File size: 130,639 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 2141 2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155 2156 2157 2158 2159 2160 2161 2162 2163 2164 2165 2166 2167 2168 2169 2170 2171 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181 2182 2183 2184 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197 2198 2199 2200 2201 2202 2203 2204 2205 2206 2207 2208 2209 2210 2211 2212 2213 2214 2215 2216 2217 2218 2219 2220 2221 2222 2223 2224 2225 2226 2227 2228 2229 2230 2231 2232 2233 2234 2235 2236 2237 2238 2239 2240 2241 2242 2243 2244 2245 2246 2247 2248 2249 2250 2251 2252 2253 2254 2255 2256 2257 2258 2259 2260 2261 2262 2263 2264 2265 2266 2267 2268 2269 2270 2271 2272 2273 2274 2275 2276 2277 2278 2279 2280 2281 2282 2283 2284 2285 2286 2287 2288 2289 2290 2291 2292 2293 2294 2295 2296 2297 2298 2299 2300 2301 2302 2303 2304 2305 2306 2307 2308 2309 2310 2311 2312 2313 2314 2315 2316 2317 2318 2319 2320 2321 2322 2323 2324 2325 2326 2327 2328 2329 2330 2331 2332 2333 2334 2335 2336 2337 2338 2339 2340 2341 2342 2343 2344 2345 2346 2347 2348 2349 2350 2351 2352 2353 2354 2355 2356 2357 2358 2359 2360 2361 2362 2363 2364 2365 2366 2367 2368 2369 2370 2371 2372 2373 2374 2375 2376 2377 2378 2379 2380 2381 2382 2383 2384 2385 2386 2387 2388 2389 2390 2391 2392 2393 2394 2395 2396 2397 2398 2399 2400 2401 2402 |
{
"paper_id": "O06-5005",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T08:07:41.042587Z"
},
"title": "A Pragmatic Chinese Word Segmentation Approach Based on Mixing Models 1",
"authors": [
{
"first": "Wei",
"middle": [],
"last": "Jiang",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Harbin Institute of Technology",
"location": {
"addrLine": "Heilongjiang Province",
"postCode": "150001",
"country": "P. R. China"
}
},
"email": "jiangwei@insun.hit.edu.cn"
},
{
"first": "Yi",
"middle": [],
"last": "Guan",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Harbin Institute of Technology",
"location": {
"addrLine": "Heilongjiang Province",
"postCode": "150001",
"country": "P. R. China"
}
},
"email": ""
},
{
"first": "Xiao-Long",
"middle": [],
"last": "Wang",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Harbin Institute of Technology",
"location": {
"addrLine": "Heilongjiang Province",
"postCode": "150001",
"country": "P. R. China"
}
},
"email": ""
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "A pragmatic Chinese word segmentation approach is presented in this paper based on mixing language models. Chinese word segmentation is composed of several hard sub-tasks, which usually encounter different difficulties. The authors apply the corresponding language model to solve each special sub-task, so as to take advantage of each model. First, a class-based trigram is adopted in basic word segmentation, which applies the Absolute Discount Smoothing algorithm to overcome data sparseness. The Maximum Entropy Model (ME) is also used to identify Named Entities. Second, the authors propose the application of rough sets and average mutual information, etc. to extract special features. Finally, some features are extended through the combination of the word cluster and the thesaurus. The authors' system participated in the Second International Chinese Word Segmentation Bakeoff, and achieved 96.7 and 97.2 in F-measure in the PKU and MSRA open tests, respectively.",
"pdf_parse": {
"paper_id": "O06-5005",
"_pdf_hash": "",
"abstract": [
{
"text": "A pragmatic Chinese word segmentation approach is presented in this paper based on mixing language models. Chinese word segmentation is composed of several hard sub-tasks, which usually encounter different difficulties. The authors apply the corresponding language model to solve each special sub-task, so as to take advantage of each model. First, a class-based trigram is adopted in basic word segmentation, which applies the Absolute Discount Smoothing algorithm to overcome data sparseness. The Maximum Entropy Model (ME) is also used to identify Named Entities. Second, the authors propose the application of rough sets and average mutual information, etc. to extract special features. Finally, some features are extended through the combination of the word cluster and the thesaurus. The authors' system participated in the Second International Chinese Word Segmentation Bakeoff, and achieved 96.7 and 97.2 in F-measure in the PKU and MSRA open tests, respectively.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "The word is a logical semantic and syntactic unit in natural language. Unlike English, there is no delimiter to mark word boundaries in Chinese language, so, in most Chinese NLP tasks, word segmentation is the foundational task which transforms the Chinese character string into a word sequence. It is a prerequisite to POS tagging, parser or further applications, such as Information Extraction, and the Question Answer system. than two decades. Various methods have been proposed, which fall into two main categories. The first category is made up of rule-based approaches that make use of linguistic knowledge. Cheng [1999] and Liang [1993] described Maximum Forward Match and Maximum Backward Match segmentation. Hockenmaier [1998] and Palmer [1997] used transformation-based error-driven learning. Wu [1998] combined segmentation with a parser and word segmentation became a by-product of the sentence parser. The second category is made up of statistical methods that make use of machine learning algorithms and training on corpus. The typical language model is n-gram [Gao 2002] . Zhang [2003] used the Hierarchical Hidden Markov Model (HMM). In addition, there are some other machine learning methods, such as EM [Peng and Schuurmans 2001] , and the channel noise model [Gao 2003 ]. Sproat [1996] used the WFST method. At present, many state-of-the-art systems use hybrid approaches. Gao [2004] proposed a unified method via the class-based model, and Zhang [2003] presented a unified approach using the Hierarchical Hidden Markov Model. Xue [2003] used Maximum Entropy. Peng [2004] used the Conditional Random Fields model. Though many methods have been proposed and many improvements have been achieved, as a challenge task, word segmentation is not well-performed. The disambiguation and the out-of-vocabulary (OOV) identification are the main bottlenecks. Due to Zipf's Law, the sparse data problem is rarely avoided, while this problem brings great difficulties in improving the performance of the disambiguation and OOV identification. A meaningful direction for exploration to overcome the sparse data problem is to collect more linguistic knowledge or features and incorporate them into the processing systems.",
"cite_spans": [
{
"start": 614,
"end": 626,
"text": "Cheng [1999]",
"ref_id": "BIBREF1"
},
{
"start": 631,
"end": 643,
"text": "Liang [1993]",
"ref_id": null
},
{
"start": 717,
"end": 735,
"text": "Hockenmaier [1998]",
"ref_id": "BIBREF6"
},
{
"start": 740,
"end": 753,
"text": "Palmer [1997]",
"ref_id": "BIBREF14"
},
{
"start": 803,
"end": 812,
"text": "Wu [1998]",
"ref_id": "BIBREF19"
},
{
"start": 1075,
"end": 1085,
"text": "[Gao 2002]",
"ref_id": "BIBREF4"
},
{
"start": 1088,
"end": 1100,
"text": "Zhang [2003]",
"ref_id": "BIBREF21"
},
{
"start": 1221,
"end": 1247,
"text": "[Peng and Schuurmans 2001]",
"ref_id": "BIBREF16"
},
{
"start": 1278,
"end": 1287,
"text": "[Gao 2003",
"ref_id": "BIBREF5"
},
{
"start": 1291,
"end": 1304,
"text": "Sproat [1996]",
"ref_id": "BIBREF18"
},
{
"start": 1392,
"end": 1402,
"text": "Gao [2004]",
"ref_id": "BIBREF3"
},
{
"start": 1460,
"end": 1472,
"text": "Zhang [2003]",
"ref_id": "BIBREF21"
},
{
"start": 1546,
"end": 1556,
"text": "Xue [2003]",
"ref_id": "BIBREF20"
},
{
"start": 1579,
"end": 1590,
"text": "Peng [2004]",
"ref_id": "BIBREF15"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1."
},
{
"text": "In this paper, the authors propose to solve the Chinese word segmentation task based on mixing models. The \"No Free Lunch Theorem\" and \"Ugly Duckling Theorem\" in Machine Learning theory have indicated that domain knowledge is essential for improving the processing performance. For this reason, different language models will be applied to solve each special sub-task, which is classified according to its linguistic phenomenon and the Natural Language Processing (NLP) technology used in the word segmentation. Another consideration is the pragmatic attribution, e.g. some successive processing may require different kinds of balance between precision and efficiency. So, this approach is a pragmatic one, which may incorporate several delicate processing modules, some of which can improve precision by introducing complicated models and utilizing more linguistic knowledge. However, this does result in a decrease in efficiency. Based on the assumption that more delicate linguistic knowledge or some fine linguistic statistical phenomenon can bring information gain to the segmentation task, the authors propose to apply Rough Set Theory and Average Mutual Information, etc. to extract complicated and long distance features. and the authors will also explore combining the word cluster and the thesaurus to extend the features so as to overcome the sparse data problem. This system participated in the Second International Chinese Word Segmentation Bakeoff (SIGHAN 2005) , and a simplified version participated in the SIGHAN 2006.",
"cite_spans": [
{
"start": 1462,
"end": 1475,
"text": "(SIGHAN 2005)",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1."
},
{
"text": "Section 2 describes the structure of the system. Section 3 describes in detail Named Entity Recognition, which is one of the difficult tasks in word segmentation. Section 4 presents experimental results obtained with the authors' system. Finally, some conclusions will be drawn and direction for future work will be given in Section 5.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1."
},
{
"text": "All words in this system are categorized into five types: Lexicon words (LW), Factoid words (FT), Morphological derived words (MDW), Named entities (NE), and New words (NW). Table 1 shows the tag, description, and some examples for each word type. To the sentence \"\u540c\u5b66\u4eec\u4e0b\u5348\u4e24\u70b9\u4e09\u5341\u5206\u5230\u5b59\u6842\u5e73\u5bb6\u505a\u5ba2\" (Some students visit Sun Gui-Ping in his home at 2:30 p.m.), the segmentation result is \"{\u540c\u5b66\u4eec/[MR_Suffix]} {\u4e0b\u5348 \u4e24\u70b9\u4e09\u5341\u5206/[TIME]} {\u5230} {\u5b59\u6842\u5e73/[PER]} {\u5bb6} {\u505a\u5ba2}\". where the word \"\u540c\u5b66\u4eec /[MR_Suffix]\" is a morphologically derived word, and \"\u4e0b\u5348\u4e24\u70b9\u4e09\u5341\u5206/[TIME]\" is a factoid word, all of which can be detected by Segmentation module while \"\u5b59\u6842\u5e73/[PER]\" is a named entity, and detected in NE Recognition module. Figure 1 shows the structure of this system.",
"cite_spans": [],
"ref_spans": [
{
"start": 174,
"end": 181,
"text": "Table 1",
"ref_id": "TABREF0"
},
{
"start": 673,
"end": 681,
"text": "Figure 1",
"ref_id": "FIGREF0"
}
],
"eq_spans": [],
"section": "System Description",
"sec_num": "2."
},
{
"text": "The input character sequence is converted into one or several sentences, which is the basic dealing unit. The internal encoding is UNICODE, and the \"Code Convert\" module is used to convert the permitted encoding, such as GB2312 and BIG5, into UNICODE. \"Basic Segmentation\" is used to deal with the LW, FT, MDW words, and \"Named Entity Recognition\" is used to detect NW words. The authors adopt the New Word Detection algorithm to detect suffix-based new words. The \"Disambiguation\" module is performed to classify complicated ambiguous words, and all the above results are connected to the final result, namely \"word sequence\", which is denoted by XML format. The sequence of each applied component is decided by the performance of the system. In the following part of this section, the authors will detail the basic theory and the implementation of the system. ",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "System Description",
"sec_num": "2."
},
{
"text": "The authors apply the Trigram model to the word segmentation task [Jiang 2005; Jiang 2007] , and make use of the Absolute Discount Smoothing algorithm to overcome the sparse data problem.",
"cite_spans": [
{
"start": 66,
"end": 78,
"text": "[Jiang 2005;",
"ref_id": "BIBREF9"
},
{
"start": 79,
"end": 90,
"text": "Jiang 2007]",
"ref_id": "BIBREF7"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Trigram and Smoothing Algorithm",
"sec_num": "2.1"
},
{
"text": "The Trigram model is used to convert the sentence into a word sequence. Let w = w 1 w 2 \u2026w n be a word sequence, then the most likely word sequence w* in Trigram is:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Trigram and Smoothing Algorithm",
"sec_num": "2.1"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "1 2 2 1 1 * argmax ( | ) n n i i i i w w w P w w w \u2212 \u2212 = = \u220f w ,",
"eq_num": "(1)"
}
],
"section": "Trigram and Smoothing Algorithm",
"sec_num": "2.1"
},
{
"text": "where let P(w 0 |w -2 w -1 ) be P(w 0 ) and let P(w 1 |w -1 w 0 ) be P(w 1 |w 0 ), and w i represents LW or a type of FT or MDW. In order to search for the best segmentation way, all the word candidates are filled into the word lattice [Jiang 2006B ], as shown in Figure 2 , and the Viterbi algorithm is used to search for the best word segmentation path over the built word lattice.",
"cite_spans": [
{
"start": 236,
"end": 248,
"text": "[Jiang 2006B",
"ref_id": "BIBREF10"
}
],
"ref_spans": [
{
"start": 264,
"end": 272,
"text": "Figure 2",
"ref_id": null
}
],
"eq_spans": [],
"section": "Trigram and Smoothing Algorithm",
"sec_num": "2.1"
},
{
"text": "FT and MDW need to be detected when constructing a word lattice (detailed in section 2.2). The data structure of the lexicon can affect the efficiency of word segmentation, so",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Figure 2. The class-based word lattice in the segmentation task",
"sec_num": null
},
{
"text": "\u5468\uf98e 1/[Number] \u7eaa\uf9a3 5 \u6708 12 \u65e5/[Date] 5 \u6708 1 2 \u65e5 \u7eaa \uf9a3 1 \u5468 \uf98e \u4e8e",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Figure 2. The class-based word lattice in the segmentation task",
"sec_num": null
},
{
"text": "lexicon words are represented as a set of TRIEs, which are tree-like structures. Words starting with the same character are represented as a TRIE, where the root represents the first Chinese character, and the children of the root represent the second character, and so on (detailed in section 2.3).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Figure 2. The class-based word lattice in the segmentation task",
"sec_num": null
},
{
"text": "When searching a word lattice, there is a zero-probability phenomenon due to the sparse data problem. For instance, if there is no co-occurrence pair \"\u6211\u4eec/\u5403/\u9999\u8549\"(we eat bananas) in the training corpus, then P(\u9999\u8549|\u6211\u4eec\uff0c\u5403) = 0. According to formula (1), the probability of the whole candidate path, which contains \"\u6211\u4eec/\u5403/\u9999\u8549\", is zero as a result of the local zero probability. In order to overcome the sparse data problem, this system has applied the Absolute Discounting Smoothing algorithm [Chen 1999 ",
"cite_spans": [
{
"start": 484,
"end": 494,
"text": "[Chen 1999",
"ref_id": "BIBREF0"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Figure 2. The class-based word lattice in the segmentation task",
"sec_num": null
},
{
"text": "]. 1 1 1 1 1 ( ) |{ : ( ) 0 }| i i i n i i n i N w w c w w \u2212 \u2212 + \u2212 + \u2212 + \u2022 = > (2)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Figure 2. The class-based word lattice in the segmentation task",
"sec_num": null
},
{
"text": "The notation N 1+ is meant to evoke the number of words that have one or more counts, and the \u2022 is meant to evoke a free variable that is summed over. The function () c represents the count of one word or the co-occurrence count of multi-words. In this case, the smoothing probability can be calculated by the Equation 3.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Figure 2. The class-based word lattice in the segmentation task",
"sec_num": null
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "1 1 1 1 2 1 max{ ( ) , 0} ( | ) (1 ) ( | ) ( ) i i i i i n i i n i i n i i n w c w D p w w p w w c w \u03bb \u2212 \u2212 \u2212 + \u2212 + \u2212 + \u2212 + \u2212 = +\u2212 \u2211 (3) where 1 1 1 1 1 ( ) ( ) i i i n i i n w D N w c w \u03bb \u2212 + \u2212 + \u2212 + \u239b \u239e \u239c \u239f \u2212 = \u2022 \u239c \u239f \u239d \u23a0 \u2211",
"eq_num": "(4)"
}
],
"section": "Figure 2. The class-based word lattice in the segmentation task",
"sec_num": null
},
{
"text": "In this trigram model, the maximum n may be 3. A fixed discount D (0 \u2264 D \u2264 1) can be set through the deleted estimation on the training data. They arrive at the estimate ",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Figure 2. The class-based word lattice in the segmentation task",
"sec_num": null
},
{
"text": "where n 1 and n 2 are the total number of n-grams with exactly one and two counts, respectively [Jiang 2006B; Jiang 2007] .",
"cite_spans": [
{
"start": 96,
"end": 109,
"text": "[Jiang 2006B;",
"ref_id": "BIBREF10"
},
{
"start": 110,
"end": 121,
"text": "Jiang 2007]",
"ref_id": "BIBREF7"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Figure 2. The class-based word lattice in the segmentation task",
"sec_num": null
},
{
"text": "After basic segmentation, some complicated ambiguous segmentation can be further disambiguated. In the Trigram model, only the previous two words are considered as context features, while in disambiguation processing (detailed in section 2.4), one can use the Maximum Entropy Model-fused features [Jiang 2006A ] or a rule-based method.",
"cite_spans": [
{
"start": 297,
"end": 309,
"text": "[Jiang 2006A",
"ref_id": "BIBREF8"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Figure 2. The class-based word lattice in the segmentation task",
"sec_num": null
},
{
"text": "As all of the Factoid words can be represented as regular expressions, the detection of factoid words can be achieved by Finite State Automaton (FSA).The categories of factoid words, which can be detected [Jiang 2006B; Jiang 2006D] Deterministic FSA (DFA) is efficient because a unique \"next state\" is determined when given an input symbol and the current state. However, it is common for a linguist to write rules, which can be represented directly as a non-deterministic FSA (NFA), i.e. which allow several \"next states\" to follow a given input and state.",
"cite_spans": [
{
"start": 205,
"end": 218,
"text": "[Jiang 2006B;",
"ref_id": "BIBREF10"
},
{
"start": 219,
"end": 231,
"text": "Jiang 2006D]",
"ref_id": "BIBREF12"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Factoid and Morphological Words",
"sec_num": "2.2"
},
{
"text": "Since every NFA has an equivalent DFA, an FT rule compiler was build to convert all the FT generative rules into a DFA [Jiang 2007] . The rule description is in Table 3 .",
"cite_spans": [
{
"start": 119,
"end": 131,
"text": "[Jiang 2007]",
"ref_id": "BIBREF7"
}
],
"ref_spans": [
{
"start": 161,
"end": 168,
"text": "Table 3",
"ref_id": null
}
],
"eq_spans": [],
"section": "Factoid and Morphological Words",
"sec_num": "2.2"
},
{
"text": "<digit> -> [0..9]| [\uff10..\uff19];",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Table 3. The demonstration of partial ELUSLex rules",
"sec_num": null
},
{
"text": "//define Arabic numerals <integer> ::= {<digit>+}; // define Arabic Integer <real> ::= <integer>(.|\uff0e|\u2022|\u70b9)<integer>; // decimal fraction <day> -> <integer>\u65e5; // define day <month> -> <integer>\u6708; // define month <year> -> <digit><integer>\uf98e; // define year <date> ::= <year><month><day>; // define date In order to provide a kind of convenient and powerful description ability, some meta descriptions are assigned to the meta language. Transferred meaning : if the token in the meta rule is the terminator, one needs to transfer its meaning, so one can use double quotation marks to bracket the terminator when it present ambiguity. e.g. \"(\", \"|\", \")\".",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Table 3. The demonstration of partial ELUSLex rules",
"sec_num": null
},
{
"text": "Rule type: \"->\" is a temporary generative rule, and \"::=\" is a real generative rule or a detected rule. This method makes the rule easily written.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Table 3. The demonstration of partial ELUSLex rules",
"sec_num": null
},
{
"text": "The authors built an FT rule compiler to convert all the FT generative rules into a DFA. Obviously, this method makes the system easy to be transferred into a different word segmentation definition, such as from PKU to MSRA. In fact, the authors have used it in SIGHAN 2005 and SIGHAN 2006 . Correspondingly, the DFA is represented by the matrix [Jiang 2007] , and a run API is provided to make this method easily used. FT detection is important in building the word lattice in word segmentation and also important in the POS tagging task.",
"cite_spans": [
{
"start": 262,
"end": 277,
"text": "SIGHAN 2005 and",
"ref_id": null
},
{
"start": 278,
"end": 289,
"text": "SIGHAN 2006",
"ref_id": null
},
{
"start": 346,
"end": 358,
"text": "[Jiang 2007]",
"ref_id": "BIBREF7"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Table 3. The demonstration of partial ELUSLex rules",
"sec_num": null
},
{
"text": "The proposed system tries to deal with five main categories of morphologically derived words in real application, the same as Wu [2003] and Gao [2004] : 1) Affixation : \uf934\u5e08\u4eec (teachers), \u670b\u53cb\u4eec(friends); 2) Reduplication: \u9ad8\u9ad8\u5174\u5174(happily); 3) Splitting:\u73a9\u4f1a\u7403(play ball for a while)\uff0c\u6d17\uf9ba\u6fa1(already wash), \u5403\uf9ba\u996d(already ate); 4) Merging: \"\u8fdb\u51fa\u53e3\" comes from \"\u8fdb\u53e3\"(importation)and \"\u51fa\u53e3\"(exportation); 5) Head Particle: \"\u8d70\u51fa\u53bb\"comes from \"\u8d70\"(walk) and \"\u51fa\u53bb\"(out).",
"cite_spans": [
{
"start": 126,
"end": 135,
"text": "Wu [2003]",
"ref_id": null
},
{
"start": 140,
"end": 150,
"text": "Gao [2004]",
"ref_id": "BIBREF3"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Table 3. The demonstration of partial ELUSLex rules",
"sec_num": null
},
{
"text": "The authors collate the possible MDW into a morphological dictionary from a large corpus, according to the morphological categories mentioned above. Then, some manual selections are needed to select fitting MDW words. As the segmentation specifications of all kinds of corpora are usually different, one needs to collect the corresponding MDW words.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Table 3. The demonstration of partial ELUSLex rules",
"sec_num": null
},
{
"text": "The data structure of a lexicon affects the efficiency of word segmentation, as the word candidate in the word lattice is generated through searching the lexicon. When given a sentence string, the candidate comes from matching the substring (starting from the current Chinese character), and judging whether this substring exists in the lexicon. The authors represent lexicon words as a set of TRIEs, which is a tree-like structure. Words starting with the same character are represented as a TRIE, where the root represents the first Chinese character, and the children of the root represent the second characters, and so on, as shown in Figure 3 .",
"cite_spans": [],
"ref_spans": [
{
"start": 639,
"end": 647,
"text": "Figure 3",
"ref_id": "FIGREF3"
}
],
"eq_spans": [],
"section": "The Data Structure of Lexicon",
"sec_num": "2.3"
},
{
"text": "The lexical word starts from the \"Start state\", and ends in the \"End state\". When matching the input sentence and generating the word candidate in the word lattice, each time \"End State\" is passed, a word candidate is formed and the properties of the current word represented in the \"End State\" are filled into word lattice. Since each Chinese character in the input sentence needs to match the word candidate, the authors build many TRIEs, as shown in Figure 3 , to form a lexicon. The example in Figure 3 , \"\u793e\u4f1a\u4e3b\u4e49\" (socialism), is a word, and this tree is used to match the candidate from the start to the end in the sentence. If one constructs a word lattice in the opposite direction, the tree needs to be built correspondingly, e.g. \"\u4e49\u4e3b\u4f1a\u793e\". This data structure can improve speed in generating the word lattice.",
"cite_spans": [],
"ref_spans": [
{
"start": 453,
"end": 461,
"text": "Figure 3",
"ref_id": "FIGREF3"
},
{
"start": 498,
"end": 507,
"text": "Figure 3",
"ref_id": "FIGREF3"
}
],
"eq_spans": [],
"section": "The Data Structure of Lexicon",
"sec_num": "2.3"
},
{
"text": "It is necessary to effectively exploit the context in the disambiguation process. The authors have proposed using rough sets to extract complicated features and long distance features for disambiguation, which has been reported in previous work [Jiang 2006A] . In that paper, the authors proposed introducing a variable precision Rough Set in feature extraction, in order to acquire a balance of features in disambiguation processing, along with attempting to process complicated and consecutive ambiguity segmentation in the paper. In this paper, the ambiguity segmentations come from the error-total results after evaluating the system.",
"cite_spans": [
{
"start": 245,
"end": 258,
"text": "[Jiang 2006A]",
"ref_id": "BIBREF8"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "The Disambiguation",
"sec_num": "2.4"
},
{
"text": "In Rough Set theory, knowledge is represented via relational tables. An Information System can be defined as follows: ",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "The Disambiguation",
"sec_num": "2.4"
},
{
"text": "( , , , ) a a a A I U A V f \u2208 = ,",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "The Disambiguation",
"sec_num": "2.4"
},
{
"text": "| | ( ) : | | x X X x x \u03b8 \u03b8 \u03b1 \u03b8 \u03b1 \u03b8 \u23a7 \u23ab = \u2265 \u23a8 \u23ac \u23a9 \u23ad \u2229 \u222a",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "The Disambiguation",
"sec_num": "2.4"
},
{
"text": ", where \u03b1 is an external parameter [Jiang 2006A] .",
"cite_spans": [
{
"start": 35,
"end": 48,
"text": "[Jiang 2006A]",
"ref_id": "BIBREF8"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "The Disambiguation",
"sec_num": "2.4"
},
{
"text": "When extracting features, \u03b1 -Approximation will probably cause unbalanced support, since each segmentation of the ambiguities possibly has disproportionate distribution. In order to let all the features that were added in provide more evidence in guiding toward the correct segmentation, \u03bb -Approximation is introduced in this model. Let filter parameter",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "The Disambiguation",
"sec_num": "2.4"
},
{
"text": "[0,1] d \u03b1 \u2208",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "The Disambiguation",
"sec_num": "2.4"
},
{
"text": ", and the n-order rough rule set of keyword t be noted as n t R , then",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "The Disambiguation",
"sec_num": "2.4"
},
{
"text": ", n t tn R G \u2208",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "The Disambiguation",
"sec_num": "2.4"
},
{
"text": ", and defined as:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "The Disambiguation",
"sec_num": "2.4"
},
{
"text": "{ } ( ) , , | ( ) i n t tn d d R r G r X \u03b8 \u03b1 = \u2208 \u2208 , where 1 f n A = \u2212 , [1, ] i K \u2208 and , t n G",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "The Disambiguation",
"sec_num": "2.4"
},
{
"text": "represents generalized LIT. In In order to make effective use of contextual knowledge, the authors adopt the Maximum Entropy model (ME), which is a conditional probabilistic model, and relax the feature independent assumption. Disambiguation is regarded as a classifying problem in ambiguous words by the Maximum Entropy model, which is defined over H\u00d7T in segmentation disambiguation, where H is the set of possible contexts around the target word that will be tagged, and T is the set of allowable tags. Then, the model's conditional probability is defined as:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "The Disambiguation",
"sec_num": "2.4"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "' ( , ) ( | ) ( , ') t T p h t p t h p h t \u2208 = \u2211",
"eq_num": "(6)"
}
],
"section": "The Disambiguation",
"sec_num": "2.4"
},
{
"text": "where, It has been pointed out that two kinds of ambiguities were dealt with. One is the simple two categories problem, such as \"\u4ece/\u5c0f\u5b66\"(from elementary school) and \"\u4ece\u5c0f/\u5b66\"(study since youth), where the tags are 0 and 1; here 0 represents the first segmentation and 1 represents the second. H includes the near context and long distance context. The former is comprised of two words around the target word, and the latter features can be obtained by Average Mutual Information, Information Gain, etc.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "The Disambiguation",
"sec_num": "2.4"
},
{
"text": "In fact, a rough statistical result showed that the \"one segmentation error\" occupied more than 90% of all errors when not considering the errors caused by Named Entity Recognition. Here, \"one segmentation error\" means that the segmentations surrounding this segmentation error are correct. So, the authors focus on \"one segmentation error\", which may be seen in two types of Chinese segmentation ambiguities: overlapping ambiguity and combining ambiguity. ",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "The Disambiguation",
"sec_num": "2.4"
},
{
"text": "New word (NW) in this system refers to the out-of-vocabulary word that isn't an FT word, MDW word or NE word. The authors do not try to detect all the NW words, since the precision is not satisfactory based on the existing methods in some applications.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "The Suffix Based New Word Detection",
"sec_num": "2.5"
},
{
"text": "On the other hand, in some applications, it is meaningful to recognize some special new words. For instance, \"\u666f\u89c2+\u706f\" (sightseeing light), \"\u6d77\u98ce+\u724c\" (Sea Breeze brand). Since some prefixes or some suffixes are paid attention to by this system, such as \"\u73b0\u4ee3 + \u5316\"(modernization), \"x + \u5f0f\"(x + way), \"x + \u706f\"(x + light), the authors propose to apply a variance algorithm to acquire the prefix or suffix candidate, leaving some minor manual selections possibly required. Hereafter, this paper takes the suffix as an instance, and collects the new words, e.g. \"\u65e5\u5149+\u706f\" (sunlight), \"\u9713\u8679+\u706f\" (neon light), \"\u666f\u89c2+\u706f\" (sightseeing light), etc. Table 4 illustrates the method. Use S 1 ..S m to represent m candidate suffixes, W 1 ..W n represent n remained word with the suffix being razed. e.g. S 1 is \"\u706f\" (light), then W 1 represents \"\u666f\u89c2\" (sightseeing), W 1 S 1 is the W 1 +S 1 =\"\u666f\u89c2\u706f\" (sightseeing light). Now, suppose C xy =Count(S x ,W y ) 2 , and N xy is the existence of a co-occurring pair (S x ,W y ) 3 , then, one gets the following formula:",
"cite_spans": [],
"ref_spans": [
{
"start": 620,
"end": 627,
"text": "Table 4",
"ref_id": "TABREF3"
}
],
"eq_spans": [],
"section": "The Suffix Based New Word Detection",
"sec_num": "2.5"
},
{
"text": "m x x i i 1 CV(S ) = N = \u2211 , m x x i i 1 Sum(S )= C = \u2211 , avg(S x )= Sum(S x )/CV(S x ), p xi =C xi /Sum(S x ),V xi =p xi *(C xi -avg(S x )) *(C xi -avg(S x )) So, the variance V (S x )= m xi i 1 V = \u2211 .",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "The Suffix Based New Word Detection",
"sec_num": "2.5"
},
{
"text": "Besides the variance, one also needs to consider two other factors: (1) the occurrence count in the corpus; (2) the type count that this suffix has constructed words in the lexicon. By considering the above two factors in Sighan2005 evaluation [Jiang 2005 ], the researchers selected 25 new word suffixes, e.g. \u5236 (method), \u724c(brand), \u578b (type)\u3001\u5f0f (way). These suffixes also seem to be useful in the Information Retrieval task.",
"cite_spans": [
{
"start": 244,
"end": 255,
"text": "[Jiang 2005",
"ref_id": "BIBREF9"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "The Suffix Based New Word Detection",
"sec_num": "2.5"
},
{
"text": "The detection process adopts the Local Maximum Entropy Model, and this process is similar to the NER module [Jiang 2007 ].",
"cite_spans": [
{
"start": 108,
"end": 119,
"text": "[Jiang 2007",
"ref_id": "BIBREF7"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "The Suffix Based New Word Detection",
"sec_num": "2.5"
},
{
"text": "Named Entity Recognition (NER) is one of the common message understanding tasks. The objective is to identify and categorize all members of certain categories of \"proper names\". In MUC-7, there are seven categories: person, organization, location, date, time, percentage, and monetary amount. Named Entities (NE) are broadly distributed in original texts from many domains. In this work, the authors only focus on those more difficult, yet commonly used categories: PER, LOC and ORG. Other NE, such as times and quantities can be recognized simply via Finite State Automata (Section 2.2), and do not need to be aided by a disambiguation algorithm (Section 2.4).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Named Entity Recognition",
"sec_num": "3."
},
{
"text": "The extensive evaluation of NER systems in recent years (such as CoNLL-2002 and CoNLL-2003) indicates the best statistical systems are typically achieved by using a linear (or log-linear) classification algorithm, such as the Maximum Entropy model, together with a vast amount of carefully designed linguistic features. This still seems true at present in terms of statistics based methods.",
"cite_spans": [
{
"start": 65,
"end": 79,
"text": "CoNLL-2002 and",
"ref_id": null
},
{
"start": 80,
"end": 91,
"text": "CoNLL-2003)",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Named Entity Recognition",
"sec_num": "3."
},
{
"text": "In this section, the authors adopt the ME model, which is a linear (or log-linear) classification, to identify the Named Entities, and the focus will be on the utilization of the features [Jiang 2006C ]. In addition, the authors propose to build double-layer fixing models to detect the Named Entities, which has also been reported in another paper [Jiang 2007 ].",
"cite_spans": [
{
"start": 188,
"end": 200,
"text": "[Jiang 2006C",
"ref_id": "BIBREF11"
},
{
"start": 349,
"end": 360,
"text": "[Jiang 2007",
"ref_id": "BIBREF7"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Named Entity Recognition",
"sec_num": "3."
},
{
"text": "The authors use w i (i=0,1,\u2026n) to denote the input sequence, then every token w i should be assigned a tag t i . B-I-O encoding, e.g., B-CPN, I-CPN as the beginning of Chinese person's name and the continued part of person's name, respectively, is adopted. Furthermore, in order to improve the ability of describing the rich tagging knowledge, part of the role tags [Zhang 2003 ] is appended, including the Named Entity prefix, suffix and infix. For example: (FPN) . In addition, the authors do not distinguish the type of infix, so the tag number for NER in this system is: 4 * 4 + 1 (O) + 1 (INFIX) = 18.",
"cite_spans": [
{
"start": 366,
"end": 377,
"text": "[Zhang 2003",
"ref_id": "BIBREF21"
}
],
"ref_spans": [
{
"start": 459,
"end": 464,
"text": "(FPN)",
"ref_id": null
}
],
"eq_spans": [],
"section": "Named Entity Recognition",
"sec_num": "3."
},
{
"text": "The \"Ugly Duckling Theorem\" has denoted that there is no generic feature extraction method suitable for all kinds of tasks. The basic feature template is shown in Table 5 . ",
"cite_spans": [],
"ref_spans": [
{
"start": 163,
"end": 170,
"text": "Table 5",
"ref_id": "TABREF4"
}
],
"eq_spans": [],
"section": "The Context Features",
"sec_num": "3.1"
},
{
"text": "w i-2 , w i-1 , w i , w i+1 , w i+2",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "The Context Features",
"sec_num": "3.1"
},
{
"text": "two order feature w i-1:i , w i:i+1",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "The Context Features",
"sec_num": "3.1"
},
{
"text": "NER tag feature",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "The Context Features",
"sec_num": "3.1"
},
{
"text": "t i-1",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "The Context Features",
"sec_num": "3.1"
},
{
"text": "In addition, in order to solve the unstable feature collection problem caused by having no delimiters to separate Chinese words, inspired by the term extraction in text classification, the authors construct a novel feature template of \"word->tag\" to extract the trigger features, which have a flexible distance between the two units [Jiang 2006C ].",
"cite_spans": [
{
"start": 333,
"end": 345,
"text": "[Jiang 2006C",
"ref_id": "BIBREF11"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "The Context Features",
"sec_num": "3.1"
},
{
"text": "Mutual Information (MI) measures the interdependence between a trigger word and a NE type, being defined as:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "The Context Features",
"sec_num": "3.1"
},
{
"text": "P(W C) MI(W, C) log P(W) P(C) \u039b = \u00d7 (9)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "The Context Features",
"sec_num": "3.1"
},
{
"text": "where P(W) represents the probability of the trigger word, and P(C) is the probability of the corresponding NE category. However, this method does not consider the influence of lacking one point. In contrast, average mutual information (AMI) is defined as: P(C|W) AMI(W, C) P(W, C)log P(C) = P(C|W) P(W, C)log P(C) + P(C|W) P(W, C)log P(C) + P(C|W) P(W, C)log P(C) +",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "The Context Features",
"sec_num": "3.1"
},
{
"text": "MI in fact is point wise information, while AMI can look like a Kullback-Leibler (KL) divergence:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "The Context Features",
"sec_num": "3.1"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "AMI(X,Y) D(P(X,Y)||P(X) P(Y)) = \u00d7",
"eq_num": "(11)"
}
],
"section": "The Context Features",
"sec_num": "3.1"
},
{
"text": "Equation 11 measures the two different probability distributions between P(X,Y) and P(Y) P(X) \u00d7 . However, MI is only a point in the whole set of distributions.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "The Context Features",
"sec_num": "3.1"
},
{
"text": "Let m be the number of the possible categories count, the average mutual information is",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "The Context Features",
"sec_num": "3.1"
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "m avg i i i 1 AMI (W) P(C ) AMI(W, C ) = = \u00d7 \u2211",
"eq_num": "(12)"
}
],
"section": "The Context Features",
"sec_num": "3.1"
},
{
"text": "or another optional formula adopted in this paper:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "The Context Features",
"sec_num": "3.1"
},
{
"text": "m max i 1 i AMI (W) MAX AMI(W, C ) = = (13)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "The Context Features",
"sec_num": "3.1"
},
{
"text": "The authors select the top triggers with higher AMI value, and acquire the trigger words.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "The Context Features",
"sec_num": "3.1"
},
{
"text": "Besides context features, entity features are also very important in the NER task, such as the suffix of Location or Organization. The authors performed statistical analysis of foreign resources, including the corpora and the collected entity name on the Internet. The authors built 8 kinds of dictionaries: Table 6 gives several kinds of resource dictionaries used in this system. Take the \"Suffix of ORG\" as an example, the suffix \"\u5c40\", \"\u7ec4\u7ec7\" is a good hint to detect the Organization Name, so the authors collected them into a \"Suffix of ORG\" dictionary. When used in the Maximum Entropy Model, this dictionary is used to judge the existing cases of the specified context feature.",
"cite_spans": [],
"ref_spans": [
{
"start": 308,
"end": 315,
"text": "Table 6",
"ref_id": "TABREF5"
}
],
"eq_spans": [],
"section": "The Entity Features",
"sec_num": "3.2"
},
{
"text": "Feature extension is used to overcome the sparse data problem and to increase robustness. In addition, semantic and pragmatic knowledge is useful in language processing, e.g., if one knows \"\u6559\u6388\" (professor) is a good hint to label a person's name, the similar words {\uf934\u5e08 teacher), \u52a9\u6559(assistant), \u8bb2\u5e08(lecturer)}, should have the same effect. So, one can build a semantic class by combining word clusters and using a thesaurus.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "The Feature Extension",
"sec_num": "3.3"
},
{
"text": "A vector for word w is derived from the close neighbors of w in the corpus. Close neighbors are all words that co-occur with w in a sentence or a larger context. The entry for word v in the vector for w records the number of times that word v occurs close to w in the corpus. The authors refer this vector space to as Word Space. Figure 4 gives a schematic example of two words being represented in a two-dimensional space. This vector representation captures the typical topic or subject matter of a word. By looking at the amount of overlap between two vectors, one can roughly determine how closely they are related semantically. This is because related meanings are often expressed by similar sets of words. Semantically related words will, therefore, co-occur with similar neighbors and their vectors will have considerable overlap.",
"cite_spans": [],
"ref_spans": [
{
"start": 330,
"end": 338,
"text": "Figure 4",
"ref_id": null
}
],
"eq_spans": [],
"section": "The Feature Extension",
"sec_num": "3.3"
},
{
"text": "The authors combine the basic semantic word in a thesaurus --HOWNET2005 --with the TF-IDF algorithm [Zhao 2005B] , and use a frequency cutoff to select the 2000 words to serve as the dimensions of Word Space. Compared with the traditional TF-IDF method, this method increases the taxonomical information, so this method can give a better measure of the word similarity.",
"cite_spans": [
{
"start": 100,
"end": 112,
"text": "[Zhao 2005B]",
"ref_id": "BIBREF24"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Figure 4. A demonstration of word vectors",
"sec_num": null
},
{
"text": "After constructing word vectors, the similarity can be measured by the cosine between two vectors. The cosine is equivalent to the normalized correlation coefficient:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Figure 4. A demonstration of word vectors",
"sec_num": null
},
{
"text": "EQUATION",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [
{
"start": 0,
"end": 8,
"text": "EQUATION",
"ref_id": "EQREF",
"raw_str": "1 2 2 1 1 ( , ) N i i i N N i i i i v w corr v w v w = = = = \u2211 \u2211 \u2211",
"eq_num": "(14)"
}
],
"section": "Figure 4. A demonstration of word vectors",
"sec_num": null
},
{
"text": "The word cluster algorithm in the word vectors is used to measure the similarity by totaling the pragmatic knowledge from the corpora.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Figure 4. A demonstration of word vectors",
"sec_num": null
},
{
"text": "The authors evaluated the system with two kinds of corpora: 1) The corpora in the International Chinese Word Segmentation Bakeoff; 2) The prior six-month corpora of Peoples' Daily (China) in 1998, which came from Peking University, and have been annotated with lexical tags, including word segmentation, POS tagging, and Named Entity Recognition tags. ",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Evaluation and Discussion",
"sec_num": "4."
},
{
"text": "This system participated in the Second International Chinese Word Segmentation Bakeoff (SIGHAN-2005) held in 2005, and also participated in SIGHAN-2006. The performance of ELUS in the SIGHAN-2005 bakeoff is presented in Table 7 and Table 8 respectively, in terms of Recall (R), Precision (P) and F score in percentages. The score software is standard and open by SIGHAN. This good performance in the R iv is due to the class-based Trigram, Absolute Discount Smoothing and Word Disambiguation module with the rough rule features. In this bakeoff, the Name Entity Recognition is a two layer mixing approach, which is reported in detail in a previous paper [Jiang 2007] . The Maximum Entropy Model in the mixing method is similar to that found in Section 3.",
"cite_spans": [
{
"start": 87,
"end": 100,
"text": "(SIGHAN-2005)",
"ref_id": null
},
{
"start": 140,
"end": 152,
"text": "SIGHAN-2006.",
"ref_id": null
},
{
"start": 655,
"end": 667,
"text": "[Jiang 2007]",
"ref_id": "BIBREF7"
}
],
"ref_spans": [
{
"start": 220,
"end": 240,
"text": "Table 7 and Table 8",
"ref_id": "TABREF6"
}
],
"eq_spans": [],
"section": "The International Chinese Word Segmentation Bakeoff",
"sec_num": "4.1"
},
{
"text": "The performance of this system in the SIGHAN-2006 bakeoff is presented in Table 9 . The system has good performance in terms of R iv measure. The R iv measure in a closed test and in an open test was 99.1% and 98.9%, respectively. This good performance is due to a class-based Trigram with the Absolute Smoothing and Word Disambiguation algorithm.",
"cite_spans": [],
"ref_spans": [
{
"start": 74,
"end": 81,
"text": "Table 9",
"ref_id": "TABREF8"
}
],
"eq_spans": [],
"section": "The International Chinese Word Segmentation Bakeoff",
"sec_num": "4.1"
},
{
"text": "In this system, the following reasons illustrate why the open test had better performance than the closed test:",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "The International Chinese Word Segmentation Bakeoff",
"sec_num": "4.1"
},
{
"text": "(1) Named Entity Recognition module is added into the open test system. And Named Entities, including PER, LOC, ORG, occupy the most of the out-of-vocabulary words.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "The International Chinese Word Segmentation Bakeoff",
"sec_num": "4.1"
},
{
"text": "(2) The system of closed test can only use the dictionary that is collected from the given training corpus, while the system of open test can use a better dictionary, which includes the words that exist in MSRA training corpus in SIGHAN-2005. As is known, the dictionary is one of the important factors that affects the performance, because the LW candidates in the word lattice are generated from the dictionary.",
"cite_spans": [
{
"start": 230,
"end": 242,
"text": "SIGHAN-2005.",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "The International Chinese Word Segmentation Bakeoff",
"sec_num": "4.1"
},
{
"text": "As for the dictionary, the authors compare the two collections in SIGHAN-2005 and SIGHAN2006 , and in evaluating the SIGHAN-2005 MSRA closed test. There are less training sentences in SIGHAN-2006 . As a result, there is at least a 1.2% performance decrease. So, this result indicates that the dictionary can have an important impact in a system.",
"cite_spans": [
{
"start": 66,
"end": 81,
"text": "SIGHAN-2005 and",
"ref_id": null
},
{
"start": 82,
"end": 92,
"text": "SIGHAN2006",
"ref_id": null
},
{
"start": 184,
"end": 195,
"text": "SIGHAN-2006",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "The International Chinese Word Segmentation Bakeoff",
"sec_num": "4.1"
},
{
"text": "In this section, some detailed evaluation results are presented. The authors mainly focus on two difficult sub-tasks in the word segmentation task, namely disambiguation and Named Entity Recognition. The measurements in the following experiments include: the precision P = the right count / the model count, the recall rate R = the right count / the corpus count, and F-measure = (2 * P * R) / (P + R). The authors firstly evaluate the disambiguation performance. Training was done with the preceding five month's Corpus of the People's Daily Newspaper, 1998, including 664,805 sentences, and the test corpus was the sixth month corpus, including 136,647 sentences. The authors applied the Rough Set (RS) theory to extract the rough rule features, and fused this theory into the Maximum Entropy Model. The basic feature templates are the w i-2 , w i-1 , w i , w i+1 , w i+2 , furthermore, the rough rule features were fused into the ME disambiguation model [Jiang 2006A] , the results are shown in Table 10 . Table 10 demonstrates that RS model may achieve improvement over the baseline ME model. There are at least two main advantages in the proposed method: 1) As a conditional probabilistic model, ME can be fused to more effective features, which relaxes the features independent assumption that is suffered from by the N-Gram model; 2) The authors apply the rough set theory to extract complicated and long distance features. Due to how more effective features are utilized, the new method overcomes the sparse data problem to a certain extent. Now, the authors evaluate the second group of difficult sub-tasks, namely, the NER module. The experimental corpora also came from the Chinese People's Daily Newspaper in the first half-year of 1998. The overview of the entity distribution is shown in Table 11 . Figure 5 shows that the distribution of the entities complies with the Zipf's law. As a result, the entities exhibit the sparse property; thereby bringing trouble to the model. The authors compared several Named Entity Recognition Models, and Table 12 gives the evaluation result. The baseline result is obtained by selecting the NER tag that is most frequently associated with the current word. The authors add several tags in the tag set (Called adding \"role\"), including the entity prefix, infix and suffix. These tags are used to enhance the ability of the context repetition. In this experiment, HMM is one order model, and ME, CRF use the feature template:W -2 ,W -1 ,W 0 ,W 1 ,W 2 ,W -1:0 ,W 0:1 ,T -1 . Table 12 indicates that the ME + Role has achieved the best performance. Compared with Hidden Markov Model (HMM), ME can fuse more context features. In another experiment, the authors selected the pairs using two methods, one is to filter by the threshold, such as AMI>0.001, the other method is to select the top pair after ranking the pair in descending order, e.g. selecting the top 500 pairs, having the maximum value. The partial pairs are shown in Table 13 , including the MI, AMI value and their rank.",
"cite_spans": [
{
"start": 528,
"end": 577,
"text": "People's Daily Newspaper, 1998, including 664,805",
"ref_id": null
},
{
"start": 957,
"end": 970,
"text": "[Jiang 2006A]",
"ref_id": "BIBREF8"
}
],
"ref_spans": [
{
"start": 998,
"end": 1006,
"text": "Table 10",
"ref_id": "TABREF0"
},
{
"start": 1009,
"end": 1017,
"text": "Table 10",
"ref_id": "TABREF0"
},
{
"start": 1802,
"end": 1810,
"text": "Table 11",
"ref_id": "TABREF0"
},
{
"start": 1813,
"end": 1821,
"text": "Figure 5",
"ref_id": "FIGREF9"
},
{
"start": 2056,
"end": 2064,
"text": "Table 12",
"ref_id": "TABREF0"
},
{
"start": 2524,
"end": 2532,
"text": "Table 12",
"ref_id": "TABREF0"
},
{
"start": 2978,
"end": 2986,
"text": "Table 13",
"ref_id": "TABREF0"
}
],
"eq_spans": [],
"section": "The Detailed Evaluation of the System",
"sec_num": "4.2"
},
{
"text": "Then, the trigger features were collected, respectively, from above corpora. Taking AMI as an example, after being put in descending order, the top 500 features were selected. Table 14 shows the compared performance with trigger selected by AMI. Table 14 gives the detailed comparison between ME and ME with AMI trigger features. The overall improvement is 2.21% in terms of F-measure. Another experiment is done to compare ME with ME + MI model trained by five month corpora. The result is exhibited in Figure 6 .",
"cite_spans": [],
"ref_spans": [
{
"start": 176,
"end": 185,
"text": "Table 14",
"ref_id": "TABREF0"
},
{
"start": 247,
"end": 255,
"text": "Table 14",
"ref_id": "TABREF0"
},
{
"start": 505,
"end": 513,
"text": "Figure 6",
"ref_id": null
}
],
"eq_spans": [],
"section": "The Detailed Evaluation of the System",
"sec_num": "4.2"
},
{
"text": "The effectiveness of the proposed method has been confirmed. A similar result is also achieved for the IG approach. Experimental results show that the new method is more efficient.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Figure 6. The comparison about MI in F-measure",
"sec_num": null
},
{
"text": "In the last part of this section, the authors evaluate the word cluster performance. The word vectors method is performed in the large-scale corpora, in the 1998 and 2000 People's Daily Newspaper, the window of size k =8 being used in this experiment.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Figure 6. The comparison about MI in F-measure",
"sec_num": null
},
{
"text": "The hierarchical cluster analysis or other cluster analysis methods can be used to obtain the word cluster result. Table 15 demonstrates the proximity matrix, and Figure 7 gives its corresponding hierarchical cluster result. The authors used a synonym dictionary \"Word Forest of The Synonym\" to reduce the cluster space and increase prior knowledge. For instance, there are about 63 synonyms to the word \"\u6559\u6388\" (professor).",
"cite_spans": [],
"ref_spans": [
{
"start": 115,
"end": 123,
"text": "Table 15",
"ref_id": "TABREF0"
},
{
"start": 163,
"end": 171,
"text": "Figure 7",
"ref_id": null
}
],
"eq_spans": [],
"section": "Figure 6. The comparison about MI in F-measure",
"sec_num": null
},
{
"text": "Though it is helpful to build the word classes for the NER task by combining the word cluster and the thesaurus, some manual correction is also needed, because the linguistic phenomenon is too complicated, therefore making it impossible to acquire all the perfect word ",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Figure 6. The comparison about MI in F-measure",
"sec_num": null
},
{
"text": "Based on the analysis of the errors, one finds that the sparse data problem is the main problem [Jiang 2006A; Jiang 2007] . In this paper, the authors apply the Smoothing Algorithm, Word Cluster Method, etc. to overcome the sparse data problem.",
"cite_spans": [
{
"start": 96,
"end": 109,
"text": "[Jiang 2006A;",
"ref_id": "BIBREF8"
},
{
"start": 110,
"end": 121,
"text": "Jiang 2007]",
"ref_id": "BIBREF7"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Figure 7. The demonstration about hierarchical cluster",
"sec_num": null
},
{
"text": "A pragmatic Chinese word segmentation approach having balance between the precision, efficiency and model complication is described in this paper. The disambiguation and out-of-vocabulary detection are the two main difficulties found in the Word Segmentation task. Accordingly, a lot of work is done in order to improve the performance of the above two problems. The contributions of this research are: 1) Apply multiple models to build a word segmentation model, and a special sub-task can be effectively solved via an optimized language model.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion",
"sec_num": "5."
},
{
"text": "2) The authors propose to apply Average Mutual Information, etc. to extract stable entity features, and also present a novel method to provide an auxiliary function in extending the features by combining the word cluster and the thesaurus.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion",
"sec_num": "5."
},
{
"text": "3) Rough Set theory is present to extract the complicated features and the long distance features for the segmentation disambiguation and for the Named Entity Recognition.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion",
"sec_num": "5."
},
{
"text": "The work in the future will concentrate on two sides: improving the NER performance and exploring New Word Detection Algorithm.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion",
"sec_num": "5."
},
{
"text": "Here, Count(x,y) represents taking count of the co-occurrence of pair (x,y).3 Namely, if C xy >0 then N xy is 1, else N xy is 0.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
},
{
"text": "\u5b66\u751f student, \u6559\u6388 professor, \u526f\u6559\u6388 associate professor, \u5bfc\u5e08 tutor, \u5927\u5b66\u751f undergraduate, \u4e2d \u5b66\u751f middle school student.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
}
],
"back_matter": [
{
"text": "The authors thank Dr. Yan Zhao and Dr. Jian Zhao for their valuable suggestions in the proposed system. The authors also thank the members of the Natural Language Computing Group at School of Computer Science and Technology of the Harbin Institute of Technology. The authors especially thank the anonymous reviewers for their insightful comments and suggestions, based on which the paper has been improved.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Acknowledgements",
"sec_num": null
}
],
"bib_entries": {
"BIBREF0": {
"ref_id": "b0",
"title": "An empirical study of smoothing techniques for language modeling",
"authors": [
{
"first": "S",
"middle": [
"F"
],
"last": "Chen",
"suffix": ""
},
{
"first": "J",
"middle": [],
"last": "Goodman",
"suffix": ""
}
],
"year": 1999,
"venue": "Computer Speech and Language",
"volume": "13",
"issue": "4",
"pages": "359--394",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Chen, S. F., and J. Goodman, \"An empirical study of smoothing techniques for language modeling,\" Computer Speech and Language, 13(4) 1999, pp. 359-394.",
"links": null
},
"BIBREF1": {
"ref_id": "b1",
"title": "A study on word-based and integral-bit Chinese text compression algorithms",
"authors": [
{
"first": "K.-S",
"middle": [],
"last": "Cheng",
"suffix": ""
},
{
"first": "G",
"middle": [],
"last": "Young",
"suffix": ""
},
{
"first": "K.-F",
"middle": [],
"last": "Wong",
"suffix": ""
}
],
"year": 1999,
"venue": "Journal of the American Society for Information Science",
"volume": "50",
"issue": "3",
"pages": "218--228",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Cheng, K.-S., G. Young, K.-F Wong, \"A study on word-based and integral-bit Chinese text compression algorithms,\" Journal of the American Society for Information Science, 50(3) 1999, pp. 218-228.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "Chinese word segmentation and named entity recognition: a pragmatic approach in Computational Linguistics",
"authors": [
{
"first": "J.-F",
"middle": [],
"last": "Gao",
"suffix": ""
},
{
"first": "A.-D",
"middle": [],
"last": "Wu",
"suffix": ""
},
{
"first": "M",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "C.-N",
"middle": [],
"last": "Huang",
"suffix": ""
}
],
"year": 2005,
"venue": "Computational Linguistics",
"volume": "31",
"issue": "4",
"pages": "531--574",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Gao, J.-F., A.-D. Wu, M. Li, and C.-N. Huang, \"Chinese word segmentation and named entity recognition: a pragmatic approach in Computational Linguistics,\" Computational Linguistics, 31(4) 2005, pp.531-574.",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "Chinese Word Segmentation: A Pragmatic Approach",
"authors": [
{
"first": "J.-F",
"middle": [],
"last": "Gao",
"suffix": ""
},
{
"first": "M",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "A.-D",
"middle": [],
"last": "Wu",
"suffix": ""
},
{
"first": "C.-N",
"middle": [],
"last": "Huang",
"suffix": ""
}
],
"year": 2004,
"venue": "Microsoft Research",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Gao, J.-F., M. Li, A.-D. Wu, and C.-N. Huang, \"Chinese Word Segmentation: A Pragmatic Approach,\" Microsoft Research,Technical Report: MSR-TR-2004-123, November 2004.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "Toward a unified approach to statistical language modeling for Chinese",
"authors": [
{
"first": "J.-F",
"middle": [],
"last": "Gao",
"suffix": ""
},
{
"first": "J",
"middle": [],
"last": "Goodman",
"suffix": ""
},
{
"first": "M",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "K.-F",
"middle": [],
"last": "Lee",
"suffix": ""
}
],
"year": 2002,
"venue": "ACMTrans, Asian Language Information Process",
"volume": "1",
"issue": "1",
"pages": "3--33",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Gao, J.-F., J. Goodman, M. Li, and K.-F. Lee, \"Toward a unified approach to statistical language modeling for Chinese,\" ACMTrans, Asian Language Information Process, 1(1) 2002, pp. 3-33.",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "Improved source-channel model for Chinese word segmentation",
"authors": [
{
"first": "J.-F",
"middle": [],
"last": "Gao",
"suffix": ""
},
{
"first": "M",
"middle": [],
"last": "Li",
"suffix": ""
},
{
"first": "C.-N",
"middle": [],
"last": "Huang",
"suffix": ""
}
],
"year": 2003,
"venue": "the 41nd Annual Meeting of the Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "272--279",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Gao, J.-F., M. Li, and C.-N. Huang, \"Improved source-channel model for Chinese word segmentation,\" In the 41nd Annual Meeting of the Association for Computational Linguistics, 2003, Sapporo, Japan, pp. 272-279.",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "Error-driven Learning of Chinese word segmentation",
"authors": [
{
"first": "J",
"middle": [],
"last": "Hockenmaier",
"suffix": ""
},
{
"first": "C",
"middle": [],
"last": "Brew",
"suffix": ""
}
],
"year": 1998,
"venue": "the 12th Pacific Conference on Language and Information",
"volume": "",
"issue": "",
"pages": "218--229",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Hockenmaier, J., and C. Brew, \"Error-driven Learning of Chinese word segmentation,\" In the 12th Pacific Conference on Language and Information, 1998, Singapore, pp. 218-229.",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "Research on Chinese Lexical Analysis System by Fusing Multiple Knowledge Sources",
"authors": [
{
"first": "W",
"middle": [],
"last": "Jiang",
"suffix": ""
},
{
"first": "X.-L",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Y",
"middle": [],
"last": "Guan",
"suffix": ""
},
{
"first": "J",
"middle": [],
"last": "Zhao",
"suffix": ""
}
],
"year": 2007,
"venue": "Chinese Journal of Computer",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jiang, W., X.-L. Wang, Y. Guan, and J. Zhao, \"Research on Chinese Lexical Analysis System by Fusing Multiple Knowledge Sources,\" Chinese Journal of Computer, January, 2007.",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "Applying Rough Sets in Word Segmentation Disambiguation Based on Maximum Entropy Model",
"authors": [
{
"first": "W",
"middle": [],
"last": "Jiang",
"suffix": ""
},
{
"first": "X.-L",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Y",
"middle": [],
"last": "Guan",
"suffix": ""
},
{
"first": "G.-H",
"middle": [],
"last": "Liang",
"suffix": ""
}
],
"year": 2006,
"venue": "Journal of Harbin Institute of Technology (New Series)",
"volume": "13",
"issue": "1",
"pages": "94--98",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jiang, W., X.-L. Wang, Y. Guan, and G.-H. Liang, \"Applying Rough Sets in Word Segmentation Disambiguation Based on Maximum Entropy Model,\" Journal of Harbin Institute of Technology (New Series), 13(1) 2006A, pp. 94-98.",
"links": null
},
"BIBREF9": {
"ref_id": "b9",
"title": "Chinese Word Segmentation based on Mixing Model",
"authors": [
{
"first": "W",
"middle": [],
"last": "Jiang",
"suffix": ""
},
{
"first": "J",
"middle": [],
"last": "Zhao",
"suffix": ""
},
{
"first": "Y",
"middle": [],
"last": "Guan",
"suffix": ""
},
{
"first": "Z.-M",
"middle": [],
"last": "Xu",
"suffix": ""
}
],
"year": 2005,
"venue": "The 4th SIGHAN Workshop",
"volume": "",
"issue": "",
"pages": "180--182",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jiang, W., J. Zhao, Y. Guan, and Z.-M. Xu, \"Chinese Word Segmentation based on Mixing Model,\" In The 4th SIGHAN Workshop, 2005, Jeju Island, Korea, pp. 180-182.",
"links": null
},
"BIBREF10": {
"ref_id": "b10",
"title": "A Pragmatic Chinese Word Segmentation System",
"authors": [
{
"first": "W",
"middle": [],
"last": "Jiang",
"suffix": ""
},
{
"first": "Y",
"middle": [],
"last": "Guan",
"suffix": ""
},
{
"first": "X.-L",
"middle": [],
"last": "Wang",
"suffix": ""
}
],
"year": 2006,
"venue": "proceedings of the Fifth SIGHAN Workshop on Chinese Language Processing",
"volume": "",
"issue": "",
"pages": "189--192",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jiang, W., Y. Guan, and X.-L. Wang, \"A Pragmatic Chinese Word Segmentation System,\" In proceedings of the Fifth SIGHAN Workshop on Chinese Language Processing, 2006B, Sydney, pp. 189-192.",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "Improving Feature extraction in Named Entity Recognition based on Maximum Entropy Model",
"authors": [
{
"first": "W",
"middle": [],
"last": "Jiang",
"suffix": ""
},
{
"first": "Y",
"middle": [],
"last": "Guan",
"suffix": ""
},
{
"first": "X.-L",
"middle": [],
"last": "Wang",
"suffix": ""
}
],
"year": 2006,
"venue": "the 2006 International Conference on Machine Learning and Cybernetics (ICMLC2006)",
"volume": "",
"issue": "",
"pages": "2630--2635",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jiang, W., Y. Guan, and X.-L. Wang, \"Improving Feature extraction in Named Entity Recognition based on Maximum Entropy Model,\" In the 2006 International Conference on Machine Learning and Cybernetics (ICMLC2006), 2006C, China, pp. 2630-2635.",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "An Improved Unknown Word Recognition Model based on Multi-Knowledge Source Method",
"authors": [
{
"first": "W",
"middle": [],
"last": "Jiang",
"suffix": ""
},
{
"first": "Y",
"middle": [],
"last": "Guan",
"suffix": ""
},
{
"first": "X.-L",
"middle": [],
"last": "Wang",
"suffix": ""
}
],
"year": 2006,
"venue": "the 6th International Conference on Intelligent Systems Design and Applications (ISDA'06)",
"volume": "2",
"issue": "",
"pages": "825--830",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jiang, W., Y. Guan, and X.-L. Wang, \"An Improved Unknown Word Recognition Model based on Multi-Knowledge Source Method,\" In the 6th International Conference on Intelligent Systems Design and Applications (ISDA'06), vol 2, 2006D, China, pp. 825-830",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "automatic word segmentation in written Chinese and an auto match word segmentation system-CDWS",
"authors": [
{
"first": "N.-Y",
"middle": [],
"last": "Liang",
"suffix": ""
}
],
"year": 1987,
"venue": "Journal of Chinese information processing",
"volume": "1",
"issue": "2",
"pages": "44--52",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Liang, N.-Y., \"automatic word segmentation in written Chinese and an auto match word segmentation system-CDWS,\" (in Chinese) Journal of Chinese information processing, 1(2), 1987, pp. 44-52.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "A trainable rule-based algorithm to word segmentation",
"authors": [
{
"first": "D",
"middle": [],
"last": "Palmer",
"suffix": ""
}
],
"year": 1997,
"venue": "proceedings of the 35th Annual Meeting of the Association of Computational Linguistics",
"volume": "",
"issue": "",
"pages": "321--328",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Palmer, D., \"A trainable rule-based algorithm to word segmentation,\" In proceedings of the 35th Annual Meeting of the Association of Computational Linguistics, 1997, Madrid, Spain, pp. 321-328.",
"links": null
},
"BIBREF15": {
"ref_id": "b15",
"title": "Chinese segmentation and new word detection using conditional random fields",
"authors": [
{
"first": "F.-C",
"middle": [],
"last": "Peng",
"suffix": ""
},
{
"first": "F.-F",
"middle": [],
"last": "Feng",
"suffix": ""
},
{
"first": "A",
"middle": [],
"last": "Mccallum",
"suffix": ""
}
],
"year": 2004,
"venue": "Proceedings of the 20th International Conference on Computational Linguistics",
"volume": "",
"issue": "",
"pages": "562--568",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Peng, F.-C., F.-F Feng, and A. McCallum, \"Chinese segmentation and new word detection using conditional random fields,\" In Proceedings of the 20th International Conference on Computational Linguistics (COLING 2004), 2004, Geneva, Switzerland, pp. 562-568.",
"links": null
},
"BIBREF16": {
"ref_id": "b16",
"title": "A hierarchical EM approach to word segmentation",
"authors": [
{
"first": "F",
"middle": [
"C"
],
"last": "Peng",
"suffix": ""
},
{
"first": "D",
"middle": [],
"last": "Schuurmans",
"suffix": ""
}
],
"year": 2001,
"venue": "6th Natural Language Processing Pacific Rim Symposium (NLPRS-2001)",
"volume": "",
"issue": "",
"pages": "475--480",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Peng, F.C., and D. Schuurmans, \"A hierarchical EM approach to word segmentation,\" In 6th Natural Language Processing Pacific Rim Symposium (NLPRS-2001), 2001, pp. 475-480.",
"links": null
},
"BIBREF17": {
"ref_id": "b17",
"title": "Automatic word sense discrimination",
"authors": [
{
"first": "H",
"middle": [],
"last": "Schutze",
"suffix": ""
}
],
"year": 1998,
"venue": "Computational Linguistics",
"volume": "24",
"issue": "1",
"pages": "97--123",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Schutze, H.. \"Automatic word sense discrimination,\" Computational Linguistics, 24(1) 1998, pp. 97-123.",
"links": null
},
"BIBREF18": {
"ref_id": "b18",
"title": "A Stochastic Finite-State Word-Segmentation Algorithm for Chinese",
"authors": [
{
"first": "R",
"middle": [
"C"
],
"last": "Sproat",
"suffix": ""
},
{
"first": "G",
"middle": [],
"last": "Shih",
"suffix": ""
},
{
"first": "N",
"middle": [],
"last": "William",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Chang",
"suffix": ""
}
],
"year": 1996,
"venue": "Computational linguistics",
"volume": "22",
"issue": "3",
"pages": "377--404",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Sproat, R. C. Shih, G. William, and N. Chang, \"A Stochastic Finite-State Word-Segmentation Algorithm for Chinese,\" Computational linguistics, 22(3) 1996, pp. 377-404.",
"links": null
},
"BIBREF19": {
"ref_id": "b19",
"title": "Word Segmentation in Sentence Analysis",
"authors": [
{
"first": "A.-D",
"middle": [],
"last": "Wu",
"suffix": ""
},
{
"first": "Z.-X",
"middle": [],
"last": "Jiang",
"suffix": ""
}
],
"year": 1998,
"venue": "1998 International Conference on Chinese Information Processing",
"volume": "",
"issue": "",
"pages": "169--180",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Wu, A.-D., and Z.-X. Jiang, \"Word Segmentation in Sentence Analysis,\" In 1998 International Conference on Chinese Information Processing, 1998, Beijing, China, pp. 169-180.",
"links": null
},
"BIBREF20": {
"ref_id": "b20",
"title": "Chinese Word Segmentation as LMR Tagging",
"authors": [
{
"first": "N.-W",
"middle": [],
"last": "Xue",
"suffix": ""
},
{
"first": "L.-B",
"middle": [],
"last": "Shen",
"suffix": ""
}
],
"year": 2003,
"venue": "the Second SIGHAN Workshop on Chinese Language Processing",
"volume": "",
"issue": "",
"pages": "176--179",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Xue, N.-W., and L.-B. Shen, \"Chinese Word Segmentation as LMR Tagging,\" In the Second SIGHAN Workshop on Chinese Language Processing, 2003, Japan, pp. 176-179.",
"links": null
},
"BIBREF21": {
"ref_id": "b21",
"title": "Chinese Lexical Analysis Using Hierarchical Hidden Markov Model",
"authors": [
{
"first": "H.-P",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Q",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "X.-Q",
"middle": [],
"last": "Cheng",
"suffix": ""
},
{
"first": "H",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "H.-K",
"middle": [],
"last": "Yu",
"suffix": ""
}
],
"year": 2003,
"venue": "the Second SIGHAN workshop affiliated with 4th ACL",
"volume": "",
"issue": "",
"pages": "63--70",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Zhang, H.-P., Q. Liu, X.-Q. Cheng, H. Zhang, and H.-K. Yu, \"Chinese Lexical Analysis Using Hierarchical Hidden Markov Model,\" In the Second SIGHAN workshop affiliated with 4th ACL, 2003, Sapporo Japan, pp. 63-70.",
"links": null
},
"BIBREF22": {
"ref_id": "b22",
"title": "Research on Conditional Probabilistic Model and Its Application in Chinese Named Entity Recognition",
"authors": [
{
"first": "J",
"middle": [],
"last": "Zhao",
"suffix": ""
}
],
"year": 2006,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Zhao, J., \"Research on Conditional Probabilistic Model and Its Application in Chinese Named Entity Recognition,\" PhD thesis, Harbin Institute of Technology, China, 2006.",
"links": null
},
"BIBREF23": {
"ref_id": "b23",
"title": "Research on Chinese Morpheme Analysis Based on Statistic Language Model",
"authors": [
{
"first": "Y",
"middle": [],
"last": "Zhao",
"suffix": ""
}
],
"year": 2005,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Zhao, Y., \"Research on Chinese Morpheme Analysis Based on Statistic Language Model,\" PhD thesis, Harbin Institute of Technology, China, 2005A.",
"links": null
},
"BIBREF24": {
"ref_id": "b24",
"title": "Solution Strategies for Word Sense Problems Based On Vector Space Model and Maximum Entropy Model",
"authors": [
{
"first": "Y",
"middle": [],
"last": "Zhao",
"suffix": ""
},
{
"first": "X.-L",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "B.-Q",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "Y",
"middle": [],
"last": "Guan",
"suffix": ""
}
],
"year": 2005,
"venue": "Chinese High Technology Letters",
"volume": "15",
"issue": "",
"pages": "1--6",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Zhao, Y., X.-L. Wang, B.-Q. Liu, and Y. Guan, \"Solution Strategies for Word Sense Problems Based On Vector Space Model and Maximum Entropy Model,\" (In Chinese), Chinese High Technology Letters, 15(1) 2005B, pp. 1-6.",
"links": null
}
},
"ref_entries": {
"FIGREF0": {
"type_str": "figure",
"text": "The structure of the proposed system",
"uris": null,
"num": null
},
"FIGREF2": {
"type_str": "figure",
"text": "Permitted meta rules: <Non-terminator>, terminator, {Loop block}, {Loop block+}, {Loop block*}, [Range block] (e.g. [a..z], [\"a\"..\"z\"]), |, (Optional block), (Optional block +), (Optional block *).",
"uris": null,
"num": null
},
"FIGREF3": {
"type_str": "figure",
"text": "The example of data structure in the lexicon (TRIEs)",
"uris": null,
"num": null
},
"FIGREF4": {
"type_str": "figure",
"text": "objects are merged, the objects of each equivalence classes are counted and potential rule precision is calculated. If one lets each d \u03b1 have the same value, namely, let d \u03b1 \u03b1 = to the decision attribute d, then \u03bb -Approximation will revert back to the conventional definition of \u03b1 -Approximation.",
"uris": null,
"num": null
},
"FIGREF6": {
"type_str": "figure",
"text": "Rough rule features are added in the ME model as a new kind of feature: = represents that the conditional attribute of r can be reconstructed in the current context, and a d = represents the decision attribute of d is equal to the tag of ambiguous word. (More details were reported in the paper \"[Jiang 2006A]\".)",
"uris": null,
"num": null
},
"FIGREF7": {
"type_str": "figure",
"text": "\uf981\u58eb/B-PER_SUFFIX (Note: It's my honor to visit Ms. Sun Gui-Ping.) As there are distinct differences between a Chinese person's name and the translation of the person's name in terms of the person construction, the person name is divided into Chinese Person Name (CPN) and the Translation Person Name",
"uris": null,
"num": null
},
"FIGREF9": {
"type_str": "figure",
"text": "The entities that exhibit Zipf's law",
"uris": null,
"num": null
},
"FIGREF10": {
"type_str": "figure",
"text": "making statistical analysis of some corpora.",
"uris": null,
"num": null
},
"TABREF0": {
"content": "<table><tr><td>TAG</td><td>Description</td><td>Examples</td></tr><tr><td>LW</td><td>The word in the Lexicon</td><td>\u6700\u8fd1(recent),\u535a\u58eb(doctor), \u5b66\u4f4d(degree)</td></tr><tr><td>FT</td><td>Number, Date, Time etc.</td><td>2910, 46.12%, 2004\uf98e05\u670812\u65e5, 01:06</td></tr><tr><td>MDW</td><td>Morphological derived words</td><td>\u670b\u53cb\u4eec(friends), \u9ad8\u9ad8\u5174\u5174(happily), \u8fdb\u51fa\u53e3(imports and exports)</td></tr><tr><td>NE</td><td>Named Entities</td><td>\u5b59\u6842\u5e73(Sun Gui-Ping), \u54c8\u5c14\u6ee8(harbin)</td></tr><tr><td>NW</td><td>The other OOV except FT, MDW, NE</td><td>\u6d77\u98ce\u724c(sea breeze brand), \u53e4\u5178\u5f0f (classical), \u666f\u89c2\u706f(sighting lamp)</td></tr></table>",
"text": "",
"html": null,
"type_str": "table",
"num": null
},
"TABREF1": {
"content": "<table><tr><td>FT type</td><td>Factoid word description</td><td>Examples</td></tr><tr><td>Number</td><td>Integer, percent, real etc.</td><td>2203, 25.78%, \uf9b2\u70b9\u4e94, 20.542</td></tr><tr><td>Date</td><td>Date</td><td>2004 \uf98e 5 \u6708 12 \u65e5, 2004-06-06</td></tr><tr><td>Time</td><td>Time</td><td>8:00, \u5341\u70b9\u4e8c\u5341\u5206, \u665a\u4e0a 6 \u70b9</td></tr><tr><td>English</td><td>English word,</td><td>Hello, How, are, you</td></tr><tr><td>www</td><td>Website, IP address</td><td>http://www.hit.edu.cn; 192.168.140.133</td></tr><tr><td>email</td><td>Email</td><td>jiangwei@insun.hit.edu.cn</td></tr><tr><td>phone</td><td>Phone, fax</td><td>+86-451-86413322; (0451)86413322</td></tr></table>",
"text": "by this system, are shown inTable 2.",
"html": null,
"type_str": "table",
"num": null
},
"TABREF2": {
"content": "<table><tr><td/><td/><td/><td/><td/><td>\u2208 , there is an</td></tr><tr><td colspan=\"5\">attribute value a V set and an information function</td><td>: f U V a a \u2192 . An equivalence \u03b8 on set</td></tr><tr><td colspan=\"6\">U is called an indiscernible relation, and lower approximation for an object set X U \u2286 is</td></tr><tr><td>defined as</td><td>X</td><td>{ \u03b8 \u03b8 \u03b8 : x x X = \u2286</td><td>}</td><td>.</td></tr></table>",
"text": "where U is a non-empty set of objects; A is a non-empty set of attribute a 's; for each attribute a A However, this formula is too strict to fit the requirements of Natural Language Processing. For this reason, the concept of \u03b1 -approximation is provided:",
"html": null,
"type_str": "table",
"num": null
},
"TABREF3": {
"content": "<table><tr><td/><td>S 1</td><td>S 2</td><td>\u2026</td><td>S m</td></tr><tr><td>W 1</td><td>c 11</td><td>c 21</td><td>\u2026</td><td>S m1</td></tr><tr><td>W 2</td><td>c 12</td><td>c 22</td><td>\u2026</td><td>S m2</td></tr><tr><td>\u2026.</td><td>\u2026</td><td>\u2026</td><td>\u2026</td><td>\u2026</td></tr><tr><td>W n</td><td>c 1n</td><td>c 2n</td><td>\u2026</td><td>S mn</td></tr></table>",
"text": "",
"html": null,
"type_str": "table",
"num": null
},
"TABREF4": {
"content": "<table><tr><td>Type</td><td>Feature Template</td></tr><tr><td>one order feature</td><td/></tr></table>",
"text": "",
"html": null,
"type_str": "table",
"num": null
},
"TABREF5": {
"content": "<table><tr><td>List Type</td><td>Lexicon</td><td>Examples</td></tr><tr><td>Word list</td><td>Place lexicon Chinese surname</td><td>\uf963\u4eac, \u7ebd\u7ea6, \u9a6c\u5bb6\u6c9f \u5f20, \u738b, \u8d75, \u6b27\u9633</td></tr><tr><td/><td>Prefix of PER</td><td>\uf934, \u963f, \u5c0f</td></tr><tr><td>String list</td><td>Suffix of PLA</td><td>\u5c71, \u6e56, \u5bfa, \u53f0, \u6d77</td></tr><tr><td/><td>Suffix of ORG</td><td>\u4f1a, \u8054\u76df, \u7ec4\u7ec7, \u5c40</td></tr><tr><td/><td>Character for CPER</td><td>\u519b,\u521a, \u83b2, \u8335, \u5029</td></tr><tr><td>Character list</td><td>Character for FPER</td><td>\u79d1, \u66fc, \u65af, \u5a03, \u8d1d</td></tr><tr><td/><td>Rare character</td><td>\u6ed7, \u80e8, \u8585</td></tr></table>",
"text": "",
"html": null,
"type_str": "table",
"num": null
},
"TABREF6": {
"content": "<table><tr><td>Closed</td><td>R</td><td>P</td><td>F</td><td>OOV</td><td>R oov</td><td>R iv</td></tr><tr><td>PKU</td><td>95.4</td><td>92.7</td><td>94.1</td><td>5.8</td><td>51.8</td><td>98.1</td></tr><tr><td>MSR</td><td>97.3</td><td>94.5</td><td>95.9</td><td>2.6</td><td>32.3</td><td>99.1</td></tr><tr><td>CITYU</td><td>93.4</td><td>86.5</td><td>89.8</td><td>7.4</td><td>24.8</td><td>98.9</td></tr><tr><td>AS</td><td>94.3</td><td>89.5</td><td>91.8</td><td>4.3</td><td>13.7</td><td>97.9</td></tr></table>",
"text": "This system has good performance in terms of F-measure in the simplified Chinese open test, including the PKU and MSR open tests. In addition, its In-vocabulary word (IV, namely, Lexical words) identification performance is remarkable, ranging from 97.7% to 99.1%, standing at the top or near the top in all the tests in which it has participated.",
"html": null,
"type_str": "table",
"num": null
},
"TABREF7": {
"content": "<table><tr><td>Open</td><td>R</td><td>P</td><td>F</td><td>OOV</td><td>R oov</td><td>R iv</td></tr><tr><td>PKU</td><td>96.8</td><td>96.6</td><td>96.7</td><td>5.8</td><td>82.6</td><td>97.7</td></tr><tr><td>MSR</td><td>98.0</td><td>96.5</td><td>97.2</td><td>2.6</td><td>59.0</td><td>99.0</td></tr><tr><td>CITYU</td><td>94.6</td><td>89.8</td><td>92.2</td><td>7.4</td><td>41.7</td><td>98.9</td></tr><tr><td>AS</td><td>95.2</td><td>92.0</td><td>93.6</td><td>4.3</td><td>35.4</td><td>97.9</td></tr></table>",
"text": "",
"html": null,
"type_str": "table",
"num": null
},
"TABREF8": {
"content": "<table><tr><td colspan=\"5\">MSRA test in SIGHAN2006 (%)</td><td/><td/></tr><tr><td>MSRA</td><td>R</td><td>P</td><td>F</td><td>OOV</td><td>R oov</td><td>R iv</td></tr><tr><td>Close</td><td>96.3</td><td>91.8</td><td>94.0</td><td>3.4</td><td>17.5</td><td>99.1</td></tr><tr><td>Open</td><td>97.7</td><td>96.0</td><td>96.8</td><td>3.4</td><td>62.4</td><td>98.9</td></tr></table>",
"text": "",
"html": null,
"type_str": "table",
"num": null
},
"TABREF9": {
"content": "<table><tr><td>Ambiguity</td><td>Type</td><td>Train Count</td><td>Test Count</td><td>ME Precision</td><td>RS model Precision</td></tr><tr><td>\u624d\u80fd</td><td>\u624d\u80fd \u624d/\u80fd</td><td>704 7612</td><td>190 300</td><td>90%</td><td>93%</td></tr><tr><td>\uf967\u8981</td><td>\uf967\u8981 \uf967/\u8981</td><td>1421 497</td><td>150 80</td><td>91%</td><td>95%</td></tr><tr><td>\u4ece\u5c0f\u5b66</td><td>\u4ece\u5c0f/\u5b66 \u4ece/\u5c0f\u5b66</td><td>170 260</td><td>40 70</td><td>88%</td><td>91%</td></tr><tr><td>\u5c06\u6765</td><td>\u5c06\u6765 \u5c06/\u6765</td><td>1200 35</td><td>200 10</td><td>92%</td><td>97%</td></tr><tr><td>\u4e2a\u4eba</td><td>\u4e2a\u4eba \u4e2a/\u4eba</td><td>1016 819</td><td>150 120</td><td>89%</td><td>94%</td></tr></table>",
"text": "",
"html": null,
"type_str": "table",
"num": null
},
"TABREF10": {
"content": "<table><tr><td>Named Entity</td><td>CPN</td><td>FPN</td><td>LOC</td><td>ORG</td></tr><tr><td>By entities</td><td>27.54%</td><td>8.86%</td><td>41.53%</td><td>22.07%</td></tr><tr><td>By corpus</td><td>1.29%</td><td>0.41%</td><td>1.94%</td><td>1.03%</td></tr><tr><td>Occur Count</td><td>92941</td><td>29912</td><td>140162</td><td>74483</td></tr></table>",
"text": "",
"html": null,
"type_str": "table",
"num": null
},
"TABREF11": {
"content": "<table><tr><td colspan=\"2\">Model</td><td colspan=\"2\">Precision</td><td>Recall</td><td/><td colspan=\"2\">F-measure</td></tr><tr><td colspan=\"2\">BaseLine</td><td colspan=\"2\">68.99%</td><td>73.54%</td><td/><td colspan=\"2\">71.19%</td></tr><tr><td colspan=\"2\">HMM</td><td colspan=\"2\">79.20%</td><td>79.96%</td><td/><td colspan=\"2\">79.58%</td></tr><tr><td/><td>ME</td><td colspan=\"2\">84.77%</td><td>83.23%</td><td/><td colspan=\"2\">83.99%</td></tr><tr><td colspan=\"2\">HMM + Role</td><td colspan=\"2\">83.68%</td><td>85.20%</td><td/><td colspan=\"2\">84.43%</td></tr><tr><td colspan=\"2\">ME + Role</td><td colspan=\"2\">87.95%</td><td>84.62%</td><td/><td colspan=\"2\">86.25%</td></tr><tr><td colspan=\"5\">Table 13. Trigger pairs draw from corpus</td><td/><td/></tr><tr><td/><td>Pair</td><td>Value</td><td>AMI</td><td>Rank</td><td>Value</td><td>MI</td><td>Rank</td></tr><tr><td colspan=\"2\">\u540c\u5fd7 CPN</td><td>3.9e-4</td><td/><td>6</td><td>2.71</td><td/><td>144</td></tr><tr><td>\u8bf4</td><td>CPN</td><td>2.3e-4</td><td/><td>11</td><td>1.85</td><td/><td>885</td></tr><tr><td colspan=\"2\">\u4e3b\u4efb ORG</td><td>1.2e-4</td><td/><td>23</td><td>2.63</td><td/><td>181</td></tr><tr><td colspan=\"2\">\u4f1a\u89c1 CPN</td><td>1.1e-4</td><td/><td>27</td><td>2.43</td><td/><td>269</td></tr><tr><td colspan=\"2\">\u4e3e\ufa08 LOC</td><td>9.5e-5</td><td/><td>34</td><td>1.61</td><td/><td>1279</td></tr><tr><td colspan=\"2\">\uf963\u90e8 LOC</td><td>3.9e-5</td><td/><td>80</td><td>2.45</td><td/><td>271</td></tr><tr><td colspan=\"2\">\u4f1a\u8bae ORG</td><td>3.8e-5</td><td/><td>83</td><td>1.39</td><td/><td>1650</td></tr><tr><td colspan=\"2\">\u6559\u6388 CPN</td><td>3.1e-5</td><td/><td>96</td><td>2.21</td><td/><td>463</td></tr></table>",
"text": "",
"html": null,
"type_str": "table",
"num": null
},
"TABREF12": {
"content": "<table><tr><td>Entity type</td><td>P</td><td>ME (%) R</td><td>F</td><td>P</td><td>ME+AMI(%) R</td><td>F</td></tr><tr><td>CPN</td><td colspan=\"6\">84.54 77.71 80.98 86.36 82.41 84.34</td></tr><tr><td>FPN</td><td colspan=\"6\">73.27 53.21 61.65 78.50 56.90 65.97</td></tr><tr><td>LOC</td><td colspan=\"6\">86.95 76.53 81.41 87.57 77.62 82.30</td></tr><tr><td>ORG</td><td colspan=\"6\">74.87 55.29 63.61 74.08 60.95 66.88</td></tr><tr><td>Overall</td><td colspan=\"6\">82.81 69.74 75.71 83.60 72.97 77.92</td></tr></table>",
"text": "",
"html": null,
"type_str": "table",
"num": null
},
"TABREF13": {
"content": "<table><tr><td>Case</td><td>\u5b66\u751f</td><td colspan=\"5\">Cosine of Vectors of Values \u6559\u6388 \u526f\u6559\u6388 \u5bfc\u5e08 \u5927\u5b66\u751f \u4e2d\u5b66\u751f</td></tr><tr><td>\u5b66\u751f</td><td>1.000</td><td>.352</td><td>.280</td><td>.288</td><td>.433</td><td>.331</td></tr><tr><td>\u6559\u6388</td><td>.352</td><td>1.000</td><td>.722</td><td>.815</td><td>.310</td><td>.174</td></tr><tr><td>\u526f\u6559\u6388</td><td>.280</td><td>.722</td><td>1.000</td><td>.641</td><td>.216</td><td>.136</td></tr><tr><td>\u5bfc\u5e08</td><td>.288</td><td>.815</td><td>.641</td><td>1.000</td><td>.226</td><td>.139</td></tr><tr><td>\u5927\u5b66\u751f</td><td>.433</td><td>.310</td><td>.216</td><td>.226</td><td>1.000</td><td>.674</td></tr><tr><td>\u4e2d\u5b66\u751f</td><td>.331</td><td>.174</td><td>.136</td><td>.139</td><td>.674</td><td>1.000</td></tr></table>",
"text": "",
"html": null,
"type_str": "table",
"num": null
}
}
}
} |