
"text": "2) Liao's cross-event is the method proposed by Liao and Grishman (2010) , which uses documentlevel information to improve the performance of ACE event extraction.",
"cite_spans": [
{
"start": 48,
"end": 72,
"text": "Liao and Grishman (2010)",
"ref_id": "BIBREF16"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Our Method vs. State-of-the-art Methods",
"sec_num": "4.2"
},
{
"text": "3) Hong's cross-entity is the method proposed by Hong et al. (2011) , which extracts event by using cross-entity inference. To the best of our knowledge, it is the best-reported feature-based system in the literature based on gold standards argument candidates. 4) Li's structure is the method proposed by Li et al. (2013) , which extracts events based on structure prediction. It is the best-reported structurebased system. Following Li et al. (2013) , we tuned the model parameters on the development through grid search. Moreover, in different stages of event extraction, we adopted different parameters in the DMCNN. Specifically, in the trigger classification, we set the window size as 3, the number of the feature map as 200, the batch size as 170 and the dimension of the PF as 5. In the argument classification, we set the window size as 3, the number of the feature map as 300, the batch size as 20 and the dimension of the PF and EF as 5. Stochastic gradient descent over shuffled mini-batches with the Adadelta update rule(Zeiler, 2012) is used for training and testing processes. It mainly contains two parameters p and \u03b5. We set p = 0.95 and \u03b5 = 1e \u22126 . For the dropout operation, we set the Table 1 : Overall performance on blind test data rate = 0.5. We train the word embedding using the Skip-gram algorithm 2 on the NYT corpus 3 . Table 1 shows the overall performance on the blind test dataset. From the results, we can see that the DMCNN model we proposed with the automatically learned features achieves the best performance among all of the compared methods. DMCNN can improve the best F 1 (Li et al., 2013) in the state-of-the-arts for trigger classification by 1.6% and argument role classification by 0.8%. This demonstrates the effectiveness of the proposed method. Moreover, a comparison of Liao's cross-event with Li's baseline illustrates that Liao's cross-event achieves a better performance. We can also make the same observation when comparing Hong's cross-entity with Liao's cross-event and comparing Li's structure with Hong's cross-entity. It proves that richer feature sets lead to better performance when using traditional human-designed features. However, our method could obtain further better results on the condition of only using automatically learned features from original words. Specifically, compared to Hong's cross-entity, it gains 0.8% improvement on trigger classification F 1 and 5.2% improvement on argument classification F 1 . We believe the reason is that the features we automatically learned can capture more meaningful semantic regularities of words. Remarkably, compared to Li's structure, our approach with sentence and lexical features achieves comparable performance even though we do not use complicated NLP tools.",
"cite_spans": [
{
"start": 49,
"end": 67,
"text": "Hong et al. (2011)",
"ref_id": "BIBREF8"
},
{
"start": 306,
"end": 322,
"text": "Li et al. (2013)",
"ref_id": "BIBREF14"
},
{
"start": 435,
"end": 451,
"text": "Li et al. (2013)",
"ref_id": "BIBREF14"
},
{
"start": 1612,
"end": 1629,
"text": "(Li et al., 2013)",
"ref_id": "BIBREF14"
}
],
"ref_spans": [
{
"start": 1206,
"end": 1213,
"text": "Table 1",
"ref_id": null
},
{
"start": 1349,
"end": 1356,
"text": "Table 1",
"ref_id": null
}
],
"eq_spans": [],
"section": "Our Method vs. State-of-the-art Methods",
"sec_num": "4.2"
},
{
"text": "In this subsection, we prove the effectiveness of the proposed DMCNN for sentence-level feature extraction. We specifically select two methods as baselines for comparison with our DMCNN: Em-beddings+T and CNN. Embeddings+T uses word embeddings as lexical-level features and traditional sentence-level features based on human design (Li et al., 2013) . A CNN is similar to a DMCNN, except that it uses a standard convolutional neural network with max-pooling to capture sentence-level features. By contrast, a DMCNN uses the dynamic multi-pooling layer in the network instead of the max-pooling layer in a CNN. Moreover, to prove that a DMCNN could capture more precise sentence-level features, especially for those sentences with multiple events, we divide the testing data into two parts according the event number in a sentence (single event and multiple events) and perform evaluations separately. Table 2 shows the proportion of sentences with multiple events or a single event and the proportion of arguments that attend one event or more events within one sentence in our dataset. Table 3 shows the results. Table 2 : The proportion of multiple events within one sentence. 1/1 means that one sentence only has one trigger or one argument plays a role in one sentence; otherwise, 1/N is used. Table 3 illustrates that the methods based on convolutional neural networks (CNN and DM-CNN) outperform Embeddings+T. It proves that convolutional neural networks could be more effective than traditional human-design strategies for sentence-level feature extraction. In table 3, for all sentences, our method achieves improvements of approximately 2.8% and 4.6% over the CNN. The results prove the effectiveness of the dynamic multi-pooling layer. Interestingly, the DMCNN yields a 7.8% improvement for trigger classification on the sentences with multiple events. This improvement is larger than in sentences with a single event. Similar observations can be made for the argument classification results. This demonstrates that the proposed DMCNN can effectively capture more valuable clues than the CNN with max-pooling, especially when one sentence contains more than one event. This subsection studies the effectiveness of our word embedding for lexical features. For comparison purposes, we select the baseline described by Li et al. (2013) as the traditional method, which uses traditional lexical features, such as n-grams, POS tags and some entity information. In contrast, we only use word embedding as our lexical feature. Moreover, to prove that word embedding could capture more valuable semantics, especially for those words in the test data that never appear to be the same event type or argument role in the training data, we divide the triggers and arguments in the testing data into two parts (1: appearing in testing data only, or 2: appearing in both testing and training data with the same event type or argument role) and perform evaluations separately. For triggers, 34.9% of the trigger words in the test data never appear to be the same event type in the training data. This proportion is 83.1% for arguments. The experimental results are shown in Table 4 . Table 4 illustrates that for all situations, our method makes significant improvements compared with the traditional lexical features in the classification of both the trigger and argument. For situation B, the lexical-level features extracted from word embedding yield a 18.8% improvement for trigger classification and an 8.5% improvement for argument classification. This occurs because the baseline only uses discrete features, so they suffer from data sparsity and could not adequately handle a situation in which a trigger or argument does not appear in the training data. ",
"cite_spans": [
{
"start": 332,
"end": 349,
"text": "(Li et al., 2013)",
"ref_id": "BIBREF14"
},
{
"start": 2327,
"end": 2343,
"text": "Li et al. (2013)",
"ref_id": "BIBREF14"
}
],
"ref_spans": [
{
"start": 901,
"end": 909,
"text": "Table 2",
"ref_id": null
},
{
"start": 1088,
"end": 1095,
"text": "Table 3",
"ref_id": "TABREF4"
},
{
"start": 1115,
"end": 1122,
"text": "Table 2",
"ref_id": null
},
{
"start": 1299,
"end": 1306,
"text": "Table 3",
"ref_id": "TABREF4"
},
{
"start": 3170,
"end": 3177,
"text": "Table 4",
"ref_id": "TABREF6"
},
{
"start": 3180,
"end": 3187,
"text": "Table 4",
"ref_id": "TABREF6"
}
],
"eq_spans": [],
"section": "Effect of The DMCNN on Extracting Sentence-Level Features",
"sec_num": "4.3"
},
{
"text": "Event extraction is one of important topics in NLP. Many approaches have been explored for event extraction. Nearly all of the ACE event extraction use supervised paradigm. We further divide supervised approaches into feature-based methods and structure-based methods.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "5"
},
{
"text": "In feature-based methods, a diverse set of strategies has been exploited to convert classification clues (such as sequences and parse trees) into feature vectors. Ahn (2006) uses the lexical features(e.g., full word, pos tag), syntactic features (e.g., dependency features) and externalknowledge features(WordNet) to extract the event. Inspired by the hypothesis of \"One Sense Per Dis-course\" (Yarowsky, 1995) , Ji and Grishman (2008) combined global evidence from related documents with local decisions for the event extraction. To capture more clues from the texts, Gupta and Ji (2009) , Liao and Grishman (2010) and Hong et al. (2011) proposed the cross-event and cross-entity inference for the ACE event task. Although these approaches achieve high performance, featurebased methods suffer from the problem of selecting a suitable feature set when converting the classification clues into feature vectors.",
"cite_spans": [
{
"start": 163,
"end": 173,
"text": "Ahn (2006)",
"ref_id": "BIBREF0"
},
{
"start": 393,
"end": 409,
"text": "(Yarowsky, 1995)",
"ref_id": "BIBREF22"
},
{
"start": 412,
"end": 434,
"text": "Ji and Grishman (2008)",
"ref_id": "BIBREF9"
},
{
"start": 568,
"end": 587,
"text": "Gupta and Ji (2009)",
"ref_id": "BIBREF6"
},
{
"start": 590,
"end": 614,
"text": "Liao and Grishman (2010)",
"ref_id": "BIBREF16"
},
{
"start": 619,
"end": 637,
"text": "Hong et al. (2011)",
"ref_id": "BIBREF8"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "5"
},
{
"text": "In structure-based methods, researchers treat event extraction as the task of predicting the structure of the event in a sentence. McClosky et al. (2011) casted the problem of biomedical event extraction as a dependency parsing problem. Li et al. (2013) presented a joint framework for ACE event extraction based on structured perceptron with beam search. To use more information from the sentence, Li et al. (2014) proposed to extract entity mentions, relations and events in ACE task based on the unified structure. These methods yield relatively high performance. However, the performance of these methods depend strongly on the quality of the designed features and endure the errors in the existing NLP tools.",
"cite_spans": [
{
"start": 131,
"end": 153,
"text": "McClosky et al. (2011)",
"ref_id": "BIBREF17"
},
{
"start": 237,
"end": 253,
"text": "Li et al. (2013)",
"ref_id": "BIBREF14"
},
{
"start": 399,
"end": 415,
"text": "Li et al. (2014)",
"ref_id": "BIBREF15"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "5"
},
{
"text": "This paper proposes a novel event extraction method, which can automatically extract lexicallevel and sentence-level features from plain texts without complicated NLP preprocessing. A wordrepresentation model is introduced to capture lexical semantic clues and a dynamic multi-pooling convolutional neural network (DMCNN) is devised to encode sentence semantic clues. The experimental results prove the effectiveness of the proposed method.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion",
"sec_num": "6"
},
{
"text": "http://projects.ldc.upenn.edu/ace/",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "https://code.google.com/p/word2vec/ 3 https://catalog.ldc.upenn.edu/LDC2008T19",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
}
],
"back_matter": [
{
"text": "This work was supported by the National Basic Research Program of China (No. 2014CB340503) and the National Natural Science Foundation of China (No. 61272332 and No. 61202329) ",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Acknowledgments",
"sec_num": null
}
],
"bib_entries": {
"BIBREF0": {
"ref_id": "b0",
"title": "The stages of event extraction",
"authors": [
{
"first": "David",
"middle": [],
"last": "Ahn",
"suffix": ""
}
],
"year": 2006,
"venue": "Proceedings of ACL",
"volume": "",
"issue": "",
"pages": "1--8",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "David Ahn. 2006. The stages of event extraction. In Proceedings of ACL, pages 1-8.",
"links": null
},
"BIBREF1": {
"ref_id": "b1",
"title": "Dont count, predict! a systematic comparison of context-counting vs. context-predicting semantic vectors",
"authors": [
{
"first": "Marco",
"middle": [],
"last": "Baroni",
"suffix": ""
},
{
"first": "Georgiana",
"middle": [],
"last": "Dinu",
"suffix": ""
},
{
"first": "Germ\u00e1n",
"middle": [],
"last": "Kruszewski",
"suffix": ""
}
],
"year": 2014,
"venue": "Proceedings of ACL",
"volume": "",
"issue": "",
"pages": "238--247",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Marco Baroni, Georgiana Dinu, and Germ\u00e1n Kruszewski. 2014. Dont count, predict! a systematic comparison of context-counting vs. context-predicting semantic vectors. In Proceedings of ACL, pages 238-247.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "A neural probabilistic language model",
"authors": [
{
"first": "Yoshua",
"middle": [],
"last": "Bengio",
"suffix": ""
},
{
"first": "R\u00e9jean",
"middle": [],
"last": "Ducharme",
"suffix": ""
},
{
"first": "Pascal",
"middle": [],
"last": "Vincent",
"suffix": ""
},
{
"first": "Christian",
"middle": [],
"last": "Jauvin",
"suffix": ""
}
],
"year": 2003,
"venue": "The Journal of Machine Learning Research",
"volume": "3",
"issue": "",
"pages": "1137--1155",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yoshua Bengio, R\u00e9jean Ducharme, Pascal Vincent, and Christian Jauvin. 2003. A neural probabilistic lan- guage model. The Journal of Machine Learning Re- search, 3:1137-1155.",
"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": "V",
"middle": [],
"last": "Incent",
"suffix": ""
},
{
"first": "N",
"middle": [
"G"
],
"last": "",
"suffix": ""
}
],
"year": 2012,
"venue": "Proceedings of COLING",
"volume": "",
"issue": "",
"pages": "529--544",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Chen Chen and V Incent NG. 2012. Joint modeling for chinese event extraction with rich linguistic features. In Proceedings of COLING, pages 529-544.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "Natural language processing (almost) from scratch",
"authors": [
{
"first": "Ronan",
"middle": [],
"last": "Collobert",
"suffix": ""
},
{
"first": "Jason",
"middle": [],
"last": "Weston",
"suffix": ""
},
{
"first": "L\u00e9on",
"middle": [],
"last": "Bottou",
"suffix": ""
},
{
"first": "Michael",
"middle": [],
"last": "Karlen",
"suffix": ""
},
{
"first": "Koray",
"middle": [],
"last": "Kavukcuoglu",
"suffix": ""
},
{
"first": "Pavel",
"middle": [],
"last": "Kuksa",
"suffix": ""
}
],
"year": 2011,
"venue": "The Journal of Machine Learning Research",
"volume": "12",
"issue": "",
"pages": "2493--2537",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ronan Collobert, Jason Weston, L\u00e9on Bottou, Michael Karlen, Koray Kavukcuoglu, and Pavel Kuksa. 2011. Natural language processing (almost) from scratch. The Journal of Machine Learning Re- search, 12:2493-2537.",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "Why does unsupervised pre-training help deep learning?",
"authors": [
{
"first": "Dumitru",
"middle": [],
"last": "Erhan",
"suffix": ""
},
{
"first": "Yoshua",
"middle": [],
"last": "Bengio",
"suffix": ""
},
{
"first": "Aaron",
"middle": [],
"last": "Courville",
"suffix": ""
},
{
"first": "Pierre-Antoine",
"middle": [],
"last": "Manzagol",
"suffix": ""
},
{
"first": "Pascal",
"middle": [],
"last": "Vincent",
"suffix": ""
},
{
"first": "Samy",
"middle": [],
"last": "Bengio",
"suffix": ""
}
],
"year": 2010,
"venue": "The Journal of Machine Learning Research",
"volume": "11",
"issue": "",
"pages": "625--660",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Dumitru Erhan, Yoshua Bengio, Aaron Courville, Pierre-Antoine Manzagol, Pascal Vincent, and Samy Bengio. 2010. Why does unsupervised pre-training help deep learning? The Journal of Machine Learn- ing Research, 11:625-660.",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "Predicting unknown time arguments based on cross-event propagation",
"authors": [
{
"first": "Prashant",
"middle": [],
"last": "Gupta",
"suffix": ""
},
{
"first": "Heng",
"middle": [],
"last": "Ji",
"suffix": ""
}
],
"year": 2009,
"venue": "Proceedings of ACL-IJCNLP",
"volume": "",
"issue": "",
"pages": "369--372",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Prashant Gupta and Heng Ji. 2009. Predicting un- known time arguments based on cross-event propa- gation. In Proceedings of ACL-IJCNLP, pages 369- 372.",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "Improving neural networks by preventing coadaptation of feature detectors",
"authors": [
{
"first": "Nitish",
"middle": [],
"last": "Geoffrey E Hinton",
"suffix": ""
},
{
"first": "Alex",
"middle": [],
"last": "Srivastava",
"suffix": ""
},
{
"first": "Ilya",
"middle": [],
"last": "Krizhevsky",
"suffix": ""
},
{
"first": "Ruslan R",
"middle": [],
"last": "Sutskever",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Salakhutdinov",
"suffix": ""
}
],
"year": 2012,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1207.0580"
]
},
"num": null,
"urls": [],
"raw_text": "Geoffrey E Hinton, Nitish Srivastava, Alex Krizhevsky, Ilya Sutskever, and Ruslan R Salakhutdinov. 2012. Improving neural networks by preventing co- adaptation of feature detectors. arXiv preprint arXiv:1207.0580.",
"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": "Jianmin",
"middle": [],
"last": "Yao",
"suffix": ""
},
{
"first": "Guodong",
"middle": [],
"last": "Zhou",
"suffix": ""
},
{
"first": "Qiaoming",
"middle": [],
"last": "Zhu",
"suffix": ""
}
],
"year": 2011,
"venue": "Proceedings of ACL-HLT",
"volume": "",
"issue": "",
"pages": "1127--1136",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yu Hong, Jianfeng Zhang, Bin Ma, Jianmin Yao, Guodong Zhou, and Qiaoming Zhu. 2011. Using cross-entity inference to improve event extraction. In Proceedings of ACL-HLT, pages 1127-1136.",
"links": null
},
"BIBREF9": {
"ref_id": "b9",
"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
},
"BIBREF10": {
"ref_id": "b10",
"title": "Recurrent convolutional neural networks for discourse compositionality",
"authors": [
{
"first": "Nal",
"middle": [],
"last": "Kalchbrenner",
"suffix": ""
},
{
"first": "Phil",
"middle": [],
"last": "Blunsom",
"suffix": ""
}
],
"year": 2013,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1306.3584"
]
},
"num": null,
"urls": [],
"raw_text": "Nal Kalchbrenner and Phil Blunsom. 2013. Recurrent convolutional neural networks for discourse compo- sitionality. arXiv preprint arXiv:1306.3584.",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "A convolutional neural network for modelling sentences",
"authors": [
{
"first": "Nal",
"middle": [],
"last": "Kalchbrenner",
"suffix": ""
},
{
"first": "Edward",
"middle": [],
"last": "Grefenstette",
"suffix": ""
},
{
"first": "Phil",
"middle": [],
"last": "Blunsom",
"suffix": ""
}
],
"year": 2014,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1404.2188"
]
},
"num": null,
"urls": [],
"raw_text": "Nal Kalchbrenner, Edward Grefenstette, and Phil Blunsom. 2014. A convolutional neural net- work for modelling sentences. arXiv preprint arXiv:1404.2188.",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "Convolutional neural networks for sentence classification",
"authors": [
{
"first": "Yoon",
"middle": [],
"last": "Kim",
"suffix": ""
}
],
"year": 2014,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1408.5882"
]
},
"num": null,
"urls": [],
"raw_text": "Yoon Kim. 2014. Convolutional neural net- works for sentence classification. arXiv preprint arXiv:1408.5882.",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "Recurrent convolutional neural networks for text classification",
"authors": [
{
"first": "Siwei",
"middle": [],
"last": "Lai",
"suffix": ""
},
{
"first": "Liheng",
"middle": [],
"last": "Xu",
"suffix": ""
},
{
"first": "Kang",
"middle": [],
"last": "Liu",
"suffix": ""
}
],
"year": 2015,
"venue": "Proceedings of AAAI",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Siwei Lai, Liheng Xu, Kang Liu, and Jun Zhao. 2015. Recurrent convolutional neural networks for text classification. In Proceedings of AAAI.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "Joint event extraction via structured prediction with global features",
"authors": [
{
"first": "Qi",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Ji",
"middle": [],
"last": "Heng",
"suffix": ""
},
{
"first": "Liang",
"middle": [],
"last": "Huang",
"suffix": ""
}
],
"year": 2013,
"venue": "Proceedings of ACL",
"volume": "",
"issue": "",
"pages": "73--82",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Qi Li, Heng Ji, and Liang Huang. 2013. Joint event extraction via structured prediction with global fea- tures. In Proceedings of ACL, pages 73-82.",
"links": null
},
"BIBREF15": {
"ref_id": "b15",
"title": "Constructing information networks using one single model",
"authors": [
{
"first": "Qi",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Heng",
"middle": [],
"last": "Ji",
"suffix": ""
},
{
"first": "Yu",
"middle": [],
"last": "Hong",
"suffix": ""
},
{
"first": "Sujian",
"middle": [],
"last": "Li",
"suffix": ""
}
],
"year": 2014,
"venue": "Proceedings of EMNLP",
"volume": "",
"issue": "",
"pages": "1846--1851",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Qi Li, Heng Ji, Yu Hong, and Sujian Li. 2014. Constructing information networks using one sin- gle model. In Proceedings of EMNLP, pages 1846- 1851.",
"links": null
},
"BIBREF16": {
"ref_id": "b16",
"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
},
"BIBREF17": {
"ref_id": "b17",
"title": "Event extraction as dependency parsing",
"authors": [
{
"first": "David",
"middle": [],
"last": "Mcclosky",
"suffix": ""
},
{
"first": "Mihai",
"middle": [],
"last": "Surdeanu",
"suffix": ""
},
{
"first": "Christopher D",
"middle": [],
"last": "Manning",
"suffix": ""
}
],
"year": 2011,
"venue": "Proceedings of ACL-HLT",
"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-HLT, pages 1626- 1635.",
"links": null
},
"BIBREF18": {
"ref_id": "b18",
"title": "Efficient estimation of word representations in vector space",
"authors": [
{
"first": "Tomas",
"middle": [],
"last": "Mikolov",
"suffix": ""
},
{
"first": "Kai",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Greg",
"middle": [],
"last": "Corrado",
"suffix": ""
},
{
"first": "Jeffrey",
"middle": [],
"last": "Dean",
"suffix": ""
}
],
"year": 2013,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1301.3781"
]
},
"num": null,
"urls": [],
"raw_text": "Tomas Mikolov, Kai Chen, Greg Corrado, and Jef- frey Dean. 2013a. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.",
"links": null
},
"BIBREF19": {
"ref_id": "b19",
"title": "Distributed representations of words and phrases and their compositionality",
"authors": [
{
"first": "Tomas",
"middle": [],
"last": "Mikolov",
"suffix": ""
},
{
"first": "Ilya",
"middle": [],
"last": "Sutskever",
"suffix": ""
},
{
"first": "Kai",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Greg",
"middle": [
"S"
],
"last": "Corrado",
"suffix": ""
},
{
"first": "Jeff",
"middle": [],
"last": "Dean",
"suffix": ""
}
],
"year": 2013,
"venue": "Proceedings of NIPS",
"volume": "",
"issue": "",
"pages": "3111--3119",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Cor- rado, and Jeff Dean. 2013b. Distributed representa- tions of words and phrases and their compositional- ity. In Proceedings of NIPS, pages 3111-3119.",
"links": null
},
"BIBREF20": {
"ref_id": "b20",
"title": "Hierarchical probabilistic neural network language model",
"authors": [
{
"first": "Frederic",
"middle": [],
"last": "Morin",
"suffix": ""
},
{
"first": "Yoshua",
"middle": [],
"last": "Bengio",
"suffix": ""
}
],
"year": 2005,
"venue": "Proceedings of AISTATS",
"volume": "",
"issue": "",
"pages": "246--252",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Frederic Morin and Yoshua Bengio. 2005. Hierarchi- cal probabilistic neural network language model. In Proceedings of AISTATS, pages 246-252.",
"links": null
},
"BIBREF21": {
"ref_id": "b21",
"title": "Word representations: a simple and general method for semi-supervised learning",
"authors": [
{
"first": "Joseph",
"middle": [],
"last": "Turian",
"suffix": ""
},
{
"first": "Lev",
"middle": [],
"last": "Ratinov",
"suffix": ""
},
{
"first": "Yoshua",
"middle": [],
"last": "Bengio",
"suffix": ""
}
],
"year": 2010,
"venue": "Proceedings of ACL",
"volume": "",
"issue": "",
"pages": "384--394",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Joseph Turian, Lev Ratinov, and Yoshua Bengio. 2010. Word representations: a simple and general method for semi-supervised learning. In Proceedings of ACL, pages 384-394.",
"links": null
},
"BIBREF22": {
"ref_id": "b22",
"title": "Unsupervised word sense disambiguation rivaling supervised methods",
"authors": [
{
"first": "David",
"middle": [],
"last": "Yarowsky",
"suffix": ""
}
],
"year": 1995,
"venue": "Proceedings of ACL",
"volume": "",
"issue": "",
"pages": "189--196",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "David Yarowsky. 1995. Unsupervised word sense dis- ambiguation rivaling supervised methods. In Pro- ceedings of ACL, pages 189-196.",
"links": null
},
"BIBREF23": {
"ref_id": "b23",
"title": "Adadelta: An adaptive learning rate method",
"authors": [
{
"first": "D",
"middle": [],
"last": "Matthew",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Zeiler",
"suffix": ""
}
],
"year": 2012,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1212.5701"
]
},
"num": null,
"urls": [],
"raw_text": "Matthew D Zeiler. 2012. Adadelta: An adaptive learn- ing rate method. arXiv preprint arXiv:1212.5701.",
"links": null
},
"BIBREF24": {
"ref_id": "b24",
"title": "Relation classification via convolutional deep neural network",
"authors": [
{
"first": "Daojian",
"middle": [],
"last": "Zeng",
"suffix": ""
},
{
"first": "Kang",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "Siwei",
"middle": [],
"last": "Lai",
"suffix": ""
},
{
"first": "Guangyou",
"middle": [],
"last": "Zhou",
"suffix": ""
},
{
"first": "Jun",
"middle": [],
"last": "Zhao",
"suffix": ""
}
],
"year": 2014,
"venue": "Proceedings of COLING",
"volume": "",
"issue": "",
"pages": "2335--2344",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Daojian Zeng, Kang Liu, Siwei Lai, Guangyou Zhou, and Jun Zhao. 2014. Relation classification via con- volutional deep neural network. In Proceedings of COLING, pages 2335-2344.",
"links": null
}
},
"ref_entries": {
"FIGREF0": {
"num": null,
"uris": null,
"type_str": "figure",
"text": "In Baghdad , a cameraman died when an American tank fired on the Palestine Hotel."
},
"FIGREF1": {
"num": null,
"uris": null,
"type_str": "figure",
"text": "The architecture for the stage of argument classification in the event extraction. It illustrates the processing of one instance with the predict trigger fired and the candidate argument cameraman."
},
"TABREF0": {
"num": null,
"html": null,
"content": "<table><tr><td/><td/><td/><td/><td/><td/><td/><td/><td/><td colspan=\"2\">S</td><td>e n t e n c e</td><td>L e v e l</td><td>F e a t u r e</td><td>E</td><td>x t r a c t i o n</td></tr><tr><td/><td/><td/><td>S</td><td colspan=\"3\">e n t e n c e</td><td colspan=\"2\">F e a t u r e</td><td colspan=\"3\">I n p u t</td><td>C o n v o l u t i o n a l</td><td>D y n a m i c</td><td>M u l t i -p o o l i n g</td></tr><tr><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td/><td>F</td><td>e</td><td>a</td><td>t</td><td>u</td><td>r</td><td>e</td><td>m</td><td>a</td><td>p</td><td>3</td></tr><tr><td/><td/><td>. . .</td><td/><td/><td/><td/><td/><td/><td/><td/><td>F</td><td>e</td><td>a</td><td>F t u e r e a</td><td>t m u r a e p m 2 a</td><td>p</td><td>1</td></tr><tr><td/><td/><td>a</td><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td>A</td><td colspan=\"2\">d i e w h e a m e r i c a n n n d</td><td/><td/><td/><td/><td/><td/><td/><td/><td>. . . . . .</td><td>13 max(c ) 12 max(c ) 11 max(c )</td></tr><tr><td/><td colspan=\"2\">t a n k</td><td/><td>C</td><td>W</td><td>F</td><td>P</td><td>F</td><td>E</td><td colspan=\"2\">F</td></tr><tr><td/><td/><td>o n . . .</td><td/><td/><td/><td/><td/><td/><td/><td/></tr><tr><td/><td>E</td><td>m b e d d i n g</td><td>L e a r n i n g</td><td/><td/><td/><td/><td/><td/><td/><td>L e x i c a l</td><td>L e v e l</td><td>F e a t u r e</td><td>R e p r e s e n t a t i o n</td><td>C l a s s i f i e r</td><td>O u t p u t</td></tr></table>",
"text": "for details.",
"type_str": "table"
},
"TABREF4": {
"num": null,
"html": null,
"content": "<table><tr><td>: Comparison of the event extraction scores</td></tr><tr><td>obtained for the Traditional, CNN and DMCNN</td></tr><tr><td>models</td></tr><tr><td>4.4 Effect of Word Embedding on Extracting</td></tr><tr><td>Lexical-Level Features</td></tr></table>",
"text": "",
"type_str": "table"
},
"TABREF6": {
"num": null,
"html": null,
"content": "<table><tr><td colspan=\"3\">4.5 Lexical features vs. Sentence Features</td></tr><tr><td colspan=\"3\">To compare the effectiveness of different levels of</td></tr><tr><td colspan=\"3\">features, we extract events by using lexical fea-</td></tr><tr><td colspan=\"3\">tures and sentence features separately. The results</td></tr><tr><td colspan=\"3\">obtained using the DMCNN are shown in table</td></tr><tr><td colspan=\"3\">5. Interestingly, in the trigger-classification stage,</td></tr><tr><td colspan=\"3\">the lexical features play an effective role, whereas</td></tr><tr><td colspan=\"3\">the sentence features play a more important role in</td></tr><tr><td colspan=\"3\">the argument-classification stage. The best results</td></tr><tr><td colspan=\"3\">are achieved when we combine lexical-level and</td></tr><tr><td colspan=\"3\">sentence-level features. This observation demon-</td></tr><tr><td colspan=\"3\">strates that both of the two-level features are im-</td></tr><tr><td colspan=\"2\">portant for event extraction.</td><td/></tr><tr><td>Feature</td><td colspan=\"2\">Trigger Argument F1 F1</td></tr><tr><td>Lexical</td><td>64.9</td><td>40.2</td></tr><tr><td>Sentence</td><td>63.8</td><td>50.7</td></tr><tr><td>Combine</td><td>69.1</td><td>53.5</td></tr></table>",
"text": "Comparison of the results for the traditional lexical feature and our lexical feature. A denotes the triggers or arguments appearing in both training and test datasets, and B indicates all other cases.",
"type_str": "table"
},
"TABREF7": {
"num": null,
"html": null,
"content": "<table/>",
"text": "Comparison of the trigger-classification score and argument-classification score obtained by lexical-level features, sentence-level features and a combination of both",
"type_str": "table"
}
}
}
} |