File size: 105,598 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 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897 1898 1899 1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097 2098 2099 2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112 2113 2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127 2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 |
{
"paper_id": "I11-1026",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T07:31:48.775130Z"
},
"title": "Improving Dependency Parsing with Fined-Grained Features",
"authors": [
{
"first": "Guangyou",
"middle": [],
"last": "Zhou",
"suffix": "",
"affiliation": {
"laboratory": "National Laboratory of Pattern Recognition",
"institution": "Chinese Academy of Sciences",
"location": {
"addrLine": "95 Zhongguancun East Road",
"postCode": "100190",
"settlement": "Beijing",
"country": "China"
}
},
"email": "gyzhou@nlpr.ia.ac.cn"
},
{
"first": "Li",
"middle": [],
"last": "Cai",
"suffix": "",
"affiliation": {
"laboratory": "National Laboratory of Pattern Recognition",
"institution": "Chinese Academy of Sciences",
"location": {
"addrLine": "95 Zhongguancun East Road",
"postCode": "100190",
"settlement": "Beijing",
"country": "China"
}
},
"email": "lcai@nlpr.ia.ac.cn"
},
{
"first": "Kang",
"middle": [],
"last": "Liu",
"suffix": "",
"affiliation": {
"laboratory": "National Laboratory of Pattern Recognition",
"institution": "Chinese Academy of Sciences",
"location": {
"addrLine": "95 Zhongguancun East Road",
"postCode": "100190",
"settlement": "Beijing",
"country": "China"
}
},
"email": "kliu@nlpr.ia.ac.cn"
},
{
"first": "Jun",
"middle": [],
"last": "Zhao",
"suffix": "",
"affiliation": {
"laboratory": "National Laboratory of Pattern Recognition",
"institution": "Chinese Academy of Sciences",
"location": {
"addrLine": "95 Zhongguancun East Road",
"postCode": "100190",
"settlement": "Beijing",
"country": "China"
}
},
"email": "jzhao@nlpr.ia.ac.cn"
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "In this paper, we present a simple and effective fine-grained feature generation scheme for dependency parsing. We focus on the problem of grammar representation, introducing fine-grained features by splitting various POS tags to different degrees using HowNet hierarchical semantic knowledge. To prevent the oversplitting, we adopt a threshold-constrained bottomup strategy to merge the derived subcategories. We conduct the experiments on the Penn Chinese Treebank. The results show that, with the fine-grained features, we can improve the dependency parsing accuracies by 0.52% (absolute) for the unlabeled first-order parser, and in the case of second-order parser, we can improve the dependency parsing accuracies by 0.61% (absolute).",
"pdf_parse": {
"paper_id": "I11-1026",
"_pdf_hash": "",
"abstract": [
{
"text": "In this paper, we present a simple and effective fine-grained feature generation scheme for dependency parsing. We focus on the problem of grammar representation, introducing fine-grained features by splitting various POS tags to different degrees using HowNet hierarchical semantic knowledge. To prevent the oversplitting, we adopt a threshold-constrained bottomup strategy to merge the derived subcategories. We conduct the experiments on the Penn Chinese Treebank. The results show that, with the fine-grained features, we can improve the dependency parsing accuracies by 0.52% (absolute) for the unlabeled first-order parser, and in the case of second-order parser, we can improve the dependency parsing accuracies by 0.61% (absolute).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "In natural language parsing, part-of-speech (POS) information is seen as crucial to resolving ambiguous relationships, yet POS tags are usually too general to encapsulate a word's syntactic behavior. It is therefore attractive to consider intermediate entities which exist at a finer level than the POS tags, and the relationship between specific words and their syntactic contexts may be best modeled.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "In this paper, we introduce the fine-grained features by splitting various POS tags to different degrees. First, we split the POS tags of each word in the Treebank using HowNet hypernym-hyponymy hierarchical semantic knowledge (Dong and Dong, 2000) .",
"cite_spans": [
{
"start": 227,
"end": 248,
"text": "(Dong and Dong, 2000)",
"ref_id": "BIBREF6"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Then we adopt a thresholdconstrained bottom-up strategy to merge the semantic-related subcategories which are plagued by the oversplitting problems. Finally, we use Figure 1 : An example of a labeled dependency tree. The tree contains a special token \"$\" which is always the root of the tree. Each arc is directed from head to modifier and has a label describing the function of the attachment.",
"cite_spans": [],
"ref_spans": [
{
"start": 165,
"end": 173,
"text": "Figure 1",
"ref_id": null
}
],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "the generated sub-categories to construct a new fine-grained feature mapping for a discriminative learner. We are thus relying on the ability of discriminative learning methods to identify and exploit informative features.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "To demonstrate the effectiveness of our approach, we conduct the dependency parsing experiments on the Penn Chinese Treebank (CTB) (Xue et al., 2005) . The results show that, with the fine-grained features, we can obtain mildly significant improvements both for first-order and secondorder parsing (e.g., the absolute improvements are 0.52% and 0.61%, respectively) (see Section 6).",
"cite_spans": [
{
"start": 131,
"end": 149,
"text": "(Xue et al., 2005)",
"ref_id": "BIBREF25"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "The remainder of this paper is organized as follows. Section 2 introduces the Motivation. Section 3 gives background on dependency parsing and HowNet hierarchical semantic knowledge. Section 4 describes the fine-grained feature generation scheme. Section 5 presents fine-grained features. Experimental evaluation and results are reported in Section 6. Section 7 discusses related work. Finally, in Section 8 we draw conclusion.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "In dependency parsing, we attempt to build headmodifier (or head-dependent) relations between words in a sentence. A simple example is shown in Figure 1 , where NN, VV, and JJ are POS tags.",
"cite_spans": [],
"ref_spans": [
{
"start": 144,
"end": 152,
"text": "Figure 1",
"ref_id": null
}
],
"eq_spans": [],
"section": "Motivation",
"sec_num": "2"
},
{
"text": "Currently, a variety of statistical methods have been developed for dependency parsing, such as graph-based (McDonald et al., 2005; McDonald and Pereira, 2006) , transition-based (Yamada and Matsumoto, 2003; Hall et al., 2006) , or hybrid methods (Nivre and McDonald, 2008; Martins et al., 2008; Zhang and Clark, 2008) . These methods mainly rely on the POS information as important features, but the POS tags are usually too general to encapsulate a word's syntactic behavior, especially for Chinese dependency parsing on CTB (e.g., it assumes that all the words with the POS tag NN share the same syntactic behavior). In the limit, each word may well have its own unique syntactic behavior (Petrov and Klein, 2006) . However, in practice, given limited data, the relationships between the specific words and their context dependencies may be best modeled at a level finer than the POS tags but coarser than the words themselves. Take the sentence in Figure 1 for example, although the words \u5916\u8d44(foreign capital) and \u589e\u957f\u70b9(growth) have the same POS tag NN, they should have different context dependencies in dependency parsing tree. In HowNet, the two words are defined with different hypernyms. The word \u5916\u8d44(foreign capital) is defined as a kind of objective things, while the word \u589e\u957f\u70b9(growth) is defined as an event role feature. Intuitively, the different senses can represent their different syntactic behavior, and we attempt to split the POS tags to different degrees based on hierarchical semantic knowledge. Figure 2 shows the number of the most frequent errors relative to POS types on the development set for the first-order parsing. From the figure, it is seen that the main errors are nominal and verbal categories. Therefore, we may suspect that whether the complex and frequent categories like NN and VV should be split heavily while barely split rare or simple ones. Our experiments demonstrate that this strategy can be quite effective in Chinese dependency parsing task (see Table 2 in Section 4 for empirical results).",
"cite_spans": [
{
"start": 108,
"end": 131,
"text": "(McDonald et al., 2005;",
"ref_id": "BIBREF18"
},
{
"start": 132,
"end": 159,
"text": "McDonald and Pereira, 2006)",
"ref_id": "BIBREF17"
},
{
"start": 179,
"end": 207,
"text": "(Yamada and Matsumoto, 2003;",
"ref_id": "BIBREF26"
},
{
"start": 208,
"end": 226,
"text": "Hall et al., 2006)",
"ref_id": "BIBREF9"
},
{
"start": 247,
"end": 273,
"text": "(Nivre and McDonald, 2008;",
"ref_id": null
},
{
"start": 274,
"end": 295,
"text": "Martins et al., 2008;",
"ref_id": "BIBREF16"
},
{
"start": 296,
"end": 318,
"text": "Zhang and Clark, 2008)",
"ref_id": "BIBREF28"
},
{
"start": 692,
"end": 716,
"text": "(Petrov and Klein, 2006)",
"ref_id": "BIBREF21"
}
],
"ref_spans": [
{
"start": 952,
"end": 960,
"text": "Figure 1",
"ref_id": null
},
{
"start": 1513,
"end": 1521,
"text": "Figure 2",
"ref_id": "FIGREF1"
},
{
"start": 1989,
"end": 1996,
"text": "Table 2",
"ref_id": "TABREF2"
}
],
"eq_spans": [],
"section": "Motivation",
"sec_num": "2"
},
{
"text": "In dependency parsing, we attempt to build headmodifier (or head-dependent) relations between words in a sentence. The discriminative parser we used in this paper is based on the part-factored model and features of the MSTParser (McDonald et al., 2005; McDonald and Pereira, 2006; Carreras, 2007 ). The parsing model can be defined as a conditional distribution p(y|x; w) over each projective parse tree y for a particular sentence x, parameterized by a vector w. The probability of a parse tree is",
"cite_spans": [
{
"start": 229,
"end": 252,
"text": "(McDonald et al., 2005;",
"ref_id": "BIBREF18"
},
{
"start": 253,
"end": 280,
"text": "McDonald and Pereira, 2006;",
"ref_id": "BIBREF17"
},
{
"start": 281,
"end": 295,
"text": "Carreras, 2007",
"ref_id": "BIBREF2"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Dependency Parsing",
"sec_num": "3.1"
},
{
"text": "p(y|x; w) = 1 Z(x; w) exp { \u2211 \u03c1\u2208y w\u2022\u03a6(x, \u03c1) } (1)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Dependency Parsing",
"sec_num": "3.1"
},
{
"text": "where Z(x; w) is the partition function and \u03a6 are part-factored feature functions that include headmodifier parts, sibling parts and grandchild parts. Given the training set",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Dependency Parsing",
"sec_num": "3.1"
},
{
"text": "{(x i , y i )} N i=1",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Dependency Parsing",
"sec_num": "3.1"
},
{
"text": ", parameter estimation for log-linear models generally resolve around optimization of a regularized conditional log-likelihood objective",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Dependency Parsing",
"sec_num": "3.1"
},
{
"text": "w * = arg min w L(w) where L(w) = \u2212C N \u2211 i=1 logp(y i |x i ; w) + 1 2 ||w|| 2 (2)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Dependency Parsing",
"sec_num": "3.1"
},
{
"text": "The parameter C > 0 is a constant dictating the level of regularization in the model. Since objective function L(w) is smooth and convex, which is convenient for standard gradient-based optimization techniques. In this paper we use the dual exponentiated gradient (EG) 1 descent, which is a particularly effective optimization algorithm for log-linear models .",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Dependency Parsing",
"sec_num": "3.1"
},
{
"text": "HowNet is a bilingual general knowledge-base describing relations between concepts and relations between the attributes of concepts in Chinese and their English equivalents (Gan and Wong, 2000) . HowNet constructs a hierarchical structure of its knowledge base from hypernym-hyponymy relations. The unit of meaning is called sememe that can not be further decomposed, which can be represented in Chinese and their English equivalents, such as the sememe fund|\u8d44 \u8d44 \u8d44 \u91d1 \u91d1 \u91d1. The explicated relations of HowNet include hypernym-hyponymy, synonymy, metonymy, antonymy, part-whole, attribute-host, materialproduct, dynamic role and concept co-occurrence, and so on. In this paper, we only consider the hypernym-hyponymy relations at different levels of granularities. Since a word may have different senses, and therefore different definitions in HowNet, we just use the first definition as the semantic-related tag of the word. Take the concept \u5916 \u8d44(foreign capital) for example, its definition and the hypernym-hyponymy relations are listed below from speciality to generality, which we call the hierarchical semantic information in this paper. Definition: DEF = {fund|\u8d44\u91d1:modifier= {foreign|\u5916 \u56fd}} Hierarchy:",
"cite_spans": [
{
"start": 173,
"end": 193,
"text": "(Gan and Wong, 2000)",
"ref_id": "BIBREF8"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "HowNet Semantic Knowledge",
"sec_num": "3.2"
},
{
"text": "fund|\u8d44 \u91d1\u2192wealth|\u94b1 \u8d22\u2192artifact|\u4eba \u5de5 \u7269\u2192inanimate|\u65e0 \u751f \u7269\u2192physical|\u7269 \u8d28\u2192thing|\u4e07\u7269\u2192entity|\u5b9e\u4f53",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "HowNet Semantic Knowledge",
"sec_num": "3.2"
},
{
"text": "In the definition, HowNet decomposes the concept into sememes 'fund|\u8d44 \u91d1', 'wealth|\u94b1 \u8d22', 'artifact|\u4eba \u5de5 \u7269', 'inanimate|\u65e0 \u751f \u7269', 'physical|\u7269\u8d28', 'thing|\u4e07\u7269', 'entity|\u5b9e\u4f53'. The sememe appearing in the first position of Definition ('fund|\u8d44 \u91d1') is the categorical attribute, which names the hypernym of the concept \u5916 \u8d44(foreign capital). Those sememes appearing in other positions (e.g., 'foreign|\u5916\u56fd') are additional attributes, which give more specific information to the concept.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "HowNet Semantic Knowledge",
"sec_num": "3.2"
},
{
"text": "It is clear that the word \u5916\u8d44(foreign capital) has hypernyms from the most special hypernym fund|\u8d44\u91d1 to the most general hypernym entity|\u5b9e \u4f53 in a hierarchical way.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "HowNet Semantic Knowledge",
"sec_num": "3.2"
},
{
"text": "HowNet contains very limited words, so there are many words which cannot be found in HowNet. In this paper, we extend HowNet with Chinese Knowledge base \"TongYiCiLin\" (abbreviation: CiLin) (Mei et al., 1983) , which represents 77,343 words in a dendrogram (or tree).",
"cite_spans": [
{
"start": 189,
"end": 207,
"text": "(Mei et al., 1983)",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "HowNet Semantic Knowledge",
"sec_num": "3.2"
},
{
"text": "CiLin is organized as a hierarchical tree structure, each node represents a semantic category. To balance the words coverage, we extract semantic categories at level 3, which covers 1,400 subcate-gories.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "HowNet Semantic Knowledge",
"sec_num": "3.2"
},
{
"text": "HowNet and CiLin have different ontologies and representations of semantic categories (Xiong et al., 2005) , we combine the two dictionaries: given a word w, if we cannot find in HowNet, but found in CiLin, we try to replace w with a synonym s in the synset defined by CiLin. If the synonym s can be found in HowNet, the corresponding semantic-related tag in HowNet will be assigned to w.",
"cite_spans": [
{
"start": 86,
"end": 106,
"text": "(Xiong et al., 2005)",
"ref_id": "BIBREF24"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "HowNet Semantic Knowledge",
"sec_num": "3.2"
},
{
"text": "4 Fine-Grained Feature Generation",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "HowNet Semantic Knowledge",
"sec_num": "3.2"
},
{
"text": "In this subsection, we split the original POS tags to different degrees based on HowNet hierarchical semantic knowledge. The challenge is how to deal with the problem of polysemous words. Since each word may have multiple senses, and therefore different definitions in HowNet. Following Xiong et al. (2005) and Lin et al. (2009) , we just use the first sense to determine the sense of each token instance of a target word (e.g., all token instances of a given word are tagged with the sense that occurs most frequently in HowNet).",
"cite_spans": [
{
"start": 287,
"end": 306,
"text": "Xiong et al. (2005)",
"ref_id": "BIBREF24"
},
{
"start": 311,
"end": 328,
"text": "Lin et al. (2009)",
"ref_id": "BIBREF13"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Splitting the POS Tags",
"sec_num": "4.1"
},
{
"text": "As mentioned in Section 3.2, the semantic information of each word can be represented as hierarchical hypernym-hyponymy relations. In this paper, we attempt to establish the mapping from top to down and split the words into different subcategories based on hypernym-hyponymy relations defined in HowNet. For easy explanation of the splitting process, we take the words with POS tag NN for example; the fine-grained feature generation is shown in Figure 3 . The left part of the figure is the word subcategories, which is split based on HowNet hierarchy. As shown by the dashed line from left to right, we generate each subcategory with the hierarchical semantic-related tag, such as NN-event, NN-entity, NN-thing, NN-time and so on. If the hypernym node has no hyponym, the corresponding subcategory will stop splitting (e.g., at the level 3 in figure 3, \"fruit\" is the most speciality hypernym of the corresponding words \"banana\" and \"apple\" in HowNet hierarchy, which cannot be further decomposed). The details of HowNet hierarchy were presented in Dong and Dong (2000) .",
"cite_spans": [
{
"start": 1051,
"end": 1071,
"text": "Dong and Dong (2000)",
"ref_id": "BIBREF6"
}
],
"ref_spans": [
{
"start": 446,
"end": 454,
"text": "Figure 3",
"ref_id": null
}
],
"eq_spans": [],
"section": "Splitting the POS Tags",
"sec_num": "4.1"
},
{
"text": "As shown in Figure 3 , the original relationships between the words and their syntactic contexts are modeled by the POS tag NN, after hierarchically split, the relationships can be best modeled at the different levels of the fine-grained subcategories. By this observation, the fine-grained feature generation is just a hierarchical clustering of words themselves with the fine-grained semantic-related tags. Unlike the previous work, such as word cluster technique , data-driven split manner (Matsuzaki et al., 2005; Petrov and Klein, 2006) , our approach does not exploit unlabeled data, and the splitting is based on hierarchical semantic knowledge instead of maximizing posterior probability, which is much simpler than their methods.",
"cite_spans": [
{
"start": 493,
"end": 517,
"text": "(Matsuzaki et al., 2005;",
"ref_id": "BIBREF15"
},
{
"start": 518,
"end": 541,
"text": "Petrov and Klein, 2006)",
"ref_id": "BIBREF21"
}
],
"ref_spans": [
{
"start": 12,
"end": 20,
"text": "Figure 3",
"ref_id": null
}
],
"eq_spans": [],
"section": "Splitting the POS Tags",
"sec_num": "4.1"
},
{
"text": "Intuitively, creating more subcategories can increase parsing accuracy. On the other hand, oversplitting can be a serious problem, the details were presented in Klein and Manning (2003) . To prevent oversplitting, we merge the subcategories based on the threshold constraint. After the splitting, each subcategory contains a group of words which share the same semantic-related tag.",
"cite_spans": [
{
"start": 161,
"end": 185,
"text": "Klein and Manning (2003)",
"ref_id": "BIBREF11"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Merging Based on Threshold Constraint",
"sec_num": "4.2"
},
{
"text": "Then we measure the size of each subcategory to determine whether the subcategory should be further merged. For easy explanation, we show an example in Figure 4 , where each node C i denotes a subcategory, C j is the nearest hypernym subcategory of C i , C k is the nearest hypernym subcategory of C j , and so on. Assuming that f (C i ) , f (C j ) and f (C j ) denote the number of the words contained in the subcategory C i , C j and C k respectively, f is the threshold. We judge C i should be further merged into C j if f (C i ) < f , and update the number of the words contained in C j using the following formula:",
"cite_spans": [],
"ref_spans": [
{
"start": 152,
"end": 160,
"text": "Figure 4",
"ref_id": null
}
],
"eq_spans": [],
"section": "Merging Based on Threshold Constraint",
"sec_num": "4.2"
},
{
"text": "f update (C j ) = { f (C i ) + f (C j ) if f (C i ) < f f (C j ) other (3) where f update (C j )",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Merging Based on Threshold Constraint",
"sec_num": "4.2"
},
{
"text": "is the number of the words contained in the updated subcategory C j . In this way, we repeatedly merge each subcategory from bottom-up through the hypernym ladders according to the formula (3). Finally, we generate appropriate granularity of the fine-grained subcategories by splitting and merging approach.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Merging Based on Threshold Constraint",
"sec_num": "4.2"
},
{
"text": "In our approach, each POS tag is divided into several subcategories. The subcategories of some POS tags with the words are shown in Table 1 . The categories compose of the original POS tags and the subcategories derived from HowNet. For example, NN is split into NN-InstitutePlace, NN-aValue, and so on. The subcategories' number of each POS tag is shown in Table 2 : The number of subcategories generated by our hierarchical semantic knowledge based split-merge procedure.",
"cite_spans": [],
"ref_spans": [
{
"start": 132,
"end": 139,
"text": "Table 1",
"ref_id": "TABREF3"
},
{
"start": 358,
"end": 365,
"text": "Table 2",
"ref_id": "TABREF2"
}
],
"eq_spans": [],
"section": "Merging Based on Threshold Constraint",
"sec_num": "4.2"
},
{
"text": "ample, common noun (NN) category is divided into the maximum number of subcategories (24). One subcategory consists primarily of objective things, whose typical semantic knowledge is an entity. Another subcategory is defined as an attribute, and so on. These kinds of semantic-related subcategories are typical, and give a division similar to the distributional clustering results like those of Schuetze (1998) . The proper noun (NR) category is split into the 5 subcategories, including entity, institute-Places, attribute, aValue, and so on, which are defined in HowNet. The temporal noun (NT) category is also split into 3 subcategories.",
"cite_spans": [
{
"start": 395,
"end": 410,
"text": "Schuetze (1998)",
"ref_id": "BIBREF22"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Merging Based on Threshold Constraint",
"sec_num": "4.2"
},
{
"text": "Verbal categories are also heavily split. Verbal subcategories sometimes reflect syntactic selectional preferences, and sometimes reflect other aspects of verbal syntax (Petrov and Klein, 2006) . For example, the common verb (VV) category is divided into the number of 17 subcategories based on hierarchical split-merge procedure. The predictive adjective (VA) category is also split into 4 subcategories.",
"cite_spans": [
{
"start": 169,
"end": 193,
"text": "(Petrov and Klein, 2006)",
"ref_id": "BIBREF21"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Merging Based on Threshold Constraint",
"sec_num": "4.2"
},
{
"text": "Functional categories generally have fewer splits shown in Table 2 . Intuitively, those categories are known to be strongly correlated with syntactic behavior. For example, determiner (DT), interjection (IJ), onomatopoeia (ON), and so on.",
"cite_spans": [],
"ref_spans": [
{
"start": 59,
"end": 66,
"text": "Table 2",
"ref_id": "TABREF2"
}
],
"eq_spans": [],
"section": "Merging Based on Threshold Constraint",
"sec_num": "4.2"
},
{
"text": "Key to the success of our approach is the use of HowNet hierarchical semantic knowledge to generate the fine-grained features to assist the dependency parsers. The feature sets we used in this paper are similar to other feature sets in the literature (McDonald et al., 2005; McDonald and Pereira, 2006; Carreras, 2007) , so we will not attempt to give an exhaustive description of the features in this Section. Rather, we describe our finegrained features at a high level and concentrate on our motivations. In the experiments, our employed two different feature sets: a baseline feature set which draws upon \"normal\" information sources such as word forms and POS, and a fine-grained feature set that also information derived from the HowNet hierarchical semantic knowledge.",
"cite_spans": [
{
"start": 251,
"end": 274,
"text": "(McDonald et al., 2005;",
"ref_id": "BIBREF18"
},
{
"start": 275,
"end": 302,
"text": "McDonald and Pereira, 2006;",
"ref_id": "BIBREF17"
},
{
"start": 303,
"end": 318,
"text": "Carreras, 2007)",
"ref_id": "BIBREF2"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Feature Design",
"sec_num": "5"
},
{
"text": "Our first-order baseline feature set is similar to the feature set of McDonald et al. (2005) and McDonald and Pereira (2006) . The second-order baseline features are the same as those of Carreras (2007) and include indicators for triples of POS tags for sibling interactions and grandparent interactions, as well as additional bigram features based on pairs of words involved these higher order interactions.",
"cite_spans": [
{
"start": 70,
"end": 92,
"text": "McDonald et al. (2005)",
"ref_id": "BIBREF18"
},
{
"start": 97,
"end": 124,
"text": "McDonald and Pereira (2006)",
"ref_id": "BIBREF17"
},
{
"start": 187,
"end": 202,
"text": "Carreras (2007)",
"ref_id": "BIBREF2"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Feature Design",
"sec_num": "5"
},
{
"text": "The first-and second-order fine-grained features are complementary with the baseline features. We generate the fine-grained features by mimicking the word-to-tag and tag-to-tag interactions between the head and modifier of a dependency. Also, We include indicators for triples of fine-grained subcategory tags for sibling and grandparent interactions. Examples of these features are provided in Table 3 .",
"cite_spans": [],
"ref_spans": [
{
"start": 395,
"end": 402,
"text": "Table 3",
"ref_id": "TABREF5"
}
],
"eq_spans": [],
"section": "Feature Design",
"sec_num": "5"
},
{
"text": "Till now, we have demonstrated our finegrained generation scheme using HowNet hierarchical semantic knowledge. With the derived subcategories, we can construct a new fine-grained feature mapping for a discriminative learner, similar to . We are relying on the ability of discriminative learning methods to identify and exploit informative features.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Feature Design",
"sec_num": "5"
},
{
"text": "In order to evaluate the effectiveness of the proposed approach, we conducted dependency parsing experiments in Chinese. The experiments were performed on the Penn Chinese Treebank (CTB) version 5.0 (Xue et al., 2005) , using a set of head-selection rules (Zhang and Clark, 2008) to convert the phrase structure syntax of the Treebank to a dependency tree representation, dependency labels were obtained via the \"Malt\" hardcoded setting. 2 We split the data into training set (files 1-270 and files 400-931), development set (files 301-325) and test set (files 271-300). The development and test set were used gold-standard segmentation and POS tags in CTB.",
"cite_spans": [
{
"start": 199,
"end": 217,
"text": "(Xue et al., 2005)",
"ref_id": "BIBREF25"
},
{
"start": 256,
"end": 279,
"text": "(Zhang and Clark, 2008)",
"ref_id": "BIBREF28"
},
{
"start": 438,
"end": 439,
"text": "2",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Experiments",
"sec_num": "6"
},
{
"text": "We measured the parser quality by the unlabeled attachment score (UAS), e.g., the percentage of tokens (excluding all punctuation tokens) with the correct HEAD. And we also evaluated on complete dependency analysis (CM).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Experiments",
"sec_num": "6"
},
{
"text": "In this subsection, we conduct the experiments only using the splitting operator.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Splitting Experiments",
"sec_num": "6.1"
},
{
"text": "The results are shown in Table 4 , where Ord1/Ord2 refers to a first-/second-order parsers (Mc-Donald et al., 2005; McDonald and Pereira, 2006; Carreras, 2007) with baseline features. Ord1f/Ord2f refers to a first-/second-order parsers with baseline+fine-grained features, and the im- Table 4 : Dependency parsing results on the test set only using the splitting operator.",
"cite_spans": [
{
"start": 91,
"end": 115,
"text": "(Mc-Donald et al., 2005;",
"ref_id": null
},
{
"start": 116,
"end": 143,
"text": "McDonald and Pereira, 2006;",
"ref_id": "BIBREF17"
},
{
"start": 144,
"end": 159,
"text": "Carreras, 2007)",
"ref_id": "BIBREF2"
}
],
"ref_spans": [
{
"start": 25,
"end": 32,
"text": "Table 4",
"ref_id": null
},
{
"start": 285,
"end": 292,
"text": "Table 4",
"ref_id": null
}
],
"eq_spans": [],
"section": "Splitting Experiments",
"sec_num": "6.1"
},
{
"text": "provements by the fine-grained features over the baseline features are shown in parentheses. There are some clear trends in the results. First, the performance increases with the order of the parser: the first-order model (Ord1) has the lowest performance, adding sibling and grandparent interactions (Ord2) yield better performance. Similar observations regarding the effect of model order have also been made by Carreras (2007) and .",
"cite_spans": [
{
"start": 414,
"end": 429,
"text": "Carreras (2007)",
"ref_id": "BIBREF2"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Splitting Experiments",
"sec_num": "6.1"
},
{
"text": "Second, note that the parsers using the finegrained features outperform the baseline, regardless of model order. Moreover, the benefits of the fine-grained features can improve the performance with the increasing of the model order. For example, increasing the model order from Ord1 to Ord1f results in a relative reduction in error of roughly 1.12%, while introducing fine-grained features from Ord2 to Ord2f yields an additional relative error reduction of roughly 2.05%.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Splitting Experiments",
"sec_num": "6.1"
},
{
"text": "To prevent oversplitting, we merge the subcategories based on the threshold constraint. For parameter f in equation 3, different POS tags (e.g., NN, VV, JJ, ON, \u2022 \u2022 \u2022 ) need different values. We do the experiments on the development set to determine the best value among 10, 20, 50, 100, 200, 300, \u2022 \u2022 \u2022 , 1,000 in terms of UAS for each POS tag. The number of the subcategories are shown in Table 2 (in Section 4). Our experiments corresponding to the best parameter values are evaluated on the test set of CTB 5.0. Table 5 shows the results. The performances can be further increased after using the merging operator. Such a fact validates the effectiveness of merging operator. Overall, for the first-order parser, we find that there is an absolute improvement of 0.52 points (UAS) by adding fine-grained features. For the second-order parser, we get an absolute improvement of 0.61 points (UAS) by including fine-grained features. The improvements of parsing with fine-grained features are mildly significant using the Z-test of Collins et al. (2005) . Wang et al. (2007) 86.6 -Yu at al. 2008-87.26 Zhao et al. (2009) 88.9 87.0 Chen et al. (2009) 92.34 89.91 Ours 90.86 88.88 Table 6 : Dependency parsing results on this data set for our second-order model and the previous work.",
"cite_spans": [
{
"start": 1032,
"end": 1053,
"text": "Collins et al. (2005)",
"ref_id": "BIBREF5"
},
{
"start": 1056,
"end": 1074,
"text": "Wang et al. (2007)",
"ref_id": "BIBREF23"
},
{
"start": 1102,
"end": 1120,
"text": "Zhao et al. (2009)",
"ref_id": "BIBREF29"
},
{
"start": 1131,
"end": 1149,
"text": "Chen et al. (2009)",
"ref_id": "BIBREF3"
}
],
"ref_spans": [
{
"start": 516,
"end": 523,
"text": "Table 5",
"ref_id": "TABREF8"
},
{
"start": 1179,
"end": 1186,
"text": "Table 6",
"ref_id": null
}
],
"eq_spans": [],
"section": "Merging Experiments",
"sec_num": "6.2"
},
{
"text": "To put our results in perspective, we also compare our second-order system with other best systems: Wang et al. (2007) , Yu at al. (2008) , Zhao et al. (2009) and Chen et al. (2009) , respectively. The results are shown in Table 6 , our approach outperforms the first three systems. Chen et al. (2009) reports a very high performance using subtree features from auto-parsed data. In our systems, our do not use such knowledge. Some researchers conducted experiments on CTB with a different data split: files 1-815 and files 1001-1136 for training, files 816-885 and files 1137-1147 for test, files 886-931 and 1148-1151 for development. The development and test sets were also performed using the gold-standard assigned POS tags. We report the experimental results as well as the performance of previous work on this data set shown in Table 7 . Our results are better than most previous work, although Zhang and Clark (2008) achieved an even higher accuracy (86.21) by combining both graph-based and transition-based parsing into a single system for training and decoding. Moreover, their technique is orthogonal to ours, and we suspect that integrating the fine-grained features into the combined parsers might get an even better performance.",
"cite_spans": [
{
"start": 100,
"end": 118,
"text": "Wang et al. (2007)",
"ref_id": "BIBREF23"
},
{
"start": 121,
"end": 137,
"text": "Yu at al. (2008)",
"ref_id": "BIBREF27"
},
{
"start": 140,
"end": 158,
"text": "Zhao et al. (2009)",
"ref_id": "BIBREF29"
},
{
"start": 163,
"end": 181,
"text": "Chen et al. (2009)",
"ref_id": "BIBREF3"
},
{
"start": 283,
"end": 301,
"text": "Chen et al. (2009)",
"ref_id": "BIBREF3"
},
{
"start": 902,
"end": 924,
"text": "Zhang and Clark (2008)",
"ref_id": "BIBREF28"
}
],
"ref_spans": [
{
"start": 223,
"end": 230,
"text": "Table 6",
"ref_id": null
},
{
"start": 835,
"end": 842,
"text": "Table 7",
"ref_id": "TABREF9"
}
],
"eq_spans": [],
"section": "Comparison with Previous Work",
"sec_num": "6.3"
},
{
"text": "Our purpose in this paper is to incorporate the finegrained features to assist the dependency parsing.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Discussion",
"sec_num": "6.4"
},
{
"text": "Systems UAS Duan et al. (2007) 84.38 Zhang and Clark (2008) 86.21 Huang and Sagae (2010) 85.20 Ours 85.45 Figure 5 shows an example of dependency trees produced by the baseline parser and our proposed approach. In Figure 5(a) , the baseline parser incorrectly assigned \u5956/NN (prize) as the modifier of \u4ee5/P (with) and the head of \u4ee5/P was also incorrectly recognized as \u6388\u4e88/VV (award). The reason may be that the POS features (P\u2192NN and VV\u2192P) are too general to model the syntactic dependencies. However, after introducing the finegrained features P-event\u2192NN-attribute and VV-AlterRelational\u2192P-event, \u540d\u5b57/NN (name) was selected as modifier of \u4ee5/P (with) and the head of \u4ee5/P (with) was correctly recognized ( Figure 5(b) ).",
"cite_spans": [
{
"start": 8,
"end": 30,
"text": "UAS Duan et al. (2007)",
"ref_id": null
},
{
"start": 37,
"end": 59,
"text": "Zhang and Clark (2008)",
"ref_id": "BIBREF28"
},
{
"start": 66,
"end": 88,
"text": "Huang and Sagae (2010)",
"ref_id": "BIBREF10"
}
],
"ref_spans": [
{
"start": 106,
"end": 114,
"text": "Figure 5",
"ref_id": "FIGREF3"
},
{
"start": 214,
"end": 225,
"text": "Figure 5(a)",
"ref_id": "FIGREF3"
},
{
"start": 702,
"end": 714,
"text": "Figure 5(b)",
"ref_id": "FIGREF3"
}
],
"eq_spans": [],
"section": "Discussion",
"sec_num": "6.4"
},
{
"text": "Besides, there exist a large number of neighborhood ambiguities in Chinese dependency parsing, such as \"NN NN NN\", \"JJ NN NN\", \"AD VV VV\", \"JJ NN CC NN\" and so on they have possible parsing trees as shown in Figure 6 . For those ambiguities, our approach can provide the finegrained features as additional information for the parser. For example, we have the following case in the data set: \"\u5916\u5546NN(foreign tradesman)/\u6295 \u8d44NN(investment) /\u4f01 \u4e1aNN(enterprise)/\". We can provide additional information about the relations of \"\u5916 \u5546NN-human(foreign tradesman)/ \u4f01 \u4e1aNN-InstitutePlace(enterprise)\" and \"\u5916 \u5546NN-human(foreign tradesman)/\u6295 \u8d44NNevent(investment)\", which can be used to help the parser make the correct decision. Our approach can also help the longer dependencies, such as \"JJ NN NN NN\" and \"NN NN NN NN\". For \"JJ NN1 CC NN2\" ambiguity, we can provide the additional information about the relations of JJ/NN1 and JJ/NN2. In this case, the dependency parser can correctly differentiate the ambiguity.",
"cite_spans": [],
"ref_spans": [
{
"start": 208,
"end": 216,
"text": "Figure 6",
"ref_id": "FIGREF4"
}
],
"eq_spans": [],
"section": "Discussion",
"sec_num": "6.4"
},
{
"text": "Our proposed approach is only a preliminary work. Despite the success, there are still some problems which should be extensively discussed in the future work.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Discussion",
"sec_num": "6.4"
},
{
"text": "(1) In this paper we split the POS tags by using the gold-standard POS tags of CTB. However, in many real application problems, the sentences to be parsed are often from the plain text and the POS tagging is an inevitable phase before dependency parsing. But the split of POS tags will bring great difficulty to the POS tagging phase. Whether the increase the parsing performance will cover the decrease of the POS tagging, this is a very appealing and challenging task in practice. We will leave it for future research.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Discussion",
"sec_num": "6.4"
},
{
"text": "(2) To deal with the problem of polysemous words, we just use the first definitions in HowNet. A natural avenue for further research would be the development of word sense disambiguation (WSD) technology to solve this problem.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Discussion",
"sec_num": "6.4"
},
{
"text": "In this paper, we have focused on developing new representations for POS information. The idea of exploiting different granularities of information for dependency parsing has previously been investigated. Liu et al. (2007) subdivided verbs according to their grammatical functions and integrated the information of verb subclasses into the dependency parsing model. They regarded the verb subdividing process as a classification task. In contrast, we split the POS tags based on HowNet hierarchical semantic knowledge and relax the subdivision to be all types of POS tags, which is much simpler than the classification-based method. introduced lexical intermediaries at a coarser level than words themselves via a cluster method. Our approach is similar to theirs in that we used the fine-grained feature generation scheme based on HowNet hierarchical semantic knowledge, and the fine-grained features can be viewed as being a kind of \"back-off\" version of the baseline features. However, we focus on the problem of POS representation instead of lexical representation.",
"cite_spans": [
{
"start": 205,
"end": 222,
"text": "Liu et al. (2007)",
"ref_id": "BIBREF14"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "7"
},
{
"text": "Recently, there are some studies focusing on parsing task using semantic knowledge. Agirre et al. (2008) used word sense information to improve English parsing and PP attachment. Xiong et al. (2005) and Lin et al. (2009) extracted hypernym features from HowNet semantic knowledge and integrated the features into a generative model for Chinese constituent parsing. As with their work, we also use semantic knowledge for parsing. However, our gold is to employ HowNet hierarchical semantic knowledge to generate fine-grained features to dependency parsing, rather than to PCFGs, requiring a substantially different model formulation. Besides, Bansal and Klein (2011) and Zhou et al. (2011) exploited web-scale semantic information for parsing.",
"cite_spans": [
{
"start": 84,
"end": 104,
"text": "Agirre et al. (2008)",
"ref_id": "BIBREF0"
},
{
"start": 179,
"end": 198,
"text": "Xiong et al. (2005)",
"ref_id": "BIBREF24"
},
{
"start": 203,
"end": 220,
"text": "Lin et al. (2009)",
"ref_id": "BIBREF13"
},
{
"start": 642,
"end": 665,
"text": "Bansal and Klein (2011)",
"ref_id": "BIBREF1"
},
{
"start": 670,
"end": 688,
"text": "Zhou et al. (2011)",
"ref_id": "BIBREF30"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "7"
},
{
"text": "In this paper, we focus on the problem of grammar representation, introducing fine-grained features by splitting various POS tags to different degrees using HowNet hierarchical semantic knowledge. To prevent the oversplitting, we adopt a threshold-constrained bottom-up strategy to merge the derived subcategories. The results show that, with the fine-grained features, we can improve the dependency parsing accuracies by 0.52% (absolute) for the unlabeled first-order parser, and in the case of second-order parser, we can improve the dependency parsing accuracies by 0.61% (absolute).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion",
"sec_num": "8"
},
{
"text": "http://groups.csail.mit.edu/nlp/egstra/",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
}
],
"back_matter": [
{
"text": "This work was supported by the National Natural Science Foundation of China (No. 60875041 and No. 61070106). We thank the anonymous reviewers for their insightful comments.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Acknowledgments",
"sec_num": null
}
],
"bib_entries": {
"BIBREF0": {
"ref_id": "b0",
"title": "Improving parsing and PP-attachment performance with sense information",
"authors": [
{
"first": "E",
"middle": [],
"last": "Agirre",
"suffix": ""
},
{
"first": "T",
"middle": [],
"last": "Baldwin",
"suffix": ""
},
{
"first": "D",
"middle": [],
"last": "Martinez",
"suffix": ""
}
],
"year": 2008,
"venue": "Proceedings of ACL-08: HLT",
"volume": "",
"issue": "",
"pages": "317--325",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "E. Agirre, T. Baldwin, and D. Martinez. 2008. Improv- ing parsing and PP-attachment performance with sense information. In Proceedings of ACL-08: HLT, pages 317-325.",
"links": null
},
"BIBREF1": {
"ref_id": "b1",
"title": "Web-Scale Features for Full-Scale Parsing",
"authors": [
{
"first": "M",
"middle": [],
"last": "Bansal",
"suffix": ""
},
{
"first": "D",
"middle": [],
"last": "Klein",
"suffix": ""
}
],
"year": 2011,
"venue": "Proceedings of ACL-HLT",
"volume": "",
"issue": "",
"pages": "693--702",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "M. Bansal and D. Klein. 2011. Web-Scale Features for Full-Scale Parsing. In Proceedings of ACL-HLT, pages 693-702.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "Experiments with a higher-order projective dependency parser",
"authors": [
{
"first": "X",
"middle": [],
"last": "Carreras",
"suffix": ""
}
],
"year": 2007,
"venue": "Proceedings of EMNLP-CoNLL",
"volume": "",
"issue": "",
"pages": "957--961",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "X. Carreras. 2007. Experiments with a higher-order projective dependency parser. In Proceedings of EMNLP-CoNLL, pages 957-961.",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "Improving dependency parsing with subtrees from auto-parsed data",
"authors": [
{
"first": "W",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "D",
"middle": [],
"last": "Kawahara",
"suffix": ""
},
{
"first": "K",
"middle": [],
"last": "Uchimoto",
"suffix": ""
},
{
"first": "Torisawa",
"middle": [],
"last": "",
"suffix": ""
}
],
"year": 2009,
"venue": "Proceedings of EMNLP",
"volume": "",
"issue": "",
"pages": "570--579",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "W. Chen, D. Kawahara, K. Uchimoto, and Torisawa. 2009. Improving dependency parsing with subtrees from auto-parsed data. In Proceedings of EMNLP, pages 570-579.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "Exponentiated gradient algorithm for conditional random fields and max-margin markov networks",
"authors": [
{
"first": "M",
"middle": [],
"last": "Collins",
"suffix": ""
},
{
"first": "A",
"middle": [],
"last": "Globerson",
"suffix": ""
},
{
"first": "T",
"middle": [],
"last": "Koo",
"suffix": ""
},
{
"first": "X",
"middle": [],
"last": "Carreras",
"suffix": ""
},
{
"first": "P",
"middle": [
"L"
],
"last": "Bartlett",
"suffix": ""
}
],
"year": 2008,
"venue": "Journal of Machine Learning Research",
"volume": "",
"issue": "",
"pages": "1775--1822",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "M. Collins, A. Globerson, T. Koo, X. Carreras, and P. L. Bartlett. 2008. Exponentiated gradient algo- rithm for conditional random fields and max-margin markov networks. Journal of Machine Learning Re- search, pages 1775-1822.",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "Clause restructuring for statistical machine translation",
"authors": [
{
"first": "M",
"middle": [],
"last": "Collins",
"suffix": ""
},
{
"first": "P",
"middle": [],
"last": "Koehn",
"suffix": ""
},
{
"first": "I",
"middle": [],
"last": "Kucerova",
"suffix": ""
}
],
"year": 2005,
"venue": "Proceedings of ACL",
"volume": "",
"issue": "",
"pages": "531--540",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "M. Collins, P. Koehn, and I. Kucerova. 2005. Clause restructuring for statistical machine translation. In Proceedings of ACL, pages 531-540.",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "HowNet Chinese-English conceptual database",
"authors": [
{
"first": "Z",
"middle": [],
"last": "Dong",
"suffix": ""
},
{
"first": "Q",
"middle": [],
"last": "Dong",
"suffix": ""
}
],
"year": 2000,
"venue": "Technical report online software database, released at ACL",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Z. Dong and Q. Dong. 2000. HowNet Chinese- English conceptual database. Technical re- port online software database, released at ACL, http://www.keenage.com.",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "Probabilistic Models for action-based Chinese dependency parsing",
"authors": [
{
"first": "X",
"middle": [],
"last": "Duan",
"suffix": ""
},
{
"first": "J",
"middle": [],
"last": "Zhao",
"suffix": ""
},
{
"first": "B",
"middle": [],
"last": "Xu",
"suffix": ""
}
],
"year": 2007,
"venue": "Proceedings of ECML/PKDD",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "X. Duan, J. Zhao, and B. Xu. 2007. Probabilistic Mod- els for action-based Chinese dependency parsing. In Proceedings of ECML/PKDD.",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "Annotating information structures in Chinese texts using HowNet",
"authors": [
{
"first": "K",
"middle": [
"W"
],
"last": "Gan",
"suffix": ""
},
{
"first": "P",
"middle": [
"W"
],
"last": "Wong",
"suffix": ""
}
],
"year": 2000,
"venue": "Proceedings of ACL",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "K. W. Gan and P. W. Wong. 2000. Annotating infor- mation structures in Chinese texts using HowNet. In Proceedings of ACL.",
"links": null
},
"BIBREF9": {
"ref_id": "b9",
"title": "Discriminative classifier for deterministic dependency parsing",
"authors": [
{
"first": "J",
"middle": [],
"last": "Hall",
"suffix": ""
},
{
"first": "J",
"middle": [],
"last": "Nivre",
"suffix": ""
},
{
"first": "J",
"middle": [],
"last": "Nilsson",
"suffix": ""
}
],
"year": 2006,
"venue": "Proceedings of ACL",
"volume": "",
"issue": "",
"pages": "316--323",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "J. Hall, J. Nivre, and J. Nilsson. 2006. Discriminative classifier for deterministic dependency parsing. In Proceedings of ACL, pages 316-323.",
"links": null
},
"BIBREF10": {
"ref_id": "b10",
"title": "Dynamic Programming for Linear-Time Incremental Parsing",
"authors": [
{
"first": "L",
"middle": [],
"last": "Huang",
"suffix": ""
},
{
"first": "K",
"middle": [],
"last": "Sagae",
"suffix": ""
}
],
"year": 2010,
"venue": "Proceedings of ACL",
"volume": "",
"issue": "",
"pages": "1077--1086",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "L. Huang and K. Sagae. 2010. Dynamic Programming for Linear-Time Incremental Parsing. In Proceed- ings of ACL, pages 1077-1086.",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "Accurate unlexicalized parsing",
"authors": [
{
"first": "D",
"middle": [],
"last": "Klein",
"suffix": ""
},
{
"first": "C",
"middle": [],
"last": "Manning",
"suffix": ""
}
],
"year": 2003,
"venue": "Proceedings of ACL",
"volume": "",
"issue": "",
"pages": "423--430",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "D. Klein and C. Manning. 2003. Accurate unlexical- ized parsing. In Proceedings of ACL, pages 423- 430.",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "Simple semi-supervised dependency parsing",
"authors": [
{
"first": "T",
"middle": [],
"last": "Koo",
"suffix": ""
},
{
"first": "X",
"middle": [],
"last": "Carreras",
"suffix": ""
},
{
"first": "M",
"middle": [],
"last": "Collins",
"suffix": ""
}
],
"year": 2008,
"venue": "Proceedings of ACL-08: HLT",
"volume": "",
"issue": "",
"pages": "595--603",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "T. Koo, X. Carreras, and M. Collins. 2008. Simple semi-supervised dependency parsing. In Proceed- ings of ACL-08: HLT, pages 595-603.",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "Refining grammars for parsing with hierarchical semantic knowledge",
"authors": [
{
"first": "X",
"middle": [],
"last": "Lin",
"suffix": ""
},
{
"first": "Y",
"middle": [],
"last": "Fan",
"suffix": ""
},
{
"first": "M",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "X",
"middle": [],
"last": "Wu",
"suffix": ""
},
{
"first": "H",
"middle": [],
"last": "Chi",
"suffix": ""
}
],
"year": 2009,
"venue": "Proceedings of EMNLP",
"volume": "",
"issue": "",
"pages": "1298--1307",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "X. Lin, Y. Fan, M. Zhang, X. Wu, H. Chi. 2009. Refin- ing grammars for parsing with hierarchical semantic knowledge. In Proceedings of EMNLP, pages 1298- 1307.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "Subdivided verbs to improve syntactic parsing",
"authors": [
{
"first": "T",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "J",
"middle": [],
"last": "Ma",
"suffix": ""
},
{
"first": "H",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "S",
"middle": [],
"last": "Li",
"suffix": ""
}
],
"year": 2007,
"venue": "Journal of electronics",
"volume": "24",
"issue": "3",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "T. Liu, J. Ma, H. Zhang, and S. Li. 2007. Subdivided verbs to improve syntactic parsing. Journal of elec- tronics, 24(3).",
"links": null
},
"BIBREF15": {
"ref_id": "b15",
"title": "Probabilistic CFG with latent annotation",
"authors": [
{
"first": "T",
"middle": [],
"last": "Matsuzaki",
"suffix": ""
},
{
"first": "Y",
"middle": [],
"last": "Miyao",
"suffix": ""
},
{
"first": "J",
"middle": [],
"last": "Tsujii",
"suffix": ""
}
],
"year": 2005,
"venue": "Proceedings of ACL",
"volume": "",
"issue": "",
"pages": "75--82",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "T. Matsuzaki, Y. Miyao, and J. Tsujii. 2005. Proba- bilistic CFG with latent annotation. In Proceedings of ACL, pages 75-82.",
"links": null
},
"BIBREF16": {
"ref_id": "b16",
"title": "Stacking dependency parsers",
"authors": [
{
"first": "A",
"middle": [
"F T"
],
"last": "Martins",
"suffix": ""
},
{
"first": "D",
"middle": [],
"last": "Das",
"suffix": ""
},
{
"first": "N",
"middle": [
"A"
],
"last": "Smith",
"suffix": ""
},
{
"first": "E",
"middle": [
"P"
],
"last": "Xing",
"suffix": ""
}
],
"year": 2008,
"venue": "Proceedings of EMNLP",
"volume": "",
"issue": "",
"pages": "157--166",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "A. F. T. Martins, D. Das, N. A. Smith, and E. P. Xing. 2008. Stacking dependency parsers. In Proceedings of EMNLP, pages 157-166.",
"links": null
},
"BIBREF17": {
"ref_id": "b17",
"title": "Online learning of approximate dependency parsing algorithms",
"authors": [
{
"first": "R",
"middle": [],
"last": "Mcdonald",
"suffix": ""
},
{
"first": "F",
"middle": [],
"last": "Pereira",
"suffix": ""
}
],
"year": 2006,
"venue": "Proceedings of EACL",
"volume": "",
"issue": "",
"pages": "81--88",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "R. McDonald and F. Pereira. 2006. Online learning of approximate dependency parsing algorithms. In Proceedings of EACL, pages 81-88.",
"links": null
},
"BIBREF18": {
"ref_id": "b18",
"title": "Online large-margin training of dependency parsers",
"authors": [
{
"first": "R",
"middle": [],
"last": "Mcdonald",
"suffix": ""
},
{
"first": "K",
"middle": [],
"last": "Crammer",
"suffix": ""
},
{
"first": "F",
"middle": [],
"last": "Pereira",
"suffix": ""
}
],
"year": 2005,
"venue": "Proceedings of ACL",
"volume": "",
"issue": "",
"pages": "91--98",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "R. McDonald, K. Crammer, and F. Pereira. 2005. On- line large-margin training of dependency parsers. In Proceedings of ACL, pages 91-98.",
"links": null
},
"BIBREF20": {
"ref_id": "b20",
"title": "Integrating graphbased and transition-based dependency parsing",
"authors": [
{
"first": "J",
"middle": [],
"last": "Nivre",
"suffix": ""
},
{
"first": "R",
"middle": [],
"last": "Mcdonld",
"suffix": ""
}
],
"year": 2008,
"venue": "Proceedings of ACL-08: HLT",
"volume": "",
"issue": "",
"pages": "950--958",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "J. Nivre and R. McDonld. 2008. Integrating graph- based and transition-based dependency parsing. In Proceedings of ACL-08: HLT, pages 950-958.",
"links": null
},
"BIBREF21": {
"ref_id": "b21",
"title": "Learning accurate, compact, and interpretable tree annotation",
"authors": [
{
"first": "S",
"middle": [],
"last": "Petrov",
"suffix": ""
},
{
"first": "D",
"middle": [],
"last": "Klein",
"suffix": ""
}
],
"year": 2006,
"venue": "Proceedings of COLING-ACL",
"volume": "",
"issue": "",
"pages": "433--440",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "S. Petrov and D. Klein. 2006. Learning accurate, com- pact, and interpretable tree annotation. In Proceed- ings of COLING-ACL, pages 433-440.",
"links": null
},
"BIBREF22": {
"ref_id": "b22",
"title": "Automatic word sense discrimination",
"authors": [
{
"first": "H",
"middle": [],
"last": "Schuetze",
"suffix": ""
}
],
"year": 1998,
"venue": "Computational Linguistics",
"volume": "24",
"issue": "1",
"pages": "97--124",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "H. Schuetze. 1998. Automatic word sense discrimina- tion. Computational Linguistics, 24(1): 97-124.",
"links": null
},
"BIBREF23": {
"ref_id": "b23",
"title": "Simple training of dependency parsers via structured boosting",
"authors": [
{
"first": "Q",
"middle": [
"I"
],
"last": "Wang",
"suffix": ""
},
{
"first": "D",
"middle": [],
"last": "Lin",
"suffix": ""
},
{
"first": "D",
"middle": [],
"last": "Schuurmans",
"suffix": ""
}
],
"year": 2007,
"venue": "Proceedings of IJCAI",
"volume": "",
"issue": "",
"pages": "1756--1762",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Q. I. Wang, D. Lin, and D. Schuurmans. 2007. Simple training of dependency parsers via structured boost- ing. In Proceedings of IJCAI, pages 1756-1762.",
"links": null
},
"BIBREF24": {
"ref_id": "b24",
"title": "Parsing the Penn Chinese Treebank with semantic knowledge",
"authors": [
{
"first": "D",
"middle": [],
"last": "Xiong",
"suffix": ""
},
{
"first": "S",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "Q",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "S",
"middle": [],
"last": "Lin",
"suffix": ""
},
{
"first": "Y",
"middle": [],
"last": "Qian",
"suffix": ""
}
],
"year": 2005,
"venue": "Proceedings of IJCNLP",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "D. Xiong, S. Li, Q. Liu, S. Lin, and Y. Qian. 2005. Parsing the Penn Chinese Treebank with semantic knowledge. In Proceedings of IJCNLP.",
"links": null
},
"BIBREF25": {
"ref_id": "b25",
"title": "The Penn Chinese Treebank: Phrase structure annotation of a large corpus",
"authors": [
{
"first": "N",
"middle": [],
"last": "Xue",
"suffix": ""
},
{
"first": "F",
"middle": [],
"last": "Xia",
"suffix": ""
},
{
"first": "F.-D",
"middle": [],
"last": "Chiou",
"suffix": ""
},
{
"first": "M",
"middle": [],
"last": "Palmer",
"suffix": ""
}
],
"year": 2005,
"venue": "Natural Language Engineering",
"volume": "10",
"issue": "4",
"pages": "1--30",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "N. Xue, F. Xia, F.-D. Chiou, and M. Palmer. 2005. The Penn Chinese Treebank: Phrase structure an- notation of a large corpus. Natural Language Engi- neering, 10(4):1-30.",
"links": null
},
"BIBREF26": {
"ref_id": "b26",
"title": "Statistical dependency analysis with support vector machines",
"authors": [
{
"first": "Matsumoto",
"middle": [],
"last": "Yamada",
"suffix": ""
}
],
"year": 2003,
"venue": "Proceedings of IWPT",
"volume": "",
"issue": "",
"pages": "195--206",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yamada and Matsumoto. 2003. Statistical dependency analysis with support vector machines. In Proceed- ings of IWPT, pages 195-206.",
"links": null
},
"BIBREF27": {
"ref_id": "b27",
"title": "Chinese dependency parsing with large scale automatically constructed case structures",
"authors": [
{
"first": "K",
"middle": [],
"last": "Yu",
"suffix": ""
},
{
"first": "D",
"middle": [],
"last": "Kawahara",
"suffix": ""
},
{
"first": "S",
"middle": [],
"last": "Kurohashi",
"suffix": ""
}
],
"year": 2008,
"venue": "Proceedings of COL-ING",
"volume": "",
"issue": "",
"pages": "1049--1056",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "K. Yu, D. Kawahara, and S. Kurohashi. 2008. Chinese dependency parsing with large scale automatically constructed case structures. In Proceedings of COL- ING, pages 1049-1056.",
"links": null
},
"BIBREF28": {
"ref_id": "b28",
"title": "A tale of two parsers: investigating and combining graphbased and transition-based dependency parsing using beam-search",
"authors": [
{
"first": "Y",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "S",
"middle": [],
"last": "Clark",
"suffix": ""
}
],
"year": 2008,
"venue": "Proceedings of EMNLP",
"volume": "",
"issue": "",
"pages": "562--571",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Y. Zhang and S. Clark. 2008. A tale of two parsers: investigating and combining graph- based and transition-based dependency parsing us- ing beam-search. In Proceedings of EMNLP, pages 562-571.",
"links": null
},
"BIBREF29": {
"ref_id": "b29",
"title": "Cross language dependency parsing using a bilingual lexicon",
"authors": [
{
"first": "H",
"middle": [],
"last": "Zhao",
"suffix": ""
},
{
"first": "Y",
"middle": [],
"last": "Song",
"suffix": ""
},
{
"first": "C",
"middle": [],
"last": "Kit",
"suffix": ""
},
{
"first": "G",
"middle": [],
"last": "Zhou",
"suffix": ""
}
],
"year": 2009,
"venue": "Proceedings of ACL",
"volume": "",
"issue": "",
"pages": "55--63",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "H. Zhao, Y. Song, C. Kit and G. Zhou. 2009. Cross language dependency parsing using a bilingual lexi- con. In Proceedings of ACL, pages 55-63.",
"links": null
},
"BIBREF30": {
"ref_id": "b30",
"title": "Exploiting web-derived selectional preference to imporve statistical dependency parsing",
"authors": [
{
"first": "G",
"middle": [],
"last": "Zhou",
"suffix": ""
},
{
"first": "J",
"middle": [],
"last": "Zhao",
"suffix": ""
},
{
"first": "K",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "L",
"middle": [],
"last": "Cai",
"suffix": ""
}
],
"year": 2011,
"venue": "Proceedings of ACL-HLT",
"volume": "",
"issue": "",
"pages": "1556--1665",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "G. Zhou, J. Zhao, K. Liu and L. Cai. 2011. Exploiting web-derived selectional preference to imporve sta- tistical dependency parsing. In Proceedings of ACL- HLT, pages 1556-1665.",
"links": null
}
},
"ref_entries": {
"FIGREF1": {
"uris": null,
"text": "The number of the most frequent errors relative to POS types on development set for firstorder parsing.",
"type_str": "figure",
"num": null
},
"FIGREF2": {
"uris": null,
"text": "Fine-grained feature generation for words with POS tag NN. The left part is word subcategory and the right part is HowNet hierarchy from generality to speciality. The merging procedure based on hypernym-hyponymy relations from bottom-up.",
"type_str": "figure",
"num": null
},
"FIGREF3": {
"uris": null,
"text": "Dependency trees of an example sentence \"\u4ee5(with) \u540d\u5b57(name) \u547d\u540d(named) \u7684(of) \u5956(prize) \u88ab(by) \u6388 \u4e88(award)\" as its English translation \"\u2022 \u2022 \u2022 prize named by \u2022 \u2022 \u2022 name is awarded \u2022 \u2022 \u2022 \". (a) Dependency tree produced by the baseline model; (b) Dependency tree produced by the proposed approach.",
"type_str": "figure",
"num": null
},
"FIGREF4": {
"uris": null,
"text": "Neighborhood ambiguities in Chinese dependency parsing.",
"type_str": "figure",
"num": null
},
"TABREF2": {
"html": null,
"content": "<table><tr><td>NN-InstitutePlace NN-aValue NN-organization NN-event NN-human NN-affairs NN-mental NN-entity NN-artifact</td><td>NN \u4f01\u4e1a(enterprise) \u516c\u53f8(company) \u7ecf\u6d4e(economy)\u56fd\u9645(international) \u56fd\u5bb6(country) \u653f\u5e9c(government) \u53d1\u5c55(developing)\u5408\u4f5c(cooperation) \u8bb0\u8005(reporter) \u4e13\u5bb6(expert) \u8d38\u6613(trading) \u91d1\u878d(financial) \u60c5\u7eea(mood) \u611f\u53d7(feelings) \u540e\u8005(latter) \u673a\u4f1a(opportunity) \u68c9\u82b1(cotton) \u7ef4\u751f\u7d20(vitamin)</td><td>VV-event VV-aValue VV-SelfMoveInDirection VV-change VV-attribute VV-entity VV-AlterRelation VV-AlterPossession VV-AlterPhysical</td><td>VV \u731c\u5230(guess) \u9884\u89c1(foresee) \u5c0f\u5fc3(care) \u53ef\u4ee5(can) \u8fdb\u884c(conduct) \u6269\u6563(spread) \u589e\u957f(increase) \u6da8\u4ef7(deform) \u7b80\u79f0(abbreviation) \u5e93\u5bb9(storage capacity) \u7ecf\u5386(experience) \u8003\u8651(consider) \u56f4\u56f0(siege) \u8131\u79bb(separate) \u501f\u7528(borrow) \u8d2d\u8fdb(buy) \u5efa\u9020(build) \u5236\u6210(make)</td></tr><tr><td>\u2022 \u2022 \u2022 AD-aValue AD-event</td><td>\u2022 \u2022 \u2022 \u4ee5\u540e(after) \u552f\u6709(only) AD \u8fd8(also) \u4e0d\u7ba1(no matter)</td><td>\u2022 \u2022 \u2022 JJ-aValue JJ-event</td><td>\u2022 \u2022 \u2022 \u5171\u540c(together) \u7279\u522b(special) JJ \u7ee7\u7eed(continue) \u76f8\u5bf9(relatively)</td></tr><tr><td>\u2022 \u2022 \u2022</td><td>\u2022 \u2022 \u2022</td><td>\u2022 \u2022 \u2022</td><td>\u2022 \u2022 \u2022</td></tr></table>",
"type_str": "table",
"text": "Nominal categories are the most heavily split. For ex-",
"num": null
},
"TABREF3": {
"html": null,
"content": "<table><tr><td>NN VV NR JJ CC DEG M VA LC PN DT</td><td>24 17 5 8 7 1 5 4 1 1 1</td><td>VC VE ON P NT CS AD SB CD DEC AS</td><td>2 1 1 4 3 3 5 1 1 1 1</td><td>MSP OD DEV BA LJ LB DER SP IJ ETC PU</td><td>1 1 1 1 1 1 1 1 1 1 1</td></tr></table>",
"type_str": "table",
"text": "The two words with their English translations in the subcategories of some POS tags.",
"num": null
},
"TABREF5": {
"html": null,
"content": "<table/>",
"type_str": "table",
"text": "Baseline (left) and fine-grained (right) feature templates. Abbreviation: ht=head POS, hw= head word, hf=fine-grained POS of head, mf=fineg-grained POS of modifier. st, gt, sf, gf= likewise for sibling and grandchild.",
"num": null
},
"TABREF8": {
"html": null,
"content": "<table><tr><td>Systems</td><td>\u226440 words (UAS)</td><td>Full (UAS)</td></tr></table>",
"type_str": "table",
"text": "Dependency parsing results on the test set after using the merging operator.",
"num": null
},
"TABREF9": {
"html": null,
"content": "<table><tr><td>(a)</td><td/><td/><td/><td/><td/><td/></tr><tr><td>P</td><td>NN</td><td>VV</td><td>DEC</td><td>NN</td><td>SB</td><td>VV</td></tr><tr><td>(b)</td><td/><td/><td/><td/><td/><td/></tr><tr><td>P</td><td>NN</td><td>VV</td><td>DEC</td><td>NN</td><td>SB</td><td>VV</td></tr><tr><td>P-event</td><td>NN-attribute</td><td colspan=\"5\">VV-AlterRelational DEC NN-event SB VV-AlterRelational</td></tr></table>",
"type_str": "table",
"text": "Comparison of our final results with other best-performing systems on this data set.",
"num": null
}
}
}
} |