File size: 82,565 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 |
{
"paper_id": "I11-1044",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T07:30:42.212479Z"
},
"title": "Extracting Relation Descriptors with Conditional Random Fields",
"authors": [
{
"first": "Yaliang",
"middle": [],
"last": "Li",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Singapore Management University",
"location": {
"country": "Singapore"
}
},
"email": "ylli@smu.edu.sg"
},
{
"first": "Jing",
"middle": [],
"last": "Jiang",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Singapore Management University",
"location": {
"country": "Singapore"
}
},
"email": "jingjiang@smu.edu.sg"
},
{
"first": "Hai",
"middle": [],
"last": "Leong Chieu",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "DSO National Laboratories",
"location": {
"country": "Singapore"
}
},
"email": ""
},
{
"first": "Ming",
"middle": [
"A"
],
"last": "Kian",
"suffix": "",
"affiliation": {},
"email": "ckianmin@dso.org.sg"
},
{
"first": "",
"middle": [],
"last": "Chai",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "DSO National Laboratories",
"location": {
"country": "Singapore"
}
},
"email": ""
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "In this paper we study a novel relation extraction problem where a general relation type is defined but relation extraction involves extracting specific relation descriptors from text. This new task can be treated as a sequence labeling problem. Although linear-chain conditional random fields (CRFs) can be used to solve this problem, we modify this baseline solution in order to better fit our task. We propose two modifications to linear-chain CRFs, namely, reducing the space of possible label sequences and introducing long-range features. Both modifications are based on some special properties of our task. Using two data sets we have annotated, we evaluate our methods and find that both modifications to linear-chain CRFs can significantly improve the performance for our task.",
"pdf_parse": {
"paper_id": "I11-1044",
"_pdf_hash": "",
"abstract": [
{
"text": "In this paper we study a novel relation extraction problem where a general relation type is defined but relation extraction involves extracting specific relation descriptors from text. This new task can be treated as a sequence labeling problem. Although linear-chain conditional random fields (CRFs) can be used to solve this problem, we modify this baseline solution in order to better fit our task. We propose two modifications to linear-chain CRFs, namely, reducing the space of possible label sequences and introducing long-range features. Both modifications are based on some special properties of our task. Using two data sets we have annotated, we evaluate our methods and find that both modifications to linear-chain CRFs can significantly improve the performance for our task.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "Relation extraction is the task of identifying and characterizing the semantic relations between entities in text. Depending on the application and the resources available, relation extraction has been studied in a number of different settings. When relation types are well defined and labeled relation mention instances are available, supervised learning is usually applied (Zelenko et al., 2003; Zhou et al., 2005; Bunescu and Mooney, 2005; Zhang et al., 2006) . When relation types are known but little training data is available, bootstrapping has been used to iteratively expand the set of seed examples and relation patterns (Agichtein and Gravano, 2000) . When no relation type is pre-defined but there is a focused corpus of interest, unsupervised relation discovery tries to cluster entity pairs in order to identify interesting relation types (Hasegawa et al., 2004; Rosenfeld and Feldman, 2006; Shinyama and Sekine, 2006) . More recently, open relation extraction has also been proposed where there is no fixed domain or predefined relation type, and the goal is to identify all possible relations from an open-domain corpus (Banko and Etzioni, 2008; Wu and Weld, 2010; Hoffmann et al., 2010) .",
"cite_spans": [
{
"start": 375,
"end": 397,
"text": "(Zelenko et al., 2003;",
"ref_id": "BIBREF13"
},
{
"start": 398,
"end": 416,
"text": "Zhou et al., 2005;",
"ref_id": "BIBREF15"
},
{
"start": 417,
"end": 442,
"text": "Bunescu and Mooney, 2005;",
"ref_id": "BIBREF2"
},
{
"start": 443,
"end": 462,
"text": "Zhang et al., 2006)",
"ref_id": "BIBREF14"
},
{
"start": 631,
"end": 660,
"text": "(Agichtein and Gravano, 2000)",
"ref_id": "BIBREF0"
},
{
"start": 853,
"end": 876,
"text": "(Hasegawa et al., 2004;",
"ref_id": "BIBREF5"
},
{
"start": 877,
"end": 905,
"text": "Rosenfeld and Feldman, 2006;",
"ref_id": "BIBREF9"
},
{
"start": 906,
"end": 932,
"text": "Shinyama and Sekine, 2006)",
"ref_id": "BIBREF11"
},
{
"start": 1136,
"end": 1161,
"text": "(Banko and Etzioni, 2008;",
"ref_id": "BIBREF1"
},
{
"start": 1162,
"end": 1180,
"text": "Wu and Weld, 2010;",
"ref_id": "BIBREF12"
},
{
"start": 1181,
"end": 1203,
"text": "Hoffmann et al., 2010)",
"ref_id": "BIBREF6"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "These different relation extraction settings suit different applications. In this paper, we focus on another setting where the relation types are defined at a general level but a more specific relation description is desired. For example, in the widely used ACE 1 data sets, relation types are defined at a fairly coarse granularity. Take for instance the \"employment\" relation, which is a major relation type defined in ACE. In ACE evaluation, extraction of this relation only involves deciding whether a person entity is employed by an organization entity. In practice, however, we often also want to find the exact job title or position this person holds at the organization if this information is mentioned in the text. Table 1 gives some examples. We refer to the segment of text that describes the specific relation between the two related entities (i.e., the two arguments) as the relation descriptor. This paper studies how to extract such relation descriptors given two arguments. One may approach this task as a sequence labeling problem and apply methods such as the linearchain conditional random fields (CRFs) (Lafferty et al., 2001 ). However, this solution ignores a useful property of the task: the space of possible label sequences is much smaller than that enumerated by a linear-chain CRF. There are two implications. First, the normalization constant in the linear-chain CRF is too large because it also enumerates the impossible sequences. Second, the restriction to the correct space of label sequence per- ",
"cite_spans": [
{
"start": 1123,
"end": 1145,
"text": "(Lafferty et al., 2001",
"ref_id": "BIBREF7"
}
],
"ref_spans": [
{
"start": 724,
"end": 731,
"text": "Table 1",
"ref_id": null
}
],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "A ARG-2 spokesman , ARG-1 , said the company now ... spokesman At ARG-2 , by contrast , ARG-1 said customers spend on ... Nil",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "vice president (PER, ORG)",
"sec_num": null
},
{
"text": "Personal/Social ARG-1 had an elder brother named ARG-2 . an elder brother (PER, PER)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "vice president (PER, ORG)",
"sec_num": null
},
{
"text": "ARG-1 was born at ... , as the son of ARG-2 of Sweden ... the son ARG-1 later married ARG-2 in 1973 , ... married Through his contact with ARG-1 , ARG-2 joined the Greek Orthodox Church . Nil Table 1 : Some examples of candidate relation instances and their relation descriptors.",
"cite_spans": [],
"ref_spans": [
{
"start": 192,
"end": 199,
"text": "Table 1",
"ref_id": null
}
],
"eq_spans": [],
"section": "vice president (PER, ORG)",
"sec_num": null
},
{
"text": "mits the use of long-range features without an exponential increase in computational cost. We compare the performance of the baseline linear-chain CRF model and our special CRF model on two data sets that we have manually annotated. Our experimental results show that both reducing the label sequence space and introducing long-range features can significantly improve the baseline performance.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "vice president (PER, ORG)",
"sec_num": null
},
{
"text": "The rest of the paper is organized as follows. In Section 2 we review related work. We then formally define our task in Section 3. In Section 4 we present a baseline linear-chain CRF-based solution and our modifications to the baseline method. We discuss the annotation of our data sets and show our experimental results in Section 5. We conclude in Section 6.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "vice president (PER, ORG)",
"sec_num": null
},
{
"text": "Most existing work on relation extraction studies binary relations between two entities. For supervised relation extraction, existing work often uses the ACE benchmark data sets for evaluation (Bunescu and Mooney, 2005; Zhou et al., 2005; Zhang et al., 2006) . In this setting, a set of relation types are defined and the task is to identify pairs of entities that are related and to classify their relations into one of the pre-defined relation types. It is assumed that the relation type itself is sufficient to characterize the relation between the two related entities. However, based on our observation, some of the relation types defined in ACE such as the \"employment\" relation and the \"personal/social\" relation are very general and can be further characterized by more specific descriptions.",
"cite_spans": [
{
"start": 193,
"end": 219,
"text": "(Bunescu and Mooney, 2005;",
"ref_id": "BIBREF2"
},
{
"start": 220,
"end": 238,
"text": "Zhou et al., 2005;",
"ref_id": "BIBREF15"
},
{
"start": 239,
"end": 258,
"text": "Zhang et al., 2006)",
"ref_id": "BIBREF14"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "Recently open relation extraction has been proposed for open-domain information extraction (Banko and Etzioni, 2008) . Since there are no fixed relation types, open relation extraction aims at extracting all possible relations between pairs of entities. The extracted results are (ARG-1, REL, ARG-2) tuples. The TextRunner system based on (Banko and Etzioni, 2008) extracts a diverse set of relations from a huge Web corpus. These extracted predicate-argument tuples are presumably the most useful to support Web search scenarios where the user is looking for specific relations. However, because of the diversity of the extracted relations and the domain independence, open relation extraction is probably not suitable for populating relational databases or knowledgebases. In contrast, the task of extracting relation descriptors as we have proposed still assumes a pre-defined general relation type, which ensures that the extracted tuples follow the same relation definition and thus can be used in applications such as populating relational databases.",
"cite_spans": [
{
"start": 91,
"end": 116,
"text": "(Banko and Etzioni, 2008)",
"ref_id": "BIBREF1"
},
{
"start": 339,
"end": 364,
"text": "(Banko and Etzioni, 2008)",
"ref_id": "BIBREF1"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "In terms of models and techniques, we use standard linear-chain CRF as our baseline, which is the main method used in (Banko and Etzioni, 2008) as well as for many other information extraction problems. The major modifications we propose for our task are the reduction of the label sequence space and the incorporation of long-range features. We note that these modifications are closely related to the semi-Markov CRF models proposed by Sarawagi and Cohen (2005) . In fact, the modified CRF model for our task can be considered as a special case of semi-Markov CRF where we only consider label sequences that contain at most one relation descriptor sequence.",
"cite_spans": [
{
"start": 118,
"end": 143,
"text": "(Banko and Etzioni, 2008)",
"ref_id": "BIBREF1"
},
{
"start": 438,
"end": 463,
"text": "Sarawagi and Cohen (2005)",
"ref_id": "BIBREF10"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "In this section we define the task of extracting relation descriptors for a given pre-defined class of relations such as \"employment.\" Given two named entities occurring in the same sentence, one acting as ARG-1 and the other as ARG-2, we aim to extract a segment of text from the sentence that best describes a pre-defined general relation between the two entities. Formally, let (w 1 , w 2 , . . . , w n ) denote the sequence of tokens in a sentence, where w p is ARG-1 and w q is ARG-2 (1 \u2264 p, q \u2264 n, p = q). Our goal is to locate a subsequence (w r , . . . , w s ) (1 \u2264 r \u2264 s \u2264 n) that best describes the relation between ARG-1 and ARG-2. If ARG-1 and ARG-2 are not related through the pre-defined general relation, Nil should be returned.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Task Definition",
"sec_num": "3"
},
{
"text": "The above definition constrains ARG-1 and ARG-2 to single tokens. In our experiments, we will replace the original lexical strings of ARG-1 and ARG-2 with the generic tokens ARG1 and ARG2. Examples of sentences with the named entities replaced with argument tokens are shown in the second column of Table 1 .",
"cite_spans": [],
"ref_spans": [
{
"start": 299,
"end": 306,
"text": "Table 1",
"ref_id": null
}
],
"eq_spans": [],
"section": "Task Definition",
"sec_num": "3"
},
{
"text": "The relation descriptor extraction task can be treated as a sequence labeling problem. Let x = (x 1 , x 2 , . . . , x n ) denote the sequence of observations in a relation instance, where x i is w i augmented with additional information such as the POS tag of w i , and the phrase boundary information. Each observation x i is associated with a label y i \u2208 Y which indicates whether w i is part of the relation descriptor. Following the commonly used BIO notation (Ramshaw and Marcus, 1995) in sequence labeling, we define Y = {B-REL, I-REL, O}. Let y = (y 1 , y 2 , . . . , y n ) denote the sequence of labels for x. Our task can be reduced to finding the best label sequence\u0177 among all the possible label sequences for x.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Method 4.1 Representation",
"sec_num": "4"
},
{
"text": "For sequence labeling tasks in NLP, linear-chain CRFs have been rather successful. It is an undirected graphical model in which the conditional probability of a label sequence y given the observation sequence x is",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "A Linear-Chain CRF Solution",
"sec_num": "4.2"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "p(y|x, \u039b) = exp \" P i P k \u03bb k f k (yi\u22121, yi, x) \" Z(x, \u039b) ,",
"eq_num": "(1)"
}
],
"section": "A Linear-Chain CRF Solution",
"sec_num": "4.2"
},
{
"text": "where \u039b = {\u03bb k } is the set of model parameters, f k is an arbitrary feature function defined over two consecutive labels and the whole observation sequence, and",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "A Linear-Chain CRF Solution",
"sec_num": "4.2"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "Z(x, \u039b) = X y exp \" X i X k \u03bb k f k (y i\u22121 , y i , x) \"",
"eq_num": "(2)"
}
],
"section": "A Linear-Chain CRF Solution",
"sec_num": "4.2"
},
{
"text": "is the normalization constant. Given a set of training instances {x j , y * j } where y * j is the correct label sequence for x j , we can learn the best model parameters\u039b as follows:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "A Linear-Chain CRF Solution",
"sec_num": "4.2"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "\u039b = arg min \u039b \u2212 X j log p(y * j |xj, \u039b) + \u03b2 X k \u03bb 2 k ! .",
"eq_num": "(3)"
}
],
"section": "A Linear-Chain CRF Solution",
"sec_num": "4.2"
},
{
"text": "Here \u03b2 k \u03bb 2 k is a regularization term.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "A Linear-Chain CRF Solution",
"sec_num": "4.2"
},
{
"text": "We note that while we can directly apply linearchain CRFs to extract relation descriptors, there are some special properties of our task that allow us to modify standard linear-chain CRFs to better suit our needs.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Improvement over Linear-Chain CRFs",
"sec_num": "4.3"
},
{
"text": "In linear-chain CRFs, the normalization constant Z considers all possible label sequences y. For the relation descriptor extraction problem, however, we expect that there is either a single relation descriptor sequence or no such sequence. In other words, for a given relation instance, we only expect two kinds of label sequences: (1) All y i are O, and (2) exactly one y i is B-REL followed by zero or more consecutive I-REL while all other y i are O. Therefore the space of label sequences should be reduced to only those that satisfy the above constraint.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Label sequence constraint",
"sec_num": null
},
{
"text": "One way to exploit this constraint within linearchain CRFs is to enforce it only during testing. We can pick the label sequence that has the highest probability in the valid label sequence space instead of the entire label sequence space. For a candidate relation instance x, let\u1ef8 denote the set of valid label sequences, i.e., those that have either one or no relation descriptor sequence. We then choose the best sequence\u0177 as follows:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Label sequence constraint",
"sec_num": null
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "y = arg max y\u2208\u1ef8 p(y|x,\u039b).",
"eq_num": "(4)"
}
],
"section": "Label sequence constraint",
"sec_num": null
},
{
"text": "Arguably, the more principled way to exploit the constraint is to modify the probabilistic model itself. So at the training stage, we should also consider only\u1ef8 by defining the normalization termZ as follows:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Label sequence constraint",
"sec_num": null
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "Z(x, \u039b) = X y \u2208\u1ef8 exp \" X i X k \u03bb k f k (y i\u22121 , y i , x) \" .",
"eq_num": "(5)"
}
],
"section": "Label sequence constraint",
"sec_num": null
},
{
"text": "The difference between Equation 5and Equation (2) is the set of label sequences considered. In other words, while in linear-chain CRFs the correct label sequence competes with all possible label sequences for probability mass, for our task the correct label sequence should compete with only other valid label sequences. In Section 5 we will compare these two different normalization terms and show the advantage of using Equation 5.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Label sequence constraint",
"sec_num": null
},
{
"text": "In linear-chain CRF models, only first-order label dependencies are considered because features are defined over two consecutive labels. Inference in linear-chain CRFs can be done efficiently using dynamic programming. More general higherorder CRF models also exist, allowing long-range features defined over more than two consecutive labels. But the computational cost of higher-order CRFs also increases exponentially with the order of dependency.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Adding long-range features",
"sec_num": null
},
{
"text": "For our task, because of the constraint on the space of label sequences, we can afford to use long-range features. In our case, inference is still efficient because the number of sequences to be enumerated has been drastically reduced due to the constraint. Let g(y, x) denote a feature function defined over the entire label sequence y and the observation sequence x. We can include such feature functions in our model as follows:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Adding long-range features",
"sec_num": null
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "p(y|x, \u0398) = 1 Z(x,\u0398) \" exp \" P i P k \u03bb k f k (yi\u22121, yi, x) + P l \u00b5 l g l (y, x) \" # ,",
"eq_num": "(6)"
}
],
"section": "Adding long-range features",
"sec_num": null
},
{
"text": "where \u0398 = {{\u03bb k }, {\u00b5 l }} is the set of all model parameters. Both {\u03bb k } and {\u00b5 l } are regularized as in Equation 3. Note that although each f (y i\u22121 , y i , x) may be subsumed under a g(y, x), here we group all features that can be captured by linear-chain CRFs under f and other real longrange features under g. In Section 5 we will see that with the additional feature functions g, relation extraction performance can also be further improved.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Adding long-range features",
"sec_num": null
},
{
"text": "We now describe the features we use in the baseline linear-chain CRF model and our modified model.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Features",
"sec_num": "4.4"
},
{
"text": "The linear-chain features are those that can be formulated as f (y i\u22121 , y i , x), i.e., those that depend on x and two consecutive labels only. We use typical features that include tokens, POS tags and phrase boundary information coupled with label values. Let t i denote the POS tag of w i and p i denote the phrase boundary tag of w i . The phrase boundary tags also follow the BIO notation. Examples include B-NP, I-VP, etc. Table 2 shows the feature templates covering only the observations. Each feature shown in Table 2 is further combined with either the value of the current label y i or the values of the previous and the current labels y i\u22121 and y i to form zeroth order and first order features. For example, a zeroth order feature is \"y i is B-REL and w i is the and w i+1 is president\", and a first order feature is \"y i\u22121 is O and y i is B-REL and t i is N\".",
"cite_spans": [],
"ref_spans": [
{
"start": 429,
"end": 436,
"text": "Table 2",
"ref_id": null
},
{
"start": 519,
"end": 526,
"text": "Table 2",
"ref_id": null
}
],
"eq_spans": [],
"section": "Linear-chain features",
"sec_num": null
},
{
"text": "Long-range features are those that cannot be defined based on only two consecutive labels. When defining long-range features, we treat the whole relation descriptor sequence as a single unit, denoted as REL. Given a label sequence y that contains a relation descriptor sequence, let (w r , w r+1 , . . . , w s ) denote the relation descriptor, that is, y r = B-REL and y t = I-REL where r + 1 \u2264 t \u2264 s. The long-range features we use are categorized and summarized in Table 3 . These features capture the context of the entire relation descriptor, its relation to the two arguments, and whether the boundary of the relation descriptor conforms to the phrase boundaries (since we expect that most relation descriptors consist of a single or a sequence of phrases).",
"cite_spans": [],
"ref_spans": [
{
"start": 467,
"end": 474,
"text": "Table 3",
"ref_id": null
}
],
"eq_spans": [],
"section": "Long-range features",
"sec_num": null
},
{
"text": "Since the task of extracting relation descriptors is new, we are not aware of any data set that can be directly used to evaluate our methods. We therefore annotated two data sets for evaluation, one for the general \"employment\" relation and the other for the general \"personal/social\" relation. 2 The first data set contains 150 business articles from New York Times. The articles were crawled from the NYT website between November 2009 Description Feature Template Example single token wi+j (\u22122 \u2264 j \u2264 2) wi+1 (next token) is president single POS tag ti+j (\u22122 \u2264 j \u2264 2) ti (current POS tag) is DET single phrase tag pi+j (\u22122 \u2264 j \u2264 2) pi\u22121 (previous phrase boundary tag) is I-NP two consecutive tokens wi+j\u22121&wi+j (\u22121 \u2264 j \u2264 2) wi is the and wi+1 is president two consecutive POS tags ti+j\u22121&ti+j (\u22121 \u2264 j \u2264 2) ti is DET and ti+1 is N two consecutive phrase tags pi+j\u22121&pi+j (\u22121 \u2264 j \u2264 2) pi is B-NP and pi+1 is I-NP Table 2 : Linear-chain feature templates. Each feature is defined with respect to a particular (current) position in the sequence. i indicates the current position and j indicates the position relative to the current position. All features are defined using observations within a window size of 5 of the current position.",
"cite_spans": [
{
"start": 295,
"end": 296,
"text": "2",
"ref_id": null
}
],
"ref_spans": [
{
"start": 912,
"end": 919,
"text": "Table 2",
"ref_id": null
}
],
"eq_spans": [],
"section": "Data Preparation",
"sec_num": "5.1"
},
{
"text": "Contextual Features word wr\u22121 or POS tag tr\u22121 preceding relation descriptor , REL word ws+1 or POS tag ts+1 following relation descriptor REL PREP",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Category Feature Template Description Example",
"sec_num": null
},
{
"text": "Path-based Features word or POS tag sequence between ARG1 and relation descriptor ARG1 is REL word or POS tag sequence between ARG2 and relation descriptor REL PREP ARG2 word or POS tag sequence containing ARG1, ARG2 and relation descriptor ARG2 's REL , ARG1",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Category Feature Template Description Example",
"sec_num": null
},
{
"text": "Phrase Boundary whether relation descriptor violates phrase boundaries 1 or 0 Feature Table 3 : Long-range feature templates. r and s are the indices of the first word and the last word of the relation descriptor, respectively. and January 2010. After sentence segmentation and tokenization, we used the Stanford NER tagger (Finkel et al., 2005) to identify PER and ORG named entities from each sentence. For named entities that contain multiple tokens we concatenated them into a single token. We then took each pair of (PER, ORG) entities that occur in the same sentence as a single candidate relation instance, where the PER entity is treated as ARG-1 and the ORG entity is treated as ARG-2.",
"cite_spans": [
{
"start": 324,
"end": 345,
"text": "(Finkel et al., 2005)",
"ref_id": "BIBREF4"
}
],
"ref_spans": [
{
"start": 86,
"end": 93,
"text": "Table 3",
"ref_id": null
}
],
"eq_spans": [],
"section": "Category Feature Template Description Example",
"sec_num": null
},
{
"text": "The second data set comes from a Wikipedia personal/social relation data set previously used in (Culotta et al., 2006) . The original data set does not contain annotations of relation descriptors such as \"sister\" or \"friend\" between the two PER arguments. We therefore also manually annotated this data set. Similarly, we performed sentence segmentation, tokenization and NER tagging, and took each pair of (PER, PER) entities occurring in the same sentence as a candidate relation instance. Because both arguments involved in the \"personal/social\" relation are PER entities, we always treat the first PER entity as ARG-1 and the second PER entity as ARG-2. 3 We go through each candidate relation instance to find whether there is an explicit sequence of words describing the relation between ARG-1 and ARG-2, and label the sequence of words, if any. Note that we only consider explicitly stated relation descriptors. If we cannot find such a relation descriptor, even if ARG-1 and ARG-2 actually have some kind of relation, we still label the instance as Nil. For example, in the instance \"he is the son of ARG1 and ARG2\", although we can infer that ARG-1 and ARG-2 have some family relation, we regard this as a negative instance.",
"cite_spans": [
{
"start": 96,
"end": 118,
"text": "(Culotta et al., 2006)",
"ref_id": "BIBREF3"
},
{
"start": 658,
"end": 659,
"text": "3",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Category Feature Template Description Example",
"sec_num": null
},
{
"text": "A relation descriptor may also contain multiple relations. For example, in the instance \"ARG1 is the CEO and president of ARG2\", we label \"the CEO and president\" as the relation descriptor, which actually contains two job titles, namely, CEO and president.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Category Feature Template Description Example",
"sec_num": null
},
{
"text": "Note that our annotated relation descriptors are not always nouns or noun phrases. An example is the third instance for personal/social relation in Table 1 , where the relation descriptor \"married\" is a verb and indicates a spouse relation.",
"cite_spans": [],
"ref_spans": [
{
"start": 148,
"end": 155,
"text": "Table 1",
"ref_id": null
}
],
"eq_spans": [],
"section": "Category Feature Template Description Example",
"sec_num": null
},
{
"text": "The total number of relation instances, the number of positive and negative instances as well as the number of distinct relation descriptors in each data set are summarized in Table 4 . ",
"cite_spans": [],
"ref_spans": [
{
"start": 176,
"end": 183,
"text": "Table 4",
"ref_id": "TABREF2"
}
],
"eq_spans": [],
"section": "Category Feature Template Description Example",
"sec_num": null
},
{
"text": "We compare the following methods in our experiments:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Experiment Setup",
"sec_num": "5.2"
},
{
"text": "\u2022 LC-CRF: This is the standard linear-chain CRF model with features described in Table 2 .",
"cite_spans": [],
"ref_spans": [
{
"start": 81,
"end": 88,
"text": "Table 2",
"ref_id": null
}
],
"eq_spans": [],
"section": "Experiment Setup",
"sec_num": "5.2"
},
{
"text": "\u2022 M-CRF-1: This is our modified linear-chain CRF model with the space of label sequences reduced but with features fixed to the same as those used in LC-CRF.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Experiment Setup",
"sec_num": "5.2"
},
{
"text": "\u2022 M-CRF-2: This is M-CRF-1 with the addition of the contextual long-range features described in Table 3 .",
"cite_spans": [],
"ref_spans": [
{
"start": 96,
"end": 103,
"text": "Table 3",
"ref_id": null
}
],
"eq_spans": [],
"section": "Experiment Setup",
"sec_num": "5.2"
},
{
"text": "\u2022 M-CRF-3: This is M-CRF-2 with the addition of the path-based long-range features described in Table 3 .",
"cite_spans": [],
"ref_spans": [
{
"start": 96,
"end": 103,
"text": "Table 3",
"ref_id": null
}
],
"eq_spans": [],
"section": "Experiment Setup",
"sec_num": "5.2"
},
{
"text": "\u2022 M-CRF-4: This is M-CRF-3 with the addition of the phrase boundary long-range feature described in Table 3 .",
"cite_spans": [],
"ref_spans": [
{
"start": 100,
"end": 107,
"text": "Table 3",
"ref_id": null
}
],
"eq_spans": [],
"section": "Experiment Setup",
"sec_num": "5.2"
},
{
"text": "For the standard linear-chain CRF model, we use the package CRF++ 4 . We implement our own version of the modified linear-chain CRF models.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Experiment Setup",
"sec_num": "5.2"
},
{
"text": "We perform 10-fold cross validation for all our experiments. For each data set we first randomly divide it into 10 subsets. Each time we take 9 subsets for training and the remaining subset for testing. We report the average performance across the 10 runs.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Experiment Setup",
"sec_num": "5.2"
},
{
"text": "Based on our preliminary experiments, we have found that using a smaller set of general POS tags instead of the Penn Treebank POS tag set could slightly improve the overall performance. We therefore only report the performance obtained using our POS tags. For example, we group NN, NNP, NNS and NNPS of the Penn Treebank set under a general tag N.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Experiment Setup",
"sec_num": "5.2"
},
{
"text": "We evaluate the performance using two different criteria: overlap match and exact match. Overlap match is a more relaxed criterion: if the extracted relation descriptor overlaps with the true relation descriptor (i.e., having at least one token in common), it is considered correct. Exact match is a much stricter criterion: it requires that the extracted relation descriptor be exactly the same as the true relation descriptor in order to be considered correct. Given these two criteria, we can define accuracy, precision, recall and F1 measures. Accuracy is the percentage of candidate relation instances whose label sequence is considered correct. Both positive and negative instances are counted when computing accuracy. Because our data sets are quite balanced, it is reasonable to use accuracy. Precision, recall and F1 are defined in the usual way at the relation instance level.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Experiment Setup",
"sec_num": "5.2"
},
{
"text": "In Table 5 , we summarize the performance in terms of the various measures on the two data sets. For both the baseline linear-chain CRF model and our modified linear-chain CRF models, we have tuned the regularization parameters and show only the results using the optimal parameter values for each data set, chosen from \u03b2 = 10 \u03b3 for \u03b3 \u2208 [\u22123, \u22122, . . . , 2, 3].",
"cite_spans": [],
"ref_spans": [
{
"start": 3,
"end": 10,
"text": "Table 5",
"ref_id": null
}
],
"eq_spans": [],
"section": "Method Comparison",
"sec_num": "5.3"
},
{
"text": "First, we can see from the table that by reducing the label sequence space, M-CRF-1 can significantly outperform the baseline LC-CRF in terms of F1 in all cases. In terms of accuracy, there is significant improvement for the NYT data set but not for the Wikipedia data set. We also notice that for both data sets the advantage of M-CRF-1 is mostly evident in the improvement of recall. This shows that a larger number of true relation descriptors are extracted when the label sequence space is reduced.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Method Comparison",
"sec_num": "5.3"
},
{
"text": "Next we see from the table that long-range features are also useful, and the improvement comes mostly from the path-based long-range features. In terms of both accuracy and F1, M-CRF-3 can significantly outperform M-CRF-1 in all settings. In this case, the improvement is a mixture of both precision and recall. This shows that by explicitly capturing the patterns between the two arguments and the relation descriptor, we can largely improve the extraction performance. On the other hand, neither the contextual long-range features nor the phrase boundary long-range features exhibit any Table 5 : Comparison of different methods on the New York Times data set and Wikipedia data set. Accu., Prec., Rec. and F1 stand for accuracy, precision, recall and F1 measures, respectively. \u2020 indicates that the current value is statistically significantly better than the value in the previous row at a 0.95 level of confidence by one-tailed paired T-test. significant impact. We hypothesize the following. For contextual long-range features, they have already been captured in the linear-chain features. For example, the long-range feature \"is REL\" is similar to the linear-chain feature \"w i\u22121 = is & y i = B-R\". For the phrase boundary long-range feature, since phrase boundary tags have also been used in the linear-chain features, this feature does not provide additional information. In addition, we have found that a large percentage of relation descriptors violate phrase boundaries: 22% in the NYT data set, and 29% in the Wikipedia data set. Therefore, it seems that phrase boundary information is not important for relation descriptor extraction. Overall, performance is much higher on the NYT data set than on the Wikipedia data set. Based on our observations during annotation, this is due to the fact that the \"employment\" relations expressed in the NYT data set often follow some standard patterns, whereas in Wikipedia the \"personal/social\" relations can be expressed in more varied ways. The lower performance achieved on the Wikipedia data set suggests that extracting relation descriptors is not an easy task even under a supervised learning setting.",
"cite_spans": [],
"ref_spans": [
{
"start": 589,
"end": 596,
"text": "Table 5",
"ref_id": null
}
],
"eq_spans": [],
"section": "Method Comparison",
"sec_num": "5.3"
},
{
"text": "Presumably relation descriptors that are not seen in the training data are harder to extract. We would therefore also like to see how well our model works on such unseen relation descriptors. We find that with 10-fold cross validation, for the NYT data set, on average our model is able to extract approximately 67% of the unseen relation descriptors in the test data using exact match criteria. For the Wikipedia data set this percentage is approximately 27%. Both numbers are lower than the overall recall values the model can achieve on the entire test data, showing that unseen relation descriptors are indeed harder to extract. However, our model is still able to pick up new relation descriptors.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Method Comparison",
"sec_num": "5.3"
},
{
"text": "In the previous experiments, we have used 90% of the data for training and the remaining 10% for testing. We now take a look at how the performance changes with different numbers of training instances. We vary the training data size from only a few instances (2, 5, and 10) to 20%, 40%, 60% and 80% of the entire data set. The results are shown in Figure 1 .",
"cite_spans": [],
"ref_spans": [
{
"start": 348,
"end": 356,
"text": "Figure 1",
"ref_id": "FIGREF0"
}
],
"eq_spans": [],
"section": "The Effect of Training Data Size",
"sec_num": "5.4"
},
{
"text": "As we can expect, when the number of training instances is small, the performance on both data sets is low. The figure also shows that the Wikipedia data set is the more difficult than the NYT data set. This is consistent with our observation in the previous section.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "The Effect of Training Data Size",
"sec_num": "5.4"
},
{
"text": "The modified linear-chain CRF model consistently outperforms the baseline linear-chain CRF model. For similar level of performance, the modified linear-chain CRF model requires less training data than the baseline linear-chain CRF model. For example, Figure 1(b) shows that the modified linear-chain CRF model achieve 0.72 F1 with about 215 training instances, while the baseline linear-chain CRF model requires about 480 training instances for a similar F1.",
"cite_spans": [],
"ref_spans": [
{
"start": 251,
"end": 262,
"text": "Figure 1(b)",
"ref_id": "FIGREF0"
}
],
"eq_spans": [],
"section": "The Effect of Training Data Size",
"sec_num": "5.4"
},
{
"text": "In this paper, we studied relation extraction under a new setting: the relation types are defined at a general level but more specific relation descriptors are desired. Based on the special properties of this new task, we found that standard linear-chain CRF models have some potential limitations for this task. We subsequently proposed some modifications to linear-chain CRFs in order to suit our task better. We annotated two data sets to evaluate our methods. The experiments showed that by restricting the space of possible label sequences and introducing certain long-range features, the performance of the modified linear-chain CRF model can perform significantly better than standard linear-chain CRFs.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusions",
"sec_num": "6"
},
{
"text": "Currently our work is only based on evaluation on two data sets and on two general relations. In the future we plan to evaluate the methods on other general relations to test its robustness. We also plan to explore how this new relation extraction task can be used within other NLP or text mining applications.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusions",
"sec_num": "6"
},
{
"text": "Automatic Content Extraction http://www.itl. nist.gov/iad/mig/tests/ace/",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "http://www.mysmu.edu/faculty/ jingjiang/data/IJCNLP2011.zip",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "Since many personal/social relations are asymmetric, ideally we should assign ARG-1 and ARG-2 based on their semantic meanings rather than their positions. Here we take a simple approach.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "http://crfpp.sourceforge.net/",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
}
],
"back_matter": [
{
"text": "This material is based on research sponsored by the Air Force Research Laboratory, under agreement number FA2386-09-1-4123. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright notation thereon. The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of the Air Force Research Laboratory or the U.S. Government.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Acknowledgments",
"sec_num": null
}
],
"bib_entries": {
"BIBREF0": {
"ref_id": "b0",
"title": "Snowball: Extracting relations from large plain-text collections",
"authors": [
{
"first": "Eugene",
"middle": [],
"last": "Agichtein",
"suffix": ""
},
{
"first": "Luis",
"middle": [],
"last": "Gravano",
"suffix": ""
}
],
"year": 2000,
"venue": "Proceedings of the Fifth ACM Conference on Digital Libraries",
"volume": "",
"issue": "",
"pages": "85--94",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Eugene Agichtein and Luis Gravano. 2000. Snow- ball: Extracting relations from large plain-text col- lections. In Proceedings of the Fifth ACM Confer- ence on Digital Libraries, pages 85-94, June.",
"links": null
},
"BIBREF1": {
"ref_id": "b1",
"title": "The tradeoffs between open and traditional relation extraction",
"authors": [
{
"first": "Michele",
"middle": [],
"last": "Banko",
"suffix": ""
},
{
"first": "Oren",
"middle": [],
"last": "Etzioni",
"suffix": ""
}
],
"year": 2008,
"venue": "Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "28--36",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Michele Banko and Oren Etzioni. 2008. The tradeoffs between open and traditional relation extraction. In Proceedings of the 46th Annual Meeting of the Asso- ciation for Computational Linguistics, pages 28-36.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "A shortest path dependency kernel for relation extraction",
"authors": [
{
"first": "Razvan",
"middle": [],
"last": "Bunescu",
"suffix": ""
},
{
"first": "Raymond",
"middle": [],
"last": "Mooney",
"suffix": ""
}
],
"year": 2005,
"venue": "Proceedings of the Human Language Technology Conference and the Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "724--731",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Razvan Bunescu and Raymond Mooney. 2005. A shortest path dependency kernel for relation extrac- tion. In Proceedings of the Human Language Tech- nology Conference and the Conference on Empiri- cal Methods in Natural Language Processing, pages 724-731, October.",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "Integrating probabilistic extraction models and data mining to discover relations and patterns in text",
"authors": [
{
"first": "Aron",
"middle": [],
"last": "Culotta",
"suffix": ""
},
{
"first": "Andrew",
"middle": [],
"last": "Mccallum",
"suffix": ""
},
{
"first": "Jonathan",
"middle": [],
"last": "Betz",
"suffix": ""
}
],
"year": 2006,
"venue": "Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "296--303",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Aron Culotta, Andrew McCallum, and Jonathan Betz. 2006. Integrating probabilistic extraction models and data mining to discover relations and patterns in text. In Proceedings of the Human Language Tech- nology Conference of the North American Chapter of the Association for Computational Linguistics, pages 296-303, June.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "Incorporating non-local information into information extraction systems by gibbs sampling",
"authors": [
{
"first": "Jenny",
"middle": [
"Rose"
],
"last": "Finkel",
"suffix": ""
},
{
"first": "Trond",
"middle": [],
"last": "Grenager",
"suffix": ""
},
{
"first": "Christopher",
"middle": [
"D"
],
"last": "Manning",
"suffix": ""
}
],
"year": 2005,
"venue": "Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "363--370",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jenny Rose Finkel, Trond Grenager, and Christo- pher D. Manning. 2005. Incorporating non-local information into information extraction systems by gibbs sampling. In Proceedings of the 43rd Annual Meeting of the Association for Computational Lin- guistics, pages 363-370, June.",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "Discovering relations among named entities from large corpora",
"authors": [
{
"first": "Takaaki",
"middle": [],
"last": "Hasegawa",
"suffix": ""
},
{
"first": "Satoshi",
"middle": [],
"last": "Sekine",
"suffix": ""
},
{
"first": "Ralph",
"middle": [],
"last": "Grishman",
"suffix": ""
}
],
"year": 2004,
"venue": "Proceedings of the 42nd Meeting of the Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "415--422",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Takaaki Hasegawa, Satoshi Sekine, and Ralph Grish- man. 2004. Discovering relations among named entities from large corpora. In Proceedings of the 42nd Meeting of the Association for Computational Linguistics, pages 415-422, July.",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "Learning 5000 relational extractors",
"authors": [
{
"first": "Raphael",
"middle": [],
"last": "Hoffmann",
"suffix": ""
},
{
"first": "Congle",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Daniel",
"middle": [
"S"
],
"last": "Weld",
"suffix": ""
}
],
"year": 2010,
"venue": "Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "286--295",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Raphael Hoffmann, Congle Zhang, and Daniel S. Weld. 2010. Learning 5000 relational extractors. In Proceedings of the 48th Annual Meeting of the As- sociation for Computational Linguistics, pages 286- 295, July.",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "Conditional random fields: Probabilistic models for segmenting and labeling sequence data",
"authors": [
{
"first": "John",
"middle": [
"D"
],
"last": "Lafferty",
"suffix": ""
},
{
"first": "Andrew",
"middle": [],
"last": "Mccallum",
"suffix": ""
},
{
"first": "Fernando",
"middle": [
"C N"
],
"last": "Pereira",
"suffix": ""
}
],
"year": 2001,
"venue": "Proceedings of the 18th International Conference on Machine Learning",
"volume": "",
"issue": "",
"pages": "282--289",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "John D. Lafferty, Andrew McCallum, and Fernando C. N. Pereira. 2001. Conditional random fields: Probabilistic models for segmenting and labeling se- quence data. In Proceedings of the 18th Interna- tional Conference on Machine Learning, pages 282- 289, June.",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "Text chunking using transformation-based learning",
"authors": [
{
"first": "A",
"middle": [],
"last": "Lance",
"suffix": ""
},
{
"first": "Mitchell",
"middle": [
"P"
],
"last": "Ramshaw",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Marcus",
"suffix": ""
}
],
"year": 1995,
"venue": "Proceedings of the Third ACL Workshop on Very Large Corpora",
"volume": "",
"issue": "",
"pages": "82--94",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Lance A. Ramshaw and Mitchell P. Marcus. 1995. Text chunking using transformation-based learning. In Proceedings of the Third ACL Workshop on Very Large Corpora, pages 82-94.",
"links": null
},
"BIBREF9": {
"ref_id": "b9",
"title": "URES : An unsupervised Web relation extraction system",
"authors": [
{
"first": "Benjamin",
"middle": [],
"last": "Rosenfeld",
"suffix": ""
},
{
"first": "Ronen",
"middle": [],
"last": "Feldman",
"suffix": ""
}
],
"year": 2006,
"venue": "Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "667--674",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Benjamin Rosenfeld and Ronen Feldman. 2006. URES : An unsupervised Web relation extraction system. In Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computa- tional Linguistics, pages 667-674, July.",
"links": null
},
"BIBREF10": {
"ref_id": "b10",
"title": "Semi-Markov conditional random fields for information extraction",
"authors": [
{
"first": "Sunita",
"middle": [],
"last": "Sarawagi",
"suffix": ""
},
{
"first": "William",
"middle": [
"W"
],
"last": "Cohen",
"suffix": ""
}
],
"year": 2005,
"venue": "Advances in Neural Information Processing Systems",
"volume": "17",
"issue": "",
"pages": "1185--1192",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Sunita Sarawagi and William W. Cohen. 2005. Semi- Markov conditional random fields for information extraction. In Advances in Neural Information Pro- cessing Systems 17, pages 1185-1192.",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "Preemptive information extraction using unrestricted relation discovery",
"authors": [
{
"first": "Yusuke",
"middle": [],
"last": "Shinyama",
"suffix": ""
},
{
"first": "Satoshi",
"middle": [],
"last": "Sekine",
"suffix": ""
}
],
"year": 2006,
"venue": "Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "304--311",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yusuke Shinyama and Satoshi Sekine. 2006. Preemp- tive information extraction using unrestricted rela- tion discovery. In Proceedings of the Human Lan- guage Technology Conference of the North Ameri- can Chapter of the Association for Computational Linguistics, pages 304-311, June.",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "Open information extraction using Wikipedia",
"authors": [
{
"first": "Fei",
"middle": [],
"last": "Wu",
"suffix": ""
},
{
"first": "S",
"middle": [],
"last": "Daniel",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Weld",
"suffix": ""
}
],
"year": 2010,
"venue": "Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "118--127",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Fei Wu and Daniel S. Weld. 2010. Open information extraction using Wikipedia. In Proceedings of the 48th Annual Meeting of the Association for Compu- tational Linguistics, pages 118-127, July.",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "Kernel methods for relation extraction",
"authors": [
{
"first": "Dmitry",
"middle": [],
"last": "Zelenko",
"suffix": ""
},
{
"first": "Chinatsu",
"middle": [],
"last": "Aone",
"suffix": ""
},
{
"first": "Anthony",
"middle": [],
"last": "Richardella",
"suffix": ""
}
],
"year": 2003,
"venue": "Journal of Machine Learning Research",
"volume": "3",
"issue": "",
"pages": "1083--1106",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Dmitry Zelenko, Chinatsu Aone, and Anthony Richardella. 2003. Kernel methods for relation ex- traction. Journal of Machine Learning Research, 3:1083-1106.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "Exploring syntactic features for relation extraction using a convolution tree kernel",
"authors": [
{
"first": "Min",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Jie",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Jian",
"middle": [],
"last": "Su",
"suffix": ""
}
],
"year": 2006,
"venue": "Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "288--295",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Min Zhang, Jie Zhang, and Jian Su. 2006. Explor- ing syntactic features for relation extraction using a convolution tree kernel. In Proceedings of the Hu- man Language Technology Conference of the North American Chapter of the Association for Computa- tional Linguistics, pages 288-295, June.",
"links": null
},
"BIBREF15": {
"ref_id": "b15",
"title": "Exploring various knowledge in relation extraction",
"authors": [
{
"first": "Guodong",
"middle": [],
"last": "Zhou",
"suffix": ""
},
{
"first": "Jian",
"middle": [],
"last": "Su",
"suffix": ""
},
{
"first": "Jie",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Min",
"middle": [],
"last": "Zhang",
"suffix": ""
}
],
"year": 2005,
"venue": "Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "427--434",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "GuoDong Zhou, Jian Su, Jie Zhang, and Min Zhang. 2005. Exploring various knowledge in relation ex- traction. In Proceedings of the 43rd Annual Meet- ing of the Association for Computational Linguis- tics, pages 427-434, June.",
"links": null
}
},
"ref_entries": {
"FIGREF0": {
"uris": null,
"text": "Performance of LC-CRF and M-CRF-3 as the training data size increases.",
"num": null,
"type_str": "figure"
},
"TABREF0": {
"text": "ARG-1 , a vice president at ARG-2 , which ... a",
"html": null,
"type_str": "table",
"content": "<table><tr><td>Relation</td><td>Candidate Relation Instance</td><td>Relation Descriptor</td></tr><tr><td>Employment</td><td>... said</td><td/></tr></table>",
"num": null
},
"TABREF2": {
"text": "Number of instances in each data set. Positive instances are those that have an explicit relation descriptor. The last column shows the number of distinct relation descriptors.",
"html": null,
"type_str": "table",
"content": "<table/>",
"num": null
}
}
}
} |