File size: 126,849 Bytes
6fa4bc9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099 2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112 2113 2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127 2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 2141 2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155 2156 2157 2158 2159 2160 2161 2162 2163 2164 2165 2166 2167 2168 2169 2170 2171 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181 2182 2183 2184 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2198 2199 2200 2201 2202 2203 2204 2205 2206 2207 2208 2209 2210 2211 2212 2213 2214 2215 2216 2217 2218 2219 2220 2221 2222 2223 2224 2225 2226 2227 2228 2229 2230 2231 2232 2233 2234 2235 2236 2237 2238 2239 2240 2241 2242 2243 2244 2245 2246 2247 2248 2249 2250 2251 2252 2253 2254 2255 2256 2257 2258 2259 2260 2261 2262 2263 2264 2265 2266 2267 2268 2269 2270 2271 2272 2273 2274 2275 2276 2277 2278 2279 2280 2281 2282 2283 2284 2285 2286 2287 2288 2289 2290 2291 2292 2293 2294 2295 2296 2297 2298 2299 2300 2301 2302 2303 2304 2305 2306 2307 2308 2309 2310 2311 2312 2313 2314 2315 2316 2317 2318 2319 2320 2321 2322 2323 2324 2325 2326 2327 2328 2329 2330 2331 2332 2333 2334 2335 2336 2337 2338 2339 2340 2341 2342 2343 2344 2345 2346 2347 2348 2349 2350 2351 2352 2353 2354 2355 2356 2357 2358 2359 2360 2361 2362 2363 2364 2365 2366 2367 2368 2369 2370 2371 2372 2373 2374 2375 2376 2377 2378 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 2403 2404 2405 2406 2407 2408 2409 2410 2411 2412 2413 2414 2415 2416 2417 2418 2419 2420 2421 2422 2423 2424 2425 2426 2427 2428 2429 2430 2431 2432 2433 2434 2435 2436 2437 2438 2439 2440 2441 2442 2443 2444 2445 2446 2447 2448 2449 2450 2451 2452 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464 2465 2466 2467 2468 2469 2470 2471 2472 2473 2474 2475 | {
"paper_id": "2020",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T13:27:38.665837Z"
},
"title": "Using Type Information to Improve Entity Coreference Resolution",
"authors": [
{
"first": "Sopan",
"middle": [],
"last": "Khosla",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Language Technologies Institute Carnegie Mellon University",
"location": {
"country": "USA"
}
},
"email": "sopank@cs.cmu.edu"
},
{
"first": "Carolyn",
"middle": [],
"last": "Rose",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Language Technologies Institute Carnegie Mellon University",
"location": {
"country": "USA"
}
},
"email": "cprose@cs.cmu.edu"
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "Coreference resolution (CR) is an essential part of discourse analysis. Most recently, neural approaches have been proposed to improve over SOTA models from earlier paradigms. So far none of the published neural models leverage external semantic knowledge such as type information. This paper offers the first such model and evaluation, demonstrating modest gains in accuracy by introducing either gold standard or predicted types. In the proposed approach, type information serves both to (1) improve mention representation and (2) create a soft type consistency check between coreference candidate mentions. Our evaluation covers two different grain sizes of types over four different benchmark corpora.",
"pdf_parse": {
"paper_id": "2020",
"_pdf_hash": "",
"abstract": [
{
"text": "Coreference resolution (CR) is an essential part of discourse analysis. Most recently, neural approaches have been proposed to improve over SOTA models from earlier paradigms. So far none of the published neural models leverage external semantic knowledge such as type information. This paper offers the first such model and evaluation, demonstrating modest gains in accuracy by introducing either gold standard or predicted types. In the proposed approach, type information serves both to (1) improve mention representation and (2) create a soft type consistency check between coreference candidate mentions. Our evaluation covers two different grain sizes of types over four different benchmark corpora.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "Coreference resolution (CR) is an extensively studied problem in computational linguistics and NLP (Hobbs, 1978; Lappin and Leass, 1994; Mitkov, 1999; Ng, 2017; Clark and Manning, 2016; Lee et al., 2017) . Solutions to this problem allow us to make meaningful links between concepts and entities within a discourse and therefore serves as a valuable pre-processing step for downstream tasks like summarization and questionanswering (Steinberger et al., 2007; Dasigi et al., 2019; Sukthanker et al., 2020a) .",
"cite_spans": [
{
"start": 99,
"end": 112,
"text": "(Hobbs, 1978;",
"ref_id": "BIBREF10"
},
{
"start": 113,
"end": 136,
"text": "Lappin and Leass, 1994;",
"ref_id": "BIBREF13"
},
{
"start": 137,
"end": 150,
"text": "Mitkov, 1999;",
"ref_id": "BIBREF17"
},
{
"start": 151,
"end": 160,
"text": "Ng, 2017;",
"ref_id": "BIBREF18"
},
{
"start": 161,
"end": 185,
"text": "Clark and Manning, 2016;",
"ref_id": "BIBREF4"
},
{
"start": 186,
"end": 203,
"text": "Lee et al., 2017)",
"ref_id": "BIBREF14"
},
{
"start": 432,
"end": 458,
"text": "(Steinberger et al., 2007;",
"ref_id": "BIBREF29"
},
{
"start": 459,
"end": 479,
"text": "Dasigi et al., 2019;",
"ref_id": "BIBREF6"
},
{
"start": 480,
"end": 505,
"text": "Sukthanker et al., 2020a)",
"ref_id": "BIBREF30"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Recently, multiple datasets including Ontonotes (Pradhan et al., 2012) , Litbank (Bamman et al., 2020) , EmailCoref (Dakle et al., 2020) , and WikiCoref (Ghaddar and Langlais, 2016) have been proposed as benchmark datasets for CR, especially in the sub-area of entity anaphora (Sukthanker et al., 2020b) . Entity anaphora is a simpler starting place for work on anaphora because unlike abstract anaphora (Webber, 1991) , entity anaphora are pronouns or noun phrases that refer to an explicitly mentioned entity in the discourse rather than an abstract idea that must be constructed from a repackaging of information revealed over an extended text. An affordance of entity anaphora is that they have easily articulated semantic types. Most of the entity CR datasets are extensively annotated for syntactic features (like constituency parse etc.) and semantic features (like entity-types). However, none of the published SOTA methods (Lee et al., 2017; Joshi et al., 2019 Joshi et al., , 2020 explicitly leverage the type information.",
"cite_spans": [
{
"start": 48,
"end": 70,
"text": "(Pradhan et al., 2012)",
"ref_id": "BIBREF24"
},
{
"start": 81,
"end": 102,
"text": "(Bamman et al., 2020)",
"ref_id": "BIBREF0"
},
{
"start": 105,
"end": 136,
"text": "EmailCoref (Dakle et al., 2020)",
"ref_id": null
},
{
"start": 153,
"end": 181,
"text": "(Ghaddar and Langlais, 2016)",
"ref_id": "BIBREF8"
},
{
"start": 277,
"end": 303,
"text": "(Sukthanker et al., 2020b)",
"ref_id": "BIBREF31"
},
{
"start": 404,
"end": 418,
"text": "(Webber, 1991)",
"ref_id": "BIBREF35"
},
{
"start": 932,
"end": 950,
"text": "(Lee et al., 2017;",
"ref_id": "BIBREF14"
},
{
"start": 951,
"end": 969,
"text": "Joshi et al., 2019",
"ref_id": "BIBREF12"
},
{
"start": 970,
"end": 990,
"text": "Joshi et al., , 2020",
"ref_id": "BIBREF11"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "In this paper, we present a proof of concept to portray the benefits of using type information in neural approaches for CR. Named entities are generally divided generically (e.g. person, organization etc.) or in a domain-specific manner (e.g. symptom, drug, test etc.). In this work, we consider CR datasets that contain generic entitytypes. One challenge is that the different corpora do not utilize the same set of type tags. For example, OntoNotes includes 18 types while EmailCoref includes only 4. Thus, we evaluate the performance of the proposed modeling approach on each dataset both with the set of type tags germaine to the dataset as well as a common set of four basic types (person, org, location, facility) inspired from research on Named Entity Recognition (NER) (Tjong Kim Sang, 2002; Tjong Kim Sang and De Meulder, 2003) .",
"cite_spans": [
{
"start": 784,
"end": 799,
"text": "Kim Sang, 2002;",
"ref_id": "BIBREF32"
},
{
"start": 800,
"end": 800,
"text": "",
"ref_id": null
},
{
"start": 807,
"end": 837,
"text": "Kim Sang and De Meulder, 2003)",
"ref_id": "BIBREF33"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Our motivation is similar to (Durrett and Klein, 2014) , which used a structured CRF with handcurated features to jointly-model the tasks of CR, entity typing, and entity linking. Their joint architecture showed an improved performance on CR over the independent baseline. However, our work differs from there's as we show the benefits of entity-type information in neural models that use contextualized representations like BERT (Peters et al., 2018) . Some prior art (Petroni et al., 2019; Roberts et al., 2020) argues that contextual- Bamman et al. (2020) for entity coreference resolution by incorporating type information at two levels.",
"cite_spans": [
{
"start": 29,
"end": 54,
"text": "(Durrett and Klein, 2014)",
"ref_id": "BIBREF7"
},
{
"start": 430,
"end": 451,
"text": "(Peters et al., 2018)",
"ref_id": "BIBREF20"
},
{
"start": 469,
"end": 491,
"text": "(Petroni et al., 2019;",
"ref_id": "BIBREF21"
},
{
"start": 492,
"end": 513,
"text": "Roberts et al., 2020)",
"ref_id": "BIBREF26"
},
{
"start": 538,
"end": 558,
"text": "Bamman et al. (2020)",
"ref_id": "BIBREF0"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "(1) Type information is concatenated with the mention span representation created by their model; and (2) A consistency check is incorporated that compares the types of two mentions under consideration to calculate the coreference score. Please refer to Section 3 for details.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "ized embeddings implicitly capture facts and relationships between real-world entities. However, in this work, we empirically show that access to explicit knowledge about entity-types benefits neural models that use BERT for CR. We show a consistent improvement in performance on four different coreference datasets from varied domains.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Our contribution is that we evaluate the impact of the introduction of type information in neural entity coreference at two different levels of granularity (which we refer to as original vs common), demonstrating their utility both in the case where gold standard type information is available, and the more typical case where it is predicted.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Neural Coreference Resolution: Recently, neural approaches to coreference (Joshi et al., 2020 (Joshi et al., , 2019 Lee et al., , 2017 have begun to show their prowess. The SOTA models show impressive performance on state-of-the-art datasets like OntoNotes (Pradhan et al., 2012) and GAP (Webster et al., 2018) . The notable architecture proposed by Lee et al. (2017) scores pairs of entity mentions independently and later uses a clustering algorithm to find coreference clusters. On the other hand, improve upon this foundation by introducing an approximated higher-order inference that iteratively updates the existing span representation using its antecedent distribution. Moreover, they propose a coarseto-fine grained approach to pairwise scoring for tackling the computational challenges caused due to the iterative higher-order inference. More recently, Joshi et al. (2019 Joshi et al. ( , 2020 showed that use of contextual representations instead of wordembeddings like GloVe (Pennington et al., 2014) can further boost the results over and above those just mentioned. Our work offers additional improvement by building on the model proposed in Bamman et al. (2020) , which is based on Lee et al. (2017) , and adds additional nuanced information grounded in semantic types. Type Information: Named Entity Recognition datasets (Tjong Kim Sang, 2002; Tjong Kim Sang and De Meulder, 2003; Li et al., 2016) often group entity mentions into different types (or categories) depending on the domain and the potential downstream applications of the corpus. For example, the medical corpus used in the i2b2 Challenge 2010 (Uzuner et al., 2011) annotates domain-specific types like problem, test, symptom etc., whereas, a more general-domain dataset like CoNLL-2002 (Tjong Kim Sang, 2002 uses generic types like person, organization, and location. Type information as a predictive signal has been shown to be beneficial for NLP tasks like relation extraction (Soares et al., 2019) and entitylinking . It affords some level of disambiguation, which assists models with filtering out some incorrect predictions in order to increase the probability of a correct prediction. In this work, we evaluate the benefits of using explicit type information for CR. We show that a model that leverages entity types associated with the anaphoric/ antecedent mentions significantly reduces the problem of type inconsistency in the output coreference clusters and thus improves the overall performance of the neural baseline on four datasets. Type Information for CR: Multiple prior works have shown type-information to be a useful feature for shallow coreference resolution classifiers (Soon et al., 2001; Bengtson and Roth, 2008; Ponzetto and Strube, 2006; Haghighi and Klein, 2010; Durrett and Klein, 2014) . (Soon et al., 2001) take the most frequent sense for each noun in WordNet as the semantic class for that noun and use a decision-tree for pairwise classification of whether two samples co-refer each other. (Bengtson and Roth, 2008) use a hypernym tree to extract the type information for different common nouns, and compare the proper names against a predefined list to determine if the mention is a person. They, then, pass this and many other features (like distance, agreement, etc.) through a regularized average perceptron for pairwise classification. This paper expands on these studies to show that entity-type information is also beneficial for neural models that use contextualized representations like BERT (Peters et al., 2018) , which have been argued to implicitly capture facts and relationships between real-world entities (Petroni et al., 2019; Roberts et al., 2020) .",
"cite_spans": [
{
"start": 74,
"end": 93,
"text": "(Joshi et al., 2020",
"ref_id": "BIBREF11"
},
{
"start": 94,
"end": 115,
"text": "(Joshi et al., , 2019",
"ref_id": "BIBREF12"
},
{
"start": 116,
"end": 134,
"text": "Lee et al., , 2017",
"ref_id": "BIBREF14"
},
{
"start": 247,
"end": 279,
"text": "OntoNotes (Pradhan et al., 2012)",
"ref_id": null
},
{
"start": 288,
"end": 310,
"text": "(Webster et al., 2018)",
"ref_id": "BIBREF36"
},
{
"start": 350,
"end": 367,
"text": "Lee et al. (2017)",
"ref_id": "BIBREF14"
},
{
"start": 862,
"end": 880,
"text": "Joshi et al. (2019",
"ref_id": "BIBREF12"
},
{
"start": 881,
"end": 902,
"text": "Joshi et al. ( , 2020",
"ref_id": "BIBREF11"
},
{
"start": 986,
"end": 1011,
"text": "(Pennington et al., 2014)",
"ref_id": "BIBREF19"
},
{
"start": 1155,
"end": 1175,
"text": "Bamman et al. (2020)",
"ref_id": "BIBREF0"
},
{
"start": 1196,
"end": 1213,
"text": "Lee et al. (2017)",
"ref_id": "BIBREF14"
},
{
"start": 1343,
"end": 1358,
"text": "Kim Sang, 2002;",
"ref_id": "BIBREF32"
},
{
"start": 1359,
"end": 1395,
"text": "Tjong Kim Sang and De Meulder, 2003;",
"ref_id": "BIBREF33"
},
{
"start": 1396,
"end": 1412,
"text": "Li et al., 2016)",
"ref_id": "BIBREF16"
},
{
"start": 1623,
"end": 1644,
"text": "(Uzuner et al., 2011)",
"ref_id": "BIBREF34"
},
{
"start": 1755,
"end": 1765,
"text": "CoNLL-2002",
"ref_id": "BIBREF32"
},
{
"start": 1766,
"end": 1787,
"text": "(Tjong Kim Sang, 2002",
"ref_id": "BIBREF32"
},
{
"start": 2671,
"end": 2690,
"text": "(Soon et al., 2001;",
"ref_id": null
},
{
"start": 2691,
"end": 2715,
"text": "Bengtson and Roth, 2008;",
"ref_id": "BIBREF1"
},
{
"start": 2716,
"end": 2742,
"text": "Ponzetto and Strube, 2006;",
"ref_id": "BIBREF23"
},
{
"start": 2743,
"end": 2768,
"text": "Haghighi and Klein, 2010;",
"ref_id": "BIBREF9"
},
{
"start": 2769,
"end": 2793,
"text": "Durrett and Klein, 2014)",
"ref_id": "BIBREF7"
},
{
"start": 2796,
"end": 2815,
"text": "(Soon et al., 2001)",
"ref_id": null
},
{
"start": 3513,
"end": 3534,
"text": "(Peters et al., 2018)",
"ref_id": "BIBREF20"
},
{
"start": 3634,
"end": 3656,
"text": "(Petroni et al., 2019;",
"ref_id": "BIBREF21"
},
{
"start": 3657,
"end": 3678,
"text": "Roberts et al., 2020)",
"ref_id": "BIBREF26"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "In this section, we explain how we introduce type information into a neural CR system.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Model",
"sec_num": "3"
},
{
"text": "We use the model proposed in Bamman et al. (2020) as our baseline. The model gives stateof-the-art scores on the LitBank corpus (Bamman et al., 2020) and is an end-to-end mention ranking system based on Lee et al. (2017) , which has shown competitive performance on the OntoNotes dataset. However, this model differs from Lee et al. (2017) as it uses BERT embeddings, omits author and genre information, and only focuses on the task of mention-linking. Since our main goal is to evaluate the benefits of type information, we too separate mention-linking from mentionidentification and only show results computed over gold-standard mentions. This controls for the effects of the mention-identification module's performance on our experiments. Impact of typeinformation incorporation in the real-world endto-end CR setting (mention identification + linking) is left as future work.",
"cite_spans": [
{
"start": 29,
"end": 49,
"text": "Bamman et al. (2020)",
"ref_id": "BIBREF0"
},
{
"start": 128,
"end": 149,
"text": "(Bamman et al., 2020)",
"ref_id": "BIBREF0"
},
{
"start": 203,
"end": 220,
"text": "Lee et al. (2017)",
"ref_id": "BIBREF14"
},
{
"start": 322,
"end": 339,
"text": "Lee et al. (2017)",
"ref_id": "BIBREF14"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Baseline",
"sec_num": "3.1"
},
{
"text": "The BERT embeddings for each token i are passed through a bi-directional LSTM (x i ). To represent a mention m with start and end positions s, e respectively, x s , x e , attention over x s , ..., x e , and features to represent the width (wi) and inclusion within quotations (qu) are concatenated.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Baseline",
"sec_num": "3.1"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "m = [x s ; x e ; Att(x s , .., x e ); wi; qu]",
"eq_num": "(1)"
}
],
"section": "Baseline",
"sec_num": "3.1"
},
{
"text": "Finally, given the representation of two mentions m j and m k , their coreference score S(m j , m k ) is computed by concatenating m j , m k , m j m k , distance (d) between the mentions and whether one mention is nested (n) within the other, which are then passed through fully-connected layers (FC).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Baseline",
"sec_num": "3.1"
},
{
"text": "S(m j , m k ) = FC([m j ; m k ; m j m k ; d; n]) (2)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Baseline",
"sec_num": "3.1"
},
{
"text": "We refer the reader to (Bamman et al., 2020; Lee et al., 2017) for more details about the architecture.",
"cite_spans": [
{
"start": 23,
"end": 44,
"text": "(Bamman et al., 2020;",
"ref_id": "BIBREF0"
},
{
"start": 45,
"end": 62,
"text": "Lee et al., 2017)",
"ref_id": "BIBREF14"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Baseline",
"sec_num": "3.1"
},
{
"text": "We improve the above model by including entitytype information on two levels ( Figure 1 ). First, we concatenate the entity-type t of the mention to m (in Eq. 1) to improve the mention representation.",
"cite_spans": [],
"ref_spans": [
{
"start": 79,
"end": 87,
"text": "Figure 1",
"ref_id": "FIGREF0"
}
],
"eq_spans": [],
"section": "Entity Type Information",
"sec_num": "3.2"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "m = [m; t]",
"eq_num": "(3)"
}
],
"section": "Entity Type Information",
"sec_num": "3.2"
},
{
"text": "This allows the model access to the entity type of the mention as an additional feature. We call this +ET-self. Second, to check the type consistency (softly) between any two mentions under consideration as possibly coreferent, we append a feature (tc) in Eq. 2, which takes the value 0 if both mentions have the same type, and 1 otherwise. For example, in Figure 1 , since Los Angeles and it have the same entity-type PLACE, tc jk = 0.",
"cite_spans": [],
"ref_spans": [
{
"start": 357,
"end": 365,
"text": "Figure 1",
"ref_id": "FIGREF0"
}
],
"eq_spans": [],
"section": "Entity Type Information",
"sec_num": "3.2"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "S (m j , m k ) = FC([m j ; m k ; m j m k ; d; n; tc jk ])",
"eq_num": "(4)"
}
],
"section": "Entity Type Information",
"sec_num": "3.2"
},
{
"text": "This part of the approach is referred to as +ETcross throughout the remainder of the paper. We decide against the use of a hard consistency check (which would filter out mentions which do not have the same type) as it might not generalize well to bridging anaphora (Clark, 1975) where the anaphor refers to an object that is associated with, but not identical to, the antecedent (Poesio et al., 2018) . In such cases, the type of the anaphora and its antecedent may not match. Finally, our architecture combines both components together as +ET (ET = ET-self + ET-cross).",
"cite_spans": [
{
"start": 265,
"end": 278,
"text": "(Clark, 1975)",
"ref_id": "BIBREF3"
},
{
"start": 379,
"end": 400,
"text": "(Poesio et al., 2018)",
"ref_id": "BIBREF22"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Entity Type Information",
"sec_num": "3.2"
},
{
"text": "We gauge the benefits of using entity-type information on the four datasets discussed below. LitBank. This dataset (Bamman et al., 2020) contains coreference annotations for 100 literary texts. 1 This dataset limits the markable mentions to six entity-types, where majority of the mentions (83.1%) point to a person. EmailCoref. This dataset (Dakle et al., 2020) comprises of 46 email threads with a total of 245 email messages. 2 Similar to LitBank, it considers a mention to be a span of text that refers to a real-world entity. In this work, we filter out pronouns that point towards multiple entities in the email (e.g. we, they) thus only focusing on singular mentions. Ontonotes. From this multi-lingual dataset, we evaluate on the subset (english) from OntoNotes that was used in the CoNLL-2012 shared task (Pradhan et al., 2012). 3 It contains 2802 training, 343 development, and 348 test documents. The dataset differs from LitBank in its annotation scheme with the biggest difference being the fact that it does not annotate singletons.",
"cite_spans": [
{
"start": 115,
"end": 136,
"text": "(Bamman et al., 2020)",
"ref_id": "BIBREF0"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Datasets",
"sec_num": "4"
},
{
"text": "It contains annotations for 18 different entitytypes. However, unlike LitBank and EmailCoref, not all mentions have an associated entity-type. For example, none of the pronoun mentions are given a type even if they act as anaphors to typed entities. We partially ameliorate this issue by extracting gold coreference clusters that contain at least one typed mention and assigning the majority type in that cluster to all of its elements. For example, in Figure 1 , if Los Angeles is typed PLACE, and it is in the gold coreference cluster of Los Angeles (no other element in the cluster), then it is also assigned the type PLACE. WikiCoref. This corpus, released by (Ghaddar and Langlais, 2016), comprises 30 documents from wikipedia annotated for coreference resolution. 4 The annotations contain additional metadata, like the associated freebase rdf link for each mention (if available). We use this rdf entry to extract the mention's entity types from freebase dump. Mentions that do not get any type are marked NA. The first 24 documents are chosen for training, the next 3 for development, and the rest for testing. The above-discussed datasets differ in the number as well as the categories of entity-types they originally annotate (Table 1) . Apart from a common list of types (like PER, ORG, LOC), they also include corpus-specific categories like DIGital (EmailCoref), MONey, and LANG (OntoNotes). We carry out experiments with two sets of types -original and common -for each dataset. The common set of types include the following 5 categories: PER, ORG, LOC, FAC, OTHER.",
"cite_spans": [
{
"start": 770,
"end": 771,
"text": "4",
"ref_id": null
}
],
"ref_spans": [
{
"start": 453,
"end": 461,
"text": "Figure 1",
"ref_id": "FIGREF0"
},
{
"start": 1236,
"end": 1245,
"text": "(Table 1)",
"ref_id": "TABREF1"
}
],
"eq_spans": [],
"section": "Datasets",
"sec_num": "4"
},
{
"text": "In this section, we provide the results of our empirical experiments. Evaluation Metrics: We convert all three datasets into the CoNLL 2012 format and report the F1 score for MUC, B 3 , and CEAF metrics using the CoNLL-2012 official scripts. The performances are compared on the average F1 of the abovementioned metrics. For EmailCoref, OntoNotes, and WikiCoref, we report the mean score of 5 independent runs of the model with different seeds. Whereas, for LitBank, we present the 10-fold cross-validation results. 5 ",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Experiments and Results",
"sec_num": "5"
},
{
"text": "In order to establish an upper bound for improvement through introduction of type information, our first experiment leverages the original list of entity-types annotated in different corpora (+ ET (orig)), using the gold standard labels for types. Inclusion of entity-type information improves over the baseline for all CR datasets. Table 2 presents the performance of the baseline model and the model with entity-type information. We find that entity-type information gives a boost of 0.96 Avg. F1 (p < 0.01) on LitBank which is the new state-of-the-art score with goldmentions. This suggests that type information is helpful for CR on LitBank despite the heavily skewed distribution of entity-types in this corpus. Similarly, type information also benefits Email-Coref and WikiCoref resulting in an absolute improvement of 1.67 and 2.9 Avg. F1 points respectively (p < 0.01). We also see a 2.4 Avg. F1 improvement (p < 0.01) on OntoNotes, the largest dataset in this study. This suggests that explicit access to type information is beneficial all over the board, despite the use of contextual representations which have been claimed to model realworld facts and relationships (Petroni et al., 2019) . Ablation Results: To understand the contribution of the inclusion of type information to improve mention representation (+ET-self) and type consistency check between candidate mentions (+ET- cross), we perform an ablation study (Table 3) . We find that both components consistently provide significant performance boosts over the baseline. However, their combination (+ET) performs the best across all datasets.",
"cite_spans": [
{
"start": 1178,
"end": 1200,
"text": "(Petroni et al., 2019)",
"ref_id": "BIBREF21"
}
],
"ref_spans": [
{
"start": 333,
"end": 340,
"text": "Table 2",
"ref_id": "TABREF3"
},
{
"start": 1431,
"end": 1440,
"text": "(Table 3)",
"ref_id": "TABREF5"
}
],
"eq_spans": [],
"section": "Performance with Original Types",
"sec_num": "5.1"
},
{
"text": "The previous experiment leverages the original entity-types assigned by dataset annotators. Due to the differences in domain and annotation guidelines among these datasets, the annotators introduce several domain-specific entity types (e.g. DIGital, Work Of Art etc.) apart from the common four (PERson, ORGanization, LOCation, FACility) that are often used in the Named Entity Recognition literature (Tjong Kim Sang, 2002) . The former can prove to be much more difficult to obtain/ learn due to dearth of relevant data. Therefore, to assess the worth of using a common entitytype list for all datasets, we map the original types (Table 1) to the above-mentioned four common types. 6 Categories that do not map to any common type are assigned Other. +ET (com) rows in Table 2 show the results for this experiment. Models trained with common types as features perform worse than +ET (orig) which was expected as several original types are now clubbed into a single category (e.g. LAW -> OTHER, LANG -> OTHER) thus somewhat reducing the effectiveness of the feature. One surprising observation is the small difference between the performance on OntoNotes dataset, despite the fact that the number of type categories reduce from 18 + Other (+ET (orig)) to 4 + Other (+ET (com)). This could either be because (1) the entities with corpus-specific types occur less frequently in Ontonotes, or (2) the baseline model does a good job in resolving them. Further research is required to understand this case which is out of scope for this work. ",
"cite_spans": [
{
"start": 412,
"end": 423,
"text": "Sang, 2002)",
"ref_id": "BIBREF32"
},
{
"start": 683,
"end": 684,
"text": "6",
"ref_id": null
}
],
"ref_spans": [
{
"start": 631,
"end": 640,
"text": "(Table 1)",
"ref_id": "TABREF1"
},
{
"start": 769,
"end": 776,
"text": "Table 2",
"ref_id": "TABREF3"
}
],
"eq_spans": [],
"section": "Performance with Common Types",
"sec_num": "5.2"
},
{
"text": "Our hypothesis around the use of entity-types was to provide additional information to the model that could be leveraged to minimize errors due to type mismatch in CR. To evaluate if the F1 score improvements achieved by +ET models are because of fewer type mismatch errors, we report the number of coreference clusters detected by the model that contain at least one element with a type that is different from the others in the cluster. Since all of the datasets used in this work only consider identity coreferences (Recasens et al., 2011) -with potentially varied definitions of identity (Bamman et al., 2020; Pradhan et al., 2012) -where the mention is a linguistic \"re-packaging\" of its antecedent, this measure makes sense. As shown in Table 2 , the models that score lower on the impurity measure get a higher Avg F1. This suggests that the aggregate performance improvements are at least partly due to the better mention-mention comparison in +ET systems.",
"cite_spans": [
{
"start": 518,
"end": 541,
"text": "(Recasens et al., 2011)",
"ref_id": "BIBREF25"
},
{
"start": 591,
"end": 612,
"text": "(Bamman et al., 2020;",
"ref_id": "BIBREF0"
},
{
"start": 613,
"end": 634,
"text": "Pradhan et al., 2012)",
"ref_id": "BIBREF24"
}
],
"ref_spans": [
{
"start": 742,
"end": 749,
"text": "Table 2",
"ref_id": "TABREF3"
}
],
"eq_spans": [],
"section": "# Impure Clusters (#IC)",
"sec_num": "5.3"
},
{
"text": "Results shown in the previous section assume the presence of gold standard types during training as well as inference, which is often impractical in the real-world. Most of the new samples that a CR model would encounter would not include type information about the candidate mentions. Therefore, we set up an additional experiment to gauge the benefits of type information using predicted types. We introduce a baseline approach to infer the type of the mentions and then use these predictions in the +ET models, in place of the gold types, for coreference resolution.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Predicted Types",
"sec_num": "6"
},
{
"text": "Given the mention and its immediate context, i.e. the sentence it occurs in (S = ..., c \u22122 , c \u22121 , e 1 , e 2 , ..., e n , c 1 , c 2 , ...), we add markers <ENT_START>/ <ENT_END> before/ after the beginning/ ending of the mention in the sentence. The new sequence (S = ..., c \u22122 , c \u22121 , <ENT_START>, e 1 , e 2 , ..., e n , <ENT_END>, c 1 , c 2 , ...) is tokenized using BERT tokenizer and passed through the BERT encoder. The output from which is then mean-pooled and passed through a fully-connected layer for classification. This architecture is motivated from (Soares et al., 2019) who show that adding markers around entities before passing the sentence through BERT performs better for relation extraction.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Type Prediction Model",
"sec_num": "6.1"
},
{
"text": "Type Prediction: Our final evaluation of the use of types in coreference is perhaps the most important one as it uses predicted types rather than annotated types, thus demonstrating that the benefits can be achieved in practice. Here we use the Type Prediction Model described just above. We limit the length of the input sequence to 128 tokens and use BERT-base-cased model for our type-prediction experiments. We perform a fivefold cross-validation to predict the type for each mention in the dataset. Since all four datasets suffer from class-imbalance, we report both Macro F1 score as well as the accuracy for the model. The model is trained for 20 epochs, with earlystopping (patience = 10), and is fine-tuned on the development set for Macro F1 to give more importance to minority type categories. We do not consider NA as a separate class during type prediction for WikiCoref and OntoNotes. For evaluation of our type-prediction model, we ignore the mentions that do not have an associated gold type (NA) from the final numbers in Table 4 .",
"cite_spans": [],
"ref_spans": [
{
"start": 1039,
"end": 1046,
"text": "Table 4",
"ref_id": "TABREF7"
}
],
"eq_spans": [],
"section": "Experiments and Results",
"sec_num": "6.2"
},
{
"text": "As shown, our model performs well on Lit-Bank, EmailCoref, and Ontonotes due to their favorable size in terms of training samples for the BERT-based type predictor. WikiCoref, however, proves more challenging as the model only manages 38.0 Macro F1 points with original (orig) types and 45.0 with common types (com), portraying its lack of ability to learn minority type categories with less data. Furthermore, our model finds it easier to predict the common (com) set of types for each dataset as combining multiple corpus-specific types into one partially alleviates the problem of class-imbalance. In line with our expectation, the largest improvement due to common types is seen for OntoNotes where the prob- lem reduces from an 18-way classification to a 5way classification.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Experiments and Results",
"sec_num": "6.2"
},
{
"text": "Coreference Resolution: Each mention in the corpus occurs in the test-sets of the five-fold cross-validation type-prediction experiments exactly once. This allows us to infer the type of each mention using the model that is trained on a different subset of the dataset. These inferred types are used in the training and testing of the CR systems in a manner similar to the annotated types. Empirically, we found that the above configuration performs better than using the +ET models trained with annotated types and testing with predicted types, as the former exposes the CR models to the noisy types during training thus allowing them to learn weights that take this noise into account. We report the results for both original (+ ET-pred (orig)) and common (+ ET-pred (com)) type categories on each dataset. Table 5 shows the results for performance of the baseline and the type-informed models on the four datasets, where the types are inferred from the model described in Section 6.1. We find that the improvements from type-information persist across LitBank, EmailCoref, and OntoNotes despite the use of predicted types, but, quite expectedly, remain smaller than the improvements from the gold annotated types. Scores on WikiCoref show no significant improvement over the baseline, which could be explained by the poor performance of the type prediction model on this dataset which reduces the potency of the feature for CR. Table 6 shows the most frequently occurring entity-types for each of the genres in OntoNotes. In line with our intuition, we find that enity-type information helps the baseline in bc, bn, wb, and mz genres which have less skew in their entity-type distribution. Genres like bc, bn and wb, although dominated by PER entities, contain a substantial minority of other entity-types like ORG and GPE. Along the same lines, mz contains a majority of GPE entities but also enough entities with type PER and ORG to make type information a potentially useful feature for CR. However, two exceptions to this are the improved performance of +ET (orig) on tc (highest skew) and no significant improvement on nw (lowest skew). These findings prompt further research in the future.",
"cite_spans": [],
"ref_spans": [
{
"start": 809,
"end": 816,
"text": "Table 5",
"ref_id": "TABREF8"
},
{
"start": 1431,
"end": 1438,
"text": "Table 6",
"ref_id": "TABREF10"
}
],
"eq_spans": [],
"section": "Experiments and Results",
"sec_num": "6.2"
},
{
"text": "Entity coreference in discourse often takes the surface form of pronouns (PRP) (like she, they, that, it etc.) or noun phrases (NP) (like LA, John's brother etc.) In Table 4 , we compare the performance of our type prediction model on different types of pronouns, and noun phrases of varying length. We find that the model does well in predicting types for personal pronouns (PRP (pers.)) like she, he and noun phrases (NP). However, it consistently underperforms on demonstrative pro- nouns (PRP (dem.)) like this, that, and it across all datasets. This reduced performance could be due to the fact that demonstrative pronouns do not contain any signal about the type of the entity they refer to. Therefore, the type prediction model has to solely rely on the context to make that decision. However, this is not the case with PRPs (pers.) and NPs where the mention string is usually a strong indicator of the type. This problem is worsened by the imbalance due to the small presence of PRP (dem.) mentions in difference CR datasets. Since, the model does not encounter enough PRPs (dem.), it might not be able to learn to give high importance to context in these cases.",
"cite_spans": [],
"ref_spans": [
{
"start": 166,
"end": 173,
"text": "Table 4",
"ref_id": "TABREF7"
}
],
"eq_spans": [],
"section": "Type Prediction: PRP vs NP",
"sec_num": "7.2"
},
{
"text": "This could be partially alleviated by creating a separate type-prediction path for PRP (dem.) where the mention span is masked before it is passed through the model. A model that is trained with masked mentions would focus more on the context for type prediction and thus could lead to better performance on PRPs (dem.). One could also experiment with training the type-prediction model on all of the mentions across the four datasets. The common list of types introduced in this work would allow for the creation of a larger training-set that includes mentions from multiple corpora (including external NER datasets) which could provide enough signal for the model to better learn the common types for PRPs (dem.).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Type Prediction: PRP vs NP",
"sec_num": "7.2"
},
{
"text": "Both these approaches could further boost the results for CR with predicted entity-types, ultimately, reducing the gap between the scores in Table 2 and 5. However, they are left as future work as they are out of scope for this paper. Table 7 provides an excerpt of an email from EmailCoref corpus. As shown, the baseline model predicts the coreference clusters for an organizer (DIG) and PricewaterhouseCoopers Calgary (ORG) incorrectly. For the former, the model mistakes it as a reference to your current home address (LOC) which is corrected by the entity-type aware models. For the latter, the baseline considers PricewaterhouseCoopers Calgary (PCC) as part of a new coreference cluster, even though it refers to the organization of the email's sender which was previously referred to as we in the email. Models with access to gold type information (+ET (orig) and +ET (com)) are able to make that connection. +ET-pred (orig), however, is unable to cluster PCC correctly which could be due to the fact that the type-prediction model incorrectly classifies the type of we as PER rather than ORG. This could lead to the CR model considering PCC (ORG) as a new entity in the discourse rather than a postcedent of we. This example demonstrates that sentencelevel context might not be sufficient in some cases for mention type-disambiguation. We intend to experiment with models that capture long-term context and leverage external knowledge in the future.",
"cite_spans": [],
"ref_spans": [
{
"start": 235,
"end": 242,
"text": "Table 7",
"ref_id": "TABREF11"
}
],
"eq_spans": [],
"section": "Type Prediction: PRP vs NP",
"sec_num": "7.2"
},
{
"text": "In this work, we show the importance of using entity-type information in neural coreference resolution (CR) models with contextualized embeddings like BERT. Models which leverage type information, annotated in the corpus, substantially outperform the baseline on four CR datasets by reducing the number of type mismatches in detected coreference clusters. Since, these datasets vary in number and categories of the types they define, we also experiment with mapping the original corpus types to four common types (PER, ORG, LOC, FAC) based on previous NER research that can be learnt more easily through large NER datasets. Models which use these common types perform slightly worse than original types but still show significant improvements over the baseline systems.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion",
"sec_num": "8"
},
{
"text": "The presence of gold standard types during CR inference is unlikely in practice. Therefore, we propose a model that infers the type of a mention given the mention span and its immediate context to use along side the proposed CR approach. In our evaluation, we find that using types predicted by our model for CR still performs significantly better than the baseline, thus offering stronger evidence that type information holds the potential for practical improvements for CR. 150 Dropout 0.2 Table A1 : Hyperparameter values for our model. We refer the reader to https://github.com/dbamman/ lrec2020-coref for the implementation of the baseline model. (com) experiments. These types are annotated in most of the named-entity recognition datasets and therefore are easier to model and learn via machine learning approaches. Tables A2, A3, A4, A5 show the mapping between the original types of each coreference dataset used in our study to the reduced common types. The most drastic difference occurs for OntoNotes (19 -> 5) and . OTHER type in WikiCoref is for freebase links that did not have an associated type stored in freebase, whereas NA is used for mentions which do not have a freebase link. For OntoNotes, NA refers to the mentions that did not get any type assigned to them even after the use of our cluster based type-propagation approach (explained in Section 4).",
"cite_spans": [],
"ref_spans": [
{
"start": 492,
"end": 500,
"text": "Table A1",
"ref_id": "TABREF1"
},
{
"start": 823,
"end": 844,
"text": "Tables A2, A3, A4, A5",
"ref_id": "TABREF3"
}
],
"eq_spans": [],
"section": "Conclusion",
"sec_num": "8"
},
{
"text": "https://github.com/dbamman/lrec2020-coref 2 https://github.com/paragdakle/emailcoref 3 https://catalog.ldc.upenn.edu/LDC2013T19",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "http://rali.iro.umontreal.ca/rali/?q=en/wikicoref 5 Hyperparameter values are provided in Appendix A.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "We provide the mapping between the original types and the common types in Appendix B.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
}
],
"back_matter": [
{
"text": "We thank the anonymous reviewers for their insightful comments. We are also grateful to the members of the TELEDIA group at LTI, CMU for the invaluable feedback. This work was funded in part by Dow Chemical, and Microsoft.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Acknowledgements",
"sec_num": null
}
],
"bib_entries": {
"BIBREF0": {
"ref_id": "b0",
"title": "An annotated dataset of coreference in english literature",
"authors": [
{
"first": "David",
"middle": [],
"last": "Bamman",
"suffix": ""
},
{
"first": "Olivia",
"middle": [],
"last": "Lewke",
"suffix": ""
},
{
"first": "Anya",
"middle": [],
"last": "Mansoor",
"suffix": ""
}
],
"year": 2020,
"venue": "Proceedings of The 12th Language Resources and Evaluation Conference",
"volume": "",
"issue": "",
"pages": "44--54",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "David Bamman, Olivia Lewke, and Anya Mansoor. 2020. An annotated dataset of coreference in english literature. In Proceedings of The 12th Language Re- sources and Evaluation Conference, pages 44-54.",
"links": null
},
"BIBREF1": {
"ref_id": "b1",
"title": "Understanding the value of features for coreference resolution",
"authors": [
{
"first": "Eric",
"middle": [],
"last": "Bengtson",
"suffix": ""
},
{
"first": "Dan",
"middle": [],
"last": "Roth",
"suffix": ""
}
],
"year": 2008,
"venue": "Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "294--303",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Eric Bengtson and Dan Roth. 2008. Understanding the value of features for coreference resolution. In Pro- ceedings of the 2008 Conference on Empirical Meth- ods in Natural Language Processing, pages 294- 303.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "Improving entity linking by modeling latent entity type information",
"authors": [
{
"first": "Shuang",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Jinpeng",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Feng",
"middle": [],
"last": "Jiang",
"suffix": ""
},
{
"first": "Chin-Yew",
"middle": [],
"last": "Lin",
"suffix": ""
}
],
"year": 2020,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:2001.01447"
]
},
"num": null,
"urls": [],
"raw_text": "Shuang Chen, Jinpeng Wang, Feng Jiang, and Chin- Yew Lin. 2020. Improving entity linking by mod- eling latent entity type information. arXiv preprint arXiv:2001.01447.",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "Bridging",
"authors": [
{
"first": "H",
"middle": [
"H"
],
"last": "Clark",
"suffix": ""
}
],
"year": 1975,
"venue": "Proceedings of TIN-LAP",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "H. H. Clark. 1975. Bridging. In Proceedings of TIN- LAP.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "Deep reinforcement learning for mention-ranking coreference models",
"authors": [
{
"first": "Kevin",
"middle": [],
"last": "Clark",
"suffix": ""
},
{
"first": "D",
"middle": [],
"last": "Christopher",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Manning",
"suffix": ""
}
],
"year": 2016,
"venue": "Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "2256--2262",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Kevin Clark and Christopher D Manning. 2016. Deep reinforcement learning for mention-ranking coref- erence models. In Proceedings of the 2016 Con- ference on Empirical Methods in Natural Language Processing, pages 2256-2262.",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "A study on entity resolution for email conversations",
"authors": [
{
"first": "Takshak",
"middle": [],
"last": "Parag Pravin Dakle",
"suffix": ""
},
{
"first": "Dan",
"middle": [],
"last": "Desai",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Moldovan",
"suffix": ""
}
],
"year": 2020,
"venue": "Proceedings of The 12th Language Resources and Evaluation Conference",
"volume": "",
"issue": "",
"pages": "65--73",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Parag Pravin Dakle, Takshak Desai, and Dan Moldovan. 2020. A study on entity resolution for email conversations. In Proceedings of The 12th Language Resources and Evaluation Conference, pages 65-73.",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "Quoref: A reading comprehension dataset with questions requiring coreferential reasoning",
"authors": [
{
"first": "Pradeep",
"middle": [],
"last": "Dasigi",
"suffix": ""
},
{
"first": "Nelson",
"middle": [
"F"
],
"last": "Liu",
"suffix": ""
},
{
"first": "Ana",
"middle": [],
"last": "Marasovi\u0107",
"suffix": ""
},
{
"first": "Noah",
"middle": [
"A"
],
"last": "Smith",
"suffix": ""
},
{
"first": "Matt",
"middle": [],
"last": "Gardner",
"suffix": ""
}
],
"year": 2019,
"venue": "Proc. of EMNLP-IJCNLP",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Pradeep Dasigi, Nelson F. Liu, Ana Marasovi\u0107, Noah A. Smith, and Matt Gardner. 2019. Quoref: A reading comprehension dataset with questions re- quiring coreferential reasoning. In Proc. of EMNLP- IJCNLP.",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "A joint model for entity analysis: Coreference, typing, and linking",
"authors": [
{
"first": "Greg",
"middle": [],
"last": "Durrett",
"suffix": ""
},
{
"first": "Dan",
"middle": [],
"last": "Klein",
"suffix": ""
}
],
"year": 2014,
"venue": "Transactions of the Association for Computational Linguistics",
"volume": "2",
"issue": "",
"pages": "477--490",
"other_ids": {
"DOI": [
"10.1162/tacl_a_00197"
]
},
"num": null,
"urls": [],
"raw_text": "Greg Durrett and Dan Klein. 2014. A joint model for entity analysis: Coreference, typing, and linking. Transactions of the Association for Computational Linguistics, 2:477-490.",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "Wikicoref: An english coreference-annotated corpus of wikipedia articles",
"authors": [
{
"first": "Abbas",
"middle": [],
"last": "Ghaddar",
"suffix": ""
},
{
"first": "Philippe",
"middle": [],
"last": "Langlais",
"suffix": ""
}
],
"year": 2016,
"venue": "Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)",
"volume": "",
"issue": "",
"pages": "136--142",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Abbas Ghaddar and Philippe Langlais. 2016. Wiki- coref: An english coreference-annotated corpus of wikipedia articles. In Proceedings of the Tenth In- ternational Conference on Language Resources and Evaluation (LREC'16), pages 136-142.",
"links": null
},
"BIBREF9": {
"ref_id": "b9",
"title": "Coreference resolution in a modular, entity-centered model",
"authors": [
{
"first": "Aria",
"middle": [],
"last": "Haghighi",
"suffix": ""
},
{
"first": "Dan",
"middle": [],
"last": "Klein",
"suffix": ""
}
],
"year": 2010,
"venue": "Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "385--393",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Aria Haghighi and Dan Klein. 2010. Coreference res- olution in a modular, entity-centered model. In Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics, pages 385-393.",
"links": null
},
"BIBREF10": {
"ref_id": "b10",
"title": "Resolving pronoun references",
"authors": [
{
"first": "R",
"middle": [],
"last": "Jerry",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Hobbs",
"suffix": ""
}
],
"year": 1978,
"venue": "Lingua",
"volume": "44",
"issue": "4",
"pages": "311--338",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jerry R Hobbs. 1978. Resolving pronoun references. Lingua, 44(4):311-338.",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "Spanbert: Improving pre-training by representing and predicting spans",
"authors": [
{
"first": "Mandar",
"middle": [],
"last": "Joshi",
"suffix": ""
},
{
"first": "Danqi",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Yinhan",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "S",
"middle": [],
"last": "Daniel",
"suffix": ""
},
{
"first": "Luke",
"middle": [],
"last": "Weld",
"suffix": ""
},
{
"first": "Omer",
"middle": [],
"last": "Zettlemoyer",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Levy",
"suffix": ""
}
],
"year": 2020,
"venue": "Transactions of the Association for Computational Linguistics",
"volume": "8",
"issue": "",
"pages": "64--77",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Mandar Joshi, Danqi Chen, Yinhan Liu, Daniel S Weld, Luke Zettlemoyer, and Omer Levy. 2020. Spanbert: Improving pre-training by representing and predict- ing spans. Transactions of the Association for Com- putational Linguistics, 8:64-77.",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "Bert for coreference resolution: Baselines and analysis",
"authors": [
{
"first": "Mandar",
"middle": [],
"last": "Joshi",
"suffix": ""
},
{
"first": "Omer",
"middle": [],
"last": "Levy",
"suffix": ""
},
{
"first": "Luke",
"middle": [],
"last": "Zettlemoyer",
"suffix": ""
},
{
"first": "Daniel",
"middle": [
"S"
],
"last": "Weld",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
"volume": "",
"issue": "",
"pages": "5807--5812",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Mandar Joshi, Omer Levy, Luke Zettlemoyer, and Daniel S Weld. 2019. Bert for coreference reso- lution: Baselines and analysis. In Proceedings of the 2019 Conference on Empirical Methods in Nat- ural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 5807-5812.",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "An algorithm for pronominal anaphora resolution",
"authors": [
{
"first": "Shalom",
"middle": [],
"last": "Lappin",
"suffix": ""
},
{
"first": "J",
"middle": [],
"last": "Herbert",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Leass",
"suffix": ""
}
],
"year": 1994,
"venue": "Computational linguistics",
"volume": "20",
"issue": "4",
"pages": "535--561",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Shalom Lappin and Herbert J Leass. 1994. An algo- rithm for pronominal anaphora resolution. Compu- tational linguistics, 20(4):535-561.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "End-to-end neural coreference resolution",
"authors": [
{
"first": "Kenton",
"middle": [],
"last": "Lee",
"suffix": ""
},
{
"first": "Luheng",
"middle": [],
"last": "He",
"suffix": ""
},
{
"first": "Mike",
"middle": [],
"last": "Lewis",
"suffix": ""
},
{
"first": "Luke",
"middle": [],
"last": "Zettlemoyer",
"suffix": ""
}
],
"year": 2017,
"venue": "Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "188--197",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Kenton Lee, Luheng He, Mike Lewis, and Luke Zettle- moyer. 2017. End-to-end neural coreference reso- lution. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Process- ing, pages 188-197.",
"links": null
},
"BIBREF15": {
"ref_id": "b15",
"title": "Higher-order coreference resolution with coarse-tofine inference",
"authors": [
{
"first": "Kenton",
"middle": [],
"last": "Lee",
"suffix": ""
},
{
"first": "Luheng",
"middle": [],
"last": "He",
"suffix": ""
},
{
"first": "Luke",
"middle": [],
"last": "Zettlemoyer",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
"volume": "2",
"issue": "",
"pages": "687--692",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Kenton Lee, Luheng He, and Luke Zettlemoyer. 2018. Higher-order coreference resolution with coarse-to- fine inference. In Proceedings of the 2018 Confer- ence of the North American Chapter of the Associ- ation for Computational Linguistics: Human Lan- guage Technologies, Volume 2 (Short Papers), pages 687-692.",
"links": null
},
"BIBREF16": {
"ref_id": "b16",
"title": "Biocreative v cdr task corpus: a resource for chemical disease relation extraction. Database",
"authors": [
{
"first": "Jiao",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Yueping",
"middle": [],
"last": "Sun",
"suffix": ""
},
{
"first": "J",
"middle": [],
"last": "Robin",
"suffix": ""
},
{
"first": "Daniela",
"middle": [],
"last": "Johnson",
"suffix": ""
},
{
"first": "Chih-Hsuan",
"middle": [],
"last": "Sciaky",
"suffix": ""
},
{
"first": "Robert",
"middle": [],
"last": "Wei",
"suffix": ""
},
{
"first": "Allan",
"middle": [
"Peter"
],
"last": "Leaman",
"suffix": ""
},
{
"first": "Carolyn",
"middle": [
"J"
],
"last": "Davis",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Mattingly",
"suffix": ""
},
{
"first": "C",
"middle": [],
"last": "Thomas",
"suffix": ""
},
{
"first": "Zhiyong",
"middle": [],
"last": "Wiegers",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Lu",
"suffix": ""
}
],
"year": 2016,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jiao Li, Yueping Sun, Robin J Johnson, Daniela Sci- aky, Chih-Hsuan Wei, Robert Leaman, Allan Peter Davis, Carolyn J Mattingly, Thomas C Wiegers, and Zhiyong Lu. 2016. Biocreative v cdr task corpus: a resource for chemical disease relation extraction. Database, 2016.",
"links": null
},
"BIBREF17": {
"ref_id": "b17",
"title": "Anaphora resolution: the state of the art",
"authors": [
{
"first": "Ruslan",
"middle": [],
"last": "Mitkov",
"suffix": ""
}
],
"year": 1999,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ruslan Mitkov. 1999. Anaphora resolution: the state of the art. Citeseer.",
"links": null
},
"BIBREF18": {
"ref_id": "b18",
"title": "Machine learning for entity coreference resolution: A retrospective look at two decades of research",
"authors": [
{
"first": "Vincent",
"middle": [],
"last": "Ng",
"suffix": ""
}
],
"year": 2017,
"venue": "Thirty-First AAAI Conference on Artificial Intelligence",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Vincent Ng. 2017. Machine learning for entity corefer- ence resolution: A retrospective look at two decades of research. In Thirty-First AAAI Conference on Ar- tificial Intelligence.",
"links": null
},
"BIBREF19": {
"ref_id": "b19",
"title": "Glove: Global vectors for word representation",
"authors": [
{
"first": "Jeffrey",
"middle": [],
"last": "Pennington",
"suffix": ""
},
{
"first": "Richard",
"middle": [],
"last": "Socher",
"suffix": ""
},
{
"first": "Christopher D",
"middle": [],
"last": "Manning",
"suffix": ""
}
],
"year": 2014,
"venue": "Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP)",
"volume": "",
"issue": "",
"pages": "1532--1543",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014. Glove: Global vectors for word representation. In Proceedings of the 2014 confer- ence on empirical methods in natural language pro- cessing (EMNLP), pages 1532-1543.",
"links": null
},
"BIBREF20": {
"ref_id": "b20",
"title": "Deep contextualized word representations",
"authors": [
{
"first": "Matthew",
"middle": [],
"last": "Peters",
"suffix": ""
},
{
"first": "Mark",
"middle": [],
"last": "Neumann",
"suffix": ""
},
{
"first": "Mohit",
"middle": [],
"last": "Iyyer",
"suffix": ""
},
{
"first": "Matt",
"middle": [],
"last": "Gardner",
"suffix": ""
},
{
"first": "Christopher",
"middle": [],
"last": "Clark",
"suffix": ""
},
{
"first": "Kenton",
"middle": [],
"last": "Lee",
"suffix": ""
},
{
"first": "Luke",
"middle": [],
"last": "Zettlemoyer",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
"volume": "1",
"issue": "",
"pages": "2227--2237",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018. Deep contextualized word rep- resentations. In Proceedings of the 2018 Confer- ence of the North American Chapter of the Associ- ation for Computational Linguistics: Human Lan- guage Technologies, Volume 1 (Long Papers), pages 2227-2237.",
"links": null
},
"BIBREF21": {
"ref_id": "b21",
"title": "Language models as knowledge bases?",
"authors": [
{
"first": "Fabio",
"middle": [],
"last": "Petroni",
"suffix": ""
},
{
"first": "Tim",
"middle": [],
"last": "Rockt\u00e4schel",
"suffix": ""
},
{
"first": "Sebastian",
"middle": [],
"last": "Riedel",
"suffix": ""
},
{
"first": "Patrick",
"middle": [],
"last": "Lewis",
"suffix": ""
},
{
"first": "Anton",
"middle": [],
"last": "Bakhtin",
"suffix": ""
},
{
"first": "Yuxiang",
"middle": [],
"last": "Wu",
"suffix": ""
},
{
"first": "Alexander",
"middle": [],
"last": "Miller",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)",
"volume": "",
"issue": "",
"pages": "2463--2473",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Fabio Petroni, Tim Rockt\u00e4schel, Sebastian Riedel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, and Alexander Miller. 2019. Language models as knowledge bases? In Proceedings of the 2019 Con- ference on Empirical Methods in Natural Language Processing and the 9th International Joint Confer- ence on Natural Language Processing (EMNLP- IJCNLP), pages 2463-2473.",
"links": null
},
"BIBREF22": {
"ref_id": "b22",
"title": "Anaphora resolution with the arrau corpus",
"authors": [
{
"first": "Massimo",
"middle": [],
"last": "Poesio",
"suffix": ""
},
{
"first": "Yulia",
"middle": [],
"last": "Grishina",
"suffix": ""
},
{
"first": "Varada",
"middle": [],
"last": "Kolhatkar",
"suffix": ""
},
{
"first": "Nafise Sadat",
"middle": [],
"last": "Moosavi",
"suffix": ""
},
{
"first": "Ina",
"middle": [],
"last": "Roesiger",
"suffix": ""
},
{
"first": "Adam",
"middle": [],
"last": "Roussel",
"suffix": ""
},
{
"first": "Fabian",
"middle": [],
"last": "Simonjetz",
"suffix": ""
},
{
"first": "Alexandra",
"middle": [],
"last": "Uma",
"suffix": ""
},
{
"first": "Olga",
"middle": [],
"last": "Uryupina",
"suffix": ""
},
{
"first": "Juntao",
"middle": [],
"last": "Yu",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the First Workshop on Computational Models of Reference, Anaphora and Coreference",
"volume": "",
"issue": "",
"pages": "11--22",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Massimo Poesio, Yulia Grishina, Varada Kolhatkar, Nafise Sadat Moosavi, Ina Roesiger, Adam Roussel, Fabian Simonjetz, Alexandra Uma, Olga Uryupina, Juntao Yu, et al. 2018. Anaphora resolution with the arrau corpus. In Proceedings of the First Workshop on Computational Models of Reference, Anaphora and Coreference, pages 11-22.",
"links": null
},
"BIBREF23": {
"ref_id": "b23",
"title": "Exploiting semantic role labeling, wordnet and wikipedia for coreference resolution",
"authors": [
{
"first": "Paolo",
"middle": [],
"last": "Simone",
"suffix": ""
},
{
"first": "Michael",
"middle": [],
"last": "Ponzetto",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Strube",
"suffix": ""
}
],
"year": 2006,
"venue": "Proceedings of the Human Language Technology Conference of the NAACL, Main Conference",
"volume": "",
"issue": "",
"pages": "192--199",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Simone Paolo Ponzetto and Michael Strube. 2006. Exploiting semantic role labeling, wordnet and wikipedia for coreference resolution. In Proceed- ings of the Human Language Technology Confer- ence of the NAACL, Main Conference, pages 192- 199.",
"links": null
},
"BIBREF24": {
"ref_id": "b24",
"title": "Conll-2012 shared task: Modeling multilingual unrestricted coreference in ontonotes",
"authors": [
{
"first": "Alessandro",
"middle": [],
"last": "Sameer Pradhan",
"suffix": ""
},
{
"first": "Nianwen",
"middle": [],
"last": "Moschitti",
"suffix": ""
},
{
"first": "Olga",
"middle": [],
"last": "Xue",
"suffix": ""
},
{
"first": "Yuchen",
"middle": [],
"last": "Uryupina",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Zhang",
"suffix": ""
}
],
"year": 2012,
"venue": "Joint Conference on EMNLP and CoNLL -Shared Task, CoNLL '12",
"volume": "",
"issue": "",
"pages": "1--40",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Sameer Pradhan, Alessandro Moschitti, Nianwen Xue, Olga Uryupina, and Yuchen Zhang. 2012. Conll- 2012 shared task: Modeling multilingual unre- stricted coreference in ontonotes. In Joint Confer- ence on EMNLP and CoNLL -Shared Task, CoNLL '12, page 1-40, USA. Association for Computa- tional Linguistics.",
"links": null
},
"BIBREF25": {
"ref_id": "b25",
"title": "Identity, non-identity, and near-identity: Addressing the complexity of coreference",
"authors": [
{
"first": "Marta",
"middle": [],
"last": "Recasens",
"suffix": ""
},
{
"first": "Eduard",
"middle": [],
"last": "Hovy",
"suffix": ""
},
{
"first": "M Ant\u00f2nia",
"middle": [],
"last": "Mart\u00ed",
"suffix": ""
}
],
"year": 2011,
"venue": "Lingua",
"volume": "121",
"issue": "6",
"pages": "1138--1152",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Marta Recasens, Eduard Hovy, and M Ant\u00f2nia Mart\u00ed. 2011. Identity, non-identity, and near-identity: Ad- dressing the complexity of coreference. Lingua, 121(6):1138-1152.",
"links": null
},
"BIBREF26": {
"ref_id": "b26",
"title": "How much knowledge can you pack into the parameters of a language model",
"authors": [
{
"first": "Adam",
"middle": [],
"last": "Roberts",
"suffix": ""
},
{
"first": "Colin",
"middle": [],
"last": "Raffel",
"suffix": ""
},
{
"first": "Noam",
"middle": [],
"last": "Shazeer",
"suffix": ""
}
],
"year": 2020,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:2002.08910"
]
},
"num": null,
"urls": [],
"raw_text": "Adam Roberts, Colin Raffel, and Noam Shazeer. 2020. How much knowledge can you pack into the pa- rameters of a language model? arXiv preprint arXiv:2002.08910.",
"links": null
},
"BIBREF27": {
"ref_id": "b27",
"title": "Matching the blanks: Distributional similarity for relation learning",
"authors": [
{
"first": "",
"middle": [],
"last": "Livio Baldini",
"suffix": ""
},
{
"first": "Nicholas",
"middle": [],
"last": "Soares",
"suffix": ""
},
{
"first": "Jeffrey",
"middle": [],
"last": "Fitzgerald",
"suffix": ""
},
{
"first": "Tom",
"middle": [],
"last": "Ling",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Kwiatkowski",
"suffix": ""
}
],
"year": 2019,
"venue": "Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "2895--2905",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Livio Baldini Soares, Nicholas FitzGerald, Jeffrey Ling, and Tom Kwiatkowski. 2019. Matching the blanks: Distributional similarity for relation learn- ing. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 2895-2905.",
"links": null
},
"BIBREF28": {
"ref_id": "b28",
"title": "A machine learning approach to coreference resolution of noun phrases",
"authors": [],
"year": 2001,
"venue": "Computational linguistics",
"volume": "27",
"issue": "4",
"pages": "521--544",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Wee Meng Soon, Hwee Tou Ng, and Daniel Chung Yong Lim. 2001. A machine learning ap- proach to coreference resolution of noun phrases. Computational linguistics, 27(4):521-544.",
"links": null
},
"BIBREF29": {
"ref_id": "b29",
"title": "Two uses of anaphora resolution in summarization. Information Processing and Management",
"authors": [
{
"first": "Josef",
"middle": [],
"last": "Steinberger",
"suffix": ""
},
{
"first": "Massimo",
"middle": [],
"last": "Poesio",
"suffix": ""
},
{
"first": "A",
"middle": [],
"last": "Mijail",
"suffix": ""
},
{
"first": "Karel",
"middle": [],
"last": "Kabadjov",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Je\u017eek",
"suffix": ""
}
],
"year": 2007,
"venue": "",
"volume": "6",
"issue": "",
"pages": "1663--1680",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Josef Steinberger, Massimo Poesio, Mijail A Kabad- jov, and Karel Je\u017eek. 2007. Two uses of anaphora resolution in summarization. Information Process- ing and Management, 6(43):1663-1680.",
"links": null
},
"BIBREF30": {
"ref_id": "b30",
"title": "Anaphora and coreference resolution: A review",
"authors": [
{
"first": "Rhea",
"middle": [],
"last": "Sukthanker",
"suffix": ""
},
{
"first": "Soujanya",
"middle": [],
"last": "Poria",
"suffix": ""
},
{
"first": "Erik",
"middle": [],
"last": "Cambria",
"suffix": ""
},
{
"first": "Ramkumar",
"middle": [],
"last": "Thirunavukarasu",
"suffix": ""
}
],
"year": 2020,
"venue": "",
"volume": "59",
"issue": "",
"pages": "139--162",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Rhea Sukthanker, Soujanya Poria, Erik Cambria, and Ramkumar Thirunavukarasu. 2020a. Anaphora and coreference resolution: A review. Information Fu- sion, 59:139-162.",
"links": null
},
"BIBREF31": {
"ref_id": "b31",
"title": "Anaphora and coreference resolution: A review",
"authors": [
{
"first": "Rhea",
"middle": [],
"last": "Sukthanker",
"suffix": ""
},
{
"first": "Soujanya",
"middle": [],
"last": "Poria",
"suffix": ""
},
{
"first": "Erik",
"middle": [],
"last": "Cambria",
"suffix": ""
},
{
"first": "Ramkumar",
"middle": [],
"last": "Thirunavukarasu",
"suffix": ""
}
],
"year": 2020,
"venue": "",
"volume": "59",
"issue": "",
"pages": "139--162",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Rhea Sukthanker, Soujanya Poria, Erik Cambria, and Ramkumar Thirunavukarasu. 2020b. Anaphora and coreference resolution: A review. Information Fu- sion, 59:139-162.",
"links": null
},
"BIBREF32": {
"ref_id": "b32",
"title": "Introduction to the CoNLL-2002 shared task: Language-independent named entity recognition",
"authors": [
{
"first": "Erik",
"middle": [
"F"
],
"last": "",
"suffix": ""
},
{
"first": "Tjong Kim",
"middle": [],
"last": "Sang",
"suffix": ""
}
],
"year": 2002,
"venue": "COLING-02: The 6th Conference on Natural Language Learning",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Erik F. Tjong Kim Sang. 2002. Introduction to the CoNLL-2002 shared task: Language-independent named entity recognition. In COLING-02: The 6th Conference on Natural Language Learning 2002 (CoNLL-2002).",
"links": null
},
"BIBREF33": {
"ref_id": "b33",
"title": "Introduction to the conll-2003 shared task: language-independent named entity recognition",
"authors": [
{
"first": "Erik F Tjong Kim",
"middle": [],
"last": "Sang",
"suffix": ""
},
{
"first": "Fien",
"middle": [],
"last": "De Meulder",
"suffix": ""
}
],
"year": 2003,
"venue": "Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003",
"volume": "4",
"issue": "",
"pages": "142--147",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Erik F Tjong Kim Sang and Fien De Meulder. 2003. Introduction to the conll-2003 shared task: language-independent named entity recognition. In Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003-Volume 4, pages 142-147.",
"links": null
},
"BIBREF34": {
"ref_id": "b34",
"title": "i2b2/va challenge on concepts, assertions, and relations in clinical text",
"authors": [
{
"first": "\u00d6zlem",
"middle": [],
"last": "Uzuner",
"suffix": ""
},
{
"first": "R",
"middle": [],
"last": "Brett",
"suffix": ""
},
{
"first": "Shuying",
"middle": [],
"last": "South",
"suffix": ""
},
{
"first": "Scott L",
"middle": [],
"last": "Shen",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Duvall",
"suffix": ""
}
],
"year": 2010,
"venue": "Journal of the American Medical Informatics Association",
"volume": "18",
"issue": "5",
"pages": "552--556",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "\u00d6zlem Uzuner, Brett R South, Shuying Shen, and Scott L DuVall. 2011. 2010 i2b2/va challenge on concepts, assertions, and relations in clinical text. Journal of the American Medical Informatics Asso- ciation, 18(5):552-556.",
"links": null
},
"BIBREF35": {
"ref_id": "b35",
"title": "Structure and ostension in the interpretation of discourse deixis",
"authors": [
{
"first": "Bonnie",
"middle": [
"Lynn"
],
"last": "Webber",
"suffix": ""
}
],
"year": 1991,
"venue": "Language and Cognitive processes",
"volume": "6",
"issue": "2",
"pages": "107--135",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Bonnie Lynn Webber. 1991. Structure and ostension in the interpretation of discourse deixis. Language and Cognitive processes, 6(2):107-135.",
"links": null
},
"BIBREF36": {
"ref_id": "b36",
"title": "Mind the gap: A balanced corpus of gendered ambiguous pronouns",
"authors": [
{
"first": "Kellie",
"middle": [],
"last": "Webster",
"suffix": ""
},
{
"first": "Marta",
"middle": [],
"last": "Recasens",
"suffix": ""
},
{
"first": "Vera",
"middle": [],
"last": "Axelrod",
"suffix": ""
},
{
"first": "Jason",
"middle": [],
"last": "Baldridge",
"suffix": ""
}
],
"year": 2018,
"venue": "Transactions of the Association for Computational Linguistics",
"volume": "6",
"issue": "",
"pages": "605--617",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Kellie Webster, Marta Recasens, Vera Axelrod, and Ja- son Baldridge. 2018. Mind the gap: A balanced corpus of gendered ambiguous pronouns. Transac- tions of the Association for Computational Linguis- tics, 6:605-617.",
"links": null
}
},
"ref_entries": {
"FIGREF0": {
"text": "We improve",
"num": null,
"uris": null,
"type_str": "figure"
},
"FIGREF1": {
"text": "Type-prediction model.",
"num": null,
"uris": null,
"type_str": "figure"
},
"FIGREF2": {
"text": ", I(1) will have {Tamara Utsch} (2) reply to you (0) on your (0) refund -I (1) believe she (2) updated you (0) last week , but we (3) 'll see if there 's new news this week . Can you (0) please send me (1) {your (0) current home address} (4) so we (3) can send you (0) {an organizer} (5) ? Thanks , {Judy Perdomo} (1) {PricewaterhouseCoopers Calgary} (3)BaselineHi Chris (0) , I(1) will have {Tamara Utsch} (2) reply to you (0) on your (0) refund -I (1) believe she(2) updated you (0) last week , but we (3) 'll see if there 's new news this week . Can you (0) please send me (1) {your (0) current home address} (4) so we (3) can send you (0) {an organizer} (4) ? Thanks , {Judy Perdomo} (1) {PricewaterhouseCoopers Calgary} (6) + ET (orig) Hi Chris (0) , I (1) will have {Tamara Utsch} (2) reply to you (0) on your (0) refund -I (1) believe she (2) updated you (0) last week , but we (3) 'll see if there 's new news this week . Can you (0) please send me (1) {your (0) current home address} (4) so we (3) can send you (0) {an organizer} (5) ? Thanks , {Judy Perdomo} (1) {PricewaterhouseCoopers Calgary} (3) + ET (com) Hi Chris (0) , I (1) will have {Tamara Utsch} (2) reply to you (0) on your (0) refund -I (1) believe she (2) updated you (0) last week , but we (3) 'll see if there 's new news this week . Can you (0) please send me (1) {your (0) current home address} (4) so we (3) can send you (0) {an organizer} (5) ? Thanks , {Judy Perdomo} (1) {PricewaterhouseCoopers Calgary} (3) + ET-pred (orig) Hi Chris (0) , I (1) will have {Tamara Utsch} (2) reply to you (0) on your (0) refund -I (1) believe she (2) updated you (0) last week , but we (3) 'll see if there 's new news this week . Can you (0) please send me (1) {your (0) current home address} (4) so we (3) can send you (0) {an organizer} (5) ? Thanks , {Judy Perdomo} (1) {PricewaterhouseCoopers Calgary} (6)",
"num": null,
"uris": null,
"type_str": "figure"
},
"TABREF1": {
"text": "Type statistics for corpora used in this study.",
"html": null,
"num": null,
"type_str": "table",
"content": "<table/>"
},
"TABREF3": {
"text": "",
"html": null,
"num": null,
"type_str": "table",
"content": "<table/>"
},
"TABREF5": {
"text": "Ablation results (ET = ET-self + ET-cross).",
"html": null,
"num": null,
"type_str": "table",
"content": "<table/>"
},
"TABREF7": {
"text": "Performance of our BERT-based model for type-prediction. The last five columns show the accuracy (# samples) on demonstrative and personal pronouns, and noun phrases of different lengths. PRP (dem.) = Demonstrative Pronouns (this, that, it, these, those), PRP (pers.) = Personal Pronouns (she, he, they, me, you, we). The scores for WikiCoref and OntoNotes do not include mentions without an associated gold type (NA).",
"html": null,
"num": null,
"type_str": "table",
"content": "<table><tr><td>Dataset</td><td colspan=\"3\">Baseline + ET-pred (orig) + ET-pred (com)</td></tr><tr><td>LitBank</td><td>79.30</td><td>79.58</td><td>79.40</td></tr><tr><td>EmailCoref</td><td>73.33</td><td>74.20</td><td>74.18</td></tr><tr><td>WikiCoref</td><td>68.45</td><td>68.37</td><td>68.62</td></tr><tr><td>OntoNotes</td><td>83.36</td><td>84.02</td><td>83.65</td></tr></table>"
},
"TABREF8": {
"text": "CR results with predicted types. Numbers in bold are significantly better (p < 0.01) than the baseline.",
"html": null,
"num": null,
"type_str": "table",
"content": "<table/>"
},
"TABREF10": {
"text": "Type Distribution (excluding OTHER) for mentions within different Ontonotes genres after clustering based typepropagation. Numbers in bold are significantly better than the baseline (p < 0.01). -represents that none of the mentions in the genre are annotated with types.",
"html": null,
"num": null,
"type_str": "table",
"content": "<table/>"
},
"TABREF11": {
"text": "An example email from EmailCoref corpus. Numbers in round brackets denote the cluster number of the mention.",
"html": null,
"num": null,
"type_str": "table",
"content": "<table/>"
},
"TABREF13": {
"text": "",
"html": null,
"num": null,
"type_str": "table",
"content": "<table><tr><td/><td>: Litbank</td></tr><tr><td colspan=\"2\">Original Common</td></tr><tr><td>PER</td><td>PER</td></tr><tr><td>ORG</td><td>ORG</td></tr><tr><td>LOC</td><td>LOC</td></tr><tr><td>DIG</td><td>Other</td></tr></table>"
},
"TABREF14": {
"text": "",
"html": null,
"num": null,
"type_str": "table",
"content": "<table><tr><td colspan=\"2\">: EmailCoref</td></tr><tr><td>Original</td><td>Common</td></tr><tr><td>Organization</td><td>ORG</td></tr><tr><td>Person</td><td>PER</td></tr><tr><td>Corporation</td><td>FAC</td></tr><tr><td>Event</td><td>Other</td></tr><tr><td>Place</td><td>LOC</td></tr><tr><td>Thing</td><td>Other</td></tr><tr><td>OTHER</td><td>Other</td></tr><tr><td>NA</td><td>Other</td></tr></table>"
},
"TABREF15": {
"text": "WikiCorefWe use four common types (PERson, LOCation, FACility, ORGanization) and Other in +ET",
"html": null,
"num": null,
"type_str": "table",
"content": "<table><tr><td>Original</td><td>Common</td></tr><tr><td>ORG</td><td>ORG</td></tr><tr><td>WORK_OF_ART</td><td>Other</td></tr><tr><td>LOC</td><td>LOC</td></tr><tr><td>CARDINAL</td><td>Other</td></tr><tr><td>EVENT</td><td>Other</td></tr><tr><td>NORP</td><td>Other</td></tr><tr><td>GPE</td><td>LOC</td></tr><tr><td>DATE</td><td>Other</td></tr><tr><td>PERSON</td><td>PER</td></tr><tr><td>FAC</td><td>FAC</td></tr><tr><td>QUANTITY</td><td>Other</td></tr><tr><td>ORDINAL</td><td>Other</td></tr><tr><td>TIME</td><td>Other</td></tr><tr><td>PRODUCT</td><td>Other</td></tr><tr><td>PERCENT</td><td>Other</td></tr><tr><td>MONEY</td><td>Other</td></tr><tr><td>LAW</td><td>Other</td></tr><tr><td>LANGUAGE</td><td>Other</td></tr><tr><td>NA</td><td>Other</td></tr></table>"
},
"TABREF16": {
"text": "OntoNotes",
"html": null,
"num": null,
"type_str": "table",
"content": "<table/>"
}
}
}
} |