File size: 79,653 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 | {
"paper_id": "P96-1018",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T09:02:57.409324Z"
},
"title": "High-Performance Bilingual Text Alignment Using Statistical and Dictionary Information",
"authors": [
{
"first": "Masahiko",
"middle": [],
"last": "Haruno",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "NTT Communication Science Labs",
"location": {
"addrLine": "1-2356 Take Yokosuka-Shi Kanagawa 238-03",
"country": "Japan"
}
},
"email": "haruno@nttkb@ntt.jp"
},
{
"first": "Takefumi",
"middle": [],
"last": "Yamazaki",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "NTT Communication Science Labs",
"location": {
"addrLine": "1-2356 Take Yokosuka-Shi Kanagawa 238-03",
"country": "Japan"
}
},
"email": ""
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "This paper describes an accurate and robust text alignment system for structurally different languages. Among structurally different languages such as Japanese and English, there is a limitation on the amount of word correspondences that can be statistically acquired. The proposed method makes use of two kinds of word correspondences in aligning bilingual texts. One is a bilingual dictionary of general use. The other is the word correspondences that are statistically acquired in the alignment process. Our method gradually determines sentence pairs (anchors) that correspond to each other by relaxing parameters. The method, by combining two kinds of word correspondences, achieves adequate word correspondences for complete alignment. As a result, texts of various length and of various genres in structurally different languages can be aligned with high precision. Experimental results show our system outperforms conventional methods for various kinds of Japanese-English texts.",
"pdf_parse": {
"paper_id": "P96-1018",
"_pdf_hash": "",
"abstract": [
{
"text": "This paper describes an accurate and robust text alignment system for structurally different languages. Among structurally different languages such as Japanese and English, there is a limitation on the amount of word correspondences that can be statistically acquired. The proposed method makes use of two kinds of word correspondences in aligning bilingual texts. One is a bilingual dictionary of general use. The other is the word correspondences that are statistically acquired in the alignment process. Our method gradually determines sentence pairs (anchors) that correspond to each other by relaxing parameters. The method, by combining two kinds of word correspondences, achieves adequate word correspondences for complete alignment. As a result, texts of various length and of various genres in structurally different languages can be aligned with high precision. Experimental results show our system outperforms conventional methods for various kinds of Japanese-English texts.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "Corpus-based approaches based on bilingual texts are promising for various applications(i.e., lexical knowledge extraction (Kupiec, 1993; Matsumoto et al., 1993; Smadja et al., 1996; Dagan and Church, 1994; Kumano and Hirakawa, 1994; Haruno et al., 1996) , machine translation (Brown and others, 1993; Sato and Nagao, 1990; Kaji et al., 1992) and information retrieval (Sato, 1992) ). Most of these works assume voluminous aligned corpora.",
"cite_spans": [
{
"start": 123,
"end": 137,
"text": "(Kupiec, 1993;",
"ref_id": "BIBREF14"
},
{
"start": 138,
"end": 161,
"text": "Matsumoto et al., 1993;",
"ref_id": "BIBREF16"
},
{
"start": 162,
"end": 182,
"text": "Smadja et al., 1996;",
"ref_id": "BIBREF20"
},
{
"start": 183,
"end": 206,
"text": "Dagan and Church, 1994;",
"ref_id": "BIBREF6"
},
{
"start": 207,
"end": 233,
"text": "Kumano and Hirakawa, 1994;",
"ref_id": "BIBREF13"
},
{
"start": 234,
"end": 254,
"text": "Haruno et al., 1996)",
"ref_id": "BIBREF10"
},
{
"start": 277,
"end": 301,
"text": "(Brown and others, 1993;",
"ref_id": null
},
{
"start": 302,
"end": 323,
"text": "Sato and Nagao, 1990;",
"ref_id": "BIBREF18"
},
{
"start": 324,
"end": 342,
"text": "Kaji et al., 1992)",
"ref_id": "BIBREF11"
},
{
"start": 369,
"end": 381,
"text": "(Sato, 1992)",
"ref_id": "BIBREF19"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Many methods have been proposed to align bilingual corpora. One of the major approaches is based on the statistics of simple features such as sentence length in words (Brown and others, 1991) or in characters (Gale and Church, 1993) . These techniques are widely used because they can be imple-mented in an efficient and simple way through dynamic programing. However, their main targets are rigid translations that are almost literal translations. In addition, the texts being aligned were structurally similar European languages (i.e., English-French, English-German).",
"cite_spans": [
{
"start": 167,
"end": 191,
"text": "(Brown and others, 1991)",
"ref_id": null
},
{
"start": 209,
"end": 232,
"text": "(Gale and Church, 1993)",
"ref_id": "BIBREF9"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "The simple-feature based approaches don't work in flexible translations for structurally different languages such as Japanese and English, mainly for the following two reasons. One is the difference in the character types of the two languages. Japanese has three types of characters (Hiragana, Katakana, and Kanji), each of which has different amounts of information. In contrast, English has only one type of characters. The other is the grammatical and rhetorical difference of the two languages. First, the systems of functional (closed) words are quite different from language to language. Japanese has a quite different system of closed words, which greatly influence the length of simple features. Second, due to rhetorical difference, the number of multiple match (i.e., 1-2, 1-3, 2-1 and so on) is more than that among European languages. Thus, it is impossible in general to apply the simple-feature based methods to Japanese-English translations.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "One alternative alignment method is the lexiconbased approach that makes use of the wordcorrespondence knowledge of the two languages. (Church, 1993) employed n-grams shared by two languages. His method is also effective for Japanese-English computer manuals both containing lots of the same alphabetic technical terms.",
"cite_spans": [
{
"start": 135,
"end": 149,
"text": "(Church, 1993)",
"ref_id": "BIBREF5"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "However, the method cannot be applied to general translations in structurally different languages. (Kay and Roscheisen, 1993) proposed a relaxation method to iteratively align bilingual texts using the word correspondences acquired during the alignment process. Although the method works well among European languages, the method does not work in aligning structurally different languages. In Japanese-English translations, the method does not capture enough word correspondences to permit alignment. As a result, it can align only some of the two texts. This is mainly because the syntax and rhetoric are greatly differ in the two languages even in literal translations. The number of confident word correspondences of words is not enough for complete alignment. Thus, the problem cannot be addressed as long as the method relies only on statistics. Other methods in the lexicon-based approach embed lexical knowledge into stochastic models (Wu, 1994; Chen, 1993) , but these methods were tested using rigid translations.",
"cite_spans": [
{
"start": 99,
"end": 125,
"text": "(Kay and Roscheisen, 1993)",
"ref_id": "BIBREF12"
},
{
"start": 942,
"end": 952,
"text": "(Wu, 1994;",
"ref_id": "BIBREF22"
},
{
"start": 953,
"end": 964,
"text": "Chen, 1993)",
"ref_id": "BIBREF4"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "To tackle the problem, we describe in this paper a text alignment system that uses both statistics and bilingual dictionaries at the same time.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Bilingual dictionaries are now widely available on-line due to advances in CD-ROM technologies.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "For example, English-Spanish, English-French, English-German, English-Japanese, Japanese-French, Japanese-Chinese and other dictionaries are now commercially available. It is reasonable to make use of these dictionaries in bilingual text alignment. The pros and cons of statistics and online dictionaries are discussed below. They show that statistics and on-line dictionaries are complementary in terms of bilingual text alignment.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Statistics Merit Statistics is robust in the sense that it can extract context-dependent usage of words and that it works well even if word segmentation 1 is not correct.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Statistics Demerit The amount of word correspondences acquired by statistics is not enough for complete alignment. Dictionaries Merit They can contain the information about words that appear only once in the corpus. Dictionaries Demerit They cannot capture context-dependent keywords in the corpus and are weak against incorrect word segmentation. Entries in the dictionaries differ from author to author and are not always the same as those in the corpus.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Our system iteratively aligns sentences by using statistical and on-line dictionary word correspondences. The characteristics of the system are as follows.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "\u2022 The system performs well and is robust for various lengths (especially short) and various genres of texts.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "\u2022 The system is very economical because it assumes only online-dictionaries of general use and doesn't require the labor-intensive construction of domain-specific dictionaries.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "\u2022 The system is extendable by registering statistically acquired word correspondences into user dictionaries.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "1In Japanese, there are no explicit delimiters between words. The first task for alignment is , therefore, to divide the text stream into words.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "We will treat hereafter Japanese-English translations although the proposed method is language independent.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "The construction of the paper is as follows. First, Section 2 offers an overview of our alignment system. Section 3 describes the entire alignment algorithm in detail. Section 4 reports experimental results for various kinds of Japanese-English texts including newspaper editorials, scientific papers and critiques on economics. The evaluation is performed from two points of view: precision-recall of alignment and word correspondences acquired during alignment. Section 5 concerns related works and Section 6 concludes the paper. Figure 1 overviews our alignment system. The input to the system is a pair of Japanese and English texts, one the translation of the other. First, sentence boundaries are found in both texts using finite state transducers. The texts are then partof-speech (POS) tagged and separated into original form words z. Original forms of English words are determined by 80 rules using the POS information. From the word sequences, we extract only nouns, adjectives, adverbs verbs and unknown words (only in Japanese) because Japanese and English closed words are different and impede text alignment. These pre-processing operation can be easily implemented with regular expressions. 2We use in this phase the JUMAN morphological analyzing system (Kurohashi et al., 1994) for tagging Japanese texts and Brill's transformation-based tagger (Brill, 1992; Brill, 1994) for tagging English texts (JU-MAN: ftp://ftp.aist-nara.ac.jp/pub/nlp/tools/juman/ Brih ftp://ftp.cs.jhu.edu/pub/brill). We would like to thank all people concerned for providing us with the tools.",
"cite_spans": [
{
"start": 1269,
"end": 1293,
"text": "(Kurohashi et al., 1994)",
"ref_id": "BIBREF15"
},
{
"start": 1361,
"end": 1374,
"text": "(Brill, 1992;",
"ref_id": "BIBREF0"
},
{
"start": 1375,
"end": 1387,
"text": "Brill, 1994)",
"ref_id": "BIBREF1"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "The initial state of the algorithm is a set of already known anchors (sentence pairs). These are determined by article boundaries, section boundaries and paragraph boundaries. In the most general case, initial anchors are only the first and final sentence pairs of both texts as depicted in Figure 2 . Possible sentence correspondences are determined from the anchors. Intuitively, the number of possible correspondences for a sentence is small near anchors, while large between the anchors. In this phase, the most important point is that each set of possible sentence correspondences should include the correct correspondence.",
"cite_spans": [],
"ref_spans": [
{
"start": 291,
"end": 299,
"text": "Figure 2",
"ref_id": null
}
],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "The main task of the system is to find anchors from the possible sentence correspondences by using two kinds of word correspondences: statistical word correspondences and word correspondences as held in a bilingual dictionary 3. By using both correspondences, the sentence pair whose correspondences exceeds a pre-defined threshold is judged as an anchor. These newly found anchors make word correspondences more precise in the subsequent session. By repeating this anchor setting process with threshold reduction, sentence correspondences are gradually determined from confident pairs to nonconfident pairs. The gradualism of the algorithm makes it robust because anchor-setting errors in the last stage of the algorithm have little effect on overall performance. The output of the algorithm is the alignment result (a sequence of anchors) and word correspondences as by-products. SAdding to the bilingual dictionary of general use, users can reuse their own dictionaries created in previous sessions.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "3 Algorithms",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "In this section, we describe the statistics used to decide word correspondences. From many similarity metrics applicable to the task, we choose mutual information and t-score because the relaxation of parameters can be controlled in a sophisticated manner. Mutual information represents the similarity on the occurrence distribution and t-score represents the confidence of the similarity. These two parameters permit more effective relaxation than the single parameter used in conventional methods (Kay and Roscheisen, 1993) .",
"cite_spans": [
{
"start": 499,
"end": 525,
"text": "(Kay and Roscheisen, 1993)",
"ref_id": "BIBREF12"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Statistics Used",
"sec_num": "3.1"
},
{
"text": "Our basic data structure is the alignable sentence matrix (ASM) and the anchor matrix (AM). ASM represents possible sentence correspondences and consists of ones and zeros. A one in ASM indicates the intersection of the column and row constitutes a possible sentence correspondence. On the contrary, AM is introduced to represent how a sentence pair is supported by word correspondences. The i-j Element of AM indicates how many times the corresponding words appear in the i-j sentence pair. As alignment proceeds, the number of ones in ASM reduces, while the elements of AM increase.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Statistics Used",
"sec_num": "3.1"
},
{
"text": "Let pi be a sentence set comprising the ith Japanese sentence and its possible English correspondences as depicted in Figure 3 . For example, P2 is the set comprising Jsentence2, Esentence2 and Esentencej, which means Jsentence2 has the possibility of aligning with Esentence2 or Esentencej. The pis can be directly derived from ASM.",
"cite_spans": [],
"ref_spans": [
{
"start": 118,
"end": 126,
"text": "Figure 3",
"ref_id": "FIGREF2"
}
],
"eq_spans": [],
"section": "Statistics Used",
"sec_num": "3.1"
},
{
"text": "Jsentence I \u00a9",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "P2 P3",
"sec_num": null
},
{
"text": "Jsentence 3 Esentence3",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Jsentence 2 Esentence2",
"sec_num": null
},
{
"text": "\u2022 \u2022 , \u00b0 \u2022 \u2022 , \u2022 \u00b0 \u2022 \u00b0 , \u00b0 \u00b0 , \u00b0 , , , \u2022 \u2022 \u2022 ,",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Jsentence 2 Esentence2",
"sec_num": null
},
{
"text": "PM Jsentence Esentence N We introduce the contingency matrix (Fung and Church, 1994) to evaluate the similarity of word occurrences. Consider the contingency matrix shown Table 1 , between Japanese word wjp n and English word Weng. The contingency matrix shows: (a) the number of pis in which both wjp, and w~ng were found, (b) the number of pis in which just w~.g was found, (c) the number of pis in which just wjp, was found, (d) the number of pis in which neither word was found. Note here that pis overlap each other and w~,~ 9 may be double counted in the contingency matrix. We count each w~,,~ only once, even if it occurs more than twice in pls.",
"cite_spans": [
{
"start": 61,
"end": 84,
"text": "(Fung and Church, 1994)",
"ref_id": "BIBREF7"
}
],
"ref_spans": [
{
"start": 171,
"end": 178,
"text": "Table 1",
"ref_id": "TABREF1"
}
],
"eq_spans": [],
"section": "Jsentence 2 Esentence2",
"sec_num": null
},
{
"text": "] Wjpn Weng I a b ",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Jsentence 2 Esentence2",
"sec_num": null
},
{
"text": "I c d",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Jsentence 2 Esentence2",
"sec_num": null
},
{
"text": "Our basic algorithm is an iterative adjustment of the Anchor Matrix (AM) using the Alignable Sentence Matrix (ASM). Given an ASM, mutual information and t-score are computed for all word pairs in possible sentence correspondences. A word combination exceeding a predefined threshold is judged as a word correspondence. In order to find new anchors, we combine these statistical word correspondences with the word correspondences in a bilingual dictionary. Each element of AM, which represents a sentence pair, is updated by adding the number of word correspondences in the sentence pair. A sentence pair containing more than a predefined number of corresponding words is determined to be a new anchor. The detailed algorithm is as follows.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Basic Alignment Algorithm",
"sec_num": "3.2"
},
{
"text": "This step constructs the initial ASM. If the texts contain M and N sentences respectively, the ASM is an M x N matrix. First, we decide a set of anchors using article boundaries, section boundaries and so on. In the most general case, initial anchors are the first and last sentences of both texts as depicted in Figure 2 . Next, possible sentence correspondences are generated. Intuitively, true correspondences are close to the diagonal linking the two anchors. We construct the initial ASM using such a function that pairs sentences near the middle of the two anchors with as many as O(~/~) (L is the number of sentences existing between two anchors) sentences in the other text because the maximum deviation can be stochastically modeled as O(~rL) (Kay and Roscheisen, 1993) . The initial ASM has little effect on the alignment performance so long as it contains all correct sentence correspondences.",
"cite_spans": [
{
"start": 752,
"end": 778,
"text": "(Kay and Roscheisen, 1993)",
"ref_id": "BIBREF12"
}
],
"ref_spans": [
{
"start": 313,
"end": 321,
"text": "Figure 2",
"ref_id": null
}
],
"eq_spans": [],
"section": "Constructing Initial ASM",
"sec_num": "3.2.1"
},
{
"text": "This step constructs an AM when given an ASM and a bilingual dictionary. Let thigh, tlow, Ihigh and Izow be two thresholds for t-score and two thresholds for mutual information, respectively. Let ANC be the minimal number of corresponding words for a sentence pair to be judged as an anchor.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Constructing AM",
"sec_num": "3.2.2"
},
{
"text": "First, mutual information and t-score are computed for all word pairs appearing in a possible sentence correspondence in ASM. We use hereafter the word correspondences whose mutual information exceeds Itow and whose t-score exceeds ttow. For all possible sentence correspondences Jsentencei and Esentencej (any pair in ASM), the following operations are applied in order.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Constructing AM",
"sec_num": "3.2.2"
},
{
"text": "1. If the following three conditions hold, add 3 to the i-j element of AM. (1) Jsentencei and Esentencej contain a bilingual dictionary word correspondence (wjpn and w,ng). (2) w~na does not occur in any other English sentence that is a possible translation of Jsentencei. (3) Jsentencei and Esentencej do not cross any sentence pair that has more than ANC word correspondences.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Constructing AM",
"sec_num": "3.2.2"
},
{
"text": "2. If the following three conditions hold, add 3 to the i-j element of AM. 1 The first operation deals with word correspondences in the bilingual dictionary. The second operation deals with stochastic word correspondences which are highly confident and in many cases involve domain specific keywords. These word correspondences are given the value of 3. The third operation is introduced because the number of highly confident corresponding words are too small to align all sentences. Although word correspondences acquired by this step are sometimes false translations of each other, they play a crucial role mainly in the final iterations phase. They are given one point.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Constructing AM",
"sec_num": "3.2.2"
},
{
"text": "This step adjusts ASM using the AM constructed by the above operations. The sentence pairs that have at least ANC word correspondences are determined to be new anchors. By using the new set of anchors, a new ASM is constructed using the same method as used for initial ASM construction.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Adjusting ASM",
"sec_num": "3.2.3"
},
{
"text": "Our algorithm implements a kind of relaxation by gradually reducing flow, Izow and ANC, which enables us to find confident sentence correspondences first. As a result, our method is more robust than dynamic programing techniques against the shortage of word-correspondence knowledge.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Adjusting ASM",
"sec_num": "3.2.3"
},
{
"text": "In this section, we report the result of experiments on aligning sentences in bilingual texts and on statistically acquired word correspondences. The texts for the experiment varied in length and genres as summarized in Table 2 . Texts 1 and 2 are editorials taken from 'Yomiuri Shinbun' and its English version 'Daily Yomiuri'. This data was distributed electrically via a WWW server 4. The first two texts clarify the systems's performance on shorter texts. Text 3 is an essay on economics taken from a quarterly publication of The International House of Japan. Text 4 is a scientific survey on brain science taken from 'Scientific American' and its Japanese version 'Nikkei Science '5. Jpn and Eng in Table2 represent the number of sentences in the Japanese and English texts respectively . The remaining table entries show 4The Yomiuri data can be obtained from www.yomiuri.co.jp. We would like to thank Yomiuri Shinbun Co. for permitting us to use the data.",
"cite_spans": [],
"ref_spans": [
{
"start": 220,
"end": 227,
"text": "Table 2",
"ref_id": null
},
{
"start": 792,
"end": 833,
"text": ". The remaining table entries show 4The",
"ref_id": "TABREF5"
}
],
"eq_spans": [],
"section": "Experimental Results",
"sec_num": "4"
},
{
"text": "~We obtained the data from paper version of the magazine by using OCR. We would like to thank Nikkei Science Co. for permitting us to use the data. categories of matches by manual alignment and indicate the difficulty of the task.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Experimental Results",
"sec_num": "4"
},
{
"text": "Our evaluation focuses on much smaller texts than those used in other study (Brown and others, 1993; Gale and Church, 1993; Wu, 1994; Fung, 1995; Kay and Roscheisen, 1993) because our main targets are well-separated articles. However, our method will work on larger and noisy sets too, by using word anchors rather than using sentence boundaries as segment boundaries. In such a case, the method constructing initial ASM needs to be modified.",
"cite_spans": [
{
"start": 76,
"end": 100,
"text": "(Brown and others, 1993;",
"ref_id": null
},
{
"start": 101,
"end": 123,
"text": "Gale and Church, 1993;",
"ref_id": "BIBREF9"
},
{
"start": 124,
"end": 133,
"text": "Wu, 1994;",
"ref_id": "BIBREF22"
},
{
"start": 134,
"end": 145,
"text": "Fung, 1995;",
"ref_id": "BIBREF8"
},
{
"start": 146,
"end": 171,
"text": "Kay and Roscheisen, 1993)",
"ref_id": "BIBREF12"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Experimental Results",
"sec_num": "4"
},
{
"text": "We briefly report here the computation time of our method. Let us consider Text 4 as an example. After 15 seconds for full preprocessing, the first iteration took 25 seconds with tto~ = 1.55 and Izow = 1.8. The rest of the algorithm took 20 seconds in all. This experiment was performed on a SPARC Station 20 Model tIS21. From the result, we may safely say that our method can be applied to voluminous corpora. Table 3 shows the performance on sentence alignments for the texts in Table 2 . Combined, Statistics and Dictionary represent the methods using both statistics and dictionary, only statistics and only dictionary, respectively. Both Combined and Dictionary use a CD-ROM version of a Japanese-English dictionary containing 40 thousands entries.",
"cite_spans": [],
"ref_spans": [
{
"start": 411,
"end": 418,
"text": "Table 3",
"ref_id": null
},
{
"start": 481,
"end": 488,
"text": "Table 2",
"ref_id": null
}
],
"eq_spans": [],
"section": "Experimental Results",
"sec_num": "4"
},
{
"text": "Statistics repeats the iteration by using statistical corresponding words only. This is identical to Kay's method (Kay and Roscheisen, 1993) except for the statistics used. Dictionary performs the iteration of the algorithm by using corresponding words of the bilingual dictionary. This delineates the coverage of the dictionary. The parameter setting used for each method was the optimum as determined by empirical tests.",
"cite_spans": [
{
"start": 114,
"end": 140,
"text": "(Kay and Roscheisen, 1993)",
"ref_id": "BIBREF12"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Sentence Alignment",
"sec_num": "4.1"
},
{
"text": "In Table 3 , PRECISION delineates how many of the aligned pairs are correct and RECALL delineates how many of the manual alignments we included in systems output. Unlike conventional sentencechunk based evaluations, our result is measured on the sentence-sentence basis. Let us consider a 3-1 matching. Although conventional evaluations can make only one error from the chunk, three errors may arise by our evaluation. Note that our evaluation is more strict than the conventional one, especially for difficult texts, because they contain more complex matches.",
"cite_spans": [],
"ref_spans": [
{
"start": 3,
"end": 10,
"text": "Table 3",
"ref_id": null
}
],
"eq_spans": [],
"section": "Sentence Alignment",
"sec_num": "4.1"
},
{
"text": "For Text 1 and Text 2, both the combined method and the dictionary method perform much better than the statistical method. This is obviously because statistics cannot capture wordcorrespondences in the case of short texts.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Sentence Alignment",
"sec_num": "4.1"
},
{
"text": "Text 3 is easy to align in terms of both the complexity of the alignment and the vocabularies used. All methods performed well on this text.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Sentence Alignment",
"sec_num": "4.1"
},
{
"text": "For Text 4, Combined and Statistics perform 63.8% Table 3 : Result of Sentence Alignment much better than Dictionary. The reason for this is that Text 4 concerns brain science and the bilingual dictionaries of general use did not contain domain specific keywords. On the other hand, the combined and statistical methods well capture the keywords as described in the next section. Note here that Combined performs better than Statistics in the case of longer texts, too. There is clearly a limitation in the amount of word correspondences that can be captured by statistics. In summary, the performance of Combined is better than either Statistics or Dictionary for all texts, regardless of text length and the domain.",
"cite_spans": [],
"ref_spans": [
{
"start": 50,
"end": 57,
"text": "Table 3",
"ref_id": null
}
],
"eq_spans": [],
"section": "Sentence Alignment",
"sec_num": "4.1"
},
{
"text": "correspondences were not used. Although these word correspondences are very effective for sentence alignment task, they are unsatisfactory when regarded as a bilingual dictionary. For example, ' 7 7 Y ~' ~ ~ ~n.MR I ' in Japanese is the translation of 'functional MRI'. In Table 4 , the correspondence of these compound nouns was captured only in their constituent level. (Haruno et al., 1996) proposes an efficient n-gram based method to extract bilingual collocations from sentence aligned bilingual corpora.",
"cite_spans": [
{
"start": 372,
"end": 393,
"text": "(Haruno et al., 1996)",
"ref_id": "BIBREF10"
}
],
"ref_spans": [
{
"start": 273,
"end": 280,
"text": "Table 4",
"ref_id": "TABREF5"
}
],
"eq_spans": [],
"section": "Sentence Alignment",
"sec_num": "4.1"
},
{
"text": "Related Work",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "5",
"sec_num": null
},
{
"text": "In this section, we will demonstrate how well the proposed method captured domain specific word correspondences by using Text 4 as an example. Table 4 shows the word correspondences that have high mutual information. These are typical keywords concerning the non-invasive approach to human brain analysis. For example, NMR, MEG, PET, CT, MRI and functional MRI are devices for measuring brain activity from outside the head. These technical terms are the subjects of the text and are essential for alignment. However, none of them have their own entry in the bilingual dictionary, which would strongly obstruct the dictionary method. It is interesting to note that the correct Japanese translation of 'MEG' is ' ~{i~i~]'. The Japanese morphological analyzer we used does not contain an entry for ' ~i~i[~' and split it into a sequence of three characters ' ~',' ~' and ' []'. Our system skillfully combined ' ~i' and ' []' with 'MEG', as a result of statistical acquisition. These word correspondences greatly improved the performance for Text 4. Thus, the statistical method well captures the domain specific keywords that are not included in general-use bilingual dictionaries. The dictionary method would yield false alignments if statistically acquired word Sentence alignment between Japanese and English was first explored by Sato and Murao (Murao, 1991) . They found (character or word) length-based approaches were not appropriate due to the structural difference of the two languages. They devised a dynamic programming method based on the number of corresponding words in a hand-crafted bilingual dictionary. Although some results were promising, the method's performance strongly depended on the domain of the texts and the dictionary entries. (Utsuro et al., 1994) introduced a statistical postprocessing step to tackle the problem. He first applied Sato's method and extracted statistical word correspondences from the result of the first path. Sato's method was then reiterated using both the acquired word correspondences and the hand-crafted dictionary. His method involves the following two problems. First, unless the hand-crafted dictionary contains domain specific key words, the first path yields false alignment, which in turn leads to false statistical correspondences. Because it is impossible in general to cover key words in all domains, it is inevitable that statistics and hand-crafted bilingual dictionaries must be used at the same time. The proposed method involves iterative alignment which simultaneously uses both statistics and a bilingual dictionary. Second, their score function is not reliable especially when the number of corresponding words contained in corresponding sentences is small. Their method selects a matching type (such as 1-1, 1-2 and 2-1) according to the number of word correspondences per contents word. However, in many cases, there are a few word translations in a set of corresponding sentences. Thus, it is essential to decide sentence alignment on the sentence-sentence basis. Our iterative approach decides sentence alignment level by level by counting the word correspondences between a Japanese and an English sentence. (Fung and Church, 1994; Fung, 1995) proposed methods to find Chinese-English word correspondences without aligning parallel texts. Their motivation is that structurally different languages such as Chinese-English and Japanese-English are difficult to align in general. Their methods bypassed aligning sentences and directly acquired word correspondences. Although their approaches are robust for noisy corpora and do not require any information source, aligned sentences are necessary for higher level applications such as well-grained translation template acquisition (Matsumoto et as., 1993; Smadja et al., 1996; Haruno et al., 1996) and example-based translation (Sato and Nagao, 1990) . Our method performs accurate alignment for such use by combining the detailed word correspondences: statistically acquired word correspondences and those from a bilingual dictionary of general use. (Church, 1993) proposed char_align that makes use of n-grams shared by two languages. This kind of matching techniques will be helpful in our dictionary-based approach in the following situation: Entries of a bilingual dictionary do not completely match the word in the corpus but partially do. By using the matching technique, we can make the most of the information compiled in bilingual dictionaries.",
"cite_spans": [
{
"start": 1332,
"end": 1360,
"text": "Sato and Murao (Murao, 1991)",
"ref_id": null
},
{
"start": 1755,
"end": 1776,
"text": "(Utsuro et al., 1994)",
"ref_id": "BIBREF21"
},
{
"start": 3184,
"end": 3207,
"text": "(Fung and Church, 1994;",
"ref_id": "BIBREF7"
},
{
"start": 3208,
"end": 3219,
"text": "Fung, 1995)",
"ref_id": "BIBREF8"
},
{
"start": 3753,
"end": 3777,
"text": "(Matsumoto et as., 1993;",
"ref_id": "BIBREF16"
},
{
"start": 3778,
"end": 3798,
"text": "Smadja et al., 1996;",
"ref_id": "BIBREF20"
},
{
"start": 3799,
"end": 3819,
"text": "Haruno et al., 1996)",
"ref_id": "BIBREF10"
},
{
"start": 3850,
"end": 3872,
"text": "(Sato and Nagao, 1990)",
"ref_id": "BIBREF18"
},
{
"start": 4073,
"end": 4087,
"text": "(Church, 1993)",
"ref_id": "BIBREF5"
}
],
"ref_spans": [
{
"start": 143,
"end": 150,
"text": "Table 4",
"ref_id": "TABREF5"
}
],
"eq_spans": [],
"section": "Word Correspondence",
"sec_num": "4.2"
},
{
"text": "We have described a text alignment method for structurally different languages.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion",
"sec_num": "6"
},
{
"text": "Our iterative method uses two kinds of word correspondences at the same time: word correspondences acquired by statistics and those of a bilingual dictionary. By combining these two types of word correspondences, the method covers both domain specific keywords not included in the dictionary and the infrequent words not detected by statistics. As a result, our method outperforms conventional methods for texts of different lengths and different domains.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion",
"sec_num": "6"
}
],
"back_matter": [
{
"text": "We would like to thank Pascale Fung and Takehito Utsuro for helpful comments and discussions.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Acknowledgement",
"sec_num": null
}
],
"bib_entries": {
"BIBREF0": {
"ref_id": "b0",
"title": "A simple rule-based part of speech tagger",
"authors": [
{
"first": "Eric",
"middle": [],
"last": "Brill",
"suffix": ""
}
],
"year": 1992,
"venue": "Proc. Third Con/erence on Apolied Natural Language Processing",
"volume": "",
"issue": "",
"pages": "152--155",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Eric Brill. 1992. A simple rule-based part of speech tagger. In Proc. Third Con/erence on Apolied Natural Language Processing, pages 152-155.",
"links": null
},
"BIBREF1": {
"ref_id": "b1",
"title": "Some advances in transformation-based part of speech tagging",
"authors": [
{
"first": "Eric",
"middle": [],
"last": "Brill",
"suffix": ""
}
],
"year": 1994,
"venue": "Proc. 1Pth AAAI",
"volume": "",
"issue": "",
"pages": "722--727",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Eric Brill. 1994. Some advances in transformation-based part of speech tagging. In Proc. 1Pth AAAI, pages 722-727.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "Aligning sentences in parallel corpora",
"authors": [
{
"first": "P F",
"middle": [],
"last": "Brown",
"suffix": ""
}
],
"year": 1991,
"venue": "the 29th Annual Meeting of ACL",
"volume": "",
"issue": "",
"pages": "169--176",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "P F Brown et al. 1991. Aligning sentences in parallel corpora. In the 29th Annual Meeting of ACL, pages 169-176.",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "The mathematics of statistical machine translation",
"authors": [
{
"first": "P F",
"middle": [],
"last": "Brown",
"suffix": ""
}
],
"year": 1993,
"venue": "Computational Linguistics",
"volume": "19",
"issue": "2",
"pages": "263--311",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "P F Brown et al. 1993. The mathematics of statisti- cal machine translation. Computational Linguistics, 19(2):263-311, June.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "Aligning sentences in bilingual corpora using lexical information",
"authors": [
{
"first": "",
"middle": [],
"last": "S F Chen",
"suffix": ""
}
],
"year": 1993,
"venue": "the 31st Annual Meeting of ACL",
"volume": "",
"issue": "",
"pages": "9--16",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "S F Chen. 1993. Aligning sentences in bilingual corpora using lexical information. In the 31st Annual Meeting of ACL, pages 9-16.",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "Char_align: A program for aligning parallel texts at the character level",
"authors": [
{
"first": "K W",
"middle": [],
"last": "Church",
"suffix": ""
}
],
"year": 1993,
"venue": "the 31st Annual Meeting of ACL",
"volume": "",
"issue": "",
"pages": "1--8",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "K W Church. 1993. Char_align: A program for align- ing parallel texts at the character level. In the 31st Annual Meeting of ACL, pages 1-8.",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "Termight: identifying and translating technical terminology",
"authors": [
{
"first": "Ido",
"middle": [],
"last": "Dagan",
"suffix": ""
},
{
"first": "Ken",
"middle": [],
"last": "Church",
"suffix": ""
}
],
"year": 1994,
"venue": "Proc. Fourth Conference on Apolied Natural Language Processing",
"volume": "",
"issue": "",
"pages": "34--40",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ido Dagan and Ken Church. 1994. Termight: identifying and translating technical terminology. In Proc. Fourth Conference on Apolied Natural Language Processing, pages 34-40.",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "K-vec: A new approach for aligning parallel texts",
"authors": [
{
"first": "Pascale",
"middle": [],
"last": "Fung",
"suffix": ""
},
{
"first": "K W",
"middle": [],
"last": "Church",
"suffix": ""
}
],
"year": 1994,
"venue": "Proc. 15th COLING",
"volume": "",
"issue": "",
"pages": "1096--1102",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Pascale Fung and K W Church. 1994. K-vec: A new approach for aligning parallel texts. In Proc. 15th COLING, pages 1096-1102.",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "A pattern matching method for finding noun and proper nouns translations from noisy parallel corpora",
"authors": [
{
"first": "Pascale",
"middle": [],
"last": "Fung",
"suffix": ""
}
],
"year": 1995,
"venue": "Proc. 33rd ACL",
"volume": "",
"issue": "",
"pages": "236--243",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Pascale Fung. 1995. A pattern matching method for finding noun and proper nouns translations from noisy parallel corpora. In Proc. 33rd ACL, pages 236-243.",
"links": null
},
"BIBREF9": {
"ref_id": "b9",
"title": "A program for aligning sentences in bilingual corpora",
"authors": [
{
"first": "W A",
"middle": [],
"last": "Gale",
"suffix": ""
},
{
"first": "K W",
"middle": [],
"last": "Church",
"suffix": ""
}
],
"year": 1993,
"venue": "Computational Linguistics",
"volume": "19",
"issue": "1",
"pages": "75--102",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "W A Gale and K W Church. 1993. A program for align- ing sentences in bilingual corpora. Computational Linguistics, 19(1):75-102, March.",
"links": null
},
"BIBREF10": {
"ref_id": "b10",
"title": "Learning Bilingual Collocations by Word-Level Sorting",
"authors": [
{
"first": "Masahiko",
"middle": [],
"last": "Haruno",
"suffix": ""
},
{
"first": "Satoru",
"middle": [],
"last": "Ikehara",
"suffix": ""
},
{
"first": "Takefumi",
"middle": [],
"last": "Yamazaki",
"suffix": ""
}
],
"year": 1996,
"venue": "Proc. 16th COLING",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Masahiko Haruno, Satoru Ikehara, and Takefumi Ya- mazaki. 1996. Learning Bilingual Collocations by Word-Level Sorting,. In Proc. 16th COLING.",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "Learning translation templates from bilingaul text",
"authors": [
{
"first": "Hiroyuki",
"middle": [],
"last": "Kaji",
"suffix": ""
},
{
"first": "Yuuko",
"middle": [],
"last": "Kida",
"suffix": ""
},
{
"first": "Yasutsugu",
"middle": [],
"last": "Morimoto",
"suffix": ""
}
],
"year": 1992,
"venue": "Proc. 14th COLING",
"volume": "",
"issue": "",
"pages": "672--678",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Hiroyuki Kaji, Yuuko Kida, and Yasutsugu Morimoto. 1992. Learning translation templates from bilingaul text. In Proc. 14th COLING, pages 672-678.",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "Texttranslation alignment",
"authors": [
{
"first": "Martin",
"middle": [],
"last": "Kay",
"suffix": ""
},
{
"first": "Martin",
"middle": [],
"last": "Roscheisen",
"suffix": ""
}
],
"year": 1993,
"venue": "Computational Linguistics",
"volume": "19",
"issue": "1",
"pages": "121--142",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Martin Kay and Martin Roscheisen. 1993. Text- translation alignment. Computational Linguistics, 19(1):121-142, March.",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "Building an MT dictionary from parallel texts based on linguisitic and statistical information",
"authors": [
{
"first": "Akira",
"middle": [],
"last": "Kumano",
"suffix": ""
},
{
"first": "Hideki",
"middle": [],
"last": "Hirakawa",
"suffix": ""
}
],
"year": 1994,
"venue": "Proc. 15th COLING",
"volume": "",
"issue": "",
"pages": "76--81",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Akira Kumano and Hideki Hirakawa. 1994. Building an MT dictionary from parallel texts based on linguisitic and statistical information. In Proc. 15th COLING, pages 76-81.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "An algorithm for finding noun phrase correspondences in bilingual corpora",
"authors": [
{
"first": "Julian",
"middle": [],
"last": "Kupiec",
"suffix": ""
}
],
"year": 1993,
"venue": "the 31st Annual Meeting of A CL",
"volume": "",
"issue": "",
"pages": "17--22",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Julian Kupiec. 1993. An algorithm for finding noun phrase correspondences in bilingual corpora. In the 31st Annual Meeting of A CL, pages 17-22.",
"links": null
},
"BIBREF15": {
"ref_id": "b15",
"title": "Improvements of Japanese morphological analyzer juman",
"authors": [
{
"first": "Sadao",
"middle": [],
"last": "Kurohashi",
"suffix": ""
},
{
"first": "Toshihisa",
"middle": [],
"last": "Nakamura",
"suffix": ""
}
],
"year": 1994,
"venue": "Proc. International Workshop on Sharable Natural Language Resources",
"volume": "",
"issue": "",
"pages": "22--28",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Sadao Kurohashi, Toshihisa Nakamura, Yuji Mat- sumoto, and Makoto Nagao. 1994. Improvements of Japanese morphological analyzer juman. In Proc. In- ternational Workshop on Sharable Natural Language Resources, pages 22-28.",
"links": null
},
"BIBREF16": {
"ref_id": "b16",
"title": "Structural matching of parallel texts",
"authors": [
{
"first": "Yuji",
"middle": [],
"last": "Matsumoto",
"suffix": ""
},
{
"first": "Hiroyuki",
"middle": [],
"last": "Ishimoto",
"suffix": ""
},
{
"first": "Takehito",
"middle": [],
"last": "Utsuro",
"suffix": ""
}
],
"year": 1993,
"venue": "the 31st Annual Meeting of ACL",
"volume": "",
"issue": "",
"pages": "23--30",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Yuji Matsumoto, Hiroyuki Ishimoto, and Takehito Ut- suro. 1993. Structural matching of parallel texts. In the 31st Annual Meeting of ACL, pages 23-30.",
"links": null
},
"BIBREF17": {
"ref_id": "b17",
"title": "Studies on bilingual text alignment",
"authors": [
{
"first": "H",
"middle": [],
"last": "Murao",
"suffix": ""
}
],
"year": 1991,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "H. Murao. 1991. Studies on bilingual text alignment. Bachelor Thesis, Kyoto University (in Japanese).",
"links": null
},
"BIBREF18": {
"ref_id": "b18",
"title": "Toward memorybased translation",
"authors": [
{
"first": "Satoshi",
"middle": [],
"last": "Sato",
"suffix": ""
},
{
"first": "Makoto",
"middle": [],
"last": "Nagao",
"suffix": ""
}
],
"year": 1990,
"venue": "Proc. 13th COLING",
"volume": "",
"issue": "",
"pages": "247--252",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Satoshi Sato and Makoto Nagao. 1990. Toward memory- based translation. In Proc. 13th COLING, pages 247- 252.",
"links": null
},
"BIBREF19": {
"ref_id": "b19",
"title": "CTM: an example-based translation aid system",
"authors": [
{
"first": "Satoshi",
"middle": [],
"last": "Sato",
"suffix": ""
}
],
"year": 1992,
"venue": "Proc. l$th COLING",
"volume": "",
"issue": "",
"pages": "1259--1263",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Satoshi Sato. 1992. CTM: an example-based translation aid system. In Proc. l$th COLING, pages 1259-1263.",
"links": null
},
"BIBREF20": {
"ref_id": "b20",
"title": "Translating collocations for bilingual lexicons: A statistical approach",
"authors": [
{
"first": "Frank",
"middle": [],
"last": "Smadja",
"suffix": ""
},
{
"first": "Kathleen",
"middle": [],
"last": "Mckeown",
"suffix": ""
},
{
"first": "Vasileios",
"middle": [],
"last": "Hatzivassiloglou",
"suffix": ""
}
],
"year": 1996,
"venue": "Computational Linguistics",
"volume": "22",
"issue": "1",
"pages": "1--38",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Frank Smadja, Kathleen McKeown, and Vasileios Hatzi- vassiloglou. 1996. Translating collocations for bilin- gual lexicons: A statistical approach. Computational Linguistics, 22(1):1-38, March.",
"links": null
},
"BIBREF21": {
"ref_id": "b21",
"title": "Bilingual text matching using bilingual dictionary and statistics",
"authors": [
{
"first": "Takehito",
"middle": [],
"last": "Utsuro",
"suffix": ""
},
{
"first": "Hiroshi",
"middle": [],
"last": "Ikeda Masaya Yamane",
"suffix": ""
},
{
"first": "Yuji",
"middle": [],
"last": "Matsumoto",
"suffix": ""
},
{
"first": "Makoto",
"middle": [],
"last": "Nagao",
"suffix": ""
}
],
"year": 1994,
"venue": "Proc. 15th COLING",
"volume": "",
"issue": "",
"pages": "1076--1082",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Takehito Utsuro, Hiroshi Ikeda Masaya Yamane, Yuji Matsumoto, and Makoto Nagao. 1994. Bilingual text matching using bilingual dictionary and statistics. In Proc. 15th COLING, pages 1076-1082.",
"links": null
},
"BIBREF22": {
"ref_id": "b22",
"title": "Aligning a parallel English-Chinese corpus statistically with lexical criteria",
"authors": [
{
"first": "Dekai",
"middle": [],
"last": "Wu",
"suffix": ""
}
],
"year": 1994,
"venue": "the 3And Annual Meeting of ACL",
"volume": "",
"issue": "",
"pages": "80--87",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Dekai Wu. 1994. Aligning a parallel English-Chinese corpus statistically with lexical criteria. In the 3And Annual Meeting of ACL, pages 80-87.",
"links": null
}
},
"ref_entries": {
"FIGREF0": {
"type_str": "figure",
"text": "Overview of the Alignment System",
"num": null,
"uris": null
},
"FIGREF1": {
"type_str": "figure",
"text": "Figure 2: Alignment Process",
"num": null,
"uris": null
},
"FIGREF2": {
"type_str": "figure",
"text": "Possible Sentence Correspondences",
"num": null,
"uris": null
},
"TABREF0": {
"type_str": "table",
"content": "<table><tr><td>2</td><td>System Overview</td></tr><tr><td/><td>Word Correspondences</td></tr><tr><td colspan=\"2\">Japanese text word seg~=~oa</td></tr><tr><td/><td>& pos tagging</td></tr><tr><td colspan=\"2\">English text</td></tr></table>",
"text": ".............................................................. :",
"num": null,
"html": null
},
"TABREF1": {
"type_str": "table",
"content": "<table><tr><td colspan=\"2\">introduce mutual information:</td></tr><tr><td>log</td><td>prob(wjpn, Weng)</td></tr><tr><td/><td>prob( w p. )prob( won9 )</td></tr><tr><td colspan=\"2\">The probabilities are:</td></tr><tr><td/><td>a+c</td><td>a+c</td></tr><tr><td colspan=\"3\">prob(wjpn) -a T b + c W d -Y</td></tr><tr><td colspan=\"3\">a+b pr ob( w eng ) -a+b+c+d a prob( wjpn , Weng ) --a+b+c+d--M a+b M a</td></tr></table>",
"text": "Contingency MatrixIf Wjpn and weng are good translations of one another, a should be large, and b and c should be small. In contrast, if the two are not good translations of each other, a should be small, and b and c should be large. To make this argument more precise, we Unfortunately, mutual information is not reliable when the number of occurrences is small. Many words occur just once which weakens the statistics approach. In order to avoid this, we employ t-score, defined below, where M is the number of Japanese sentences. Insignificant mutual information values are filtered out by thresholding t-score. For example, t-scores above 1.65 are significant at the p > 0.95 confidence level.",
"num": null,
"html": null
},
"TABREF2": {
"type_str": "table",
"content": "<table><tr><td>Jsentencei and</td></tr><tr><td>Esentencej contain a stochastic word corre-</td></tr><tr><td>spondence (wjpn and w~na) that has mutual</td></tr><tr><td>information Ihig h and whose t-score exceeds thigh. (2) w~g does not occur in any other</td></tr><tr><td>English sentence that is a possible translation</td></tr><tr><td>of Jsentencei. (3) Jsentencei and Esentencej</td></tr><tr><td>do not cross any sentence pair that has more</td></tr><tr><td>than ANC word correspondences.</td></tr></table>",
"text": "Itoto and whose t-score exceeds ttow. (2) w~na does not occur in any other English sentence that is a possible translation of Jsentencei. (3) Jsentencei and Esentencej does not cross any sentence pair that has more than ANC word correspondences.",
"num": null,
"html": null
},
"TABREF5": {
"type_str": "table",
"content": "<table/>",
"text": "Statistically Acquired Keywords",
"num": null,
"html": null
}
}
}
} |