File size: 84,169 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 |
{
"paper_id": "R11-1037",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T15:04:26.344949Z"
},
"title": "Modelling Entity Instantiations",
"authors": [
{
"first": "Andrew",
"middle": [],
"last": "Mckinlay",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "University of Leeds",
"location": {
"country": "UK"
}
},
"email": ""
},
{
"first": "Katja",
"middle": [],
"last": "Markert",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "University of Leeds",
"location": {
"country": "UK"
}
},
"email": "markert@comp.leeds.ac.uk"
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "We introduce the problem of detecting Entity Instantiations, a type of entity relation in which a set of entities is introduced, and either a member or subset of this set is mentioned afterwards. We perform the first, reliable, corpus study of Entity Instantiations, concentrating on intersentential annotation. We then develop the first automatic instantiation detector, which incorporates lexical, contextual and world knowledge and shows significant improvements over a strong baseline.",
"pdf_parse": {
"paper_id": "R11-1037",
"_pdf_hash": "",
"abstract": [
{
"text": "We introduce the problem of detecting Entity Instantiations, a type of entity relation in which a set of entities is introduced, and either a member or subset of this set is mentioned afterwards. We perform the first, reliable, corpus study of Entity Instantiations, concentrating on intersentential annotation. We then develop the first automatic instantiation detector, which incorporates lexical, contextual and world knowledge and shows significant improvements over a strong baseline.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "In this paper we annotate and classify Entity Instantiations. An Entity Instantiation is a noncoreferent entity relationship, where a set of entities is mentioned, and then a member or subset 1 of this set is introduced. Example 1 shows a pair of sentences with the set in bold and set member in italics. 2 Examples 2 and 3 show a pair of sentences with a set in bold and subset in italics.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "(1) a. Some European funds recently have skyrocketed. b. Spain Fund has surged to a startling 120% premium.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "(2) a. Bids totalling $515 million were submitted. b. Accepted offers ranged from 8.38% to 8.395%",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "1 When we refer to a subset, we mean a proper subset. We consider two equal sets to be coreferent, and not participating in an Entity Instantiation.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "2 Examples 1, 2, 3, 8 and 9 are adapted from the Penn Treebank Wall Street Journal Corpus (Marcus et al., 1993) .",
"cite_spans": [
{
"start": 90,
"end": 111,
"text": "(Marcus et al., 1993)",
"ref_id": "BIBREF17"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "(3) a. In the aftermath of the downturn many manufacturers have struggled. b. Those relying on foreign imports have had the most difficulty.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "The detection of Entity Instantiations is not tackled in ACE (ACE, 2000 (ACE, -2005 or MUC (MUC, 1987 (MUC, -1998 , the two most popular schemes of semantic relation annotation. It is, however, important as it can supplement knowledge about the member or subset. In Example 4 below, the Entity Instantiation between 'several EU countries' and 'the UK' gives us the knowledge that not only are interest rates dropping in the UK, but inflation is rising as well. Entity Instantiations can also aid the interpretation of sentiment -in Example 5, the author's thoughts about the pay of Wayne Rooney can be inferred from the negative sentiment of the first sentence. In some instances, the member or subset is even uninterpretable without the set. In Example 3, 'Those relying on foreign imports' requires 'many manufacturers' to interpret the missing head noun. The problem of detecting these types of Entity Instantiation overlaps with bridging anaphora.",
"cite_spans": [
{
"start": 61,
"end": 71,
"text": "(ACE, 2000",
"ref_id": null
},
{
"start": 72,
"end": 83,
"text": "(ACE, -2005",
"ref_id": null
},
{
"start": 91,
"end": 101,
"text": "(MUC, 1987",
"ref_id": null
},
{
"start": 102,
"end": 113,
"text": "(MUC, -1998",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "(4) a. Inflation has increased sharply in several EU countries. b. In the UK, this has accompanied a drop in interest rates. In Examples 6 and 7, the contextual information about the attitudes of the workers is necessary to establish whether an Entity Instantiation exists.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "(6) a. Some workers are opposed to strike action. b. John Smith fears that a strike could damage the industry's public perception.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "(7) a. Some workers are opposed to strike action. b. David Jones, however, is willing to put his job on the line for the cause. (Not an instantiation.)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "In this paper we present an annotated corpus of Entity Instantiations, containing 648 annotated instantiations over 25 texts. We then use this corpus to train and test an automatic Entity Instantiation identifier, which gains significant improvements over a unigram baseline.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Our work is related to Relation Extraction (RE), which is the discovery of semantic relations between pairs of entities. Much of the work in this field is connected to the Message Understanding Conferences (MUC, 1987 (MUC, -1998 and the NIST Automatic Content Extraction (ACE, 2000 (ACE, -2005 programs, both of which provide annotated corpora of semantic relations. The ACE-2004 scheme includes 7 broad relation types, divided into a total of 23 subtypes, such as ART.User-Owner to indicate the ownership of an object by a person, and ORG-AFF.Employment to represent the employment of a person by an organisation. Entity Instantiations are not considered in the MUC and ACE annotation schemes, which consider relationships between different types of entity, such as those between persons and locations, rather than our groups and instances of entities of the same type. However, the algorithms used to classify these semantic relationship might still be applicable to our problem.",
"cite_spans": [
{
"start": 206,
"end": 216,
"text": "(MUC, 1987",
"ref_id": null
},
{
"start": 217,
"end": 228,
"text": "(MUC, -1998",
"ref_id": null
},
{
"start": 271,
"end": 281,
"text": "(ACE, 2000",
"ref_id": null
},
{
"start": 282,
"end": 293,
"text": "(ACE, -2005",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "A variety of automatic RE algorithms have been developed, falling largely into two groups; those that learn from tree-kernels and those that use traditional, flat features. In one approach of the first type, (Zhou et al., 2007) use tree kernels to capture the structured information held in the parse trees of entities. They implement an algorithm which dynamically decides how much context to include as part of the tree, and in conjunction with some flat features it achieves an F-score of 75.8% on the 7 broad relation types in the ACE-2004 dataset.",
"cite_spans": [
{
"start": 208,
"end": 227,
"text": "(Zhou et al., 2007)",
"ref_id": "BIBREF32"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "Two recent flat-featured approaches successfully exploit background knowledge to improve RE. (Chan and Roth, 2010) implement features which use Wikipedia queries to search for parentchild relationships between entities. They attain an F-score of 68.2% at the coarse-grained level and 54.4% at the fine-grained level on a set of directed, sentence-internal relations from the ACE-2004 dataset. (Sun et al., 2011) generate largescale word clusters from the TDT5 corpus and incorporate information regarding which cluster the mention head word belongs to. This method results in an F-score of 71.5%.",
"cite_spans": [
{
"start": 93,
"end": 114,
"text": "(Chan and Roth, 2010)",
"ref_id": "BIBREF4"
},
{
"start": 393,
"end": 411,
"text": "(Sun et al., 2011)",
"ref_id": "BIBREF30"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "Our work is also related to the problem of bridging anaphora. A bridging anaphor is an anaphor that is not coreferent to its antecedent, but connected by another relationship, such as meronymy. Prior work in theoretical linguistics and corpus linguistics (Asher and Lascarides, 1998; Fraurud, 1990; Poesio and Vieira, 1998) has offered significant insight into bridging. A number of bridging publications also refer to set membership or subset relationships specifically (Clark, 1975; Prince, 1981; Gardent et al., 2003) . Further work has concentrated on the development of algorithms for the resolution of bridging anaphora. (Markert et al., 1996; Vieira and Poesio, 2000) create end-to-end systems for bridging resolution, while both (Markert et al., 2003) and (Poesio et al., 2004) tackle solely part-of bridging references.",
"cite_spans": [
{
"start": 255,
"end": 283,
"text": "(Asher and Lascarides, 1998;",
"ref_id": null
},
{
"start": 284,
"end": 298,
"text": "Fraurud, 1990;",
"ref_id": "BIBREF10"
},
{
"start": 299,
"end": 323,
"text": "Poesio and Vieira, 1998)",
"ref_id": "BIBREF24"
},
{
"start": 471,
"end": 484,
"text": "(Clark, 1975;",
"ref_id": "BIBREF6"
},
{
"start": 485,
"end": 498,
"text": "Prince, 1981;",
"ref_id": "BIBREF27"
},
{
"start": 499,
"end": 520,
"text": "Gardent et al., 2003)",
"ref_id": "BIBREF11"
},
{
"start": 627,
"end": 649,
"text": "(Markert et al., 1996;",
"ref_id": "BIBREF19"
},
{
"start": 650,
"end": 674,
"text": "Vieira and Poesio, 2000)",
"ref_id": "BIBREF31"
},
{
"start": 737,
"end": 759,
"text": "(Markert et al., 2003)",
"ref_id": "BIBREF20"
},
{
"start": 764,
"end": 785,
"text": "(Poesio et al., 2004)",
"ref_id": "BIBREF25"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "Our work differs from bridging in that often Entity Instantiations are not anaphoric (see Examples 1, 4, 5 and 6). There is, however, some overlap. For instance, in Example 3 the subset 'Those relying on foreign imports' requires knowledge of the set 'manufacturers' to be understood.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "Our work is also related to (Recasens et al., 2010) , in which the authors develop a typology of near-identity coreference relationships, including largely overlapping sets. Set membership relations, however, are not tackled.",
"cite_spans": [
{
"start": 28,
"end": 51,
"text": "(Recasens et al., 2010)",
"ref_id": "BIBREF28"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "To create a gold standard corpus creation we annotate full texts from the Penn Treebank (PTB) Wall Street Journal corpus (Marcus et al., 1993) for the presence of two types of Entity Instantiation:",
"cite_spans": [
{
"start": 121,
"end": 142,
"text": "(Marcus et al., 1993)",
"ref_id": "BIBREF17"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Corpus Study",
"sec_num": "3"
},
{
"text": "Set Member A set of entities is introduced, and a single member of that set is mentioned.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Corpus Study",
"sec_num": "3"
},
{
"text": "Subset A set of entities is introduced, and a smaller subset of these is mentioned.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Corpus Study",
"sec_num": "3"
},
{
"text": "We limit our annotation to instantiations that occur between adjacent sentences. We do not annotate intrasententially, as we suspect that many intrasentential instantiations may be easily discoverable by syntactic analysis (for example, the instantiations in 'Some football managers, such as Sir Alex Ferguson' and 'Among these workers, John Smith').. Our annotation tool automatically identifies plural and singular noun phrases (NPs) that are candidates for participating in Entity Instantiations, separately displaying plural-plural NP pairs for subset annotation and plural-singular NP pairs for set member annotation. We automatically remove NPs that are appositions or predicates, and therefore not mentions. Our tool also includes the option to manually mark noun phrases as \"Not a mention\". We use this to exclude instances of nonreferential it, noun phrases that are idiomaticsuch as pie in the sky -and generic pronouns.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Corpus Study",
"sec_num": "3"
},
{
"text": "The annotator then indicates whether each pair of NPs forms an Entity Instantiation. We annotate each pair of sentences twice; once with potential sets in first sentence and potential set members and subsets in the second sentence, and once with potential sets in the second sentence and potential set members and subsets in the first sentence.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Corpus Study",
"sec_num": "3"
},
{
"text": "To ascertain the reliability and replicability of our annotations, we undertook a short agreement study. Five texts containing a total of 6,177 NP pairs were independently annotated by the two authors of this study, and their agreement was measured in the following three variations: 3. Is there an Entity Instantiation between these two sentences?",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Agreement Study",
"sec_num": "3.1"
},
{
"text": "The results of the agreement study, including percentage agreement and chance corrected agreement (Kappa, (Cohen, 1960) ), are presented in Table 1. Our agreement about which candidates were \"Not a mention\" was \u03ba = 0.7146. These agreement statistics show reasonable agreement on the task, and that our annotation scheme is reliable and replicable.",
"cite_spans": [
{
"start": 106,
"end": 119,
"text": "(Cohen, 1960)",
"ref_id": "BIBREF7"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Agreement Study",
"sec_num": "3.1"
},
{
"text": "There were several re-occurring types of disagreements. It was often difficult for annotators to establish whether a pair of sets were subsets, coreferent or overlapping. In Example 8, one can interpret 'men' to mean either the men belonging to Baker or the general set of men, and this interpretation directly affects whether 'them' is considered a subset.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Agreement Study",
"sec_num": "3.1"
},
{
"text": "Another problematic issue was systematic polysemy. In Example 9, 'Most cosmetic purchases' might comprise a set of transactions or a set of products. The result of this interpretation then affects whether one considers 'lipstick' to be a set member.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Agreement Study",
"sec_num": "3.1"
},
{
"text": "We also found that disagreements often propagated. A single decision about the relationship between two entities early on in a text can result in a large number of follow-on disagreements.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Agreement Study",
"sec_num": "3.1"
},
{
"text": "(8) a. Baker had lots of men.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Agreement Study",
"sec_num": "3.1"
},
{
"text": "b. But she didn't trust them and didn't reward trust.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Agreement Study",
"sec_num": "3.1"
},
{
"text": "(9) a. Most cosmetic purchases are unplanned. b. Lipstick is often bought on a whim.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Agreement Study",
"sec_num": "3.1"
},
{
"text": "After the successful agreement study, a further 20 texts were annotated by the first author of this study in order to complete the corpus. The frequency of Entity Instantiations over the final 25 Table 2 . We found that a mean of 26 instantiations occurred per text, and that set membership instantiations occur considerably more frequently than subset instantiations.",
"cite_spans": [],
"ref_spans": [
{
"start": 196,
"end": 203,
"text": "Table 2",
"ref_id": "TABREF4"
}
],
"eq_spans": [],
"section": "Further Annotation",
"sec_num": "3.2"
},
{
"text": "We use a supervised machine learning approach to detect which NP pairs comprise Entity Instantiations. Below we detail our feature set, experimental set-up and results.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Automatic Instantiation Detection",
"sec_num": "4"
},
{
"text": "Our features fall into five broad categories; surface, salience, syntactic, contextual and knowledge. These categories contain both features that pertain to a single NP, and those that represent cross-NP relationships.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Features",
"sec_num": "4.1"
},
{
"text": "Surface features. Our surface features consist of unigrams, part-of-speech tags, lemmas, and dependency-parse 3 derived heads of each NP. We calculate Levenshtein's distance between the strings representing the unigrams, lemmas, head word and head lemma of each NP, hoping to capture pairs like 'funds' and 'fund' (see Example 1). We also calculate the distance in characters and words between NP pairs, and include these along with versions normalised by the total length of the two sentences containing the NPs. Additionally we include a boolean feature which represents the order of the NPs -True for candidate set NP in the first sentence and candidate set member/subset NP in the second sentence and False for the reverse order.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Features",
"sec_num": "4.1"
},
{
"text": "Salience features. As an indicator of the salience of each NP we include: its grammatical role, derived from dependency parse data; whether it is the first mention of that entity in the sentence or document; the number of mentions of the entity prior to this in the document; and the overall number of mentions of the entity in the document. We approximate the number of entity mentions by judging noun phrases with identical heads to be coreferent, as in (Barzilay and Lapata, 2008) .",
"cite_spans": [
{
"start": 456,
"end": 483,
"text": "(Barzilay and Lapata, 2008)",
"ref_id": "BIBREF2"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Features",
"sec_num": "4.1"
},
{
"text": "Syntactic features. We include five syntactic features, representing syntactic parallelism and pre-and post-modification. The modification type includes values that represent apposition, conjunction, pre modification and bare nouns. Our intuition is that set members and subsets are often more heavily modified than the sets that they are part of, as in footballers \u2192 footballers playing in the Premiership, European countries \u2192 European nations that use the Euro.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Features",
"sec_num": "4.1"
},
{
"text": "Contextual features. We include several contextual features, hypothesising that NPs that occur in similar contexts may be more likely to be Entity Instantiations. We retrieve the Levin class (Levin, 1993) of each NP's head verb, as well as the verb itself, noting examples such as Example 1 which has two similar verbs, 'surge' and 'skyrocket'. We also calculate whether each NP is in a quotation, and include an approximation of the discourse relations present in the two sentences by identifying likely discourse connectives and mapping them to their most frequent explicit relation in the Penn Discourse Treebank (PDTB) (Prasad et al., 2008) . In cases such as Example 7, the presence of the discourse connective 'however' appears useful in establishing that no instantiation is present. Note that we do not use any PDTB annotations to discover the presence of implicit or explicit discourse relations in the two sentences.",
"cite_spans": [
{
"start": 191,
"end": 204,
"text": "(Levin, 1993)",
"ref_id": "BIBREF16"
},
{
"start": 623,
"end": 644,
"text": "(Prasad et al., 2008)",
"ref_id": "BIBREF26"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Features",
"sec_num": "4.1"
},
{
"text": "Knowledge-based features. Our knowledgebased features are organised into four categories:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Features",
"sec_num": "4.1"
},
{
"text": "WordNet. We use WordNet to establish whether the head words of NPs that are not named entities are synonyms or hyponyms, in an effort to identify pairs such as 'offers' and 'bids' in Example 2.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Features",
"sec_num": "4.1"
},
{
"text": "Freebase. We use Freebase (Bollacker et al., 2008) , a freely-available repository of structured knowledge, to attempt to establish the relatedness of NPs. Each entity in Freebase is associated with a list of topics, which loosely represent hyponyms of the entity. For example, the topics listed for 'Wayne Rooney' include ['Person','Football player','Athlete','2010 World Cup Athlete'] . For each NP representing a potential set member or subset, we search Freebase using their Search API, choosing those matching entities that have a relevance score over 35. We then retrieve a list of topics for each entity and compare these topics to our potential set NP. If one of the topics is equal to, synonymous with, or has a Levenshtein distance of 1 from our potential set, the feature is True. Otherwise the feature is False.",
"cite_spans": [
{
"start": 26,
"end": 50,
"text": "(Bollacker et al., 2008)",
"ref_id": "BIBREF3"
},
{
"start": 323,
"end": 386,
"text": "['Person','Football player','Athlete','2010 World Cup Athlete']",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Features",
"sec_num": "4.1"
},
{
"text": "Google PMI. We also use Google for discovering potential set membership and subset relations. We calculate Point-wise Mutual Information from hit counts for our potential Entity Instantiations, based on the notion that the pattern \"X and other Y \", where X is a potential set member or subset and Y is a potential set, indicates hyponymy (Hearst, 1992; Markert and Nissim, 2005) . We use the following formula to calculate the value of our feature: G-PMI(X, Y ) = hits(\"X and other Y \") hits(\"X\") \u00d7 hits(\"and other Y \") Animacy. We attempt to establish whether the animacy of the two NPs match, reasoning that pairs of NPs that do not have the same animacy are highly unlikely to participate in an Entity Instantiation.",
"cite_spans": [
{
"start": 338,
"end": 352,
"text": "(Hearst, 1992;",
"ref_id": "BIBREF12"
},
{
"start": 353,
"end": 378,
"text": "Markert and Nissim, 2005)",
"ref_id": "BIBREF18"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Features",
"sec_num": "4.1"
},
{
"text": "We use a list of animate pronouns, lists of animate and inanimate words distributed as part of the Stanford Deterministic Coreference Resolution System (Ji and Lin, 2009; Lee et al., 2011) , and named entity information generated by the Stanford Named Entity Recognizer (Finkel et al., 2005) to ascertain the animacy of each NP. Our feature has three possible values; Match if the two NPs have the same animacy, No Match if they do not, and Not Present if we cannot calculate the animacy of one of the NPs. Not Present occurs in only 6% of pairs.",
"cite_spans": [
{
"start": 152,
"end": 170,
"text": "(Ji and Lin, 2009;",
"ref_id": "BIBREF13"
},
{
"start": 171,
"end": 188,
"text": "Lee et al., 2011)",
"ref_id": "BIBREF15"
},
{
"start": 270,
"end": 291,
"text": "(Finkel et al., 2005)",
"ref_id": "BIBREF9"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Features",
"sec_num": "4.1"
},
{
"text": "We divide our data set into two; plural-plural NP pairs that are labelled either subset or noinstantiation and plural-singular NP pairs that are labelled either set member or no-instantiation. We use the machine learner ICSIBoost (Favre et al., 2007) . ICSIBoost is an open source implementation of Boostexter (Schapire and Singer, 2000) , an algorithm which combines simple 'rules-ofthumb' -in this case, decision stumps -to produce a classifier. We apply 10-fold crossvalidation for testing and training in all our experiments, keeping pairs from the same text in the same fold, to avoid rewarding the learning of very specific rules about the unigrams present which will not generalise well.",
"cite_spans": [
{
"start": 230,
"end": 250,
"text": "(Favre et al., 2007)",
"ref_id": null
},
{
"start": 310,
"end": 337,
"text": "(Schapire and Singer, 2000)",
"ref_id": "BIBREF29"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Experimental Set-up and Results",
"sec_num": "4.2"
},
{
"text": "Due to the nature of the annotation study, there are many more pairs of candidates between which no Entity Instantiation has been annotated than those that have. Only 2.32% of the 28,966 pairs of candidates in the corpus have a set member or subset annotation. We therefore experiment with two different datasets.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Experimental Set-up and Results",
"sec_num": "4.2"
},
{
"text": "Firstly, we used random sub-sampling to produce a balanced data set in which only 50% of the annotated pairs were non-relations, and used this for both training and testing. Results on the subsampled data are shown in Table 3 .",
"cite_spans": [],
"ref_spans": [
{
"start": 218,
"end": 225,
"text": "Table 3",
"ref_id": "TABREF6"
}
],
"eq_spans": [],
"section": "Experimental Set-up and Results",
"sec_num": "4.2"
},
{
"text": "Secondly, we experimented with the original, highly skewed data. Training on the original data resulted in a classifier that almost never predicted an instantiation, so we experimented with some simple techniques to improve precision and recall. These comprised randomly subsampling the negative examples so that they made up 50% or 75% of the training data, and oversampling the positive examples in the training data by a factor of 10, 20 or 40. The results of these experiments are shown in Table 4 .",
"cite_spans": [],
"ref_spans": [
{
"start": 494,
"end": 501,
"text": "Table 4",
"ref_id": null
}
],
"eq_spans": [],
"section": "Experimental Set-up and Results",
"sec_num": "4.2"
},
{
"text": "For comparison, results for a baseline whose sole features are the unigrams of the two NPs are also included. The Precision, Recall and F-Measure scores shown are for the positive examples in each set.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Experimental Set-up and Results",
"sec_num": "4.2"
},
{
"text": "On a balanced data set, our best features show highly significant improvements over the unigram baseline 4 . We performed a feature ablation study, removing each group of features from our model in turn, the results of which are present in Table 3. Our knowledge-based features are particularly good for identifying instantiations. Upon further investigation, we discovered that our Google PMI feature is the most effective of this feature group, with large PMI values often being indicative of instantiations.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Discussion",
"sec_num": "4.3"
},
{
"text": "Our salience features aid classification significantly for set members but not subsets. This indicates that set members are often first mentions of an entity that are mediated from a set, but subsets function less often in this way. In general, sub- Table 4 : Results on unbalanced data set sets appear harder to detect than set membership relations, but the smaller size of the subset data set likely contributes to this.",
"cite_spans": [],
"ref_spans": [
{
"start": 250,
"end": 257,
"text": "Table 4",
"ref_id": null
}
],
"eq_spans": [],
"section": "Discussion",
"sec_num": "4.3"
},
{
"text": "Learning from the original, highly skewed data is much more difficult, and our highest F-scores are 0.1938 and 0.1414 for set members and subsets, respectively (see Table 4 ). Learning from data with this sort of distribution is difficult, regardless of the domain. In future we intend to use techniques such as SMOTE (Chawla et al., 2002) and One-Sided Selection (Kubat and Matwin, 1997) to address this heavy skew.",
"cite_spans": [
{
"start": 318,
"end": 339,
"text": "(Chawla et al., 2002)",
"ref_id": "BIBREF5"
},
{
"start": 364,
"end": 388,
"text": "(Kubat and Matwin, 1997)",
"ref_id": "BIBREF14"
}
],
"ref_spans": [
{
"start": 165,
"end": 172,
"text": "Table 4",
"ref_id": null
}
],
"eq_spans": [],
"section": "Discussion",
"sec_num": "4.3"
},
{
"text": "We propose a novel Information Extraction task: the detection of Entity Instantiations. This task is potentially important for a variety of NLP problems, such as question answering and sentiment analysis. We have presented the first corpus study of Entity Instantiations, achieving good levels of annotator agreement. Our supervised machine learning classifier achieves an F-score of 0.628 for set member relations and 0.586 for subset relations on a balanced set, making good use of a variety of features, including world-knowledge and salience criteria.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion and Future Work",
"sec_num": "5"
},
{
"text": "In the future, we intend to expand our annotation to include intrasentential and further dis-tant Entity Instantiations, as well as our current instantiations between adjacent sentences. Future machine learning approaches to consider are tree-kernel based approaches such as (Zhou et al., 2007) . To tackle the high skew in our data, we will use techniques such as those detailed in (Kubat and Matwin, 1997) and (Chawla et al., 2002) , and also look to methods such as active learning to acquire more positive instantiation examples.",
"cite_spans": [
{
"start": 275,
"end": 294,
"text": "(Zhou et al., 2007)",
"ref_id": "BIBREF32"
},
{
"start": 383,
"end": 407,
"text": "(Kubat and Matwin, 1997)",
"ref_id": "BIBREF14"
},
{
"start": 412,
"end": 433,
"text": "(Chawla et al., 2002)",
"ref_id": "BIBREF5"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion and Future Work",
"sec_num": "5"
},
{
"text": "Our dependency parses are generated from the gold standard PTB tree.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "p < 10 \u2212 8 and 10 \u2212 4 for set members and subsets respectively with McNemar's \u03c7 2 test(McNemar, 1947).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
}
],
"back_matter": [
{
"text": "Andrew McKinlay is funded by an EPSRC Doctoral Training Grant. This research draws on data provided by the University Research Program for Google Search, a service provided by Google to promote a greater understanding of the web.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Acknowledgements",
"sec_num": null
}
],
"bib_entries": {
"BIBREF0": {
"ref_id": "b0",
"title": "Automatic Content Extraction",
"authors": [],
"year": 2000,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "ACE. 2000-2005. Automatic Content Extraction. http://www.ldc.upenn.edu/Projects/ ACE/.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "Modeling local coherence: An entity-based approach",
"authors": [
{
"first": "R",
"middle": [],
"last": "Barzilay",
"suffix": ""
},
{
"first": "M",
"middle": [],
"last": "Lapata",
"suffix": ""
}
],
"year": 2008,
"venue": "Computational Linguistics",
"volume": "34",
"issue": "1",
"pages": "1--34",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "R. Barzilay and M. Lapata. 2008. Modeling local co- herence: An entity-based approach. Computational Linguistics, 34(1):1-34.",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "Freebase: a collaboratively created graph database for structuring human knowledge",
"authors": [
{
"first": "K",
"middle": [],
"last": "Bollacker",
"suffix": ""
},
{
"first": "C",
"middle": [],
"last": "Evans",
"suffix": ""
},
{
"first": "P",
"middle": [],
"last": "Paritosh",
"suffix": ""
},
{
"first": "T",
"middle": [],
"last": "Sturge",
"suffix": ""
},
{
"first": "J",
"middle": [],
"last": "Taylor",
"suffix": ""
}
],
"year": 2008,
"venue": "Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data",
"volume": "",
"issue": "",
"pages": "1247--1250",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "K. Bollacker, C. Evans, P. Paritosh, T. Sturge, and J. Taylor. 2008. Freebase: a collaboratively cre- ated graph database for structuring human knowl- edge. In Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pages 1247-1250.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "Exploiting background knowledge for relation extraction",
"authors": [
{
"first": "Y",
"middle": [
"S"
],
"last": "Chan",
"suffix": ""
},
{
"first": "D",
"middle": [],
"last": "Roth",
"suffix": ""
}
],
"year": 2010,
"venue": "Proceedings of COLING 2010",
"volume": "",
"issue": "",
"pages": "152--160",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Y.S. Chan and D. Roth. 2010. Exploiting background knowledge for relation extraction. In Proceedings of COLING 2010, pages 152-160.",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "Smote: synthetic minority oversampling technique",
"authors": [
{
"first": "N",
"middle": [
"V"
],
"last": "Chawla",
"suffix": ""
},
{
"first": "K",
"middle": [
"W"
],
"last": "Bowyer",
"suffix": ""
},
{
"first": "L",
"middle": [
"O"
],
"last": "Hall",
"suffix": ""
},
{
"first": "W",
"middle": [
"P"
],
"last": "Kegelmeyer",
"suffix": ""
}
],
"year": 2002,
"venue": "Journal of Artificial Intelligence Research",
"volume": "16",
"issue": "1",
"pages": "321--357",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "N.V. Chawla, K.W. Bowyer, L.O. Hall, and W.P. Kegelmeyer. 2002. Smote: synthetic minority over- sampling technique. Journal of Artificial Intelli- gence Research, 16(1):321-357.",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "Bridging",
"authors": [
{
"first": "H",
"middle": [
"H"
],
"last": "Clark",
"suffix": ""
}
],
"year": 1975,
"venue": "Proceedings of the 1975 Workshop on Theoretical Issues in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "169--174",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "H.H. Clark. 1975. Bridging. In Proceedings of the 1975 Workshop on Theoretical Issues in Natural Language Processing, pages 169-174.",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "A coefficient of agreement for nominal scales. Educational and psychological measurement",
"authors": [
{
"first": "J",
"middle": [],
"last": "Cohen",
"suffix": ""
}
],
"year": 1960,
"venue": "",
"volume": "20",
"issue": "",
"pages": "37--46",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "J. Cohen. 1960. A coefficient of agreement for nomi- nal scales. Educational and psychological measure- ment, 20(1):37-46.",
"links": null
},
"BIBREF9": {
"ref_id": "b9",
"title": "Incorporating non-local information into information extraction systems by Gibbs sampling",
"authors": [
{
"first": "J",
"middle": [
"R"
],
"last": "Finkel",
"suffix": ""
},
{
"first": "T",
"middle": [],
"last": "Grenager",
"suffix": ""
},
{
"first": "C",
"middle": [],
"last": "Manning",
"suffix": ""
}
],
"year": 2005,
"venue": "Proceedings of ACL 2005",
"volume": "",
"issue": "",
"pages": "363--370",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "J.R. Finkel, T. Grenager, and C. Manning. 2005. In- corporating non-local information into information extraction systems by Gibbs sampling. In Proceed- ings of ACL 2005, pages 363-370.",
"links": null
},
"BIBREF10": {
"ref_id": "b10",
"title": "Definiteness and the processing of noun phrases in natural discourse",
"authors": [
{
"first": "K",
"middle": [],
"last": "Fraurud",
"suffix": ""
}
],
"year": 1990,
"venue": "Journal of Semantics",
"volume": "7",
"issue": "4",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "K. Fraurud. 1990. Definiteness and the processing of noun phrases in natural discourse. Journal of Se- mantics, 7(4):395.",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "Which bridges for bridging definite descriptions",
"authors": [
{
"first": "C",
"middle": [],
"last": "Gardent",
"suffix": ""
},
{
"first": "H",
"middle": [],
"last": "Manuelian",
"suffix": ""
},
{
"first": "E",
"middle": [],
"last": "Kow",
"suffix": ""
}
],
"year": 2003,
"venue": "Proceedings of the EACL 2003 Workshop on Linguistically Interpreted Corpora",
"volume": "",
"issue": "",
"pages": "69--76",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "C. Gardent, H. Manuelian, and E. Kow. 2003. Which bridges for bridging definite descriptions. In Pro- ceedings of the EACL 2003 Workshop on Linguisti- cally Interpreted Corpora, pages 69-76.",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "Automatic acquisition of hyponyms from large text corpora",
"authors": [
{
"first": "M",
"middle": [
"A"
],
"last": "Hearst",
"suffix": ""
}
],
"year": 1992,
"venue": "Proceedings of COLING 1992",
"volume": "",
"issue": "",
"pages": "539--545",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "M.A. Hearst. 1992. Automatic acquisition of hy- ponyms from large text corpora. In Proceedings of COLING 1992, pages 539-545.",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "Gender and animacy knowledge discovery from web-scale n-grams for unsupervised person mention detection",
"authors": [
{
"first": "H",
"middle": [],
"last": "Ji",
"suffix": ""
},
{
"first": "D",
"middle": [],
"last": "Lin",
"suffix": ""
}
],
"year": 2009,
"venue": "Proceedings of PACLIC",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "H. Ji and D. Lin. 2009. Gender and animacy knowl- edge discovery from web-scale n-grams for unsuper- vised person mention detection. In Proceedings of PACLIC 2009.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "Addressing the curse of imbalanced training sets: one-sided selection",
"authors": [
{
"first": "M",
"middle": [],
"last": "Kubat",
"suffix": ""
},
{
"first": "S",
"middle": [],
"last": "Matwin",
"suffix": ""
}
],
"year": 1997,
"venue": "Proceedings of ICML 1997",
"volume": "",
"issue": "",
"pages": "179--186",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "M. Kubat and S. Matwin. 1997. Addressing the curse of imbalanced training sets: one-sided selection. In Proceedings of ICML 1997, pages 179-186.",
"links": null
},
"BIBREF15": {
"ref_id": "b15",
"title": "Stanfords multipass sieve coreference resolution system at the conll-2011 shared task",
"authors": [
{
"first": "H",
"middle": [],
"last": "Lee",
"suffix": ""
},
{
"first": "Y",
"middle": [],
"last": "Peirsman",
"suffix": ""
},
{
"first": "A",
"middle": [],
"last": "Chang",
"suffix": ""
},
{
"first": "N",
"middle": [],
"last": "Chambers",
"suffix": ""
},
{
"first": "M",
"middle": [],
"last": "Surdeanu",
"suffix": ""
},
{
"first": "D",
"middle": [],
"last": "Jurafsky",
"suffix": ""
}
],
"year": 2011,
"venue": "Proceedings of the CoNLL-2011 Shared Task",
"volume": "",
"issue": "",
"pages": "28--34",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "H. Lee, Y. Peirsman, A. Chang, N. Chambers, M. Sur- deanu, and D. Jurafsky. 2011. Stanfords multi- pass sieve coreference resolution system at the conll- 2011 shared task. In Proceedings of the CoNLL- 2011 Shared Task, pages 28-34.",
"links": null
},
"BIBREF16": {
"ref_id": "b16",
"title": "English verb classes and alternations: A preliminary investigation",
"authors": [
{
"first": "B",
"middle": [],
"last": "Levin",
"suffix": ""
}
],
"year": 1993,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "B. Levin. 1993. English verb classes and alternations: A preliminary investigation. University of Chicago press.",
"links": null
},
"BIBREF17": {
"ref_id": "b17",
"title": "Building a large annotated corpus of English: The Penn Treebank",
"authors": [
{
"first": "M",
"middle": [
"P"
],
"last": "Marcus",
"suffix": ""
},
{
"first": "M",
"middle": [
"A"
],
"last": "Marcinkiewicz",
"suffix": ""
},
{
"first": "B",
"middle": [],
"last": "Santorini",
"suffix": ""
}
],
"year": 1993,
"venue": "Computational Linguistics",
"volume": "19",
"issue": "2",
"pages": "313--330",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "M.P. Marcus, M.A. Marcinkiewicz, and B. Santorini. 1993. Building a large annotated corpus of En- glish: The Penn Treebank. Computational Linguis- tics, 19(2):313-330.",
"links": null
},
"BIBREF18": {
"ref_id": "b18",
"title": "Comparing knowledge sources for nominal anaphora resolution",
"authors": [
{
"first": "K",
"middle": [],
"last": "Markert",
"suffix": ""
},
{
"first": "M",
"middle": [],
"last": "Nissim",
"suffix": ""
}
],
"year": 2005,
"venue": "Computational Linguistics",
"volume": "31",
"issue": "3",
"pages": "367--402",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "K. Markert and M. Nissim. 2005. Comparing knowl- edge sources for nominal anaphora resolution. Com- putational Linguistics, 31(3):367-402.",
"links": null
},
"BIBREF19": {
"ref_id": "b19",
"title": "Inferential realization constraints on functional anaphora in the centering model",
"authors": [
{
"first": "K",
"middle": [],
"last": "Markert",
"suffix": ""
},
{
"first": "M",
"middle": [],
"last": "Strube",
"suffix": ""
},
{
"first": "U",
"middle": [],
"last": "Hahn",
"suffix": ""
}
],
"year": 1996,
"venue": "Proceedings of CogSci",
"volume": "",
"issue": "",
"pages": "609--614",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "K. Markert, M. Strube, and U. Hahn. 1996. Inferential realization constraints on functional anaphora in the centering model. In Proceedings of CogSci 1996, pages 609-614.",
"links": null
},
"BIBREF20": {
"ref_id": "b20",
"title": "Using the web for nominal anaphora resolution",
"authors": [
{
"first": "K",
"middle": [],
"last": "Markert",
"suffix": ""
},
{
"first": "N",
"middle": [],
"last": "Modjeska",
"suffix": ""
},
{
"first": "M",
"middle": [],
"last": "Nissim",
"suffix": ""
}
],
"year": 2003,
"venue": "Proceedings of EACL 2003 Workshop on the Computational Treatment of Anaphora",
"volume": "",
"issue": "",
"pages": "39--46",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "K. Markert, N. Modjeska, and M. Nissim. 2003. Using the web for nominal anaphora resolution. In Pro- ceedings of EACL 2003 Workshop on the Computa- tional Treatment of Anaphora, pages 39-46.",
"links": null
},
"BIBREF21": {
"ref_id": "b21",
"title": "Note on the sampling error of the difference between correlated proportions or percentages",
"authors": [
{
"first": "Q",
"middle": [],
"last": "Mcnemar",
"suffix": ""
}
],
"year": 1947,
"venue": "Psychometrika",
"volume": "12",
"issue": "2",
"pages": "153--157",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Q. McNemar. 1947. Note on the sampling error of the difference between correlated proportions or per- centages. Psychometrika, 12(2):153-157.",
"links": null
},
"BIBREF24": {
"ref_id": "b24",
"title": "A corpus-based investigation of definite description use",
"authors": [
{
"first": "M",
"middle": [],
"last": "Poesio",
"suffix": ""
},
{
"first": "R",
"middle": [],
"last": "Vieira",
"suffix": ""
}
],
"year": 1998,
"venue": "Computational Linguistics",
"volume": "24",
"issue": "",
"pages": "183--216",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "M. Poesio and R. Vieira. 1998. A corpus-based inves- tigation of definite description use. Computational Linguistics, 24:183-216, June.",
"links": null
},
"BIBREF25": {
"ref_id": "b25",
"title": "Learning to resolve bridging references",
"authors": [
{
"first": "M",
"middle": [],
"last": "Poesio",
"suffix": ""
},
{
"first": "R",
"middle": [],
"last": "Mehta",
"suffix": ""
},
{
"first": "A",
"middle": [],
"last": "Maroudas",
"suffix": ""
},
{
"first": "J",
"middle": [],
"last": "Hitzeman",
"suffix": ""
}
],
"year": 2004,
"venue": "Proceedings of ACL 2004",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "M. Poesio, R. Mehta, A. Maroudas, and J. Hitzeman. 2004. Learning to resolve bridging references. In Proceedings of ACL 2004, page 143.",
"links": null
},
"BIBREF26": {
"ref_id": "b26",
"title": "The Penn Discourse TreeBank 2.0",
"authors": [
{
"first": "R",
"middle": [],
"last": "Prasad",
"suffix": ""
},
{
"first": "N",
"middle": [],
"last": "Dinesh",
"suffix": ""
},
{
"first": "A",
"middle": [],
"last": "Lee",
"suffix": ""
},
{
"first": "E",
"middle": [],
"last": "Miltsakaki",
"suffix": ""
},
{
"first": "L",
"middle": [],
"last": "Robaldo",
"suffix": ""
},
{
"first": "A",
"middle": [],
"last": "Joshi",
"suffix": ""
},
{
"first": "B",
"middle": [],
"last": "Webber",
"suffix": ""
}
],
"year": 2008,
"venue": "Proceedings of LREC 2008",
"volume": "",
"issue": "",
"pages": "2961--2968",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "R. Prasad, N. Dinesh, A. Lee, E. Miltsakaki, L. Robaldo, A. Joshi, and B. Webber. 2008. The Penn Discourse TreeBank 2.0. In Proceedings of LREC 2008, pages 2961-2968.",
"links": null
},
"BIBREF27": {
"ref_id": "b27",
"title": "Toward a Taxonomy of Given-New Information. Radical Pragmatics",
"authors": [
{
"first": "E",
"middle": [
"F"
],
"last": "Prince",
"suffix": ""
}
],
"year": 1981,
"venue": "",
"volume": "3",
"issue": "",
"pages": "223--255",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "E.F. Prince. 1981. Toward a Taxonomy of Given-New Information. Radical Pragmatics, 3:223-255.",
"links": null
},
"BIBREF28": {
"ref_id": "b28",
"title": "A typology of near-identity relations for coreference (NIDENT)",
"authors": [
{
"first": "M",
"middle": [],
"last": "Recasens",
"suffix": ""
},
{
"first": "E",
"middle": [],
"last": "Hovy",
"suffix": ""
},
{
"first": "M",
"middle": [
"A"
],
"last": "Mart\u0131",
"suffix": ""
}
],
"year": 2010,
"venue": "Proceedings of LREC 2010",
"volume": "",
"issue": "",
"pages": "149--156",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "M. Recasens, E. Hovy, and M.A. Mart\u0131. 2010. A typology of near-identity relations for coreference (NIDENT). In Proceedings of LREC 2010, pages 149-156.",
"links": null
},
"BIBREF29": {
"ref_id": "b29",
"title": "BoosTexter: A boosting-based system for text categorization. Machine learning",
"authors": [
{
"first": "R",
"middle": [
"E"
],
"last": "Schapire",
"suffix": ""
},
{
"first": "Y",
"middle": [],
"last": "Singer",
"suffix": ""
}
],
"year": 2000,
"venue": "",
"volume": "39",
"issue": "",
"pages": "135--168",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "R.E. Schapire and Y. Singer. 2000. BoosTexter: A boosting-based system for text categorization. Ma- chine learning, 39(2):135-168.",
"links": null
},
"BIBREF30": {
"ref_id": "b30",
"title": "Semisupervised relation extraction with large-scale word clustering",
"authors": [
{
"first": "A",
"middle": [],
"last": "Sun",
"suffix": ""
},
{
"first": "R",
"middle": [],
"last": "Grishman",
"suffix": ""
},
{
"first": "S",
"middle": [],
"last": "Sekine",
"suffix": ""
}
],
"year": 2011,
"venue": "Proceedings of ACL-HLT 2011",
"volume": "",
"issue": "",
"pages": "521--529",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "A. Sun, R. Grishman, and S. Sekine. 2011. Semi- supervised relation extraction with large-scale word clustering. In Proceedings of ACL-HLT 2011, pages 521-529.",
"links": null
},
"BIBREF31": {
"ref_id": "b31",
"title": "An empirically based system for processing definite descriptions",
"authors": [
{
"first": "R",
"middle": [],
"last": "Vieira",
"suffix": ""
},
{
"first": "M",
"middle": [],
"last": "Poesio",
"suffix": ""
}
],
"year": 2000,
"venue": "Computational Linguistics",
"volume": "26",
"issue": "4",
"pages": "539--593",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "R. Vieira and M. Poesio. 2000. An empirically based system for processing definite descriptions. Compu- tational Linguistics, 26(4):539-593.",
"links": null
},
"BIBREF32": {
"ref_id": "b32",
"title": "Tree Kernel-Based Relation Extraction with Context-Sensitive Structured Parse Tree Information",
"authors": [
{
"first": "G",
"middle": [
"D"
],
"last": "Zhou",
"suffix": ""
},
{
"first": "M",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "D",
"middle": [
"H"
],
"last": "Ji",
"suffix": ""
},
{
"first": "Q",
"middle": [
"M"
],
"last": "Zhu",
"suffix": ""
}
],
"year": 2007,
"venue": "Proceedings of EMNLP-CoNLL",
"volume": "",
"issue": "",
"pages": "728--736",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "G.D. Zhou, M. Zhang, D.H. Ji, and Q.M. Zhu. 2007. Tree Kernel-Based Relation Extraction with Context-Sensitive Structured Parse Tree Informa- tion. In Proceedings of EMNLP-CoNLL 2007, pages 728-736.",
"links": null
}
},
"ref_entries": {
"TABREF2": {
"content": "<table><tr><td>: Agreement Statistics</td></tr><tr><td>2. Does this candidate set member/subset par-</td></tr><tr><td>ticipate in a set membership/subset relation-</td></tr><tr><td>ship with any potential set or not?</td></tr></table>",
"text": "",
"num": null,
"type_str": "table",
"html": null
},
"TABREF4": {
"content": "<table/>",
"text": "Frequency of Entity Instantiations in 25 texts texts is shown in",
"num": null,
"type_str": "table",
"html": null
},
"TABREF6": {
"content": "<table><tr><td/><td/><td colspan=\"2\">Set Members</td><td/><td/><td/><td>Subsets</td></tr><tr><td>Method</td><td colspan=\"2\">Accuracy P</td><td>R</td><td>F</td><td colspan=\"2\">Accuracy P</td><td>R</td><td>F</td></tr><tr><td>Original Set</td><td>97.39%</td><td colspan=\"4\">0.2979 0.0289 0.0527 97.90%</td><td colspan=\"2\">0.1852 0.0266 0.0465</td></tr><tr><td colspan=\"2\">Undersampling 50/50 83.31%</td><td colspan=\"4\">0.0782 0.5227 0.1361 76.47%</td><td colspan=\"2\">0.0453 0.5585 0.0839</td></tr><tr><td colspan=\"2\">Undersampling 75/25 94.60%</td><td colspan=\"4\">0.1275 0.1963 0.1546 93.28%</td><td colspan=\"2\">0.0838 0.2500 0.1255</td></tr><tr><td>Oversampling x10</td><td>96.89%</td><td colspan=\"4\">0.2500 0.1178 0.1601 97.47%</td><td colspan=\"2\">0.1685 0.0798 0.1083</td></tr><tr><td>Oversampling x20</td><td>96.38%</td><td colspan=\"4\">0.2129 0.1632 0.1848 97.21%</td><td colspan=\"2\">0.1557 0.1011 0.1226</td></tr><tr><td>Oversampling x40</td><td>95.24%</td><td colspan=\"4\">0.1690 0.2272 0.1938 96.51%</td><td colspan=\"2\">0.1346 0.1489 0.1414</td></tr></table>",
"text": "Results on balanced data set \u2663 Algorithm with highest accuracy \u2660 Significantly worse than \u2663 , significance p < 0.005, McNemar's \u03c7 2 test. Significantly worse than \u2663 , significance p < 0.001, McNemar's \u03c7 2 test.",
"num": null,
"type_str": "table",
"html": null
}
}
}
} |