File size: 89,974 Bytes
6fa4bc9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697 1698 1699 1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797 1798 1799 1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869 1870 1871 | {
"paper_id": "P05-1012",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T09:37:31.396989Z"
},
"title": "Online Large-Margin Training of Dependency Parsers",
"authors": [
{
"first": "Ryan",
"middle": [],
"last": "Mcdonald",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "University of Pennsylvania Philadelphia",
"location": {
"region": "PA"
}
},
"email": "ryantm@cis.upenn.edu"
},
{
"first": "Koby",
"middle": [],
"last": "Crammer",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "University of Pennsylvania Philadelphia",
"location": {
"region": "PA"
}
},
"email": "crammer@cis.upenn.edu"
},
{
"first": "Fernando",
"middle": [],
"last": "Pereira",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "University of Pennsylvania Philadelphia",
"location": {
"region": "PA"
}
},
"email": "pereira@cis.upenn.edu"
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "We present an effective training algorithm for linearly-scored dependency parsers that implements online largemargin multi-class training (Crammer and Singer, 2003; Crammer et al., 2003) on top of efficient parsing techniques for dependency trees (Eisner, 1996). The trained parsers achieve a competitive dependency accuracy for both English and Czech with no language specific enhancements.",
"pdf_parse": {
"paper_id": "P05-1012",
"_pdf_hash": "",
"abstract": [
{
"text": "We present an effective training algorithm for linearly-scored dependency parsers that implements online largemargin multi-class training (Crammer and Singer, 2003; Crammer et al., 2003) on top of efficient parsing techniques for dependency trees (Eisner, 1996). The trained parsers achieve a competitive dependency accuracy for both English and Czech with no language specific enhancements.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "Research on training parsers from annotated data has for the most part focused on models and training algorithms for phrase structure parsing. The best phrase-structure parsing models represent generatively the joint probability P (x, y) of sentence x having the structure y (Collins, 1999; Charniak, 2000) . Generative parsing models are very convenient because training consists of computing probability estimates from counts of parsing events in the training set. However, generative models make complicated and poorly justified independence assumptions and estimations, so we might expect better performance from discriminatively trained models, as has been shown for other tasks like document classification (Joachims, 2002) and shallow parsing (Sha and Pereira, 2003 ). Ratnaparkhi's conditional maximum entropy model (Ratnaparkhi, 1999) , trained to maximize conditional likelihood P (y|x) of the training data, performed nearly as well as generative models of the same vintage even though it scores parsing decisions in isolation and thus may suffer from the label bias problem (Lafferty et al., 2001) .",
"cite_spans": [
{
"start": 275,
"end": 290,
"text": "(Collins, 1999;",
"ref_id": "BIBREF6"
},
{
"start": 291,
"end": 306,
"text": "Charniak, 2000)",
"ref_id": "BIBREF2"
},
{
"start": 713,
"end": 729,
"text": "(Joachims, 2002)",
"ref_id": "BIBREF18"
},
{
"start": 750,
"end": 772,
"text": "(Sha and Pereira, 2003",
"ref_id": "BIBREF25"
},
{
"start": 824,
"end": 843,
"text": "(Ratnaparkhi, 1999)",
"ref_id": "BIBREF23"
},
{
"start": 1086,
"end": 1109,
"text": "(Lafferty et al., 2001)",
"ref_id": "BIBREF19"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Discriminatively trained parsers that score entire trees for a given sentence have only recently been investigated (Riezler et al., 2002; Clark and Curran, 2004; Collins and Roark, 2004; Taskar et al., 2004) . The most likely reason for this is that discriminative training requires repeatedly reparsing the training corpus with the current model to determine the parameter updates that will improve the training criterion. The reparsing cost is already quite high for simple context-free models with O(n 3 ) parsing complexity, but it becomes prohibitive for lexicalized grammars with O(n 5 ) parsing complexity.",
"cite_spans": [
{
"start": 115,
"end": 137,
"text": "(Riezler et al., 2002;",
"ref_id": "BIBREF24"
},
{
"start": 138,
"end": 161,
"text": "Clark and Curran, 2004;",
"ref_id": "BIBREF3"
},
{
"start": 162,
"end": 186,
"text": "Collins and Roark, 2004;",
"ref_id": "BIBREF4"
},
{
"start": 187,
"end": 207,
"text": "Taskar et al., 2004)",
"ref_id": "BIBREF28"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Dependency trees are an alternative syntactic representation with a long history (Hudson, 1984) . Dependency trees capture important aspects of functional relationships between words and have been shown to be useful in many applications including relation extraction (Culotta and Sorensen, 2004) , paraphrase acquisition (Shinyama et al., 2002) and machine translation (Ding and Palmer, 2005 ). Yet, they can be parsed in O(n 3 ) time (Eisner, 1996) . Therefore, dependency parsing is a potential \"sweet spot\" that deserves investigation. We focus here on projective dependency trees in which a word is the parent of all of its arguments, and dependencies are non-crossing with respect to word order (see Figure 1) . However, there are cases where crossing dependencies may occur, as is the case for Czech (Haji\u010d, 1998) . Edges in a dependency tree may be typed (for instance to indicate grammatical function). Though we focus on the simpler non-typed case, all algorithms are easily extendible to typed structures.",
"cite_spans": [
{
"start": 81,
"end": 95,
"text": "(Hudson, 1984)",
"ref_id": "BIBREF17"
},
{
"start": 267,
"end": 295,
"text": "(Culotta and Sorensen, 2004)",
"ref_id": "BIBREF11"
},
{
"start": 321,
"end": 344,
"text": "(Shinyama et al., 2002)",
"ref_id": "BIBREF26"
},
{
"start": 369,
"end": 391,
"text": "(Ding and Palmer, 2005",
"ref_id": "BIBREF12"
},
{
"start": 435,
"end": 449,
"text": "(Eisner, 1996)",
"ref_id": "BIBREF14"
},
{
"start": 705,
"end": 714,
"text": "Figure 1)",
"ref_id": null
},
{
"start": 806,
"end": 819,
"text": "(Haji\u010d, 1998)",
"ref_id": "BIBREF15"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "The following work on dependency parsing is most relevant to our research. Eisner (1996) gave a generative model with a cubic parsing algorithm based on an edge factorization of trees. Yamada and Matsumoto (2003) trained support vector machines (SVM) to make parsing decisions in a shift-reduce dependency parser. As in Ratnaparkhi's parser, the classifiers are trained on individual decisions rather than on the overall quality of the parse. Nivre and Scholz (2004) developed a history-based learning model. Their parser uses a hybrid bottom-up/topdown linear-time heuristic parser and the ability to label edges with semantic types. The accuracy of their parser is lower than that of Yamada and Matsumoto (2003) .",
"cite_spans": [
{
"start": 75,
"end": 88,
"text": "Eisner (1996)",
"ref_id": "BIBREF14"
},
{
"start": 185,
"end": 212,
"text": "Yamada and Matsumoto (2003)",
"ref_id": "BIBREF29"
},
{
"start": 443,
"end": 466,
"text": "Nivre and Scholz (2004)",
"ref_id": "BIBREF21"
},
{
"start": 686,
"end": 713,
"text": "Yamada and Matsumoto (2003)",
"ref_id": "BIBREF29"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "We present a new approach to training dependency parsers, based on the online large-margin learning algorithms of and . Unlike the SVM parser of Yamada and Matsumoto (2003) and Ratnaparkhi's parser, our parsers are trained to maximize the accuracy of the overall tree.",
"cite_spans": [
{
"start": 145,
"end": 172,
"text": "Yamada and Matsumoto (2003)",
"ref_id": "BIBREF29"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Our approach is related to those of Collins and Roark (2004) and Taskar et al. (2004) for phrase structure parsing. Collins and Roark (2004) presented a linear parsing model trained with an averaged perceptron algorithm. However, to use parse features with sufficient history, their parsing algorithm must prune heuristically most of the possible parses. Taskar et al. (2004) formulate the parsing problem in the large-margin structured classification setting (Taskar et al., 2003) , but are limited to parsing sentences of 15 words or less due to computation time. Though these approaches represent good first steps towards discriminatively-trained parsers, they have not yet been able to display the benefits of discriminative training that have been seen in namedentity extraction and shallow parsing.",
"cite_spans": [
{
"start": 36,
"end": 60,
"text": "Collins and Roark (2004)",
"ref_id": "BIBREF4"
},
{
"start": 65,
"end": 85,
"text": "Taskar et al. (2004)",
"ref_id": "BIBREF28"
},
{
"start": 116,
"end": 140,
"text": "Collins and Roark (2004)",
"ref_id": "BIBREF4"
},
{
"start": 355,
"end": 375,
"text": "Taskar et al. (2004)",
"ref_id": "BIBREF28"
},
{
"start": 460,
"end": 481,
"text": "(Taskar et al., 2003)",
"ref_id": "BIBREF27"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Besides simplicity, our method is efficient and accurate, as we demonstrate experimentally on English and Czech treebank data.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "In what follows, the generic sentence is denoted by x (possibly subscripted); the ith word of x is denoted by x i . The generic dependency tree is denoted by y. If y is a dependency tree for sentence x, we write (i, j) \u2208 y to indicate that there is a directed edge from word x i to word x j in the tree, that is, x i is the parent of",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Definitions and Background",
"sec_num": "2.1"
},
{
"text": "x j . T = {(x t , y t )} T",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Definitions and Background",
"sec_num": "2.1"
},
{
"text": "t=1 denotes the training data.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Definitions and Background",
"sec_num": "2.1"
},
{
"text": "We follow the edge based factorization method of Eisner (1996) and define the score of a dependency tree as the sum of the score of all edges in the tree,",
"cite_spans": [
{
"start": 49,
"end": 62,
"text": "Eisner (1996)",
"ref_id": "BIBREF14"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Definitions and Background",
"sec_num": "2.1"
},
{
"text": "s(x, y) = (i,j)\u2208y s(i, j) = (i,j)\u2208y w \u2022 f(i, j)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Definitions and Background",
"sec_num": "2.1"
},
{
"text": "where f(i, j) is a high-dimensional binary feature representation of the edge from x i to x j . For example, in the dependency tree of Figure 1 , the following feature would have a value of 1:",
"cite_spans": [],
"ref_spans": [
{
"start": 135,
"end": 143,
"text": "Figure 1",
"ref_id": "FIGREF0"
}
],
"eq_spans": [],
"section": "Definitions and Background",
"sec_num": "2.1"
},
{
"text": "f (i, j) = 1 if x i ='hit' and x j ='ball' 0 otherwise.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Definitions and Background",
"sec_num": "2.1"
},
{
"text": "In general, any real-valued feature may be used, but we use binary features for simplicity. The feature weights in the weight vector w are the parameters that will be learned during training. Our training algorithms are iterative. We denote by w (i) the weight vector after the i th training iteration. Finally we define dt(x) as the set of possible dependency trees for the input sentence x and best k (x; w) as the set of k dependency trees in dt(x) that are given the highest scores by weight vector w, with ties resolved by an arbitrary but fixed rule.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Definitions and Background",
"sec_num": "2.1"
},
{
"text": "Three basic questions must be answered for models of this form: how to find the dependency tree y with highest score for sentence x; how to learn an appropriate weight vector w from the training data; and finally, what feature representation f(i, j) should be used. The following sections address each of these questions.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Definitions and Background",
"sec_num": "2.1"
},
{
"text": "Given a feature representation for edges and a weight vector w, we seek the dependency tree or Figure 2 : O(n 3 ) algorithm of Eisner (1996) , needs to keep 3 indices at any given stage.",
"cite_spans": [
{
"start": 127,
"end": 140,
"text": "Eisner (1996)",
"ref_id": "BIBREF14"
}
],
"ref_spans": [
{
"start": 95,
"end": 103,
"text": "Figure 2",
"ref_id": null
}
],
"eq_spans": [],
"section": "Parsing Algorithm",
"sec_num": "2.2"
},
{
"text": "trees that maximize the score function, s(x, y). The primary difficulty is that for a given sentence of length n there are exponentially many possible dependency trees. Using a slightly modified version of a lexicalized CKY chart parsing algorithm, it is possible to generate and represent these sentences in a forest that is O(n 5 ) in size and takes O(n 5 ) time to create. Eisner (1996) made the observation that if the head of each chart item is on the left or right periphery, then it is possible to parse in O(n 3 ). The idea is to parse the left and right dependents of a word independently and combine them at a later stage. This removes the need for the additional head indices of the O(n 5 ) algorithm and requires only two additional binary variables that specify the direction of the item (either gathering left dependents or gathering right dependents) and whether an item is complete (available to gather more dependents). Figure 2 shows the algorithm schematically. As with normal CKY parsing, larger elements are created bottom-up from pairs of smaller elements.",
"cite_spans": [
{
"start": 376,
"end": 389,
"text": "Eisner (1996)",
"ref_id": "BIBREF14"
}
],
"ref_spans": [
{
"start": 937,
"end": 945,
"text": "Figure 2",
"ref_id": null
}
],
"eq_spans": [],
"section": "Parsing Algorithm",
"sec_num": "2.2"
},
{
"text": "Eisner showed that his algorithm is sufficient for both searching the space of dependency parses and, with slight modification, finding the highest scoring tree y for a given sentence x under the edge factorization assumption. Eisner and Satta (1999) give a cubic algorithm for lexicalized phrase structures. However, it only works for a limited class of languages in which tree spines are regular. Furthermore, there is a large grammar constant, which is typically in the thousands for treebank parsers. Figure 3 gives pseudo-code for the generic online learning setting. A single training instance is considered on each iteration, and parameters updated by applying an algorithm-specific update rule to the instance under consideration. The algorithm in 1. w0 = 0; v = 0; i = 0 2. for n : 1..N 3. for t : 1..T",
"cite_spans": [
{
"start": 227,
"end": 250,
"text": "Eisner and Satta (1999)",
"ref_id": "BIBREF13"
}
],
"ref_spans": [
{
"start": 505,
"end": 513,
"text": "Figure 3",
"ref_id": "FIGREF2"
}
],
"eq_spans": [],
"section": "Parsing Algorithm",
"sec_num": "2.2"
},
{
"text": "w (i+1) = update w (i) according to instance (xt, yt) i+1) 6. the values of w after each iteration, and the returned weight vector is the average of all the weight vectors throughout training. Averaging has been shown to help reduce overfitting (Collins, 2002) .",
"cite_spans": [
{
"start": 19,
"end": 22,
"text": "(i)",
"ref_id": null
},
{
"start": 54,
"end": 58,
"text": "i+1)",
"ref_id": null
},
{
"start": 245,
"end": 260,
"text": "(Collins, 2002)",
"ref_id": "BIBREF7"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "4.",
"sec_num": null
},
{
"text": "5. v = v + w (",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "4.",
"sec_num": null
},
{
"text": "i = i + 1 7. w = v/(N * T )",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "4.",
"sec_num": null
},
{
"text": "Crammer and Singer (2001) developed a natural method for large-margin multi-class classification, which was later extended by Taskar et al. (2003) to structured classification:",
"cite_spans": [
{
"start": 126,
"end": 146,
"text": "Taskar et al. (2003)",
"ref_id": "BIBREF27"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "MIRA",
"sec_num": "2.3.1"
},
{
"text": "min w s.t. s(x, y) \u2212 s(x, y ) \u2265 L(y, y ) \u2200(x, y) \u2208 T , y \u2208 dt(x)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "MIRA",
"sec_num": "2.3.1"
},
{
"text": "where L(y, y ) is a real-valued loss for the tree y relative to the correct tree y. We define the loss of a dependency tree as the number of words that have the incorrect parent. Thus, the largest loss a dependency tree can have is the length of the sentence.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "MIRA",
"sec_num": "2.3.1"
},
{
"text": "Informally, this update looks to create a margin between the correct dependency tree and each incorrect dependency tree at least as large as the loss of the incorrect tree. The more errors a tree has, the farther away its score will be from the score of the correct tree. In order to avoid a blow-up in the norm of the weight vector we minimize it subject to constraints that enforce the desired margin between the correct and incorrect trees 1 .",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "MIRA",
"sec_num": "2.3.1"
},
{
"text": "The Margin Infused Relaxed Algorithm (MIRA) employs this optimization directly within the online framework. On each update, MIRA attempts to keep the norm of the change to the parameter vector as small as possible, subject to correctly classifying the instance under consideration with a margin at least as large as the loss of the incorrect classifications. This can be formalized by substituting the following update into line 4 of the generic online algorithm,",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "MIRA",
"sec_num": "2.3.1"
},
{
"text": "min w (i+1) \u2212 w (i) s.t. s(x t , y t ) \u2212 s(x t , y ) \u2265 L(y t , y ) \u2200y \u2208 dt(x t ) (1)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "MIRA",
"sec_num": "2.3.1"
},
{
"text": "This is a standard quadratic programming problem that can be easily solved using Hildreth's algorithm (Censor and Zenios, 1997) . and provide an analysis of both the online generalization error and convergence properties of MIRA. In equation 1, s(x, y) is calculated with respect to the weight vector after optimization, w (i+1) .",
"cite_spans": [
{
"start": 102,
"end": 127,
"text": "(Censor and Zenios, 1997)",
"ref_id": "BIBREF1"
},
{
"start": 323,
"end": 328,
"text": "(i+1)",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "MIRA",
"sec_num": "2.3.1"
},
{
"text": "To apply MIRA to dependency parsing, we can simply see parsing as a multi-class classification problem in which each dependency tree is one of many possible classes for a sentence. However, that interpretation fails computationally because a general sentence has exponentially many possible dependency trees and thus exponentially many margin constraints.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "MIRA",
"sec_num": "2.3.1"
},
{
"text": "To circumvent this problem we make the assumption that the constraints that matter for large margin optimization are those involving the incorrect trees y with the highest scores s(x, y ). The resulting optimization made by MIRA (see Figure 3 , line 4) would then be:",
"cite_spans": [],
"ref_spans": [
{
"start": 234,
"end": 242,
"text": "Figure 3",
"ref_id": "FIGREF2"
}
],
"eq_spans": [],
"section": "MIRA",
"sec_num": "2.3.1"
},
{
"text": "min w (i+1) \u2212 w (i) s.t. s(x t , y t ) \u2212 s(x t , y ) \u2265 L(y t , y ) \u2200y \u2208 best k (x t ; w (i) )",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "MIRA",
"sec_num": "2.3.1"
},
{
"text": "reducing the number of constraints to the constant k. We tested various values of k on a development data set and found that small values of k are sufficient to achieve close to best performance, justifying our assumption. In fact, as k grew we began to observe a slight degradation of performance, indicating some overfitting to the training data. All the experiments presented here use k = 5. The Eisner (1996) algorithm can be modified to find the k-best trees while only adding an additional O(k log k) factor to the runtime (Huang and Chiang, 2005) .",
"cite_spans": [
{
"start": 399,
"end": 412,
"text": "Eisner (1996)",
"ref_id": "BIBREF14"
},
{
"start": 529,
"end": 553,
"text": "(Huang and Chiang, 2005)",
"ref_id": "BIBREF16"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "MIRA",
"sec_num": "2.3.1"
},
{
"text": "A more common approach is to factor the structure of the output space to yield a polynomial set of local constraints (Taskar et al., 2003; Taskar et al., 2004) . One such factorization for dependency trees is min",
"cite_spans": [
{
"start": 117,
"end": 138,
"text": "(Taskar et al., 2003;",
"ref_id": "BIBREF27"
},
{
"start": 139,
"end": 159,
"text": "Taskar et al., 2004)",
"ref_id": "BIBREF28"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "MIRA",
"sec_num": "2.3.1"
},
{
"text": "w (i+1) \u2212 w (i) s.t. s(l, j) \u2212 s(k, j) \u2265 1 \u2200(l, j) \u2208 y t , (k, j) / \u2208 y t",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "MIRA",
"sec_num": "2.3.1"
},
{
"text": "It is trivial to show that if these O(n 2 ) constraints are satisfied, then so are those in (1). We implemented this model, but found that the required training time was much larger than the k-best formulation and typically did not improve performance. Furthermore, the k-best formulation is more flexible with respect to the loss function since it does not assume the loss function can be factored into a sum of terms for each dependency.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "MIRA",
"sec_num": "2.3.1"
},
{
"text": "Finally, we need a suitable feature representation f(i, j) for each dependency. The basic features in our model are outlined in Table 1a and b. All features are conjoined with the direction of attachment as well as the distance between the two words being attached. These features represent a system of backoff from very specific features over words and partof-speech tags to less sparse features over just partof-speech tags. These features are added for both the entire words as well as the 5-gram prefix if the word is longer than 5 characters. Using just features over the parent-child node pairs in the tree was not enough for high accuracy, because all attachment decisions were made outside of the context in which the words occurred. To solve this problem, we added two other types of features, which can be seen in Table 1c . Features of the first type look at words that occur between a child and its parent. These features take the form of a POS trigram: the POS of the parent, of the child, and of a word in between, for all words linearly between the parent and the child. This feature was particularly helpful for nouns identifying their parent, since it would typically rule out situations when a noun attached to another noun with a verb in between, which is a very uncommon phenomenon.",
"cite_spans": [],
"ref_spans": [
{
"start": 128,
"end": 136,
"text": "Table 1a",
"ref_id": null
},
{
"start": 824,
"end": 832,
"text": "Table 1c",
"ref_id": null
}
],
"eq_spans": [],
"section": "Feature Set",
"sec_num": "2.4"
},
{
"text": "The second type of feature provides the local context of the attachment, that is, the words before and after the parent-child pair. This feature took the form of a POS 4-gram: The POS of the parent, child, word before/after parent and word before/after child. The system also used back-off features to various trigrams where one of the local context POS tags was removed. Adding these two features resulted in a large improvement in performance and brought the system to state-of-the-art accuracy.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Feature Set",
"sec_num": "2.4"
},
{
"text": "Besides performance (see Section 3), the approach to dependency parsing we described has several other advantages. The system is very general and contains no language specific enhancements. In fact, the results we report for English and Czech use identical features, though are obviously trained on different data. The online learning algorithms themselves are intuitive and easy to implement.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "System Summary",
"sec_num": "2.5"
},
{
"text": "The efficient O(n 3 ) parsing algorithm of Eisner allows the system to search the entire space of dependency trees while parsing thousands of sentences in a few minutes, which is crucial for discriminative training. We compare the speed of our model to a standard lexicalized phrase structure parser in Section 3.1 and show a significant improvement in parsing times on the testing data.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "System Summary",
"sec_num": "2.5"
},
{
"text": "The major limiting factor of the system is its restriction to features over single dependency attachments. Often, when determining the next depen-dent for a word, it would be useful to know previous attachment decisions and incorporate these into the features. It is fairly straightforward to modify the parsing algorithm to store previous attachments. However, any modification would result in an asymptotic increase in parsing complexity.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "System Summary",
"sec_num": "2.5"
},
{
"text": "We tested our methods experimentally on the English Penn Treebank (Marcus et al., 1993) and on the Czech Prague Dependency Treebank (Haji\u010d, 1998) . All experiments were run on a dual 64-bit AMD Opteron 2.4GHz processor.",
"cite_spans": [
{
"start": 66,
"end": 87,
"text": "(Marcus et al., 1993)",
"ref_id": "BIBREF20"
},
{
"start": 132,
"end": 145,
"text": "(Haji\u010d, 1998)",
"ref_id": "BIBREF15"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Experiments",
"sec_num": "3"
},
{
"text": "To create dependency structures from the Penn Treebank, we used the extraction rules of Yamada and Matsumoto (2003) , which are an approximation to the lexicalization rules of Collins (1999) . We split the data into three parts: sections 02-21 for training, section 22 for development and section 23 for evaluation. Currently the system has 6, 998, 447 features. Each instance only uses a tiny fraction of these features making sparse vector calculations possible. Our system assumes POS tags as input and uses the tagger of Ratnaparkhi (1996) to provide tags for the development and evaluation sets. Table 2 shows the performance of the systems that were compared. Y&M2003 is the SVM-shiftreduce parsing model of Yamada and Matsumoto (2003) , N&S2004 is the memory-based learner of Nivre and Scholz (2004) and MIRA is the the system we have described. We also implemented an averaged perceptron system (Collins, 2002) Table 2 : Dependency parsing results for English and Czech. Accuracy is the number of words that correctly identified their parent in the tree. Root is the number of trees in which the root word was correctly identified. For Czech this is f-measure since a sentence may have multiple roots. Complete is the number of sentences for which the entire dependency tree was correct.",
"cite_spans": [
{
"start": 88,
"end": 115,
"text": "Yamada and Matsumoto (2003)",
"ref_id": "BIBREF29"
},
{
"start": 176,
"end": 190,
"text": "Collins (1999)",
"ref_id": "BIBREF6"
},
{
"start": 525,
"end": 543,
"text": "Ratnaparkhi (1996)",
"ref_id": "BIBREF22"
},
{
"start": 714,
"end": 741,
"text": "Yamada and Matsumoto (2003)",
"ref_id": "BIBREF29"
},
{
"start": 783,
"end": 806,
"text": "Nivre and Scholz (2004)",
"ref_id": "BIBREF21"
},
{
"start": 903,
"end": 918,
"text": "(Collins, 2002)",
"ref_id": "BIBREF7"
}
],
"ref_spans": [
{
"start": 601,
"end": 608,
"text": "Table 2",
"ref_id": null
},
{
"start": 919,
"end": 926,
"text": "Table 2",
"ref_id": null
}
],
"eq_spans": [],
"section": "Experiments",
"sec_num": "3"
},
{
"text": "not exploit phrase structure. We ensured that the gold standard dependencies of all systems compared were identical. Table 2 shows that the model described here performs as well or better than previous comparable systems, including that of Yamada and Matsumoto (2003) . Their method has the potential advantage that SVM batch training takes into account all of the constraints from all training instances in the optimization, whereas online training only considers constraints from one instance at a time. However, they are fundamentally limited by their approximate search algorithm. In contrast, our system searches the entire space of dependency trees and most likely benefits greatly from this. This difference is amplified when looking at the percentage of trees that correctly identify the root word. The models that search the entire space will not suffer from bad approximations made early in the search and thus are more likely to identify the correct root, whereas the approximate algorithms are prone to error propagation, which culminates with attachment decisions at the top of the tree. When comparing the two online learning models, it can be seen that MIRA outperforms the averaged perceptron method. This difference is statistically significant, p < 0.005 (McNemar test on head selection accuracy).",
"cite_spans": [
{
"start": 240,
"end": 267,
"text": "Yamada and Matsumoto (2003)",
"ref_id": "BIBREF29"
}
],
"ref_spans": [
{
"start": 117,
"end": 124,
"text": "Table 2",
"ref_id": null
}
],
"eq_spans": [],
"section": "Experiments",
"sec_num": "3"
},
{
"text": "In our Czech experiments, we used the dependency trees annotated in the Prague Treebank, and the predefined training, development and evaluation sections of this data. The number of sentences in this data set is nearly twice that of the English treebank, leading to a very large number of features -13, 450, 672. But again, each instance uses just a handful of these features. For POS tags we used the automatically generated tags in the data set. Though we made no language specific model changes, we did need to make some data specific changes. In particular, we used the method of to simplify part-of-speech tags since the rich tags used by Czech would have led to a large but rarely seen set of POS features.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Experiments",
"sec_num": "3"
},
{
"text": "The model based on MIRA also performs well on Czech, again slightly outperforming averaged perceptron. Unfortunately, we do not know of any other parsing systems tested on the same data set. The Czech parser of was run on a different data set and most other dependency parsers are evaluated using English. Learning a model from the Czech training data is somewhat problematic since it contains some crossing dependencies which cannot be parsed by the Eisner algorithm. One trick is to rearrange the words in the training set so that all trees are nested. This at least allows the training algorithm to obtain reasonably low error on the training set. We found that this did improve performance slightly to 83.6% accuracy.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Experiments",
"sec_num": "3"
},
{
"text": "It is well known that dependency trees extracted from lexicalized phrase structure parsers (Collins, 1999; Charniak, 2000) typically are more accurate than those produced by pure dependency parsers (Yamada and Matsumoto, 2003) . We compared our system to the Bikel re-implementation of the Collins parser (Bikel, 2004; Collins, 1999) trained with the same head rules of our system. There are two ways to extract dependencies from lexicalized phrase structure. The first is to use the automatically generated dependencies that are explicit in the lexicalization of the trees, we call this system Collinsauto. The second is to take just the phrase structure output of the parser and run the automatic head rules over it to extract the dependencies, we call this sys- Table 3 shows the results comparing our system, MIRA-Normal, to the Collins parser for English. All systems are implemented in Java and run on the same machine.",
"cite_spans": [
{
"start": 91,
"end": 106,
"text": "(Collins, 1999;",
"ref_id": "BIBREF6"
},
{
"start": 107,
"end": 122,
"text": "Charniak, 2000)",
"ref_id": "BIBREF2"
},
{
"start": 198,
"end": 226,
"text": "(Yamada and Matsumoto, 2003)",
"ref_id": "BIBREF29"
},
{
"start": 305,
"end": 318,
"text": "(Bikel, 2004;",
"ref_id": "BIBREF0"
},
{
"start": 319,
"end": 333,
"text": "Collins, 1999)",
"ref_id": "BIBREF6"
}
],
"ref_spans": [
{
"start": 765,
"end": 772,
"text": "Table 3",
"ref_id": "TABREF3"
}
],
"eq_spans": [],
"section": "Lexicalized Phrase Structure Parsers",
"sec_num": "3.1"
},
{
"text": "Interestingly, the dependencies that are automatically produced by the Collins parser are worse than those extracted statically using the head rules. Arguably, this displays the artificialness of English dependency parsing using dependencies automatically extracted from treebank phrase-structure trees. Our system falls in-between, better than the automatically generated dependency trees and worse than the head-rule extracted trees.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Lexicalized Phrase Structure Parsers",
"sec_num": "3.1"
},
{
"text": "Since the dependencies returned from our system are better than those actually learnt by the Collins parser, one could argue that our model is actually learning to parse dependencies more accurately. However, phrase structure parsers are built to maximize the accuracy of the phrase structure and use lexicalization as just an additional source of information. Thus it is not too surprising that the dependencies output by the Collins parser are not as accurate as our system, which is trained and built to maximize accuracy on dependency trees. In complexity and run-time, our system is a huge improvement over the Collins parser. Table 3 takes the output of Collins-rules and adds a feature to MIRA-Normal that indicates for given edge, whether the Collins parser believed this dependency actually exists, we call this system MIRA-Collins. This is a well known discriminative training trick -using the suggestions of a generative system to influence decisions. This system can essentially be considered a corrector of the Collins parser and represents a significant improvement over it. However, there is an added complexity with such a model as it requires the output of the O(n 5 ) Collins parser. Table 4 : Evaluation of k-best MIRA approximation.",
"cite_spans": [],
"ref_spans": [
{
"start": 632,
"end": 639,
"text": "Table 3",
"ref_id": "TABREF3"
},
{
"start": 1202,
"end": 1209,
"text": "Table 4",
"ref_id": null
}
],
"eq_spans": [],
"section": "Lexicalized Phrase Structure Parsers",
"sec_num": "3.1"
},
{
"text": "One question that can be asked is how justifiable is the k-best MIRA approximation. Table 4 indicates the accuracy on testing and the time it took to train models with k = 1, 2, 5, 10, 20 for the English data set. Even though the parsing algorithm is proportional to O(k log k), empirically, the training times scale linearly with k. Peak performance is achieved very early with a slight degradation around k=20. The most likely reason for this phenomenon is that the model is overfitting by ensuring that even unlikely trees are separated from the correct tree proportional to their loss.",
"cite_spans": [],
"ref_spans": [
{
"start": 84,
"end": 91,
"text": "Table 4",
"ref_id": null
}
],
"eq_spans": [],
"section": "k-best MIRA Approximation",
"sec_num": "3.2"
},
{
"text": "We described a successful new method for training dependency parsers. We use simple linear parsing models trained with margin-sensitive online training algorithms, achieving state-of-the-art performance with relatively modest training times and no need for pruning heuristics. We evaluated the system on both English and Czech data to display state-of-theart performance without any language specific enhancements. Furthermore, the model can be augmented to include features over lexicalized phrase structure parsing decisions to increase dependency accuracy over those parsers. We plan on extending our parser in two ways. First, we would add labels to dependencies to represent grammatical roles. Those labels are very important for using parser output in tasks like information extraction or machine translation. Second, we are looking at model extensions to allow nonprojective dependencies, which occur in languages such as Czech, German and Dutch.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Summary",
"sec_num": "4"
},
{
"text": "The constraints may be unsatisfiable, in which case we can relax them with slack variables as in SVM training.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
}
],
"back_matter": [
{
"text": "We thank Jan Haji\u010d for answering queries on the Prague treebank, and Joakim Nivre for providing the Yamada and Matsumoto (2003) head rules for English that allowed for a direct comparison with our systems. This work was supported by NSF ITR grants 0205456, 0205448, and 0428193.",
"cite_spans": [
{
"start": 100,
"end": 127,
"text": "Yamada and Matsumoto (2003)",
"ref_id": "BIBREF29"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Acknowledgments:",
"sec_num": null
}
],
"bib_entries": {
"BIBREF0": {
"ref_id": "b0",
"title": "Intricacies of Collins parsing model",
"authors": [
{
"first": "M",
"middle": [],
"last": "Bikel",
"suffix": ""
}
],
"year": 2004,
"venue": "Computational Linguistics",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "M. Bikel. 2004. Intricacies of Collins parsing model. Computational Linguistics.",
"links": null
},
"BIBREF1": {
"ref_id": "b1",
"title": "Parallel optimization : theory, algorithms, and applications",
"authors": [
{
"first": "Y",
"middle": [],
"last": "Censor",
"suffix": ""
},
{
"first": "S",
"middle": [
"A"
],
"last": "Zenios",
"suffix": ""
}
],
"year": 1997,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Y. Censor and S.A. Zenios. 1997. Parallel optimization : theory, algorithms, and applications. Oxford Univer- sity Press.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "A maximum-entropy-inspired parser",
"authors": [
{
"first": "E",
"middle": [],
"last": "Charniak",
"suffix": ""
}
],
"year": 2000,
"venue": "Proc. NAACL",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "E. Charniak. 2000. A maximum-entropy-inspired parser. In Proc. NAACL.",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "Parsing the WSJ using CCG and log-linear models",
"authors": [
{
"first": "S",
"middle": [],
"last": "Clark",
"suffix": ""
},
{
"first": "J",
"middle": [
"R"
],
"last": "Curran",
"suffix": ""
}
],
"year": 2004,
"venue": "Proc. ACL",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "S. Clark and J.R. Curran. 2004. Parsing the WSJ using CCG and log-linear models. In Proc. ACL.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "Incremental parsing with the perceptron algorithm",
"authors": [
{
"first": "M",
"middle": [],
"last": "Collins",
"suffix": ""
},
{
"first": "B",
"middle": [],
"last": "Roark",
"suffix": ""
}
],
"year": 2004,
"venue": "Proc. ACL",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "M. Collins and B. Roark. 2004. Incremental parsing with the perceptron algorithm. In Proc. ACL.",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "A statistical parser for Czech",
"authors": [
{
"first": "M",
"middle": [],
"last": "Collins",
"suffix": ""
},
{
"first": "J",
"middle": [],
"last": "Haji\u010d",
"suffix": ""
},
{
"first": "L",
"middle": [],
"last": "Ramshaw",
"suffix": ""
},
{
"first": "C",
"middle": [],
"last": "Tillmann",
"suffix": ""
}
],
"year": 1999,
"venue": "Proc. ACL",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "M. Collins, J. Haji\u010d, L. Ramshaw, and C. Tillmann. 1999. A statistical parser for Czech. In Proc. ACL.",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "Head-Driven Statistical Models for Natural Language Parsing",
"authors": [
{
"first": "M",
"middle": [],
"last": "Collins",
"suffix": ""
}
],
"year": 1999,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "M. Collins. 1999. Head-Driven Statistical Models for Natural Language Parsing. Ph.D. thesis, University of Pennsylvania.",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "Discriminative training methods for hidden Markov models: Theory and experiments with perceptron algorithms",
"authors": [
{
"first": "M",
"middle": [],
"last": "Collins",
"suffix": ""
}
],
"year": 2002,
"venue": "Proc. EMNLP",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "M. Collins. 2002. Discriminative training methods for hidden Markov models: Theory and experiments with perceptron algorithms. In Proc. EMNLP.",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "On the algorithmic implementation of multiclass kernel based vector machines",
"authors": [
{
"first": "K",
"middle": [],
"last": "Crammer",
"suffix": ""
},
{
"first": "Y",
"middle": [],
"last": "Singer",
"suffix": ""
}
],
"year": 2001,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "K. Crammer and Y. Singer. 2001. On the algorithmic implementation of multiclass kernel based vector ma- chines. JMLR.",
"links": null
},
"BIBREF9": {
"ref_id": "b9",
"title": "Ultraconservative online algorithms for multiclass problems",
"authors": [
{
"first": "K",
"middle": [],
"last": "Crammer",
"suffix": ""
},
{
"first": "Y",
"middle": [],
"last": "Singer",
"suffix": ""
}
],
"year": 2003,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "K. Crammer and Y. Singer. 2003. Ultraconservative on- line algorithms for multiclass problems. JMLR.",
"links": null
},
"BIBREF10": {
"ref_id": "b10",
"title": "Online passive aggressive algorithms",
"authors": [
{
"first": "K",
"middle": [],
"last": "Crammer",
"suffix": ""
},
{
"first": "O",
"middle": [],
"last": "Dekel",
"suffix": ""
},
{
"first": "S",
"middle": [],
"last": "Shalev-Shwartz",
"suffix": ""
},
{
"first": "Y",
"middle": [],
"last": "Singer",
"suffix": ""
}
],
"year": 2003,
"venue": "Proc. NIPS",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "K. Crammer, O. Dekel, S. Shalev-Shwartz, and Y. Singer. 2003. Online passive aggressive algorithms. In Proc. NIPS.",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "Dependency tree kernels for relation extraction",
"authors": [
{
"first": "A",
"middle": [],
"last": "Culotta",
"suffix": ""
},
{
"first": "J",
"middle": [],
"last": "Sorensen",
"suffix": ""
}
],
"year": 2004,
"venue": "Proc. ACL",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "A. Culotta and J. Sorensen. 2004. Dependency tree ker- nels for relation extraction. In Proc. ACL.",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "Machine translation using probabilistic synchronous dependency insertion grammars",
"authors": [
{
"first": "Y",
"middle": [],
"last": "Ding",
"suffix": ""
},
{
"first": "M",
"middle": [],
"last": "Palmer",
"suffix": ""
}
],
"year": 2005,
"venue": "Proc. ACL",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Y. Ding and M. Palmer. 2005. Machine translation using probabilistic synchronous dependency insertion gram- mars. In Proc. ACL.",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "Efficient parsing for bilexical context-free grammars and head-automaton grammars",
"authors": [
{
"first": "J",
"middle": [],
"last": "Eisner",
"suffix": ""
},
{
"first": "G",
"middle": [],
"last": "Satta",
"suffix": ""
}
],
"year": 1999,
"venue": "Proc. ACL",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "J. Eisner and G. Satta. 1999. Efficient parsing for bilexi- cal context-free grammars and head-automaton gram- mars. In Proc. ACL.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "Three new probabilistic models for dependency parsing: An exploration",
"authors": [
{
"first": "J",
"middle": [],
"last": "Eisner",
"suffix": ""
}
],
"year": 1996,
"venue": "Proc. COLING",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "J. Eisner. 1996. Three new probabilistic models for de- pendency parsing: An exploration. In Proc. COLING.",
"links": null
},
"BIBREF15": {
"ref_id": "b15",
"title": "Building a syntactically annotated corpus: The Prague dependency treebank. Issues of Valency and Meaning",
"authors": [
{
"first": "J",
"middle": [],
"last": "Haji\u010d",
"suffix": ""
}
],
"year": 1998,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "J. Haji\u010d. 1998. Building a syntactically annotated cor- pus: The Prague dependency treebank. Issues of Va- lency and Meaning.",
"links": null
},
"BIBREF16": {
"ref_id": "b16",
"title": "Better k-best parsing",
"authors": [
{
"first": "L",
"middle": [],
"last": "Huang",
"suffix": ""
},
{
"first": "D",
"middle": [],
"last": "Chiang",
"suffix": ""
}
],
"year": 2005,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "L. Huang and D. Chiang. 2005. Better k-best parsing. Technical Report MS-CIS-05-08, University of Penn- sylvania.",
"links": null
},
"BIBREF17": {
"ref_id": "b17",
"title": "Word Grammar",
"authors": [
{
"first": "Richard",
"middle": [],
"last": "Hudson",
"suffix": ""
}
],
"year": 1984,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Richard Hudson. 1984. Word Grammar. Blackwell.",
"links": null
},
"BIBREF18": {
"ref_id": "b18",
"title": "Learning to Classify Text using Support Vector Machines",
"authors": [
{
"first": "T",
"middle": [],
"last": "Joachims",
"suffix": ""
}
],
"year": 2002,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "T. Joachims. 2002. Learning to Classify Text using Sup- port Vector Machines. Kluwer.",
"links": null
},
"BIBREF19": {
"ref_id": "b19",
"title": "Conditional random fields: Probabilistic models for segmenting and labeling sequence data",
"authors": [
{
"first": "J",
"middle": [],
"last": "Lafferty",
"suffix": ""
},
{
"first": "A",
"middle": [],
"last": "Mccallum",
"suffix": ""
},
{
"first": "F",
"middle": [],
"last": "Pereira",
"suffix": ""
}
],
"year": 2001,
"venue": "Proc. ICML",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "J. Lafferty, A. McCallum, and F. Pereira. 2001. Con- ditional random fields: Probabilistic models for seg- menting and labeling sequence data. In Proc. ICML.",
"links": null
},
"BIBREF20": {
"ref_id": "b20",
"title": "Building a large annotated corpus of english: the penn treebank",
"authors": [
{
"first": "M",
"middle": [],
"last": "Marcus",
"suffix": ""
},
{
"first": "B",
"middle": [],
"last": "Santorini",
"suffix": ""
},
{
"first": "M",
"middle": [],
"last": "Marcinkiewicz",
"suffix": ""
}
],
"year": 1993,
"venue": "Computational Linguistics",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "M. Marcus, B. Santorini, and M. Marcinkiewicz. 1993. Building a large annotated corpus of english: the penn treebank. Computational Linguistics.",
"links": null
},
"BIBREF21": {
"ref_id": "b21",
"title": "Deterministic dependency parsing of english text",
"authors": [
{
"first": "J",
"middle": [],
"last": "Nivre",
"suffix": ""
},
{
"first": "M",
"middle": [],
"last": "Scholz",
"suffix": ""
}
],
"year": 2004,
"venue": "Proc. COLING",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "J. Nivre and M. Scholz. 2004. Deterministic dependency parsing of english text. In Proc. COLING.",
"links": null
},
"BIBREF22": {
"ref_id": "b22",
"title": "A maximum entropy model for part-of-speech tagging",
"authors": [
{
"first": "A",
"middle": [],
"last": "Ratnaparkhi",
"suffix": ""
}
],
"year": 1996,
"venue": "Proc. EMNLP",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "A. Ratnaparkhi. 1996. A maximum entropy model for part-of-speech tagging. In Proc. EMNLP.",
"links": null
},
"BIBREF23": {
"ref_id": "b23",
"title": "Learning to parse natural language with maximum entropy models",
"authors": [
{
"first": "A",
"middle": [],
"last": "Ratnaparkhi",
"suffix": ""
}
],
"year": 1999,
"venue": "Machine Learning",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "A. Ratnaparkhi. 1999. Learning to parse natural language with maximum entropy models. Machine Learning.",
"links": null
},
"BIBREF24": {
"ref_id": "b24",
"title": "Parsing the Wall Street Journal using a lexical-functional grammar and discriminative estimation techniques",
"authors": [
{
"first": "S",
"middle": [],
"last": "Riezler",
"suffix": ""
},
{
"first": "T",
"middle": [],
"last": "King",
"suffix": ""
},
{
"first": "R",
"middle": [],
"last": "Kaplan",
"suffix": ""
},
{
"first": "R",
"middle": [],
"last": "Crouch",
"suffix": ""
},
{
"first": "J",
"middle": [],
"last": "Maxwell",
"suffix": ""
},
{
"first": "M",
"middle": [],
"last": "Johnson",
"suffix": ""
}
],
"year": 2002,
"venue": "Proc. ACL",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "S. Riezler, T. King, R. Kaplan, R. Crouch, J. Maxwell, and M. Johnson. 2002. Parsing the Wall Street Journal using a lexical-functional grammar and discriminative estimation techniques. In Proc. ACL.",
"links": null
},
"BIBREF25": {
"ref_id": "b25",
"title": "Shallow parsing with conditional random fields",
"authors": [
{
"first": "F",
"middle": [],
"last": "Sha",
"suffix": ""
},
{
"first": "F",
"middle": [],
"last": "Pereira",
"suffix": ""
}
],
"year": 2003,
"venue": "Proc. HLT-NAACL",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "F. Sha and F. Pereira. 2003. Shallow parsing with condi- tional random fields. In Proc. HLT-NAACL.",
"links": null
},
"BIBREF26": {
"ref_id": "b26",
"title": "Automatic paraphrase acquisition from news articles",
"authors": [
{
"first": "Y",
"middle": [],
"last": "Shinyama",
"suffix": ""
},
{
"first": "S",
"middle": [],
"last": "Sekine",
"suffix": ""
},
{
"first": "K",
"middle": [],
"last": "Sudo",
"suffix": ""
},
{
"first": "R",
"middle": [],
"last": "Grishman",
"suffix": ""
}
],
"year": 2002,
"venue": "Proc. HLT",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Y. Shinyama, S. Sekine, K. Sudo, and R. Grishman. 2002. Automatic paraphrase acquisition from news ar- ticles. In Proc. HLT.",
"links": null
},
"BIBREF27": {
"ref_id": "b27",
"title": "Max-margin Markov networks",
"authors": [
{
"first": "B",
"middle": [],
"last": "Taskar",
"suffix": ""
},
{
"first": "C",
"middle": [],
"last": "Guestrin",
"suffix": ""
},
{
"first": "D",
"middle": [],
"last": "Koller",
"suffix": ""
}
],
"year": 2003,
"venue": "Proc. NIPS",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "B. Taskar, C. Guestrin, and D. Koller. 2003. Max-margin Markov networks. In Proc. NIPS.",
"links": null
},
"BIBREF28": {
"ref_id": "b28",
"title": "Max-margin parsing",
"authors": [
{
"first": "B",
"middle": [],
"last": "Taskar",
"suffix": ""
},
{
"first": "D",
"middle": [],
"last": "Klein",
"suffix": ""
},
{
"first": "M",
"middle": [],
"last": "Collins",
"suffix": ""
},
{
"first": "D",
"middle": [],
"last": "Koller",
"suffix": ""
},
{
"first": "C",
"middle": [],
"last": "Manning",
"suffix": ""
}
],
"year": 2004,
"venue": "Proc. EMNLP",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "B. Taskar, D. Klein, M. Collins, D. Koller, and C. Man- ning. 2004. Max-margin parsing. In Proc. EMNLP.",
"links": null
},
"BIBREF29": {
"ref_id": "b29",
"title": "Statistical dependency analysis with support vector machines",
"authors": [
{
"first": "H",
"middle": [],
"last": "Yamada",
"suffix": ""
},
{
"first": "Y",
"middle": [],
"last": "Matsumoto",
"suffix": ""
}
],
"year": 2003,
"venue": "Proc. IWPT",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "H. Yamada and Y. Matsumoto. 2003. Statistical depen- dency analysis with support vector machines. In Proc. IWPT.",
"links": null
}
},
"ref_entries": {
"FIGREF0": {
"num": null,
"uris": null,
"text": "An example dependency tree.",
"type_str": "figure"
},
"FIGREF1": {
"num": null,
"uris": null,
"text": "Figure 3 returns an averaged weight vector: an auxiliary weight vector v is maintained that accumulates Training data: T = {(xt, yt)} T t=1",
"type_str": "figure"
},
"FIGREF2": {
"num": null,
"uris": null,
"text": "Generic online learning algorithm.",
"type_str": "figure"
},
"FIGREF3": {
"num": null,
"uris": null,
"text": ", p-pos+1, c-pos-1, c-pos p-pos-1, p-pos, c-pos-1, c-pos p-pos, p-pos+1, c-pos, c-pos+1 p-pos-1, p-pos, c-pos, c-pos+1Table 1: Features used by system. p-word: word of parent node in dependency tree. c-word: word of child node. p-pos: POS of parent node. c-pos: POS of child node. p-pos+1: POS to the right of parent in sentence. p-pos-1: POS to the left of parent. c-pos+1: POS to the right of child. c-pos-1: POS to the left of child. b-pos: POS of a word in between parent and child nodes.",
"type_str": "figure"
},
"TABREF1": {
"num": null,
"text": "(another online learning algorithm) for comparison. This table compares only pure dependency parsers that do",
"content": "<table><tr><td/><td>English</td><td/><td/><td>Czech</td><td/><td/></tr><tr><td/><td colspan=\"3\">Accuracy Root Complete</td><td colspan=\"3\">Accuracy Root Complete</td></tr><tr><td>Y&M2003</td><td>90.3</td><td>91.6</td><td>38.4</td><td>-</td><td>-</td><td>-</td></tr><tr><td>N&S2004</td><td>87.3</td><td>84.3</td><td>30.4</td><td>-</td><td>-</td><td>-</td></tr><tr><td>Avg. Perceptron</td><td>90.6</td><td>94.0</td><td>36.5</td><td>82.9</td><td>88.0</td><td>30.3</td></tr><tr><td>MIRA</td><td>90.9</td><td>94.2</td><td>37.5</td><td>83.3</td><td>88.6</td><td>31.3</td></tr></table>",
"type_str": "table",
"html": null
},
"TABREF3": {
"num": null,
"text": "Results comparing our system to those based on the Collins parser. Complexity represents the computational complexity of each parser and Time the CPU time to parse sec. 23 of the Penn Treebank.",
"content": "<table/>",
"type_str": "table",
"html": null
}
}
}
} |