File size: 99,012 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 | {
"paper_id": "P13-1013",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T09:37:14.579102Z"
},
"title": "Chinese Parsing Exploiting Characters",
"authors": [
{
"first": "Meishan",
"middle": [],
"last": "Zhang",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Harbin Institute of Technology",
"location": {
"country": "China"
}
},
"email": "mszhang@ir.hit.edu.cn"
},
{
"first": "Yue",
"middle": [],
"last": "Zhang",
"suffix": "",
"affiliation": {},
"email": "zhang@sutd.edu.sg"
},
{
"first": "Wanxiang",
"middle": [],
"last": "Che",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Harbin Institute of Technology",
"location": {
"country": "China"
}
},
"email": ""
},
{
"first": "Ting",
"middle": [],
"last": "Liu",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Harbin Institute of Technology",
"location": {
"country": "China"
}
},
"email": "tliu@ir.hit.edu.cn"
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "Characters play an important role in the Chinese language, yet computational processing of Chinese has been dominated by word-based approaches, with leaves in syntax trees being words. We investigate Chinese parsing from the character-level, extending the notion of phrase-structure trees by annotating internal structures of words. We demonstrate the importance of character-level information to Chinese processing by building a joint segmentation, part-of-speech (POS) tagging and phrase-structure parsing system that integrates character-structure features. Our joint system significantly outperforms a state-of-the-art word-based baseline on the standard CTB5 test, and gives the best published results for Chinese parsing.",
"pdf_parse": {
"paper_id": "P13-1013",
"_pdf_hash": "",
"abstract": [
{
"text": "Characters play an important role in the Chinese language, yet computational processing of Chinese has been dominated by word-based approaches, with leaves in syntax trees being words. We investigate Chinese parsing from the character-level, extending the notion of phrase-structure trees by annotating internal structures of words. We demonstrate the importance of character-level information to Chinese processing by building a joint segmentation, part-of-speech (POS) tagging and phrase-structure parsing system that integrates character-structure features. Our joint system significantly outperforms a state-of-the-art word-based baseline on the standard CTB5 test, and gives the best published results for Chinese parsing.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "Characters play an important role in the Chinese language. They act as basic phonetic, morphosyntactic and semantic units in a Chinese sentence. Frequently-occurring character sequences that express certain meanings can be treated as words, while most Chinese words have syntactic structures. For example, Figure 1(b) shows the structure of the word \"\u5efa\u7b51\u4e1a (construction and building industry)\", where the characters \"\u5efa (construction)\" and \"\u7b51 (building)\" form a coordination, and modify the character \"\u4e1a (industry)\".",
"cite_spans": [],
"ref_spans": [
{
"start": 306,
"end": 317,
"text": "Figure 1(b)",
"ref_id": null
}
],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "However, computational processing of Chinese is typically based on words. Words are treated as the atomic units in syntactic parsing, machine translation, question answering and other NLP tasks. Manually annotated corpora, such as the Chinese Treebank (CTB) (Xue et al., 2005) , usually have words as the basic syntactic elements 1: Word-based and character-level phrasestructure trees for the sentence \"\u4e2d\u56fd\u5efa\u7b51\u4e1a\u5448\u73b0 \u65b0\u683c\u5c40 (China's architecture industry shows new patterns)\", where \"l\", \"r\", \"c\" denote the directions of head characters (see section 2).",
"cite_spans": [
{
"start": 258,
"end": 276,
"text": "(Xue et al., 2005)",
"ref_id": "BIBREF14"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "( Figure 1(a) ). This form of annotation does not give character-level syntactic structures for words, a source of linguistic information that is more fundamental and less sparse than atomic words.",
"cite_spans": [],
"ref_spans": [
{
"start": 2,
"end": 13,
"text": "Figure 1(a)",
"ref_id": null
}
],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "In this paper, we investigate Chinese syntactic parsing with character-level information by extending the notation of phrase-structure (constituent) trees, adding recursive structures of characters for words. We manually annotate the structures of 37,382 words, which cover the entire CTB5. Using these annotations, we transform CTB-style constituent trees into character-level trees (Figure 1(b) ). Our word structure corpus, together with a set of tools to transform CTB-style trees into character-level trees, is released at https://github.com/zhangmeishan/wordstructures. Our annotation work is in line with the work of Vadas and Curran (2007) and Li (2011) , which provide extended annotations of Penn Treebank (PTB) noun phrases and CTB words (on the morphological level), respectively.",
"cite_spans": [
{
"start": 624,
"end": 647,
"text": "Vadas and Curran (2007)",
"ref_id": "BIBREF12"
},
{
"start": 652,
"end": 661,
"text": "Li (2011)",
"ref_id": "BIBREF5"
}
],
"ref_spans": [
{
"start": 384,
"end": 396,
"text": "(Figure 1(b)",
"ref_id": null
}
],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "We build a character-based Chinese parsing model to parse the character-level syntax trees. Given an input Chinese sentence, our parser produces its character-level syntax trees (Figure 1(b) ). With richer information than word-level trees, this form of parse trees can be useful for all the aforementioned Chinese NLP applications.",
"cite_spans": [],
"ref_spans": [
{
"start": 178,
"end": 190,
"text": "(Figure 1(b)",
"ref_id": null
}
],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "With regard to task of parsing itself, an important advantage of the character-level syntax trees is that they allow word segmentation, part-of-speech (POS) tagging and parsing to be performed jointly, using an efficient CKY-style or shift-reduce algorithm. Luo (2003) exploited this advantage by adding flat word structures without manually annotation to CTB trees, and building a generative character-based parser. Compared to a pipeline system, the advantages of a joint system include reduction of error propagation, and the integration of segmentation, POS tagging and syntax features. With hierarchical structures and head character information, our annotated words are more informative than flat word structures, and hence can bring further improvements to phrase-structure parsing.",
"cite_spans": [
{
"start": 258,
"end": 268,
"text": "Luo (2003)",
"ref_id": "BIBREF6"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "To analyze word structures in addition to phrase structures, our character-based parser naturally performs joint word segmentation, POS tagging and parsing jointly. Our model is based on the discriminative shift-reduce parser of Zhang and Clark (2009; , which is a state-of-the-art word-based phrase-structure parser for Chinese. We extend their shift-reduce framework, adding more transition actions for word segmentation and POS tagging, and defining novel features that capture character information. Even when trained using character-level syntax trees with flat word structures, our joint parser outperforms a strong pipelined baseline that consists of a state-of-the-",
"cite_spans": [
{
"start": 229,
"end": 251,
"text": "Zhang and Clark (2009;",
"ref_id": "BIBREF18"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "NN-c NN-i NN-b \u79d1 (science) \u6280 (technology) V VV-b \u70e7 (burn) NN-r NN-i NN-b \u5e93 (repository) \u5b58 (saving) NN-l VV-i VV-b \u8003 (investigate) \u53e4 (ancient) NN-r NN-i NN-b \u8d25 (bad) \u7c7b (kind) A AD-b \u5f92 (vain) (a) subject-predicate. NN-c NN-i NN-b \u79d1 (science) \u6280 (technology) VV-l VV-b \u70e7 (burn) NN-r NN-i NN-b \u5e93 (repository) \u5b58 (saving) NN-l VV-i VV-b \u8003 (investigate) \u53e4 (ancient) NN-r NN-i NN-b \u8d25 (bad) \u7c7b (kind) AD-l AD-b \u5f92 (vain) (b) verb-object. NN-c NN-i NN-b \u79d1 (science) \u6280 (technology) VV-b \u70e7 (burn) NN-r NN-i NN-b \u5e93 (repository) \u5b58 (saving) NN-l VV-i VV-b \u8003 (investigate) \u53e4 (ancient) NN-r NN-i NN-b \u8d25 (bad) \u7c7b (kind) AD-b \u5f92 (vain) (c) coordination. NN-c NN-i NN-b \u79d1 (science) \u6280 (technology) VV-l VV-b \u70e7 (burn) NN-r NN-i NN-b \u5e93 (repository) \u5b58 (saving) NN-l VV-i VV-b \u8003 (investigate) \u53e4 (ancient) NN-r NN-i NN-b \u8d25 (bad) \u7c7b (kind) AD-l AD-b \u5f92 (vain) (d) modifier-noun.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Figure 2: Inner word structures of \"\u5e93\u5b58 (repertory)\",\"\u8003\u53e4 (archaeology)\", \"\u79d1\u6280 (science and technology)\" and \"\u8d25\u7c7b (degenerate)\". art joint segmenter and POS tagger, and our baseline word-based parser. Our word annotations lead to further improvements to the joint system, especially for phrase-structure parsing accuracy.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Our parser work falls in line with recent work of joint segmentation, POS tagging and parsing (Hatori et al., 2012; Li and Zhou, 2012; Qian and Liu, 2012) . Compared with related work, our model gives the best published results for joint segmentation and POS tagging, as well as joint phrase-structure parsing on standard CTB5 evaluations. With linear-time complexity, our parser is highly efficient, processing over 30 sentences per second with a beam size of 16. An open release of the parser is freely available at http://sourceforge.net/projects/zpar/, version 0.6.",
"cite_spans": [
{
"start": 94,
"end": 115,
"text": "(Hatori et al., 2012;",
"ref_id": "BIBREF2"
},
{
"start": 116,
"end": 134,
"text": "Li and Zhou, 2012;",
"ref_id": "BIBREF4"
},
{
"start": 135,
"end": 154,
"text": "Qian and Liu, 2012)",
"ref_id": "BIBREF10"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "The Chinese language is a character-based language. Unlike alphabetical languages, Chinese characters convey meanings, and the meaning of most Chinese words takes roots in their character. For example, the word \"\u8ba1\u7b97\u673a (computer)\" is composed of the characters \"\u8ba1 (count)\", \"\u7b97 (calculate)\" and \"\u673a (machine)\". An informal name of \"computer\" is \"\u7535\u8111\", which is composed of \"\u7535 (electronic)\" and \"\u8111 (brain)\".",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Word Structures and Syntax Trees",
"sec_num": "2"
},
{
"text": "Chinese words have internal structures (Xue, 2001; Ma et al., 2012) . The way characters interact within words can be similar to the way words interact within phrases. Figure 2 shows the structures of the four words \"\u5e93\u5b58 (repertory)\", \" Figure 3 : Character-level word structure of \"\u5367\u864e \u85cf\u9f99 (crouching tiger hidden dragon)\". (archaeology)\", \"\u79d1\u6280 (science and technology)\" and \"\u8d25 \u7c7b (degenerate)\", which demonstrate four typical syntactic structures of two-character words, including subject-predicate, verb-object, coordination and modifier-noun structures. Multicharacter words can also have recursive syntactic structures. Figure 3 illustrates the structure of the word \"\u5367\u864e\u85cf\u9f99 (crouching tiger hidden dragon)\", which is composed of two subwords \"\u5367 \u864e (crouching tiger)\" and \"\u85cf\u9f99 (hidden dragon)\", both having a modifier-noun structure.",
"cite_spans": [
{
"start": 39,
"end": 50,
"text": "(Xue, 2001;",
"ref_id": "BIBREF15"
},
{
"start": 51,
"end": 67,
"text": "Ma et al., 2012)",
"ref_id": "BIBREF7"
}
],
"ref_spans": [
{
"start": 168,
"end": 176,
"text": "Figure 2",
"ref_id": null
},
{
"start": 236,
"end": 244,
"text": "Figure 3",
"ref_id": null
},
{
"start": 620,
"end": 628,
"text": "Figure 3",
"ref_id": null
}
],
"eq_spans": [],
"section": "Word Structures and Syntax Trees",
"sec_num": "2"
},
{
"text": "\u8003\u53e4 V-l VV-i \u5b8c (up) AD-l AD-i \u7136 (so) NN-r NN-i NN-b \u5367 (crouching) \u864e (tiger) NN-r NN-i NN-i \u85cf (hidden) \u9f99 (dragon) NN-c VV-i VV-b \u6a2a (fiercely) \u626b (sweep) VV-i VV-i \u5343 (thousands) \u519b (troops) NN-c NN-i NN-b \u6559 (teach) \u80b2 (education) NN-i \u754c (field) NN NN-f NN-f \u6559\u80b2 (education) \u754c (field) NN-c NN-i NN-b \u670b (friend) \u53cb (friend) NN-i \u4eec (plural) NN NN-f NN-f \u670b\u53cb (friend) \u4eec (plural)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Word Structures and Syntax Trees",
"sec_num": "2"
},
{
"text": "The meaning of characters can be a useful source of information for computational processing of Chinese, and some recent work has started to exploit this information. Zhang and Clark (2010) found that the first character in a Chinese word is a useful indicator of the word's POS. They made use of this information to help joint word segmentation and POS tagging.",
"cite_spans": [
{
"start": 167,
"end": 189,
"text": "Zhang and Clark (2010)",
"ref_id": "BIBREF19"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Word Structures and Syntax Trees",
"sec_num": "2"
},
{
"text": "Li (2011) studied the morphological structures of Chinese words, showing that 35% percent of the words in CTB5 can be treated as having morphemes. Figure 4 (a) illustrates the morphological structures of the words \" \u670b\u53cb\u4eec (friends)\" and \"\u6559\u80b2\u754c (educational world)\", in which the characters \"\u4eec (plural)\" and \"\u754c (field)\" can be treated as suffix morphemes. They studied the influence of such morphology to Chinese dependency parsing (Li and Zhou, 2012) .",
"cite_spans": [
{
"start": 427,
"end": 446,
"text": "(Li and Zhou, 2012)",
"ref_id": "BIBREF4"
}
],
"ref_spans": [
{
"start": 147,
"end": 155,
"text": "Figure 4",
"ref_id": "FIGREF4"
}
],
"eq_spans": [],
"section": "Word Structures and Syntax Trees",
"sec_num": "2"
},
{
"text": "The aforementioned work explores the influence of particular types of characters to Chinese processing, yet not the full potentials of complete word structures. We take one step further in this line of work, annotating the full syntactic structures of 37,382 Chinese words in the form of Figure 3 . Our annotation covers the entire vocabulary of CTB5. In addition to difference in coverage (100% vs 35%), our annotation is structurally more informative than that of Li (2011) , as illustrated in Figure 4 (b).",
"cite_spans": [
{
"start": 466,
"end": 475,
"text": "Li (2011)",
"ref_id": "BIBREF5"
}
],
"ref_spans": [
{
"start": 288,
"end": 296,
"text": "Figure 3",
"ref_id": null
},
{
"start": 496,
"end": 504,
"text": "Figure 4",
"ref_id": "FIGREF4"
}
],
"eq_spans": [],
"section": "Word Structures and Syntax Trees",
"sec_num": "2"
},
{
"text": "Our annotations are binarized recursive word",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Word Structures and Syntax Trees",
"sec_num": "2"
},
{
"text": "-l VV-i \u5b8c (up) D-l AD-i \u7136 (so) NN-r NN-i NN-b \u5367 (crouching) \u864e (tiger) NN-r NN-i NN-i \u85cf (hidden) \u9f99 (dragon) NN-c \u6a2a (fiercely) \u626b (sweep) \u5343 (thousands) \u519b (troops) NN-i NN-b \u6559 (teach) \u80b2 (education) \u754c (field) NN NN-f NN-f \u6559\u80b2 (education) \u754c (field) NN-i NN-b \u670b (friend) \u53cb (friend) \u4eec (plural) NN NN-f NN-f \u670b\u53cb (friend) \u4eec (plural)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Word Structures and Syntax Trees",
"sec_num": "2"
},
{
"text": "(a) morphological-level word structures, where \"f\" denotes a special mark for fine-grained words. structures. For each word or subword, we specify its POS and head direction. We use \"l\", \"r\" and \"c\" to indicate the \"left\", \"right\" and \"coordination\" head directions, respectively. The \"coordination\" direction is mostly used in coordination structures, while a very small number of transliteration words, such as \"\u5965\u5df4\u9a6c (Obama)\" and \"\u6d1b \u6749\u77f6 (Los Angeles)\", have flat structures, and we use \"coordination\" for their left binarization. For leaf characters, we follow previous work on word segmentation (Xue, 2003; Ng and Low, 2004) , and use \"b\" and \"i\" to indicate the beginning and nonbeginning characters of a word, respectively.",
"cite_spans": [
{
"start": 596,
"end": 607,
"text": "(Xue, 2003;",
"ref_id": "BIBREF16"
},
{
"start": 608,
"end": 625,
"text": "Ng and Low, 2004)",
"ref_id": "BIBREF8"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Word Structures and Syntax Trees",
"sec_num": "2"
},
{
"text": "VV-l VV-i VV-b \u70e7 (burn) \u5b8c (up) AD-l AD-i AD-b \u5f92 (vain) \u7136 (so) NN-r NN-i NN-b \u5367 (crouching) \u864e (tiger) NN-r NN-i NN-i \u85cf (hidden) \u9f99 (dragon) NN-c VV-r VV-i VV-b \u6a2a (fiercely) \u626b (sweep) VV-r VV-i VV-i \u5343 (thousands) \u519b (troops) VV-l NN-c NN-i NN-b \u6559 (teach) \u80b2 (education) NN-i \u754c (field) NN-r NN NN-f NN-f \u6559\u80b2 (education) \u754c (field) NN-c NN-i NN-b \u670b (friend) \u53cb (friend) NN-i \u4eec (plural) NN-l NN NN-f NN-f \u670b\u53cb (friend) \u4eec (plural)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Word Structures and Syntax Trees",
"sec_num": "2"
},
{
"text": "The vast majority of words do not have structural ambiguities. However, the structures of some words may vary according to different POS. For example, \"\u5236 \u670d\" means \"dominate\" when it is tagged as a verb, of which the head is the left character; the same word means \"uniform dress\" when tagged as a noun, of which the head is the right character. Thus the input of the word structure annotation is a word together with its POS. The annotation work was conducted by three persons, with one person annotating the entire corpus, and the other two checking the annotations.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Word Structures and Syntax Trees",
"sec_num": "2"
},
{
"text": "Using our annotations, we can extend CTBstyle syntax trees (Figure 1(a) ) into characterlevel trees (Figure 1(b) ). In particular, we mark the original nodes that represent POS tags in CTBstyle trees with \"-t\", and insert our word structures as unary subnodes of the \"-t\" nodes. For the rest of the paper, we refer to the \"-t\" nodes as full-word nodes, all nodes above full-word nodes as phrase nodes, and all nodes below full-word nodes as subword nodes.",
"cite_spans": [],
"ref_spans": [
{
"start": 59,
"end": 71,
"text": "(Figure 1(a)",
"ref_id": null
},
{
"start": 100,
"end": 112,
"text": "(Figure 1(b)",
"ref_id": null
}
],
"eq_spans": [],
"section": "Word Structures and Syntax Trees",
"sec_num": "2"
},
{
"text": "Our character-level trees contain additional syntactic information, which are potentially useful to Chinese processing. For example, the head characters of words can be populated up to phraselevel nodes, and serve as an additional source of information that is less sparse than head words. In this paper, we build a parser that yields characterlevel trees from raw character sequences. In addition, we use this parser to study the effects of our annotations to character-based statistical Chinese parsing, showing that they are useful in improving parsing accuracies.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Word Structures and Syntax Trees",
"sec_num": "2"
},
{
"text": "To produce character-level trees for Chinese NLP tasks, we develop a character-based parsing model, which can jointly perform word segmentation, POS tagging and phrase-structure parsing. To our knowledge, this is the first work to develop a transition-based system that jointly performs the above three tasks. Trained using annotated word structures, our parser also analyzes the internal structures of Chinese words.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Character-based Chinese Parsing",
"sec_num": "3"
},
{
"text": "Our character-based Chinese parsing model is based on the work of Zhang and Clark (2009) , which is a transition-based model for lexicalized constituent parsing. They use a beam-search decoder so that the transition action sequence can be globally optimized. The averaged perceptron with early-update (Collins and Roark, 2004) is used to train the model parameters. Their transition system contains four kinds of actions: (1) SHIFT, (2) REDUCE-UNARY, (3) REDUCE-BINARY and (4) TERMINATE. The system can provide binarzied CFG trees in Chomsky Norm Form, and they present a reversible conversion procedure to map arbitrary CFG trees into binarized trees.",
"cite_spans": [
{
"start": 66,
"end": 88,
"text": "Zhang and Clark (2009)",
"ref_id": "BIBREF18"
},
{
"start": 301,
"end": 326,
"text": "(Collins and Roark, 2004)",
"ref_id": "BIBREF0"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Character-based Chinese Parsing",
"sec_num": "3"
},
{
"text": "In this work, we remain consistent with their work, using the head-finding rules of Zhang and Clark (2008) , and the same binarization algorithm. 1 We apply the same beam-search algorithm for decoding, and employ the averaged perceptron with early-update to train our model. We make two extensions to their work to enable joint segmentation, POS tagging and phrasestructure parsing from the character level. First, we modify the actions of the transition system for",
"cite_spans": [
{
"start": 84,
"end": 106,
"text": "Zhang and Clark (2008)",
"ref_id": "BIBREF17"
},
{
"start": 146,
"end": 147,
"text": "1",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Character-based Chinese Parsing",
"sec_num": "3"
},
{
"text": "S 2 stack ... ... queue Q 0 Q 1 ... S 1 S 1l S 1r ... ... S 0 S 0l S 0r",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Character-based Chinese Parsing",
"sec_num": "3"
},
{
"text": "... ... parsing the inner structures of words. Second, we extend the feature set for our parsing problem.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Character-based Chinese Parsing",
"sec_num": "3"
},
{
"text": "In a transition-based system, an input sentence is processed in a linear left-to-right pass, and the output is constructed by a state-transition process. We learn a model for scoring the transition A i from one state ST i to the next ST i+1 . As shown in Figure 5 , a state ST consists of a stack S and a queue Q, where S = (\u2022 \u2022 \u2022 , S 1 , S 0 ) contains partially constructed parse trees, and",
"cite_spans": [],
"ref_spans": [
{
"start": 255,
"end": 263,
"text": "Figure 5",
"ref_id": "FIGREF5"
}
],
"eq_spans": [],
"section": "The Transition System",
"sec_num": "3.1"
},
{
"text": "Q = (Q 0 , Q 1 , \u2022 \u2022 \u2022 , Q n\u2212j ) = (c j , c j+1 , \u2022 \u2022 \u2022 , c n )",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "The Transition System",
"sec_num": "3.1"
},
{
"text": "is the sequence of input characters that have not been processed. The candidate transition action A at each step is defined as follows:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "The Transition System",
"sec_num": "3.1"
},
{
"text": "\u2022 SHIFT-SEPARATE(t): remove the head character c j from Q, pushing a subword node S c j 2 onto S, assigning S .t = t. Note that the parse tree S 0 must correspond to a full-word or a phrase node, and the character c j is the first character of the next word. The argument t denotes the POS of S .",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "The Transition System",
"sec_num": "3.1"
},
{
"text": "\u2022 SHIFT-APPEND: remove the head character c j from Q, pushing a subword node S c j onto S. c j will eventually be combined with all the subword nodes on top of S to form a word, and thus we must have S .t = S 0 .t.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "The Transition System",
"sec_num": "3.1"
},
{
"text": "\u2022 REDUCE-SUBWORD(d): pop the top two nodes S 0 and S 1 off S, pushing a new subword node S S 1 S 0 onto S. The argument d denotes the head direction of S , of which the value can be \"left\", \"right\" or \"coordination\". 3 Both S 0 and S 1 must be subword nodes and S .t = S 0 .t = S 1 .t.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "The Transition System",
"sec_num": "3.1"
},
{
"text": "When to Apply \u2022 REDUCE-WORD: pop the top node S 0 off S, pushing a full-word node S S 0 onto S. This reduce action generates a full-word node from S 0 , which must be a subword node.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Category Feature templates",
"sec_num": null
},
{
"text": "Structure S0ntl S0nwl S1ntl S1nwl S2ntl S2nwl S3ntl S3nwl, All features Q0c Q1c Q2c Q3c Q0c \u2022 Q1c Q1c \u2022 Q2c Q2c \u2022 Q3c, S 0l twl S0rtwl S0utwl S 1l twl S1rtwl S1utwl, S0nw \u2022 S1nw S0nw \u2022 S1nl S0nl \u2022 S1nw S0nl \u2022 S1nl, S0nw \u2022 Q0c S0nl \u2022 Q0c S1nw \u2022 Q0c S1nlQ0c, S0nl \u2022 S1nl \u2022 S2nl S0nw \u2022 S1nl \u2022 S2nl S0nl \u2022 S1nw \u2022 S2nl S0nl \u2022 S1nl \u2022 S2nw, S0nw \u2022 S1nl \u2022 Q0c S0nl \u2022 S1nw \u2022 Q0c S0nl \u2022 S1nl \u2022 Q0c, S0ncl S0nct S0nctl S1ncl S1nct S1nctl, S2ncl S2nct S2nctl S3ncl S3nct S3nctl, S0nc \u2022 S1nc S0ncl \u2022 S1nl S0nl \u2022 S1ncl S0ncl \u2022 S1ncl, S0nc \u2022 Q0c S0nl \u2022 Q0c S1nc \u2022 Q0c S1nl \u2022 Q0c, S0nc \u2022 S1nc \u2022 Q0c S0nc \u2022 S1nc \u2022 Q0c \u2022 Q1c start(S0w) \u2022 start(S1w) start(S0w) \u2022 end(S1w), REDUCE-SUBWORD indict(S1wS0w) \u2022 len(S1wS0w) indict(S1wS0w, S0t) \u2022 len(S1wS0w) String t\u22121 \u2022 t0 t\u22122 \u2022 t\u22121t0 w\u22121 \u2022 t0 c0 \u2022 t0 start(w\u22121) \u2022 t0 c\u22121 \u2022 c0 \u2022 t\u22121 \u2022 t0, SHIFT-SEPARATE features w\u22121 w\u22122 \u2022 w\u22121 w\u22121, where len(w\u22121) = 1 end(w\u22121) \u2022 c0, REDUCE-WORD start(w\u22121) \u2022 len(w\u22121) end(w\u22121) \u2022 len(w\u22121) start(w\u22121) \u2022 end(w\u22121), w\u22121 \u2022 c0 end(w\u22122) \u2022 w\u22121 start(w\u22121) \u2022 c0 end(w\u22122) \u2022 end(w\u22121), w\u22121 \u2022 len(w\u22122) w\u22122 \u2022 len(w\u22121) w\u22121 \u2022 t\u22121 w\u22121 \u2022 t\u22122 w\u22121 \u2022 t\u22121 \u2022 c0, w\u22121 \u2022 t\u22121 \u2022 end(w\u22122) c\u22122 \u2022 c\u22121 \u2022 c0 \u2022 t\u22121, where len(w\u22121) = 1 end(w\u22121) \u2022 t\u22121, c \u2022 t\u22121 \u2022 end(w\u22121), where c \u2208 w\u22121 and c = end(w\u22121) c0 \u2022 t\u22121 c\u22121 \u2022 c0 start(w\u22121) \u2022 c0t\u22121 c\u22121 \u2022 c0 \u2022 t\u22121 SHIFT-APPEND",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Category Feature templates",
"sec_num": null
},
{
"text": "\u2022 REDUCE-BINARY(d, l): pop the top two nodes S 0 and S 1 off S, pushing a binary phrase node S S 1 S 0 onto S. The argument l denotes the constituent label of S , and the argument d specifies the lexical head direction of S , which can be either \"left\" or \"right\". Both S 0 and S 1 must be a full-word node or a phrase node.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Category Feature templates",
"sec_num": null
},
{
"text": "\u2022 REDUCE-UNARY(l): pop the top node S 0 off S, pushing a unary phrase node S S 0 onto S. l denotes the constituent label of S .",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Category Feature templates",
"sec_num": null
},
{
"text": "\u2022 TERMINATE: mark parsing complete.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Category Feature templates",
"sec_num": null
},
{
"text": "Compared to set of actions in our baseline transition-based phrase-structure parser, we have made three major changes. First, we split the original SHIFT action into SHIFT-SEPARATE(t) and SHIFT-APPEND, which jointly perform the word segmentation and POS tagging tasks. Second, we add an extra REDUCE-SUBWORD(d) operation, which is used for parsing the inner struc-tures of words. Third, we add REDUCE-WORD, which applies a unary rule to mark a completed subword node as a full-word node. The new node corresponds to a unary \"-t\" node in Figure 1(b) . Table 1 shows the feature templates of our model. The feature set consists of two categories: (1) structure features, which encode the structural information of subwords, full-words and phrases. (2) string features, which encode the information of neighboring characters and words.",
"cite_spans": [],
"ref_spans": [
{
"start": 537,
"end": 548,
"text": "Figure 1(b)",
"ref_id": null
},
{
"start": 551,
"end": 558,
"text": "Table 1",
"ref_id": "TABREF0"
}
],
"eq_spans": [],
"section": "Category Feature templates",
"sec_num": null
},
{
"text": "For the structure features, the symbols S 0 , S 1 , S 2 , S 3 represent the top four nodes on the stack; Q 0 , Q 1 , Q 2 , Q 3 denote the first four characters in the queue; S 0l , S 0r , S 0u represent the left, right child for a binary branching S 0 , and the single child for a unary branching S 0 , respectively; S 1l , S 1r , S 1u represent the left, right child for a binary branching S 1 , and the single child for a unary branching S 1 , respectively; n represents the type for a node; it is a binary value that indicates whether the node is a subword node; c, w, t and l represent the head character, word (or subword), POS tag and constituent label of a node, respectively. The structure features are mostly taken from the work of Zhang and Clark (2009) . The feature templates in bold are novel, are designed to encode head character information. In particular, the indict function denotes whether a word is in a tag dictionary, which is collected by extracting all multi-character subwords that occur more than five times in the training corpus.",
"cite_spans": [
{
"start": 741,
"end": 763,
"text": "Zhang and Clark (2009)",
"ref_id": "BIBREF18"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Features",
"sec_num": "3.2"
},
{
"text": "For string features, c 0 , c \u22121 and c \u22122 represent the current character and its previous two characters, respectively; w \u22121 and w \u22122 represent the previous two words to the current character, respectively; t 0 , t \u22121 and t \u22122 represent the POS tags of the current word and the previous two words, respectively. The string features are used for word segmentation and POS tagging, and are adapted from a state-of-the-art joint segmentation and tagging model (Zhang and Clark, 2010) .",
"cite_spans": [
{
"start": 457,
"end": 480,
"text": "(Zhang and Clark, 2010)",
"ref_id": "BIBREF19"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Features",
"sec_num": "3.2"
},
{
"text": "In summary, our character-based parser contains the word-based features of constituent parser presented in Zhang and Clark (2009) , the wordbased and shallow character-based features of joint word segmentation and POS tagging presented in Zhang and Clark (2010) , and additionally the deep character-based features that encode word structure information, which are the first presented by this paper.",
"cite_spans": [
{
"start": 107,
"end": 129,
"text": "Zhang and Clark (2009)",
"ref_id": "BIBREF18"
},
{
"start": 239,
"end": 261,
"text": "Zhang and Clark (2010)",
"ref_id": "BIBREF19"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Features",
"sec_num": "3.2"
},
{
"text": "We conduct our experiments on the CTB5 corpus, using the standard split of data, with sections 1-270,400-931 and 1001-1151 for training, sections 301-325 for system development, and sections 271-300 for testing. We apply the same preprocessing step as Harper and Huang (2011) , so that the non-terminal yield unary chains are collapsed to single unary rules.",
"cite_spans": [
{
"start": 252,
"end": 275,
"text": "Harper and Huang (2011)",
"ref_id": "BIBREF1"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Setting",
"sec_num": "4.1"
},
{
"text": "Since our model can jointly process word segmentation, POS tagging and phrase-structure parsing, we evaluate our model for the three tasks, respectively. For word segmentation and POS tagging, standard metrics of word precision, recall and F-score are used, where the tagging accuracy is the joint accuracy of word segmentation and POS tagging. For phrase-structure parsing, we use the standard parseval evaluation metrics on bracketing precision, recall and F-score. As our constituent trees are based on characters, we follow previous work and redefine the boundary of a constituent span by its start and end characters. In addition, we evaluate the performance of word structures, using the word precision, recall and Fscore metrics. A word structure is correct only if the word and its internal structure are both correct. Figure 6 shows the accuracies of our model using different beam sizes with respect to the training epoch. The performance of our model increases as the beam size increases. The amount of increases becomes smaller as the size of the beam grows larger. Tested using gcc 4.7.2 and Fedora 17 on an Intel Core i5-3470 CPU (3.20GHz), the decoding speeds are 318.2, 98.0, 30.3 and 7.9 sentences per second with beam size 1, 4, 16 and 64, respectively. Based on this experiment, we set the beam size 64 for the rest of our experiments. The character-level parsing model has the advantage that deep character information can be extracted as features for parsing. For example, the head character of a word is exploited in our model. We conduct feature ablation experiments to evaluate the effectiveness of these features. We find that the parsing accuracy decreases about 0.6% when the head character related features (the bold feature templates in Table 1 ) are removed, which demonstrates the usefulness of these features.",
"cite_spans": [],
"ref_spans": [
{
"start": 827,
"end": 835,
"text": "Figure 6",
"ref_id": "FIGREF7"
},
{
"start": 1766,
"end": 1773,
"text": "Table 1",
"ref_id": "TABREF0"
}
],
"eq_spans": [],
"section": "Setting",
"sec_num": "4.1"
},
{
"text": "In this section, we present the final results of our model, and compare it to two baseline systems, a pipelined system and a joint system that is trained with automatically generated flat words structures. The baseline pipelined system consists of the joint segmentation and tagging model proposed by Table 2 : Final results on test corpus. Zhang and Clark (2010) , and the phrase-structure parsing model of Zhang and Clark (2009) . Both models give state-of-the-art performances, and are freely available. 4 The model for joint segmentation and POS tagging is trained with a 16beam, since it achieves the best performance. The phrase-structure parsing model is trained with a 64-beam. We train the parsing model using the automatically generated POS tags by 10-way jackknifing, which gives about 1.5% increases in parsing accuracy when tested on automatic segmented and POS tagged inputs.",
"cite_spans": [
{
"start": 341,
"end": 363,
"text": "Zhang and Clark (2010)",
"ref_id": "BIBREF19"
},
{
"start": 408,
"end": 430,
"text": "Zhang and Clark (2009)",
"ref_id": "BIBREF18"
},
{
"start": 507,
"end": 508,
"text": "4",
"ref_id": null
}
],
"ref_spans": [
{
"start": 301,
"end": 308,
"text": "Table 2",
"ref_id": null
}
],
"eq_spans": [],
"section": "Final Results",
"sec_num": "4.3"
},
{
"text": "The joint system trained with flat word structures serves to test the effectiveness of our joint parsing system over the pipelined baseline, since flat word structures do not contain additional sources of information over the baseline. It is also used to test the usefulness of our annotation in improving parsing accuracy. Table 2 shows the final results of our model and the two baseline systems on the test data. We can see that both character-level joint models outperform the pipelined system; our model with annotated word structures gives an improvement of 0.97% in tagging accuracy and 2.17% in phrase-structure parsing accuracy. The results also demonstrate that the annotated word structures are highly effective for syntactic parsing, giving an absolute improvement of 0.82% in phrase-structure parsing accuracy over the joint model with flat word structures.",
"cite_spans": [],
"ref_spans": [
{
"start": 324,
"end": 331,
"text": "Table 2",
"ref_id": null
}
],
"eq_spans": [],
"section": "Final Results",
"sec_num": "4.3"
},
{
"text": "Row \"WS\" in Table 2 shows the accuracy of hierarchical word-structure recovery of our joint system. This figure can be useful for high-level applications that make use of character-level trees by 4 http://sourceforge.net/projects/zpar/, version 0.5. our parser, as it reflects the capability of our parser in analyzing word structures. In particular, the performance of parsing OOV word structure is an important metric of our parser. The recall of OOV word structures is 60.43%, while if we do not consider the influences of segmentation and tagging errors, counting only the correctly segmented and tagged words, the recall is 87.96%.",
"cite_spans": [],
"ref_spans": [
{
"start": 12,
"end": 19,
"text": "Table 2",
"ref_id": null
}
],
"eq_spans": [],
"section": "Final Results",
"sec_num": "4.3"
},
{
"text": "In this section, we compare our model to previous systems on the performance of joint word segmentation and POS tagging, and the performance of joint phrase-structure parsing. Table 3 shows the results. Kruengkrai+ '09 denotes the results of Kruengkrai et al. (2009) , which is a lattice-based joint word segmentation and POS tagging model; Sun '11 denotes a subword based stacking model for joint segmentation and POS tagging (Sun, 2011) , which uses a dictionary of idioms; Wang+ '11 denotes a semisupervised model proposed by Wang et al. (2011) , which additionally uses the Chinese Gigaword Corpus; Li '11 denotes a generative model that can perform word segmentation, POS tagging and phrase-structure parsing jointly (Li, 2011) ; Li+ '12 denotes a unified dependency parsing model that can perform joint word segmentation, POS tagging and dependency parsing (Li and Zhou, 2012 ); Li '11 and Li+ '12 exploited annotated morphological-level word structures for Chinese; Hatori+ '12 denotes an incremental joint model for word segmentation, POS tagging and dependency parsing (Hatori et al., 2012) ; they use external dictionary resources including HowNet Word List and page names from the Chinese Wikipedia; Qian+ '12 denotes a joint segmentation, POS tagging and parsing system using a unified framework for decoding, incorporating a word segmentation model, a POS tagging model and a phrasestructure parsing model together (Qian and Liu, 2012) ; their word segmentation model is a combination of character-based model and word-based model. Our model achieved the best performance on both joint segmentation and tagging as well as joint phrase-structure parsing.",
"cite_spans": [
{
"start": 242,
"end": 266,
"text": "Kruengkrai et al. (2009)",
"ref_id": "BIBREF3"
},
{
"start": 427,
"end": 438,
"text": "(Sun, 2011)",
"ref_id": "BIBREF11"
},
{
"start": 529,
"end": 547,
"text": "Wang et al. (2011)",
"ref_id": "BIBREF13"
},
{
"start": 722,
"end": 732,
"text": "(Li, 2011)",
"ref_id": "BIBREF5"
},
{
"start": 863,
"end": 881,
"text": "(Li and Zhou, 2012",
"ref_id": "BIBREF4"
},
{
"start": 1078,
"end": 1099,
"text": "(Hatori et al., 2012)",
"ref_id": "BIBREF2"
},
{
"start": 1428,
"end": 1448,
"text": "(Qian and Liu, 2012)",
"ref_id": "BIBREF10"
}
],
"ref_spans": [
{
"start": 176,
"end": 183,
"text": "Table 3",
"ref_id": null
}
],
"eq_spans": [],
"section": "Comparison with Previous Work",
"sec_num": "4.4"
},
{
"text": "Our final performance on constituent parsing is by far the best that we are aware of for the Chinese data, and even better than some state-of-the-art models with gold segmentation. For example, the un-lexicalized PCFG model of Petrov Table 3 : Comparisons of our final model with state-of-the-art systems, where \"*\" denotes that external dictionary or corpus has been used.",
"cite_spans": [
{
"start": 227,
"end": 233,
"text": "Petrov",
"ref_id": null
}
],
"ref_spans": [
{
"start": 234,
"end": 241,
"text": "Table 3",
"ref_id": null
}
],
"eq_spans": [],
"section": "Comparison with Previous Work",
"sec_num": "4.4"
},
{
"text": "(2007) achieves 83.45% 5 in parsing accuracy on the test corpus, and our pipeline constituent parsing model achieves 83.55% with gold segmentation. They are lower than the performance of our character-level model, which is 84.43% without gold segmentation. The main differences between word-based and character-level parsing models are that character-level model can exploit character features. This further demonstrates the effectiveness of characters in Chinese parsing.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Comparison with Previous Work",
"sec_num": "4.4"
},
{
"text": "Recent work on using the internal structure of words to help Chinese processing gives important motivations to our work. Zhao (2009) studied character-level dependencies for Chinese word segmentation by formalizing segmentsion task in a dependency parsing framework. Their results demonstrate that annotated word dependencies can be helpful for word segmentation. Li (2011) pointed out that the word's internal structure is very important for Chinese NLP. They annotated morphological-level word structures, and a unified generative model was proposed to parse the Chinese morphological and phrase-structures. Li and Zhou (2012) also exploited the morphologicallevel word structures for Chinese dependency parsing. They proposed a unified transition-based model to parse the morphological and dependency structures of a Chinese sentence in a unified framework. The morphological-level word struc-tures concern only prefixes and suffixes, which cover only 35% of entire words in CTB. According to their results, the final performances of their model on word segmentation and POS tagging are below the state-of-the-art joint segmentation and POS tagging models. Compared to their work, we consider the character-level word structures for Chinese parsing, presenting a unified framework for segmentation, POS tagging and phrasestructure parsing. We can achieve improved segmentation and tagging performance.",
"cite_spans": [
{
"start": 121,
"end": 132,
"text": "Zhao (2009)",
"ref_id": "BIBREF21"
},
{
"start": 364,
"end": 373,
"text": "Li (2011)",
"ref_id": "BIBREF5"
},
{
"start": 610,
"end": 628,
"text": "Li and Zhou (2012)",
"ref_id": "BIBREF4"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "5"
},
{
"text": "Our character-level parsing model is inspired by the work of Zhang and Clark (2009) , which is a transition-based model with a beam-search decoder for word-based constituent parsing. Our work is based on the shift-reduce operations of their work, while we introduce additional operations for segmentation and POS tagging. By such an extension, our model can include all the features in their work, together with the features for segmentation and POS tagging. In addition, we propose novel features related to word structures and interactions between word segmentation, POS tagging and word-based constituent parsing. Luo (2003) was the first work to introduce the character-based syntax parsing. They use it as a joint framework to perform Chinese word segmentation, POS tagging and syntax parsing. They exploit a generative maximum entropy model for character-based constituent parsing, and find that POS information is very useful for Chinese word segmentation, but high-level syntactic information seems to have little effect on segmentation. Compared to their work, we use a transition-based discriminative model, which can benefit from large amounts of flexible features. In addition, instead of using flat structures, we manually annotate hierarchal tree structures of Chinese words for converting word-based constituent trees into character-based constituent trees. Hatori et al. (2012) proposed the first joint work for the word segmentation, POS tagging and dependency parsing. They used a single transitionbased model to perform the three tasks. Their work demonstrates that a joint model can improve the performance of the three tasks, particularly for POS tagging and dependency parsing. Qian and Liu (2012) proposed a joint decoder for word segmentation, POS tagging and word-based constituent parsing, although they trained models for the three tasks separately. They reported better performances when using a joint decoder. In our work, we employ a single character-based discriminative model to perform segmentation, POS tagging and phrase-structure parsing jointly, and study the influence of annotated word structures.",
"cite_spans": [
{
"start": 61,
"end": 83,
"text": "Zhang and Clark (2009)",
"ref_id": "BIBREF18"
},
{
"start": 617,
"end": 627,
"text": "Luo (2003)",
"ref_id": "BIBREF6"
},
{
"start": 1373,
"end": 1393,
"text": "Hatori et al. (2012)",
"ref_id": "BIBREF2"
},
{
"start": 1700,
"end": 1719,
"text": "Qian and Liu (2012)",
"ref_id": "BIBREF10"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "5"
},
{
"text": "We studied the internal structures of more than 37,382 Chinese words, analyzing their structures as the recursive combinations of characters. Using these word structures, we extended the CTB into character-level trees, and developed a characterbased parser that builds such trees from raw character sequences. Our character-based parser performs segmentation, POS tagging and parsing simultaneously, and significantly outperforms a pipelined baseline. We make both our annotations and our parser available online.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusions and Future Work",
"sec_num": "6"
},
{
"text": "In summary, our contributions include:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusions and Future Work",
"sec_num": "6"
},
{
"text": "\u2022 We annotated the internal structures of Chinese words, which are potentially useful to character-based studies of Chinese NLP. We extend CTB-style constituent trees into character-level trees using our annotations.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusions and Future Work",
"sec_num": "6"
},
{
"text": "\u2022 We developed a character-based parsing model that can produce our character-level constituent trees. Our parser jointly performs word segmentation, POS tagging and syntactic parsing.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusions and Future Work",
"sec_num": "6"
},
{
"text": "\u2022 We investigated the effectiveness of our joint parser over pipelined baseline, and the effectiveness of our annotated word structures in improving parsing accuracies.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusions and Future Work",
"sec_num": "6"
},
{
"text": "Future work includes investigations of our parser and annotations on Chinese NLP tasks.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusions and Future Work",
"sec_num": "6"
},
{
"text": "We use a left-binarization process for flat word structures that contain more than two characters.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "We use this notation for a compact representation of a tree node, where the numerator represents a father node, and the denominator represents the children.3 For the head direction \"coordination\", we extract the head character from the left node.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "We rerun the parser and evaluate it using the publiclyavailable code on http://code.google.com/p/berkeleyparser by ourselves, since we have a preprocessing step for the CTB5 corpus.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
}
],
"back_matter": [
{
"text": "This work was supported by National Natural Science Foundation of China (NSFC) via grant 61133012, the National \"863\" Major Projects via grant 2011AA01A207, the National \"863\" Leading Technology Research Project via grant 2012AA011102, and SRG ISTD 2012 038 from Singapore University of Technology and Design.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Acknowledgments",
"sec_num": null
}
],
"bib_entries": {
"BIBREF0": {
"ref_id": "b0",
"title": "Incremental parsing with the perceptron algorithm",
"authors": [
{
"first": "Michael",
"middle": [],
"last": "Collins",
"suffix": ""
},
{
"first": "Brian",
"middle": [],
"last": "Roark",
"suffix": ""
}
],
"year": 2004,
"venue": "Proceedings of the 42nd Meeting of the Association for Computational Linguistics (ACL'04), Main Volume",
"volume": "",
"issue": "",
"pages": "111--118",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Michael Collins and Brian Roark. 2004. Incremen- tal parsing with the perceptron algorithm. In Pro- ceedings of the 42nd Meeting of the Association for Computational Linguistics (ACL'04), Main Volume, pages 111-118, Barcelona, Spain, July.",
"links": null
},
"BIBREF1": {
"ref_id": "b1",
"title": "Chinese statistical parsing. Handbook of Natural Language Processing and Machine Translation",
"authors": [
{
"first": "Mary",
"middle": [],
"last": "Harper",
"suffix": ""
},
{
"first": "Zhongqiang",
"middle": [],
"last": "Huang",
"suffix": ""
}
],
"year": 2011,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Mary Harper and Zhongqiang Huang. 2011. Chinese statistical parsing. Handbook of Natural Language Processing and Machine Translation.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "Incremental joint approach to word segmentation, pos tagging, and dependency parsing in chinese",
"authors": [
{
"first": "Jun",
"middle": [],
"last": "Hatori",
"suffix": ""
},
{
"first": "Takuya",
"middle": [],
"last": "Matsuzaki",
"suffix": ""
},
{
"first": "Yusuke",
"middle": [],
"last": "Miyao",
"suffix": ""
},
{
"first": "Jun'ichi",
"middle": [],
"last": "Tsujii",
"suffix": ""
}
],
"year": 2012,
"venue": "Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics",
"volume": "1",
"issue": "",
"pages": "1045--1053",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jun Hatori, Takuya Matsuzaki, Yusuke Miyao, and Jun'ichi Tsujii. 2012. Incremental joint approach to word segmentation, pos tagging, and dependency parsing in chinese. In Proceedings of the 50th An- nual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1045- 1053, Jeju Island, Korea, July. Association for Com- putational Linguistics.",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "An error-driven word-character hybrid model for joint chinese word segmentation and pos tagging",
"authors": [
{
"first": "Canasai",
"middle": [],
"last": "Kruengkrai",
"suffix": ""
},
{
"first": "Kiyotaka",
"middle": [],
"last": "Uchimoto",
"suffix": ""
},
{
"first": "Yiou",
"middle": [],
"last": "Jun'ichi Kazama",
"suffix": ""
},
{
"first": "Kentaro",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Hitoshi",
"middle": [],
"last": "Torisawa",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Isahara",
"suffix": ""
}
],
"year": 2009,
"venue": "Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP",
"volume": "",
"issue": "",
"pages": "513--521",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Canasai Kruengkrai, Kiyotaka Uchimoto, Jun'ichi Kazama, Yiou Wang, Kentaro Torisawa, and Hitoshi Isahara. 2009. An error-driven word-character hy- brid model for joint chinese word segmentation and pos tagging. In Proceedings of the Joint Confer- ence of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Lan- guage Processing of the AFNLP, pages 513-521, Suntec, Singapore, August. Association for Compu- tational Linguistics.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "Unified dependency parsing of chinese morphological and syntactic structures",
"authors": [
{
"first": "Zhongguo",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Guodong",
"middle": [],
"last": "Zhou",
"suffix": ""
}
],
"year": 2012,
"venue": "Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning",
"volume": "",
"issue": "",
"pages": "1445--1454",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Zhongguo Li and Guodong Zhou. 2012. Unified de- pendency parsing of chinese morphological and syn- tactic structures. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Lan- guage Processing and Computational Natural Lan- guage Learning, pages 1445-1454, Jeju Island, Ko- rea, July. Association for Computational Linguistics.",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "Parsing the internal structure of words: A new paradigm for chinese word segmentation",
"authors": [
{
"first": "Zhongguo",
"middle": [],
"last": "Li",
"suffix": ""
}
],
"year": 2011,
"venue": "Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies",
"volume": "",
"issue": "",
"pages": "1405--1414",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Zhongguo Li. 2011. Parsing the internal structure of words: A new paradigm for chinese word segmen- tation. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pages 1405-1414, Portland, Oregon, USA, June. Association for Com- putational Linguistics.",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "A maximum entropy Chinese character-based parser",
"authors": [
{
"first": "Xiaoqiang",
"middle": [],
"last": "Luo",
"suffix": ""
}
],
"year": 2003,
"venue": "Proceedings of the 2003 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "192--199",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Xiaoqiang Luo. 2003. A maximum entropy Chi- nese character-based parser. In Michael Collins and Mark Steedman, editors, Proceedings of the 2003 Conference on Empirical Methods in Natural Lan- guage Processing, pages 192-199.",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "Semi-automatic annotation of chinese word structure",
"authors": [
{
"first": "Jianqiang",
"middle": [],
"last": "Ma",
"suffix": ""
},
{
"first": "Chunyu",
"middle": [],
"last": "Kit",
"suffix": ""
},
{
"first": "Dale",
"middle": [],
"last": "Gerdemann",
"suffix": ""
}
],
"year": 2012,
"venue": "Proceedings of the Second CIPS-SIGHAN Joint Conference on Chinese Language Processing",
"volume": "",
"issue": "",
"pages": "9--17",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jianqiang Ma, Chunyu Kit, and Dale Gerdemann. 2012. Semi-automatic annotation of chinese word structure. In Proceedings of the Second CIPS- SIGHAN Joint Conference on Chinese Language Processing, pages 9-17, Tianjin, China, December. Association for Computational Linguistics.",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "Chinese partof-speech tagging: One-at-a-time or all-at-once? word-based or character-based?",
"authors": [
{
"first": "Tou",
"middle": [],
"last": "Hwee",
"suffix": ""
},
{
"first": "Jin",
"middle": [
"Kiat"
],
"last": "Ng",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Low",
"suffix": ""
}
],
"year": 2004,
"venue": "Proceedings of EMNLP 2004",
"volume": "",
"issue": "",
"pages": "277--284",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Hwee Tou Ng and Jin Kiat Low. 2004. Chinese part- of-speech tagging: One-at-a-time or all-at-once? word-based or character-based? In Dekang Lin and Dekai Wu, editors, Proceedings of EMNLP 2004, pages 277-284, Barcelona, Spain, July. Association for Computational Linguistics.",
"links": null
},
"BIBREF9": {
"ref_id": "b9",
"title": "Improved inference for unlexicalized parsing",
"authors": [
{
"first": "Slav",
"middle": [],
"last": "Petrov",
"suffix": ""
},
{
"first": "Dan",
"middle": [],
"last": "Klein",
"suffix": ""
}
],
"year": 2007,
"venue": "Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computational Linguistics; Proceedings of the Main Conference",
"volume": "",
"issue": "",
"pages": "404--411",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Slav Petrov and Dan Klein. 2007. Improved infer- ence for unlexicalized parsing. In Human Language Technologies 2007: The Conference of the North American Chapter of the Association for Computa- tional Linguistics; Proceedings of the Main Confer- ence, pages 404-411, Rochester, New York, April. Association for Computational Linguistics.",
"links": null
},
"BIBREF10": {
"ref_id": "b10",
"title": "Joint chinese word segmentation, pos tagging and parsing",
"authors": [
{
"first": "Xian",
"middle": [],
"last": "Qian",
"suffix": ""
},
{
"first": "Yang",
"middle": [],
"last": "Liu",
"suffix": ""
}
],
"year": 2012,
"venue": "Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning",
"volume": "",
"issue": "",
"pages": "501--511",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Xian Qian and Yang Liu. 2012. Joint chinese word segmentation, pos tagging and parsing. In Pro- ceedings of the 2012 Joint Conference on Empiri- cal Methods in Natural Language Processing and Computational Natural Language Learning, pages 501-511, Jeju Island, Korea, July. Association for Computational Linguistics.",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "A stacked sub-word model for joint chinese word segmentation and part-of-speech tagging",
"authors": [
{
"first": "Weiwei",
"middle": [],
"last": "Sun",
"suffix": ""
}
],
"year": 2011,
"venue": "Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies",
"volume": "",
"issue": "",
"pages": "1385--1394",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Weiwei Sun. 2011. A stacked sub-word model for joint chinese word segmentation and part-of-speech tagging. In Proceedings of the 49th Annual Meet- ing of the Association for Computational Linguis- tics: Human Language Technologies, pages 1385- 1394, Portland, Oregon, USA, June. Association for Computational Linguistics.",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "Adding noun phrase structure to the penn treebank",
"authors": [
{
"first": "David",
"middle": [],
"last": "Vadas",
"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": "240--247",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "David Vadas and James Curran. 2007. Adding noun phrase structure to the penn treebank. In Proceed- ings of the 45th Annual Meeting of the Associa- tion of Computational Linguistics, pages 240-247, Prague, Czech Republic, June. Association for Com- putational Linguistics.",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "Improving chinese word segmentation and pos tagging with semi-supervised methods using large auto-analyzed data",
"authors": [
{
"first": "Yiou",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Yoshimasa",
"middle": [],
"last": "Kazama",
"suffix": ""
},
{
"first": "Wenliang",
"middle": [],
"last": "Tsuruoka",
"suffix": ""
},
{
"first": "Yujie",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Kentaro",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Torisawa",
"suffix": ""
}
],
"year": 2011,
"venue": "Proceedings of 5th International Joint Conference on Natural Language Processing",
"volume": "",
"issue": "",
"pages": "309--317",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yiou Wang, Jun'ichi Kazama, Yoshimasa Tsuruoka, Wenliang Chen, Yujie Zhang, and Kentaro Tori- sawa. 2011. Improving chinese word segmentation and pos tagging with semi-supervised methods using large auto-analyzed data. In Proceedings of 5th In- ternational Joint Conference on Natural Language Processing, pages 309-317, Chiang Mai, Thailand, November. Asian Federation of Natural Language Processing.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "The penn chinese treebank: Phrase structure annotation of a large corpus",
"authors": [
{
"first": "Nianwen",
"middle": [],
"last": "Xue",
"suffix": ""
},
{
"first": "Fei",
"middle": [],
"last": "Xia",
"suffix": ""
},
{
"first": "Fu-Dong",
"middle": [],
"last": "Chiou",
"suffix": ""
},
{
"first": "Martha",
"middle": [],
"last": "Palmer",
"suffix": ""
}
],
"year": 2005,
"venue": "Natural Language Engineering",
"volume": "11",
"issue": "2",
"pages": "207--238",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Nianwen Xue, Fei Xia, Fu-Dong Chiou, and Martha Palmer. 2005. The penn chinese treebank: Phrase structure annotation of a large corpus. Natural Lan- guage Engineering, 11(2):207-238.",
"links": null
},
"BIBREF15": {
"ref_id": "b15",
"title": "Defining and Automatically Identifying Words in Chinese",
"authors": [
{
"first": "Nianwen",
"middle": [],
"last": "Xue",
"suffix": ""
}
],
"year": 2001,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Nianwen Xue. 2001. Defining and Automatically Identifying Words in Chinese. Ph.D. thesis, Univer- sity of Delaware.",
"links": null
},
"BIBREF16": {
"ref_id": "b16",
"title": "Chinese word segmentation as character tagging",
"authors": [
{
"first": "Nianwen",
"middle": [],
"last": "Xue",
"suffix": ""
}
],
"year": 2003,
"venue": "International Journal of Computational Linguistics and Chinese Language Processing",
"volume": "8",
"issue": "1",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Nianwen Xue. 2003. Chinese word segmentation as character tagging. International Journal of Compu- tational Linguistics and Chinese Language Process- ing, 8(1).",
"links": null
},
"BIBREF17": {
"ref_id": "b17",
"title": "A tale of two parsers: Investigating and combining graph-based and transition-based dependency parsing",
"authors": [
{
"first": "Yue",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Stephen",
"middle": [],
"last": "Clark",
"suffix": ""
}
],
"year": 2008,
"venue": "Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "562--571",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yue Zhang and Stephen Clark. 2008. A tale of two parsers: Investigating and combining graph-based and transition-based dependency parsing. In Pro- ceedings of the 2008 Conference on Empirical Meth- ods in Natural Language Processing, pages 562- 571, Honolulu, Hawaii, October. Association for Computational Linguistics.",
"links": null
},
"BIBREF18": {
"ref_id": "b18",
"title": "Transitionbased parsing of the chinese treebank using a global discriminative model",
"authors": [
{
"first": "Yue",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Stephen",
"middle": [],
"last": "Clark",
"suffix": ""
}
],
"year": 2009,
"venue": "Proceedings of the 11th International Conference on Parsing Technologies (IWPT'09)",
"volume": "",
"issue": "",
"pages": "162--171",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yue Zhang and Stephen Clark. 2009. Transition- based parsing of the chinese treebank using a global discriminative model. In Proceedings of the 11th International Conference on Parsing Technologies (IWPT'09), pages 162-171, Paris, France, October. Association for Computational Linguistics.",
"links": null
},
"BIBREF19": {
"ref_id": "b19",
"title": "A fast decoder for joint word segmentation and POS-tagging using a single discriminative model",
"authors": [
{
"first": "Yue",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Stephen",
"middle": [],
"last": "Clark",
"suffix": ""
}
],
"year": 2010,
"venue": "Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "843--852",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yue Zhang and Stephen Clark. 2010. A fast decoder for joint word segmentation and POS-tagging using a single discriminative model. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pages 843-852, Cambridge, MA, October. Association for Computational Lin- guistics.",
"links": null
},
"BIBREF20": {
"ref_id": "b20",
"title": "Syntactic processing using the generalized perceptron and beam search",
"authors": [
{
"first": "Yue",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Stephen",
"middle": [],
"last": "Clark",
"suffix": ""
}
],
"year": 2011,
"venue": "Computational Linguistics",
"volume": "37",
"issue": "1",
"pages": "105--151",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yue Zhang and Stephen Clark. 2011. Syntactic pro- cessing using the generalized perceptron and beam search. Computational Linguistics, 37(1):105-151.",
"links": null
},
"BIBREF21": {
"ref_id": "b21",
"title": "Character-level dependencies in chinese: Usefulness and learning",
"authors": [
{
"first": "Hai",
"middle": [],
"last": "Zhao",
"suffix": ""
}
],
"year": 2009,
"venue": "Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009)",
"volume": "",
"issue": "",
"pages": "879--887",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Hai Zhao. 2009. Character-level dependencies in chi- nese: Usefulness and learning. In Proceedings of the 12th Conference of the European Chapter of the ACL (EACL 2009), pages 879-887, Athens, Greece, March. Association for Computational Linguistics.",
"links": null
}
},
"ref_entries": {
"FIGREF0": {
"text": "character-level syntax tree with hierarchal word structures for \"\u4e2d (middle)\u56fd (nation) \u5efa (construction) \u7b51 (building) \u4e1a (industry) \u5448 (present) \u73b0 (show) \u65b0 (new) \u683c (style) \u5c40 (situation)\".",
"num": null,
"type_str": "figure",
"uris": null
},
"FIGREF1": {
"text": "Figure 1: Word-based and character-level phrasestructure trees for the sentence \"\u4e2d\u56fd\u5efa\u7b51\u4e1a\u5448\u73b0 \u65b0\u683c\u5c40 (China's architecture industry shows new patterns)\", where \"l\", \"r\", \"c\" denote the directions of head characters (see section 2).",
"num": null,
"type_str": "figure",
"uris": null
},
"FIGREF3": {
"text": "(b) character-level word structures.",
"num": null,
"type_str": "figure",
"uris": null
},
"FIGREF4": {
"text": "Comparison between character-level and morphological-level word structures.",
"num": null,
"type_str": "figure",
"uris": null
},
"FIGREF5": {
"text": "A state in a transition-based model.",
"num": null,
"type_str": "figure",
"uris": null
},
"FIGREF7": {
"text": "Accuracies against the training epoch for joint segmentation and tagging as well as joint phrase-structure parsing using beam sizes 1, 4, 16 and 64, respectively.",
"num": null,
"type_str": "figure",
"uris": null
},
"TABREF0": {
"text": "",
"num": null,
"type_str": "table",
"content": "<table/>",
"html": null
}
}
}
} |