File size: 96,002 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 | {
"paper_id": "P09-1043",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T08:53:57.124267Z"
},
"title": "Cross-Domain Dependency Parsing Using a Deep Linguistic Grammar",
"authors": [
{
"first": "Yi",
"middle": [],
"last": "Zhang",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Saarland University",
"location": {
"postCode": "D-66123",
"settlement": "Saarbr\u00fccken",
"country": "Germany"
}
},
"email": "yzhang@coli.uni-sb.de"
},
{
"first": "Rui",
"middle": [],
"last": "Wang",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Saarland University",
"location": {
"postCode": "66123",
"settlement": "Saarbr\u00fccken",
"country": "Germany"
}
},
"email": "rwang@coli.uni-sb.de"
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "Pure statistical parsing systems achieves high in-domain accuracy but performs poorly out-domain. In this paper, we propose two different approaches to produce syntactic dependency structures using a large-scale hand-crafted HPSG grammar. The dependency backbone of an HPSG analysis is used to provide general linguistic insights which, when combined with state-of-the-art statistical dependency parsing models, achieves performance improvements on out-domain tests. \u2020",
"pdf_parse": {
"paper_id": "P09-1043",
"_pdf_hash": "",
"abstract": [
{
"text": "Pure statistical parsing systems achieves high in-domain accuracy but performs poorly out-domain. In this paper, we propose two different approaches to produce syntactic dependency structures using a large-scale hand-crafted HPSG grammar. The dependency backbone of an HPSG analysis is used to provide general linguistic insights which, when combined with state-of-the-art statistical dependency parsing models, achieves performance improvements on out-domain tests. \u2020",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "Syntactic dependency parsing is attracting more and more research focus in recent years, partially due to its theory-neutral representation, but also thanks to its wide deployment in various NLP tasks (machine translation, textual entailment recognition, question answering, information extraction, etc.). In combination with machine learning methods, several statistical dependency parsing models have reached comparable high parsing accuracy (McDonald et al., 2005b; Nivre et al., 2007b) . In the meantime, successful continuation of CoNLL Shared Tasks since 2006 (Buchholz and Marsi, 2006; Nivre et al., 2007a; Surdeanu et al., 2008) have witnessed how easy it has become to train a statistical syntactic dependency parser provided that there is annotated treebank.",
"cite_spans": [
{
"start": 444,
"end": 468,
"text": "(McDonald et al., 2005b;",
"ref_id": "BIBREF12"
},
{
"start": 469,
"end": 489,
"text": "Nivre et al., 2007b)",
"ref_id": "BIBREF16"
},
{
"start": 566,
"end": 592,
"text": "(Buchholz and Marsi, 2006;",
"ref_id": "BIBREF1"
},
{
"start": 593,
"end": 613,
"text": "Nivre et al., 2007a;",
"ref_id": "BIBREF15"
},
{
"start": 614,
"end": 636,
"text": "Surdeanu et al., 2008)",
"ref_id": "BIBREF20"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "While the dissemination continues towards various languages, several issues arise with such purely data-driven approaches. One common observation is that statistical parser performance drops significantly when tested on a dataset different from the training set. For instance, when using the Wall Street Journal (WSJ) sections of the Penn Treebank (Marcus et al., 1993) as training set, tests on BROWN Sections typically result in a 6-8% drop in labeled attachment scores, although the average sentence length is much shorter in BROWN than that in WSJ. The common interpretation is that the test set is heterogeneous to the training set, hence in a different \"domain\" (in a loose sense). The typical cause of this is that the model overfits the training domain. The concerns over random choice of training corpus leading to linguistically inadequate parsing systems increase over time.",
"cite_spans": [
{
"start": 348,
"end": 369,
"text": "(Marcus et al., 1993)",
"ref_id": "BIBREF7"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "While the statistical revolution in the field of computational linguistics gaining high publicity, the conventional symbolic grammar-based parsing approaches have undergone a quiet period of development during the past decade, and reemerged very recently with several large scale grammar-driven parsing systems, benefiting from the combination of well-established linguistic theories and data-driven stochastic models. The obvious advantage of such systems over pure statistical parsers is their usage of hand-coded linguistic knowledge irrespective of the training data. A common problem with grammar-based parser is the lack of robustness. Also it is difficult to derive grammar compatible annotations to train the statistical components.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "In recent years, two statistical dependency parsing systems, MaltParser (Nivre et al., 2007b) and MSTParser (McDonald et al., 2005b) , representing different threads of research in data-driven machine learning approaches have obtained high publicity, for their state-of-the-art performances in open competitions such as CoNLL Shared Tasks. MaltParser follows the transition-based approach, where parsing is done through a series of actions deterministically predicted by an oracle model. MSTParser, on the other hand, follows the graph-based approach where the best parse tree is acquired by searching for a spanning tree which maximizes the score on either a partially or a fully connected graph with all words in the sentence as nodes (Eisner, 1996; McDonald et al., 2005b) .",
"cite_spans": [
{
"start": 72,
"end": 93,
"text": "(Nivre et al., 2007b)",
"ref_id": "BIBREF16"
},
{
"start": 98,
"end": 132,
"text": "MSTParser (McDonald et al., 2005b)",
"ref_id": null
},
{
"start": 737,
"end": 751,
"text": "(Eisner, 1996;",
"ref_id": "BIBREF3"
},
{
"start": 752,
"end": 775,
"text": "McDonald et al., 2005b)",
"ref_id": "BIBREF12"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Parser Domain Adaptation",
"sec_num": "2"
},
{
"text": "As reported in various evaluation competitions, the two systems achieved comparable performances. More recently, approaches of combining these two parsers achieved even better dependency accuracy (Nivre and McDonald, 2008) . Granted for the differences between their approaches, both systems heavily rely on machine learning methods to estimate the parsing model from an annotated corpus as training set. Due to the heavy cost of developing high quality large scale syntactically annotated corpora, even for a resource-rich language like English, only very few of them meets the criteria for training a general purpose statistical parsing model. For instance, the text style of WSJ is newswire, and most of the sentences are statements. Being lack of non-statements in the training data could cause problems, when the testing data contain many interrogative or imperative sentences as in the BROWN corpus. Therefore, the unbalanced distribution of linguistic phenomena in the training data leads to inadequate parser output structures. Also, the financial domain specific terminology seen in WSJ can skew the interpretation of daily life sentences seen in BROWN.",
"cite_spans": [
{
"start": 196,
"end": 222,
"text": "(Nivre and McDonald, 2008)",
"ref_id": "BIBREF14"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Parser Domain Adaptation",
"sec_num": "2"
},
{
"text": "There has been a substantial amount of work on parser adaptation, especially from WSJ to BROWN. Gildea (2001) compared results from different combinations of the training and testing data to demonstrate that the size of the feature model can be reduced via excluding \"domain-dependent\" features, while the performance could still be preserved. Furthermore, he also pointed out that if the additional training data is heterogeneous from the original one, the parser will not obtain a substantially better performance. Bacchiani et al. (2006) generalized the previous approaches using a maximum a posteriori (MAP) framework and proposed both supervised and unsupervised adaptation of statistical parsers. McClosky et al. (2006) and Mc-Closky et al. (2008) have shown that out-domain parser performance can be improved with selftraining on a large amount of unlabeled data. Most of these approaches focused on the machine learning perspective instead of the linguistic knowledge embraced in the parsers. Little study has been re-ported on approaches of incorporating linguistic features to make the parser less dependent on the nature of training and testing dataset, without resorting to huge amount of unlabeled out-domain data. In addition, most of the previous work have been focusing on constituent-based parsing, while the domain adaptation of the dependency parsing has not been fully explored.",
"cite_spans": [
{
"start": 96,
"end": 109,
"text": "Gildea (2001)",
"ref_id": "BIBREF5"
},
{
"start": 517,
"end": 540,
"text": "Bacchiani et al. (2006)",
"ref_id": "BIBREF0"
},
{
"start": 703,
"end": 725,
"text": "McClosky et al. (2006)",
"ref_id": "BIBREF9"
},
{
"start": 730,
"end": 753,
"text": "Mc-Closky et al. (2008)",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Parser Domain Adaptation",
"sec_num": "2"
},
{
"text": "Taking a different approach towards parsing, grammar-based parsers appear to have much linguistic knowledge encoded within the grammars. In recent years, several of these linguistically motivated grammar-driven parsing systems achieved high accuracy which are comparable to the treebank-based statistical parsers. Notably are the constraint-based linguistic frameworks with mathematical rigor, and provide grammatical analyses for a large variety of phenomena. For instance, the Head-Driven Phrase Structure Grammar (Pollard and Sag, 1994) has been successfully applied in several parsing systems for more than a dozen of languages. Some of these grammars, such as the English Resource Grammar (ERG; Flickinger (2002)), have undergone over decades of continuous development, and provide precise linguistic analyses for a broad range of phenomena. These linguistic knowledge are encoded in highly generalized form according to linguists' reflection for the target languages, and tend to be largely independent from any specific domain.",
"cite_spans": [
{
"start": 516,
"end": 539,
"text": "(Pollard and Sag, 1994)",
"ref_id": "BIBREF18"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Parser Domain Adaptation",
"sec_num": "2"
},
{
"text": "The main issue of parsing with precision grammars is that broad coverage and high precision on linguistic phenomena do not directly guarantee robustness of the parser with noisy real world texts. Also, the detailed linguistic analysis is not always of the highest interest to all NLP applications. It is not always straightforward to scale down the detailed analyses embraced by deep grammars to a shallower representation which is more accessible for specific NLP tasks. On the other hand, since the dependency representation is relatively theory-neutral, it is possible to convert from other frameworks into its backbone representation in dependencies. For HPSG, this is further assisted by the clear marking of head daughters in headed phrases. Although the statistical components of the grammar-driven parser might be still biased by the training domain, the hand-coded grammar rules guarantee the basic linguistic constraints to be met. This not to say that domain adaptation is not an issue for grammar-based parsing systems, but the built-in linguistic knowledge can be explored to reduce the performance drop in pure statistical approaches.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Parser Domain Adaptation",
"sec_num": "2"
},
{
"text": "In this section, we explore two possible applications of the HPSG parsing onto the syntactic dependency parsing task. One is to extract dependency backbone from the HPSG analyses of the sentences and directly convert them into the target representation; the other way is to encode the HPSG outputs as additional features into the existing statistical dependency parsing models. In the previous work, Nivre and McDonald (2008) have integrated MSTParser and MaltParser by feeding one parser's output as features into the other. The relationships between our work and their work are roughly shown in Figure 1 .",
"cite_spans": [
{
"start": 400,
"end": 425,
"text": "Nivre and McDonald (2008)",
"ref_id": "BIBREF14"
}
],
"ref_spans": [
{
"start": 597,
"end": 605,
"text": "Figure 1",
"ref_id": null
}
],
"eq_spans": [],
"section": "Dependency Parsing with HPSG",
"sec_num": "3"
},
{
"text": "Given a sentence, each parse produced by the parser is represented by a typed feature structure, which recursively embeds smaller feature structures for lower level phrases or words. For the purpose of dependency backbone extraction, we only look at the derivation tree which corresponds to the constituent tree of an HPSG analysis, with all non-terminal nodes labeled by the names of the grammar rules applied. Figure 2 shows an example. Note that all grammar rules in ERG are either unary or binary, giving us relatively deep trees when compared with annotations such as Penn Treebank. Conceptually, this conversion is similar to the conversions from deeper structures to GR reprsentations reported by Clark and Curran (2007) and . The dependency backbone extraction works by first identifying the head daughter for each binary grammar rule, and then propagating the head word of the head daughter upwards to their parents, and finally creating a dependency relation, labeled with the HPSG rule name of the parent node, from the head word of the parent to the head word of the non-head daughter. See Figure 3 for an example of such an extracted backbone.",
"cite_spans": [
{
"start": 704,
"end": 727,
"text": "Clark and Curran (2007)",
"ref_id": "BIBREF2"
}
],
"ref_spans": [
{
"start": 412,
"end": 420,
"text": "Figure 2",
"ref_id": "FIGREF1"
},
{
"start": 1102,
"end": 1110,
"text": "Figure 3",
"ref_id": "FIGREF2"
}
],
"eq_spans": [],
"section": "Extracting Dependency Backbone from HPSG Derivation Tree",
"sec_num": "3.1"
},
{
"text": "For the experiments in this paper, we used July-08 version of the ERG, which contains in total 185 grammar rules (morphological rules are not counted). Among them, 61 are unary rules, and 124 are binary. Many of the binary rules are clearly marked as headed phrases. The grammar also indicates whether the head is on the left (head-initial) or on the right (head-final). However, there are still quite a few binary rules which are not marked as headed-phrases (according to the linguistic theory), e.g. rules to handle coordinations, appositions, compound nouns, etc. For these rules, we refer to the conversion of the Penn Treebank into dependency structures used in the CoNLL 2008 Shared Task, and mark the heads of these rules in a way that will arrive at a compatible dependency backbone. For instance, the left most daughters of coordination rules are marked as heads. In combination with the right-branching analysis of coordination in ERG, this leads to the same dependency attachment in the CoNLL syntax. Eventually, 37 binary rules are marked with a head daughter on the left, and 86 with a head daughter on the right.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Extracting Dependency Backbone from HPSG Derivation Tree",
"sec_num": "3.1"
},
{
"text": "Although the extracted dependency is similar to the CoNLL shared task dependency structures, minor systematic differences still exist for some phenomena. For example, the possessive \"'s\" is annotated to be governed by its preceding word in CoNLL dependency; while in HPSG, it is treated as the head of a \"specifier-head\" construction, hence governing the preceding word in the dependency backbone. With several simple tree rewriting rules, we are able to fix the most frequent inconsistencies. With the rule-based backbone extraction and repair, we can finally turn our HPSG parser outputs into dependency structures 1 . The unlabeled attachment agreement between the HPSG backbone and CoNLL dependency annotation will be shown in Section 4.2.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Extracting Dependency Backbone from HPSG Derivation Tree",
"sec_num": "3.1"
},
{
"text": "As mentioned in Section 2, one pitfall of using a precision-oriented grammar in parsing is its lack of robustness. Even with a large scale broad coverage grammar like ERG, using our settings we only achieved 75% of sentential coverage 2 . Given that the grammar has never been fine-tuned for the financial domain, such coverage is very encouraging. But still, the remaining unparsed sentences comprise a big coverage gap. Different strategies can be taken here. One can either keep the high precision by only looking at full parses from the HPSG parser, of which the analyses are completely admitted by grammar constraints. Or one can trade precision for extra robustness by looking at the most probable incomplete analysis. Several partial parsing strategies have been proposed (Kasper et al., 1999; Zhang and Kordoni, 2008) as the robust fallbacks for the parser when no available analysis can be derived. In our experiment, we select the sequence of most likely fragment analyses according to their local disambiguation scores as the partial parse. When combined with the dependency backbone extraction, partial parses generate disjoint tree fragments. We simply attach all fragments onto the virtual root node.",
"cite_spans": [
{
"start": 779,
"end": 800,
"text": "(Kasper et al., 1999;",
"ref_id": "BIBREF6"
},
{
"start": 801,
"end": 825,
"text": "Zhang and Kordoni, 2008)",
"ref_id": "BIBREF22"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Robust Parsing with HPSG",
"sec_num": "3.2"
},
{
"text": "1 It is also possible map from HPSG rule names (together with the part-of-speech of head and dependent) to CoNLL dependency labels. This remains to be explored in the future.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Robust Parsing with HPSG",
"sec_num": "3.2"
},
{
"text": "2 More recent study shows that with carefully designed retokenization and preprocessing rules, over 80% sentential coverage can be achieved on the WSJ sections of the Penn Treebank data using the same version of ERG. The numbers reported in this paper are based on a simpler preprocessor, using rather strict time/memory limits for the parser. Hence the coverage number reported here should not be taken as an absolute measure of grammar performance.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Robust Parsing with HPSG",
"sec_num": "3.2"
},
{
"text": "Besides directly using the dependency backbone of the HPSG output, we could also use it for building feature-based models of statistical dependency parsers. Since we focus on the domain adaptation issue, we incorporate a less domain dependent language resource (i.e. the HPSG parsing outputs using ERG) into the features models of statistical parsers. As mordern grammar-based parsers has achieved high runtime efficency (with our HPSG parser parsing at an average speed of \u223c3 sentences per second), this adds up to an acceptable overhead.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Using Feature-Based Models",
"sec_num": "3.3"
},
{
"text": "As mentioned before, MSTParser is a graphbased statistical dependency parser, whose learning procedure can be viewed as the assignment of different weights to all kinds of dependency arcs. Therefore, the feature model focuses on each kind of head-child pair in the dependency tree, and mainly contains four categories of features (Mcdonald et al., 2005a) : basic uni-gram features, basic bi-gram features, in-between POS features, and surrounding POS features. It is emphasized by the authors that the last two categories contribute a large improvement to the performance and bring the parser to the state-of-the-art accuracy.",
"cite_spans": [
{
"start": 330,
"end": 354,
"text": "(Mcdonald et al., 2005a)",
"ref_id": "BIBREF11"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Feature Model with MSTParser",
"sec_num": "3.3.1"
},
{
"text": "Therefore, we extend this feature set by adding four more feature categories, which are similar to the original ones, but the dependency relation was replaced by the dependency backbone of the HPSG outputs. The extended feature set is shown in Table 1.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Feature Model with MSTParser",
"sec_num": "3.3.1"
},
{
"text": "MaltParser is another trend of dependency parser, which is based on transitions. The learning procedure is to train a statistical model, which can help the parser to decide which operation to take at each parsing status. The basic data structures are a stack, where the constructed dependency graph is stored, and an input queue, where the unprocessed data are put. Therefore, the feature model focuses on the tokens close to the top of the stack and also the head of the queue.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Feature Model with MaltParser",
"sec_num": "3.3.2"
},
{
"text": "Provided with the original features used in MaltParser, we add extra ones about the top token in the stack and the head token of the queue derived from the HPSG dependency backbone. The extended feature set is shown in Table 2 (the new features are listed separately).",
"cite_spans": [],
"ref_spans": [
{
"start": 219,
"end": 226,
"text": "Table 2",
"ref_id": "TABREF2"
}
],
"eq_spans": [],
"section": "Feature Model with MaltParser",
"sec_num": "3.3.2"
},
{
"text": "Uni-gram Features: h-w,h-p; h-w; h-p; c-w,c-p; c-w; c-p With the extra features, we hope that the training of the statistical model will not overfit the indomain data, but be able to deal with domain independent linguistic phenomena as well.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Feature Model with MaltParser",
"sec_num": "3.3.2"
},
{
"text": "Bi-gram Features: h-w,h-p,c-w,c-p; h-p,c-w,c-p; h-w,c-w,c-p; h-w,h-p,c-p; h-w,h-p,c-w; h-w,c-w; h-p,c-p POS Features of words in between: h-p,b-p,c-p POS Features of words surround: h-p,h-p+1,c-p-1,c-p; h-p-1,h-p,c-p-1,c-p; h-p,h-p+1,c-p,c-p+1; h-p-1,h-p,c-p,c-p+1",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Feature Model with MaltParser",
"sec_num": "3.3.2"
},
{
"text": "To evaluate the performance of our different dependency parsing models, we tested our approaches on several dependency treebanks for English in a similar spirit to the CoNLL 2006-2008 Shared Tasks. In this section, we will first describe the datasets, then present the results. An error analysis is also carried out to show both pros and cons of different models.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Experiment Results & Error Analyses",
"sec_num": "4"
},
{
"text": "In previous years of CoNLL Shared Tasks, several datasets have been created for the purpose of dependency parser evaluation. Most of them are converted automatically from existing treebanks in various forms. Our experiments adhere to the CoNLL 2008 dependency syntax (Yamada et al. 2003 , Johansson et al. 2007 which was used to convert Penn-Treebank constituent trees into single-head, single-root, traceless and nonprojective dependencies.",
"cite_spans": [
{
"start": 267,
"end": 286,
"text": "(Yamada et al. 2003",
"ref_id": null
},
{
"start": 287,
"end": 310,
"text": ", Johansson et al. 2007",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Datasets",
"sec_num": "4.1"
},
{
"text": "WSJ This dataset comprises of three portions. The larger part is converted from the Penn Treebank Wall Street Journal Sections #2-#21, and is used for training statistical dependency parsing models; the smaller part, which covers sentences from Section #23, is used for testing.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Datasets",
"sec_num": "4.1"
},
{
"text": "Brown This dataset contains a subset of converted sentences from BROWN sections of the Penn Treebank. It is used for the out-domain test.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Datasets",
"sec_num": "4.1"
},
{
"text": "PChemtb This dataset was extracted from the PennBioIE CYP corpus, containing 195 sentences from biomedical domain. The same dataset has been used for the domain adaptation track of the CoNLL 2007 Shared Task. Although the original annotation scheme is similar to the Penn Treebank, the dependency extraction setting is slightly different to the CoNLLWSJ dependencies (e.g. the coordinations).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Datasets",
"sec_num": "4.1"
},
{
"text": "Childes This is another out-domain test set from the children language component of the TalkBank, containing dialogs between parents and children. This is the other datasets used in the domain adaptation track of the CoNLL 2007 Shared Task. The dataset is annotated with unlabeled dependencies. As have been reported by others, several systematic differences in the original CHILDES annotation scheme has led to the poor system performances on this track of the Shared Task in 2007. Two main differences concern a) root attachments, and b) coordinations. With several simple heuristics, we change the annotation scheme of the original dataset to match the Penn Treebankbased datasets. The new dataset is referred to as CHILDES*.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Datasets",
"sec_num": "4.1"
},
{
"text": "First we test the agreement between HPSG dependency backbone and CoNLL dependency. While approximating a target dependency structure with rule-based conversion is not the main focus of this work, the agreement between two representations gives indication on how similar and consistent the two representations are, and a rough impression of whether the feature-based models can benefit from the HPSG backbone. The PET parser, an efficient parser HPSG parser is used in combination with ERG to parse the test sets. Note that the training set is not used. The grammar is not adapted for any of these specific domain. To pick the most probable reading from HPSG parsing outputs, we used a discriminative parse selection model as described in (Toutanova et al., 2002) trained on the LOGON Treebank (Oepen et al., 2004) , which is significantly different from any of the test domain. The treebank contains about 9K sentences for which HPSG analyses are manually disambiguated. The difference in annotation make it difficult to simply merge this HPSG treebank into the training set of the dependency parser. Also, as Gildea (2001) suggests, adding such heterogeneous data to the training set will not automatically lead to performance improvement. It should be noted that domain adaptation also presents a challenge to the disambiguation model of the HPSG parser. All datasets we use in our should be considered outdomain to the HPSG disambiguation model. Table 3 shows the agreement between the HPSG backbone and CoNLL dependency in unlabeled attachment score (UAS). The parser is set in either full parsing or partial parsing mode. Partial parsing is used as a fallback when full parse is not available. UAS are reported on all complete test sets, as well as fully parsed subsets (suffixed with \"-p\").",
"cite_spans": [
{
"start": 738,
"end": 762,
"text": "(Toutanova et al., 2002)",
"ref_id": "BIBREF21"
},
{
"start": 793,
"end": 813,
"text": "(Oepen et al., 2004)",
"ref_id": "BIBREF17"
},
{
"start": 1110,
"end": 1123,
"text": "Gildea (2001)",
"ref_id": "BIBREF5"
}
],
"ref_spans": [
{
"start": 1449,
"end": 1456,
"text": "Table 3",
"ref_id": "TABREF4"
}
],
"eq_spans": [],
"section": "HPSG Backbone as Dependency Parser",
"sec_num": "4.2"
},
{
"text": "It is not surprising to see that, without a decent fallback strategy, the full parse HPSG backbone suffers from insufficient coverage. Since the grammar coverage is statistically correlated to the average sentence length, the worst performance is observed for the PCHEMTB. Although sentences in CHILDES* are significantly shorter than those in BROWN, there is a fairly large amount of less well-formed sentences (either as a nature of child language, or due to the transcription from spoken dialogs). This leads to the close performance between these two datasets. PCHEMTB appears to be the most difficult one for the HPSG parser. The partial parsing fallback sets up a good safe net for sentences that fail to parse. Without resorting to any external resource, the performance was significantly improved on all complete test sets.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "HPSG Backbone as Dependency Parser",
"sec_num": "4.2"
},
{
"text": "When we set the coverage of the HPSG grammar aside and only compare performance on the subsets of these datasets which are fully parsed by the HPSG grammar, the unlabeled attachment score jumps up significantly. Most notable is that the dependency backbone achieved over 80% UAS on BROWN, which is close to the performance of state-of-the-art statistical dependency parsing systems trained on WSJ (see Table 5 and Table 4 ). The performance difference across data sets correlates to varying levels of difficulties in linguists' view. Our error analysis does confirm that frequent errors occur in WSJ test with financial terminology missing from the grammar lexicon. The relative performance difference between the WSJ and BROWN test is contrary to the results observed for statistical parsers trained on WSJ.",
"cite_spans": [],
"ref_spans": [
{
"start": 402,
"end": 409,
"text": "Table 5",
"ref_id": "TABREF7"
},
{
"start": 414,
"end": 421,
"text": "Table 4",
"ref_id": "TABREF6"
}
],
"eq_spans": [],
"section": "HPSG Backbone as Dependency Parser",
"sec_num": "4.2"
},
{
"text": "To further investigate the effect of HPSG parse disambiguation model on the dependency backbone accuracy, we used a set of 222 sentences from section of WSJ which have been parsed with ERG and manually disambiguated. Comparing to the WSJ-P result in Table 3 , we improved the agreement with CoNLL dependency by another 8% (an upper-bound in case of a perfect disambiguation model).",
"cite_spans": [],
"ref_spans": [
{
"start": 250,
"end": 257,
"text": "Table 3",
"ref_id": "TABREF4"
}
],
"eq_spans": [],
"section": "HPSG Backbone as Dependency Parser",
"sec_num": "4.2"
},
{
"text": "Similar evaluations were carried out for the statistical parsers using extra HPSG dependency backbone as features. It should be noted that the performance comparison between MSTParser and MaltParser is not the aim of this experiment, and the difference might be introduced by the specific settings we use for each parser. Instead, performance variance using different feature models is the main subject. Also, performance drop on out-domain tests shows how domain dependent the feature models are. For MaltParser, we use Arc-Eager algo-rithm, and polynomial kernel with d = 2. For MSTParser, we use 1st order features and a projective decoder (Eisner, 1996) .",
"cite_spans": [
{
"start": 643,
"end": 657,
"text": "(Eisner, 1996)",
"ref_id": "BIBREF3"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Statistical Dependency Parsing with HPSG Features",
"sec_num": "4.3"
},
{
"text": "When incorporating HPSG features, two settings are used. The PARTIAL model is derived by robust-parsing the entire training data set and extract features from every sentence to train a unified model. When testing, the PARTIAL model is used alone to determine the dependency structures of the input sentences. The FULL model, on the other hand is only trained on the full parsed subset of sentences, and only used to predict dependency structures for sentences that the grammar parses. For the unparsed sentences, the original models without HPSG features are used.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Statistical Dependency Parsing with HPSG Features",
"sec_num": "4.3"
},
{
"text": "Parser performances are measured using both labeled and unlabeled attachment scores (LAS/UAS). For unlabeled CHILDES* data, only UAS numbers are reported. Table 4 and 5 summarize results for MSTParser and MaltParser, respectively.",
"cite_spans": [],
"ref_spans": [
{
"start": 155,
"end": 162,
"text": "Table 4",
"ref_id": "TABREF6"
}
],
"eq_spans": [],
"section": "Statistical Dependency Parsing with HPSG Features",
"sec_num": "4.3"
},
{
"text": "With both parsers, we see slight performance drops with both HPSG feature models on indomain tests (WSJ), compared with the original models. However, on out-domain tests, full-parse HPSG feature models consistently outperform the original models for both parsers. The difference is even larger when only the HPSG fully parsed subsets of the test sets are concerned. When we look at the performance difference between in-domain and out-domain tests for each feature model, we observe that the drop is significantly smaller for the extended models with HPSG features.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Statistical Dependency Parsing with HPSG Features",
"sec_num": "4.3"
},
{
"text": "We should note that we have not done any feature selection for our HPSG feature models. Nor have we used the best known configurations of the existing parsers (e.g. second order features in MSTParser). Admittedly the results on PCHEMTB are lower than the best reported results in CoNLL 2007 Shared Task, we shall note that we are not using any in-domain unlabeled data. Also, the poor performance of the HPSG parser on this dataset indicates that the parser performance drop is more related to domain-specific phenomena and not general linguistic knowledge. Nevertheless, the drops when compared to in-domain tests are constantly decreased with the help of HPSG analyses features. With the results on BROWN, the performance of our HPSG feature models will rank 2 nd on the out-domain test for the CoNLL 2008 Shared Task.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Statistical Dependency Parsing with HPSG Features",
"sec_num": "4.3"
},
{
"text": "Unlike the observations in Section 4.2, the partial parsing mode does not work well as a fallback in the feature models. In most cases, its performances are between the original models and the full-parse HPSG feature models. The partial parsing features obscure the linguistic certainty of grammatical structures produced in the full model. When used as features, such uncertainty leads to further confusion. Practically, falling back to the original models works better when HPSG full parse is not available.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Statistical Dependency Parsing with HPSG Features",
"sec_num": "4.3"
},
{
"text": "Qualitative error analysis is also performed. Since our work focuses on the domain adaptation, we manually compare the outputs of the original statistical models, the dependency backbone, and the feature-based models on the out-domain data, i.e. the BROWN data set (both labeled and unlabeled results) and the CHILDES* data set (only unlabeled results).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Error Analyses",
"sec_num": "4.4"
},
{
"text": "For the dependency attachment (i.e. unlabeled dependency relation), fine-grained HPSG features do help the parser to deal with colloquial sentences, such as \"What's wrong with you?\". The original parser wrongly takes \"what\" as the root of the dependency tree and \"'s\" is attached to \"what\". The dependency backbone correctly finds out the root, and thus guide the extended model to make the right prediction. A correct structure of \"..., were now neither active nor really relaxed.\" is also predicted by our model, while the original model wrongly attaches \"really\" to \"nor\" and \"relaxed\" to \"were\". The rich linguistic knowledge from the HPSG outputs also shows its usefulness. For example, in a sentence from the CHILDES* data, \"Did you put dolly's shoes on?\", the verb phrase \"put on\" can be captured by the HPSG backbone, while the original model attaches \"on\" to the adjacent token \"shoes\".",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Error Analyses",
"sec_num": "4.4"
},
{
"text": "For the dependency labels, the most difficulty comes from the prepositions. For example, \"Scotty drove home alone in the Plymouth\", all the systems get the head of \"in\" correct, which is \"drove\". However, none of the dependency labels is correct. The original model predicts the \"DIR\" relation, the extended feature-based model says \"TMP\", but the gold standard annotation is \"LOC\". This is because the HPSG dependency backbone knows that \"in the Plymouth\" is an adjunct of \"drove\", but whether it is a temporal or locative expression cannot be easily predicted at the pure syntactic level. This also suggests a joint learning of syntactic and semantic dependencies, as proposed in the CoNLL 2008 Shared Task. Instances of wrong HPSG analyses have also been observed as one source of errors. For most of the cases, a correct reading exists, but not picked by our parse selection model. This happens more often with the WSJ test set, partially contributing to the low performance.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Error Analyses",
"sec_num": "4.4"
},
{
"text": "Similar to our work, also considered the combination of dependency parsing with an HPSG parser, although their work was to use statistical dependency parser outputs as soft constraints to improve the HPSG parsing. Nevertheless, a similar backbone extraction algorithm was used to map between different representations. Similar work also exists in the constituentbased approaches, where CFG backbones were used to improve the efficiency and robustness of HPSG parsers (Matsuzaki et al., 2007; Zhang and Kordoni, 2008) .",
"cite_spans": [
{
"start": 467,
"end": 491,
"text": "(Matsuzaki et al., 2007;",
"ref_id": "BIBREF8"
},
{
"start": 492,
"end": 516,
"text": "Zhang and Kordoni, 2008)",
"ref_id": "BIBREF22"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion & Future Work",
"sec_num": "5"
},
{
"text": "In this paper, we restricted our investigation on the syntactic evaluation using labeled/unlabeled attachment scores. Recent discussions in the parsing community about meaningful cross-framework evaluation metrics have suggested to use measures that are semantically informed. In this spirit, showed that the semantic outputs of the same HPSG parser helps in the semantic role labeling task. Consistent with the results reported in this paper, more improvement was achieved on the out-domain tests in their work as well.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion & Future Work",
"sec_num": "5"
},
{
"text": "Although the experiments presented in this paper were carried out on a HPSG grammar for English, the method can be easily adapted to work with other grammar frameworks (e.g. LFG, CCG, TAG, etc.), as well as on langugages other than English. We chose to use a hand-crafted grammar, so that the effect of training corpus on the deep parser is minimized (with the exception of the lexical coverage and disambiguation model).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion & Future Work",
"sec_num": "5"
},
{
"text": "As mentioned in Section 4.4, the performance of our HPSG parse selection model varies across different domains. This indicates that, although the deep grammar embraces domain independent linguistic knowledge, the lexical coverage and the disambiguation process among permissible readings is still domain dependent. With the mapping between HPSG analyses and their dependency backbones, one can potentially use existing dependency treebanks to help overcome the insufficient data problem for deep parse selection models.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion & Future Work",
"sec_num": "5"
},
{
"text": "\u2020 The first author thanks the German Excellence Cluster of Multimodal Computing and Interaction for the support of the work. The second author is funded by the PIRE PhD scholarship program.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
}
],
"back_matter": [],
"bib_entries": {
"BIBREF0": {
"ref_id": "b0",
"title": "Map adaptation of stochastic grammars. Computer speech and language",
"authors": [
{
"first": "Michiel",
"middle": [],
"last": "Bacchiani",
"suffix": ""
},
{
"first": "Michael",
"middle": [],
"last": "Riley",
"suffix": ""
},
{
"first": "Brian",
"middle": [],
"last": "Roark",
"suffix": ""
},
{
"first": "Richard",
"middle": [],
"last": "Sproat",
"suffix": ""
}
],
"year": 2006,
"venue": "",
"volume": "20",
"issue": "",
"pages": "41--68",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Michiel Bacchiani, Michael Riley, Brian Roark, and Richard Sproat. 2006. Map adaptation of stochastic grammars. Computer speech and language, 20(1):41-68.",
"links": null
},
"BIBREF1": {
"ref_id": "b1",
"title": "CoNLL-X shared task on multilingual dependency parsing",
"authors": [
{
"first": "Sabine",
"middle": [],
"last": "Buchholz",
"suffix": ""
},
{
"first": "Erwin",
"middle": [],
"last": "Marsi",
"suffix": ""
}
],
"year": 2006,
"venue": "Proceedings of the 10th Conference on Computational Natural Language Learning (CoNLL-X)",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Sabine Buchholz and Erwin Marsi. 2006. CoNLL-X shared task on multilingual dependency parsing. In Proceedings of the 10th Conference on Computational Natural Lan- guage Learning (CoNLL-X), New York City, USA.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "Formalismindependent parser evaluation with ccg and depbank",
"authors": [
{
"first": "Stephen",
"middle": [],
"last": "Clark",
"suffix": ""
},
{
"first": "James",
"middle": [],
"last": "Curran",
"suffix": ""
}
],
"year": 2007,
"venue": "Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics",
"volume": "",
"issue": "",
"pages": "248--255",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Stephen Clark and James Curran. 2007. Formalism- independent parser evaluation with ccg and depbank. In Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 248-255, Prague, Czech Republic.",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "Three new probabilistic models for dependency parsing: An exploration",
"authors": [
{
"first": "Jason",
"middle": [],
"last": "Eisner",
"suffix": ""
}
],
"year": 1996,
"venue": "Proceedings of the 16th International Conference on Computational Linguistics (COLING-96)",
"volume": "",
"issue": "",
"pages": "340--345",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jason Eisner. 1996. Three new probabilistic models for de- pendency parsing: An exploration. In Proceedings of the 16th International Conference on Computational Linguis- tics (COLING-96), pages 340-345, Copenhagen, Den- mark.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "On building a more efficient grammar by exploiting types",
"authors": [
{
"first": "Dan",
"middle": [],
"last": "Flickinger",
"suffix": ""
}
],
"year": 2002,
"venue": "Collaborative Language Engineering",
"volume": "",
"issue": "",
"pages": "1--17",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Dan Flickinger. 2002. On building a more efficient grammar by exploiting types. In Stephan Oepen, Dan Flickinger, Jun'ichi Tsujii, and Hans Uszkoreit, editors, Collaborative Language Engineering, pages 1-17. CSLI Publications.",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "Corpus variation and parser performance",
"authors": [
{
"first": "Daniel",
"middle": [],
"last": "Gildea",
"suffix": ""
}
],
"year": 2001,
"venue": "Proceedings of the 2001 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "167--202",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Daniel Gildea. 2001. Corpus variation and parser perfor- mance. In Proceedings of the 2001 Conference on Em- pirical Methods in Natural Language Processing, pages 167-202, Pittsburgh, USA.",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "Charting the depths of robust speech processing",
"authors": [
{
"first": "Walter",
"middle": [],
"last": "Kasper",
"suffix": ""
},
{
"first": "Bernd",
"middle": [],
"last": "Kiefer",
"suffix": ""
},
{
"first": "Hans-Ulrich",
"middle": [],
"last": "Krieger",
"suffix": ""
},
{
"first": "C",
"middle": [
"J"
],
"last": "Rupp",
"suffix": ""
},
{
"first": "Karsten",
"middle": [],
"last": "Worm",
"suffix": ""
}
],
"year": 1999,
"venue": "Proceedings of the 37th Annual Meeting of the Association for Computational Linguistics (ACL 1999)",
"volume": "",
"issue": "",
"pages": "405--412",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Walter Kasper, Bernd Kiefer, Hans-Ulrich Krieger, C.J. Rupp, and Karsten Worm. 1999. Charting the depths of robust speech processing. In Proceedings of the 37th An- nual Meeting of the Association for Computational Lin- guistics (ACL 1999), pages 405-412, Maryland, USA.",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "Building a large annotated corpus of english: The penn treebank",
"authors": [
{
"first": "Mitchell",
"middle": [
"P"
],
"last": "Marcus",
"suffix": ""
},
{
"first": "Beatrice",
"middle": [],
"last": "Santorini",
"suffix": ""
},
{
"first": "Mary",
"middle": [
"Ann"
],
"last": "Marcinkiewicz",
"suffix": ""
}
],
"year": 1993,
"venue": "Computational Linguistics",
"volume": "19",
"issue": "2",
"pages": "313--330",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Mitchell P. Marcus, Beatrice Santorini, and Mary Ann Marcinkiewicz. 1993. Building a large annotated corpus of english: The penn treebank. Computational Linguis- tics, 19(2):313-330.",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "Efficient HPSG parsing with supertagging and CFGfiltering",
"authors": [
{
"first": "Takuya",
"middle": [],
"last": "Matsuzaki",
"suffix": ""
},
{
"first": "Yusuke",
"middle": [],
"last": "Miyao",
"suffix": ""
},
{
"first": "Jun'ichi",
"middle": [],
"last": "Tsujii",
"suffix": ""
}
],
"year": 2007,
"venue": "Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI 2007)",
"volume": "",
"issue": "",
"pages": "1671--1676",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Takuya Matsuzaki, Yusuke Miyao, and Jun'ichi Tsujii. 2007. Efficient HPSG parsing with supertagging and CFG- filtering. In Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI 2007), pages 1671-1676, Hyderabad, India.",
"links": null
},
"BIBREF9": {
"ref_id": "b9",
"title": "Reranking and self-training for parser adaptation",
"authors": [
{
"first": "David",
"middle": [],
"last": "Mcclosky",
"suffix": ""
},
{
"first": "Eugene",
"middle": [],
"last": "Charniak",
"suffix": ""
},
{
"first": "Mark",
"middle": [],
"last": "Johnson",
"suffix": ""
}
],
"year": 2006,
"venue": "Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "337--344",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "David McClosky, Eugene Charniak, and Mark Johnson. 2006. Reranking and self-training for parser adaptation. In Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics, pages 337-344, Sydney, Australia.",
"links": null
},
"BIBREF10": {
"ref_id": "b10",
"title": "When is self-training effective for parsing",
"authors": [
{
"first": "David",
"middle": [],
"last": "Mcclosky",
"suffix": ""
},
{
"first": "Eugene",
"middle": [],
"last": "Charniak",
"suffix": ""
},
{
"first": "Mark",
"middle": [],
"last": "Johnson",
"suffix": ""
}
],
"year": 2008,
"venue": "Proceedings of the 22nd International Conference on Computational Linguistics",
"volume": "",
"issue": "",
"pages": "561--568",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "David McClosky, Eugene Charniak, and Mark Johnson. 2008. When is self-training effective for parsing? In Proceedings of the 22nd International Conference on Computational Linguistics (Coling 2008), pages 561-568, Manchester, UK.",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "Online large-margin training of dependency parsers",
"authors": [
{
"first": "Ryan",
"middle": [],
"last": "Mcdonald",
"suffix": ""
},
{
"first": "Koby",
"middle": [],
"last": "Crammer",
"suffix": ""
},
{
"first": "Fernando",
"middle": [],
"last": "Pereira",
"suffix": ""
}
],
"year": 2005,
"venue": "Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL'05)",
"volume": "",
"issue": "",
"pages": "91--98",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ryan Mcdonald, Koby Crammer, and Fernando Pereira. 2005a. Online large-margin training of dependency parsers. In Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL'05), pages 91-98, Ann Arbor, Michigan.",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "Non-Projective Dependency Parsing using Spanning Tree Algorithms",
"authors": [
{
"first": "Ryan",
"middle": [],
"last": "Mcdonald",
"suffix": ""
},
{
"first": "Fernando",
"middle": [],
"last": "Pereira",
"suffix": ""
},
{
"first": "Kiril",
"middle": [],
"last": "Ribarov",
"suffix": ""
}
],
"year": 2005,
"venue": "Proceedings of HLT-EMNLP 2005",
"volume": "",
"issue": "",
"pages": "523--530",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ryan McDonald, Fernando Pereira, Kiril Ribarov, and Jan Hajic. 2005b. Non-Projective Dependency Parsing us- ing Spanning Tree Algorithms. In Proceedings of HLT- EMNLP 2005, pages 523-530, Vancouver, Canada.",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "Towards framework-independent evaluation of deep linguistic parsers",
"authors": [
{
"first": "Yusuke",
"middle": [],
"last": "Miyao",
"suffix": ""
},
{
"first": "Kenji",
"middle": [],
"last": "Sagae",
"suffix": ""
},
{
"first": "Jun'ichi",
"middle": [],
"last": "Tsujii",
"suffix": ""
}
],
"year": 2007,
"venue": "Proceedings of the GEAF07 Workshop",
"volume": "",
"issue": "",
"pages": "238--258",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yusuke Miyao, Kenji Sagae, and Jun'ichi Tsujii. 2007. To- wards framework-independent evaluation of deep linguis- tic parsers. In Proceedings of the GEAF07 Workshop, pages 238-258, Stanford, CA.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "Integrating graphbased and transition-based dependency parsers",
"authors": [
{
"first": "Joakim",
"middle": [],
"last": "Nivre",
"suffix": ""
},
{
"first": "Ryan",
"middle": [],
"last": "Mcdonald",
"suffix": ""
}
],
"year": 2008,
"venue": "Proceedings of ACL-08: HLT",
"volume": "",
"issue": "",
"pages": "950--958",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Joakim Nivre and Ryan McDonald. 2008. Integrating graph- based and transition-based dependency parsers. In Pro- ceedings of ACL-08: HLT, pages 950-958, Columbus, Ohio, June.",
"links": null
},
"BIBREF15": {
"ref_id": "b15",
"title": "The CoNLL 2007 shared task on dependency parsing",
"authors": [
{
"first": "Joakim",
"middle": [],
"last": "Nivre",
"suffix": ""
},
{
"first": "Johan",
"middle": [],
"last": "Hall",
"suffix": ""
},
{
"first": "Sandra",
"middle": [],
"last": "K\u00fcbler",
"suffix": ""
},
{
"first": "Ryan",
"middle": [],
"last": "Mcdonald",
"suffix": ""
},
{
"first": "Jens",
"middle": [],
"last": "Nilsson",
"suffix": ""
},
{
"first": "Sebastian",
"middle": [],
"last": "Riedel",
"suffix": ""
},
{
"first": "Deniz",
"middle": [],
"last": "Yuret",
"suffix": ""
}
],
"year": 2007,
"venue": "Proceedings of EMNLP-CoNLL",
"volume": "",
"issue": "",
"pages": "915--932",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Joakim Nivre, Johan Hall, Sandra K\u00fcbler, Ryan McDonald, Jens Nilsson, Sebastian Riedel, and Deniz Yuret. 2007a. The CoNLL 2007 shared task on dependency parsing. In Proceedings of EMNLP-CoNLL 2007, pages 915-932, Prague, Czech Republic.",
"links": null
},
"BIBREF16": {
"ref_id": "b16",
"title": "Maltparser: A languageindependent system for data-driven dependency parsing",
"authors": [
{
"first": "Joakim",
"middle": [],
"last": "Nivre",
"suffix": ""
},
{
"first": "Jens",
"middle": [],
"last": "Nilsson",
"suffix": ""
},
{
"first": "Johan",
"middle": [],
"last": "Hall",
"suffix": ""
},
{
"first": "Atanas",
"middle": [],
"last": "Chanev",
"suffix": ""
},
{
"first": "G\u00fclsen",
"middle": [],
"last": "Eryigit",
"suffix": ""
},
{
"first": "Sandra",
"middle": [],
"last": "K\u00fcbler",
"suffix": ""
},
{
"first": "Svetoslav",
"middle": [],
"last": "Marinov",
"suffix": ""
},
{
"first": "Erwin",
"middle": [],
"last": "Marsi",
"suffix": ""
}
],
"year": 2007,
"venue": "Natural Language Engineering",
"volume": "13",
"issue": "1",
"pages": "1--41",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Joakim Nivre, Jens Nilsson, Johan Hall, Atanas Chanev, G\u00fclsen Eryigit, Sandra K\u00fcbler, Svetoslav Marinov, and Erwin Marsi. 2007b. Maltparser: A language- independent system for data-driven dependency parsing. Natural Language Engineering, 13(1):1-41.",
"links": null
},
"BIBREF17": {
"ref_id": "b17",
"title": "Som\u00e5 kapp-ete med trollet? Towards MRS-Based Norwegian-English Machine Translation",
"authors": [
{
"first": "Stephan",
"middle": [],
"last": "Oepen",
"suffix": ""
},
{
"first": "Helge",
"middle": [],
"last": "Dyvik",
"suffix": ""
},
{
"first": "Jan",
"middle": [
"Tore"
],
"last": "L\u00f8nning",
"suffix": ""
},
{
"first": "Erik",
"middle": [],
"last": "Velldal",
"suffix": ""
},
{
"first": "Dorothee",
"middle": [],
"last": "Beermann",
"suffix": ""
},
{
"first": "John",
"middle": [],
"last": "Carroll",
"suffix": ""
},
{
"first": "Dan",
"middle": [],
"last": "Flickinger",
"suffix": ""
},
{
"first": "Lars",
"middle": [],
"last": "Hellan",
"suffix": ""
},
{
"first": "Janne",
"middle": [],
"last": "Bondi Johannessen",
"suffix": ""
},
{
"first": "Paul",
"middle": [],
"last": "Meurer",
"suffix": ""
},
{
"first": "Torbj\u00f8rn",
"middle": [],
"last": "Nordg\u00e5rd",
"suffix": ""
},
{
"first": "Victoria",
"middle": [],
"last": "Ros\u00e9n",
"suffix": ""
}
],
"year": 2004,
"venue": "Proceedings of the 10th International Conference on Theoretical and Methodological Issues in Machine Translation",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Stephan Oepen, Helge Dyvik, Jan Tore L\u00f8nning, Erik Vell- dal, Dorothee Beermann, John Carroll, Dan Flickinger, Lars Hellan, Janne Bondi Johannessen, Paul Meurer, Torbj\u00f8rn Nordg\u00e5rd, and Victoria Ros\u00e9n. 2004. Som\u00e5 kapp-ete med trollet? Towards MRS-Based Norwegian- English Machine Translation. In Proceedings of the 10th International Conference on Theoretical and Methodolog- ical Issues in Machine Translation, Baltimore, USA.",
"links": null
},
"BIBREF18": {
"ref_id": "b18",
"title": "Head-Driven Phrase Structure Grammar",
"authors": [
{
"first": "J",
"middle": [],
"last": "Carl",
"suffix": ""
},
{
"first": "Ivan",
"middle": [
"A"
],
"last": "Pollard",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Sag",
"suffix": ""
}
],
"year": 1994,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Carl J. Pollard and Ivan A. Sag. 1994. Head-Driven Phrase Structure Grammar. University of Chicago Press, Chicago, USA.",
"links": null
},
"BIBREF19": {
"ref_id": "b19",
"title": "Hpsg parsing with shallow dependency constraints",
"authors": [
{
"first": "Kenji",
"middle": [],
"last": "Sagae",
"suffix": ""
},
{
"first": "Yusuke",
"middle": [],
"last": "Miyao",
"suffix": ""
},
{
"first": "Jun'ichi",
"middle": [],
"last": "Tsujii",
"suffix": ""
}
],
"year": 2007,
"venue": "Proceedings of the 45th Annual Meeting of the Association of Computational Linguistics",
"volume": "",
"issue": "",
"pages": "624--631",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Kenji Sagae, Yusuke Miyao, and Jun'ichi Tsujii. 2007. Hpsg parsing with shallow dependency constraints. In Pro- ceedings of the 45th Annual Meeting of the Association of Computational Linguistics, pages 624-631, Prague, Czech Republic.",
"links": null
},
"BIBREF20": {
"ref_id": "b20",
"title": "The CoNLL-2008 shared task on joint parsing of syntactic and semantic dependencies",
"authors": [
{
"first": "Mihai",
"middle": [],
"last": "Surdeanu",
"suffix": ""
},
{
"first": "Richard",
"middle": [],
"last": "Johansson",
"suffix": ""
},
{
"first": "Adam",
"middle": [],
"last": "Meyers",
"suffix": ""
},
{
"first": "Llu\u00eds",
"middle": [],
"last": "M\u00e0rquez",
"suffix": ""
},
{
"first": "Joakim",
"middle": [],
"last": "Nivre",
"suffix": ""
}
],
"year": 2008,
"venue": "Proceedings of the 12th Conference on Computational Natural Language Learning",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Mihai Surdeanu, Richard Johansson, Adam Meyers, Llu\u00eds M\u00e0rquez, and Joakim Nivre. 2008. The CoNLL-2008 shared task on joint parsing of syntactic and semantic dependencies. In Proceedings of the 12th Conference on Computational Natural Language Learning (CoNLL- 2008), Manchester, UK.",
"links": null
},
"BIBREF21": {
"ref_id": "b21",
"title": "Parse ranking for a rich HPSG grammar",
"authors": [
{
"first": "Kristina",
"middle": [],
"last": "Toutanova",
"suffix": ""
},
{
"first": "Christoper",
"middle": [
"D"
],
"last": "Manning",
"suffix": ""
},
{
"first": "Stuart",
"middle": [
"M"
],
"last": "Shieber",
"suffix": ""
},
{
"first": "Dan",
"middle": [],
"last": "Flickinger",
"suffix": ""
},
{
"first": "Stephan",
"middle": [],
"last": "Oepen",
"suffix": ""
}
],
"year": 2002,
"venue": "Proceedings of the 1st Workshop on Treebanks and Linguistic Theories",
"volume": "",
"issue": "",
"pages": "253--263",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Kristina Toutanova, Christoper D. Manning, Stuart M. Shieber, Dan Flickinger, and Stephan Oepen. 2002. Parse ranking for a rich HPSG grammar. In Proceedings of the 1st Workshop on Treebanks and Linguistic Theories (TLT 2002), pages 253-263, Sozopol, Bulgaria.",
"links": null
},
"BIBREF22": {
"ref_id": "b22",
"title": "Robust Parsing with a Large HPSG Grammar",
"authors": [
{
"first": "Yi",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Valia",
"middle": [],
"last": "Kordoni",
"suffix": ""
}
],
"year": 2008,
"venue": "Proceedings of the Sixth International Language Resources and Evaluation (LREC'08)",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yi Zhang and Valia Kordoni. 2008. Robust Parsing with a Large HPSG Grammar. In Proceedings of the Sixth Inter- national Language Resources and Evaluation (LREC'08), Marrakech, Morocco.",
"links": null
},
"BIBREF23": {
"ref_id": "b23",
"title": "Hybrid Learning of Dependency Structures from Heterogeneous Linguistic Resources",
"authors": [
{
"first": "Yi",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Rui",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Hans",
"middle": [],
"last": "Uszkoreit",
"suffix": ""
}
],
"year": 2008,
"venue": "Proceedings of the Twelfth Conference on Computational Natural Language Learning",
"volume": "",
"issue": "",
"pages": "198--202",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yi Zhang, Rui Wang, and Hans Uszkoreit. 2008. Hy- brid Learning of Dependency Structures from Heteroge- neous Linguistic Resources. In Proceedings of the Twelfth Conference on Computational Natural Language Learn- ing (CoNLL 2008), pages 198-202, Manchester, UK.",
"links": null
}
},
"ref_entries": {
"FIGREF1": {
"type_str": "figure",
"uris": null,
"num": null,
"text": "An example of an HPSG derivation tree with ERG"
},
"FIGREF2": {
"type_str": "figure",
"uris": null,
"num": null,
"text": "An HPSG dependency backbone structure"
},
"TABREF1": {
"type_str": "table",
"content": "<table><tr><td>POS Features: s[0]-p; s[1]-p; i[0]-p; i[1]-p; i[2]-p; i[3]-p</td></tr><tr><td>Word Form Features: s[0]-h-w; s[0]-w; i[0]-w; i[1]-w</td></tr><tr><td>Dependency Features: s[0]-lmc-d; s[0]-d; s[0]-rmc-d; i[0]-lmc-d</td></tr><tr><td>New Features: s[0]-hh-w; s[0]-hh-p; s[0]-hr; i[0]-hh-w; i[0]-hh-p; i[0]-hr</td></tr></table>",
"text": "The Extra Feature Set for MSTParser. h: the HPSG head of the current token; c: the current token; b: each token in between; -1/+1: the previous/next token; w: word form; p: POS",
"html": null,
"num": null
},
"TABREF2": {
"type_str": "table",
"content": "<table><tr><td>s[0]/s[1]: the first and second token on the top of</td></tr></table>",
"text": "The Extended Feature Set for MaltParser. front tokens in the input queue; h: head of the token; hh: HPSG DB head of the token; w: word form; p: POS; d: dependency relation; hr: HPSG rule; lmc/rmc: left-/right-most child",
"html": null,
"num": null
},
"TABREF4": {
"type_str": "table",
"content": "<table><tr><td>: Agreement between HPSG dependency</td></tr><tr><td>backbone and CoNLL 2008 dependency in unla-</td></tr><tr><td>beled attachment score. DB(F): full parsing mode;</td></tr><tr><td>DB(P): partial parsing mode; Punctuations are ex-</td></tr><tr><td>cluded from the evaluation.</td></tr></table>",
"text": "",
"html": null,
"num": null
},
"TABREF5": {
"type_str": "table",
"content": "<table><tr><td/><td colspan=\"2\">Original</td><td colspan=\"2\">PARTIAL</td><td colspan=\"2\">FULL</td></tr><tr><td/><td>LAS%</td><td>UAS%</td><td>LAS%</td><td>UAS%</td><td>LAS%</td><td>UAS%</td></tr><tr><td>WSJ</td><td>87.38</td><td>90.35</td><td>87.06</td><td>90.03</td><td>86.87</td><td>89.91</td></tr><tr><td>BROWN</td><td>80.46 (-6.92)</td><td>86.26 (-4.09)</td><td>80.55 (-6.51)</td><td>86.17 (-3.86)</td><td>80.92 (-5.95)</td><td>86.58 (-3.33)</td></tr><tr><td colspan=\"4\">PCHEMTB 62.11 (CHILDES* 53.37 (-33.8) -72.17 (-18.18) -</td><td colspan=\"2\">74.91 (-15.12) -</td><td>75.64 (-14.27)</td></tr><tr><td>WSJ-P</td><td>87.86</td><td>90.88</td><td>87.78</td><td>90.85</td><td>87.12</td><td>90.25</td></tr><tr><td>BROWN-P</td><td>81.58 (-6.28)</td><td>87.41 (-3.47)</td><td>81.92 (-5.86)</td><td>87.51 (-3.34)</td><td>82.14 (-4.98)</td><td>87.80 (-2.45)</td></tr><tr><td>PCHEMTB-P</td><td colspan=\"6\">56.32 (-31.54) 65.26 (-25.63) 59.36 (-28.42) 69.20 (-21.65) 60.69 (-26.43) 70.45 (-19.80)</td></tr><tr><td>CHILDES*-P</td><td>-</td><td colspan=\"2\">72.88 (-18.00) -</td><td colspan=\"2\">76.02 (-14.83) -</td><td>76.76 (-13.49)</td></tr></table>",
"text": "-28.24) 54.69 (-32.37) 64.09 (-25.94) 56.45(-30.42) 65.77 (-24.14)",
"html": null,
"num": null
},
"TABREF6": {
"type_str": "table",
"content": "<table><tr><td/><td colspan=\"2\">Original</td><td colspan=\"2\">PARTIAL</td><td colspan=\"2\">FULL</td></tr><tr><td/><td>LAS%</td><td>UAS%</td><td>LAS%</td><td>UAS%</td><td>LAS%</td><td>UAS%</td></tr><tr><td>WSJ</td><td>86.47</td><td>88.97</td><td>85.39</td><td>88.10</td><td>85.66</td><td>88.40</td></tr><tr><td>BROWN</td><td>79.41 (-7.06)</td><td>84.75 (-4.22)</td><td>79.10 (-6.29)</td><td>84.58 (-3.52)</td><td>79.56 (-6.10)</td><td>85.24 (-3.16)</td></tr><tr><td>PCHEMTB</td><td colspan=\"6\">61.05 (-25.42) 71.32 (-17.65) 61.01 (-24.38) 70.99 (-17.11) 60.93 (-24.73) 70.89 (-17.51)</td></tr><tr><td>CHILDES*</td><td>-</td><td colspan=\"2\">74.97 (-14.00) -</td><td colspan=\"2\">75.64 (-12.46) -</td><td>76.18 (-12.22)</td></tr><tr><td>WSJ-P</td><td>86.99</td><td>89.58</td><td>86.09</td><td>88.83</td><td>85.82</td><td>88.76</td></tr><tr><td>BROWN-P</td><td>80.43 (-6.56)</td><td>85.78 (-3.80)</td><td>80.46 (-5.63)</td><td>85.94 (-2.89)</td><td>80.62 (-5.20)</td><td>86.38 (-2.38)</td></tr><tr><td>PCHEMTB-P</td><td colspan=\"6\">63.33 (-23.66) 73.54 (-16.04) 63.27 (-22.82) 73.31 (-15.52) 63.16 (-22.66) 73.06 (-15.70)</td></tr><tr><td>CHILDES*-P</td><td>-</td><td colspan=\"2\">75.95 (-13.63) -</td><td colspan=\"2\">77.05 (-11.78) -</td><td>77.30 (-11.46)</td></tr></table>",
"text": "Performance of the MSTParser with different feature models. Numbers in parentheses are performance drops in out-domain tests, comparing to in-domain results. The upper part represents the results on the complete data sets, and the lower part is on the fully parsed subsets, indicated by \"-P\".",
"html": null,
"num": null
},
"TABREF7": {
"type_str": "table",
"content": "<table/>",
"text": "Performance of the MaltParser with different feature models.",
"html": null,
"num": null
}
}
}
} |