File size: 82,891 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 |
{
"paper_id": "I11-1039",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T07:30:45.626131Z"
},
"title": "Predicting Opinion Dependency Relations for Opinion Analysis",
"authors": [
{
"first": "Lun-Wei",
"middle": [],
"last": "Ku",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "National Yunlin University of Science and Technology Douliou",
"location": {
"postCode": "64002",
"settlement": "Yunlin",
"country": "Taiwan"
}
},
"email": "lwku@yuntech.edu.tw"
},
{
"first": "Ting-Hao",
"middle": [
"Kenneth"
],
"last": "Huang",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "National Taiwan University",
"location": {
"addrLine": "No.1, Sec.4, Roosevelt Rd",
"settlement": "Taipei",
"country": "Taiwan"
}
},
"email": "tinghaoh@andrew.cmu.edu"
},
{
"first": "Hsin-Hsi",
"middle": [],
"last": "Chen",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "National Taiwan University",
"location": {
"addrLine": "No.1, Sec.4, Roosevelt Rd",
"settlement": "Taipei",
"country": "Taiwan"
}
},
"email": "hhchen@ntu.edu.tw"
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "Syntactic structures have been good features for opinion analysis, but it is not easy to use them. To find these features by supervised learning methods, correct syntactic labels are indispensible. Two possible sources to acquire syntactic structures are parsing trees and dependency trees. For the annotation processing, parsing trees are more readable for annotators, while dependency trees are easier to use by programs. To use syntactic structures as features, this paper tried to annotate on human friendly materials and transform these annotations to the corresponding machine friendly materials. We annotated the gold answers of opinion syntactic structures on the parsing tree from Chinese Treebank, and then proposed methods to find their corresponding dependency relations on the dependency trees generated from the same sentence. With these relations, we could train a model to annotate opinion dependency relations automatically to provide an opinion dependency parser, which is language independent if language resources are incorporated. Experiment results show that the annotated syntactic structures and their corresponding dependency relations improve at least 8% of the performance of opinion analysis.",
"pdf_parse": {
"paper_id": "I11-1039",
"_pdf_hash": "",
"abstract": [
{
"text": "Syntactic structures have been good features for opinion analysis, but it is not easy to use them. To find these features by supervised learning methods, correct syntactic labels are indispensible. Two possible sources to acquire syntactic structures are parsing trees and dependency trees. For the annotation processing, parsing trees are more readable for annotators, while dependency trees are easier to use by programs. To use syntactic structures as features, this paper tried to annotate on human friendly materials and transform these annotations to the corresponding machine friendly materials. We annotated the gold answers of opinion syntactic structures on the parsing tree from Chinese Treebank, and then proposed methods to find their corresponding dependency relations on the dependency trees generated from the same sentence. With these relations, we could train a model to annotate opinion dependency relations automatically to provide an opinion dependency parser, which is language independent if language resources are incorporated. Experiment results show that the annotated syntactic structures and their corresponding dependency relations improve at least 8% of the performance of opinion analysis.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "Opinion analysis has drawn much attention in research communities of machine learning and natural language processing. In the early stages, words in documents were used as the main features (Pang et al., 2002) . Some opinion dictionaries were created for this demand . However, researchers soon realized that word features were not sufficient for acquiring good performances, so they started to include syntactic structures and semantic in-formation (Qiu et al., 2008) . Their researches showed that linguistic knowledge is helpful in determining opinions.",
"cite_spans": [
{
"start": 190,
"end": 209,
"text": "(Pang et al., 2002)",
"ref_id": "BIBREF12"
},
{
"start": 450,
"end": 468,
"text": "(Qiu et al., 2008)",
"ref_id": "BIBREF13"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "For various applications related to opinions, syntactic structures have become powerful tools for extracting useful clues. To find opinions in product reviews, modification relations were used to identify the product and their features (Lu et al., 2009) , e.g., a good price (feature) of this camera (product). To find opinion holders and targets, templates and linguistic rules were adopted (Breck et al., 2007) . To find more opinion words, dependency relations were utilized (Qiu et al., 2011) . Even when applying the basic negation rule that flips opinion polarity over, we need to find its modified word first by syntactic clues. However, we will show that syntactic relations do not directly suggest opinions.",
"cite_spans": [
{
"start": 236,
"end": 253,
"text": "(Lu et al., 2009)",
"ref_id": "BIBREF10"
},
{
"start": 392,
"end": 412,
"text": "(Breck et al., 2007)",
"ref_id": "BIBREF2"
},
{
"start": 478,
"end": 496,
"text": "(Qiu et al., 2011)",
"ref_id": "BIBREF14"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Syntactic relations are obtained usually from all kinds of syntax trees. Parsing trees (phrase structured) and dependency trees (grammatical) are the most commonly seen ones. Parsing trees are in-order trees which keep the order of words in sentences, so they are more readable for people. Instead, nodes in dependency trees are displayed by the head-modifier relations, in which the sentence sequence probably is not remained. People could find the opinion passages if they can understand the whole sentence, i.e. from parsing trees. However, when the linguistic background is needed, it could be difficult for most people to reconstruct the whole sentence from the dependency trees in order to find the opinion passage. Therefore, if we want to find annotators to build a corpus which could be used to train an opinion relation recognizer, parsing trees are the better materials compared to dependency trees. However, compared to relations between words, complicated tree structures are more challenge to be utilized by algorithms (Doan et al., 2008) . This paper focuses on extracting opinionated dependency relations from relations generated by the Stanford parser. We design an annotation mechanism on the syntactic structures on the sentence from Chinese Treebank to create an annotation environment with a lower entry barrier so that sufficient annotations can be labeled. Then these annotations are aligned to the relations in the corresponding dependency trees generated by the same parser from the same sentence as the gold standard for training the automatic annotator of the opinion dependency relations. We conduct experiments on the annotated opinion syntactic structures in parsing trees, and on the opinion dependency relations corresponding to them. The proposed process demonstrates a feasible direction toward the development of an opinion dependency parser.",
"cite_spans": [
{
"start": 1033,
"end": 1052,
"text": "(Doan et al., 2008)",
"ref_id": "BIBREF4"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Given a set of non-collapsed dependencies parsed from a specific sentence by the Stanford dependency parser (de Marneffe and Manning, 2008; Chang et al., 2009) , each associated with a dependency relation between two words in this sentence, our goal is to identify which of them are with sentiment, i.e., those which reveal a part of opinions or the aroused emotions. For example, in the sentence \"\u6d3b\u52a8 \u53d6\u5f97 \u4e86 \u5706\u6ee1 \u6210\u529f (Activities scored le perfect success)\", the Stanford dependency parser gives three relations: nmod(\u6210\u529f <success>, \u5706 \u6ee1 <perfect>), nsubj( \u53d6 \u5f97 <scored>, \u6d3b \u52a8 <activities>), dobj(\u53d6\u5f97 <scored>, \u6210\u529f<success>), and asp(\u53d6\u5f97 <scored>, \u4e86<le>). The goal is to identify the former three may bear sentiment or opinions. The corresponding dependency tree is shown in Figure 1 . From Figure 1 we can also see that it is not easy to read the original sentence without the linguistic background. Formally, the collection of the noncollapsed dependency relations of a sentence S, generated by the Stanford dependency parser, is denoted by Rdep(S) = {r 1 , r 2 , \u2026}, where each ) (S Rdep r i \uf0ce is associate with an opinion judgment of op(r).",
"cite_spans": [
{
"start": 108,
"end": 139,
"text": "(de Marneffe and Manning, 2008;",
"ref_id": "BIBREF11"
},
{
"start": 140,
"end": 159,
"text": "Chang et al., 2009)",
"ref_id": "BIBREF3"
}
],
"ref_spans": [
{
"start": 762,
"end": 770,
"text": "Figure 1",
"ref_id": "FIGREF0"
},
{
"start": 778,
"end": 786,
"text": "Figure 1",
"ref_id": "FIGREF0"
}
],
"eq_spans": [],
"section": "Problem Definition",
"sec_num": "2"
},
{
"text": "Definition: Dependency Relation The dependency relation r, generated by the Stanford parser, is composed of the type of relation rel, the head word w h and the modifier word w m in the form of rel(w h , w m ). w h and w m are two individual words in S. For example, in one relation in Figure 1 , r = nmod(\u6210\u529f <success>, \u5706\u6ee1 <perfect>), where rel = nmod, w h =\u6210\u529f <suc-cess>, and w m =\u5706\u6ee1<perfect>. A list of rel is available in Stanford Parser Manual (de Marneffe and Manning, 2008; Chang et al., 2009) .",
"cite_spans": [
{
"start": 451,
"end": 478,
"text": "Marneffe and Manning, 2008;",
"ref_id": "BIBREF11"
},
{
"start": 479,
"end": 498,
"text": "Chang et al., 2009)",
"ref_id": "BIBREF3"
}
],
"ref_spans": [
{
"start": 285,
"end": 293,
"text": "Figure 1",
"ref_id": "FIGREF0"
}
],
"eq_spans": [],
"section": "Problem Definition",
"sec_num": "2"
},
{
"text": "Definition: Opinion Judgment The opinion judgment op(r), generated by the proposed system, indicates whether the corresponding dependency relation r is opinionated, and \uf07b \uf07d false true r op , ) ( \uf0ce . For example, when r = nmod(\u6210\u529f <success>, \u5706\u6ee1<perfect>), op(r) =",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Problem Definition",
"sec_num": "2"
},
{
"text": "Definition: Gold Opinion Judgment The gold opinion judgment, generated by mapping from manually annotated data, indicates whether the corresponding dependency relation r is opinionated, and",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "true.",
"sec_num": null
},
{
"text": "\uf07b \uf07d false true r gop , ) ( \uf0ce .",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "true.",
"sec_num": null
},
{
"text": "The gold answers come from the annotations on Chinese Treebank 5.1. In a parsing tree T of the sentence S, generated by the Stanford parser, an in-ordered set of tree nodes O = {o 1 , o 2 , \u2026} is used to draw a parsing tree for the annotation process, and its corresponding order, i.e., its index, is used as the node ID to record the annotations.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "true.",
"sec_num": null
},
{
"text": "The way we annotate an opinion relation on a parsing tree is annotating an opinion trio (Ku et al., 2009 Note that because the annotation of trios is on nodes of parsing trees, which appear inorderly, o left will always appear before o right in a sentence, and keeping this in mind will help understand the meaning of each inter-word relation t.",
"cite_spans": [
{
"start": 88,
"end": 104,
"text": "(Ku et al., 2009",
"ref_id": "BIBREF7"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "true.",
"sec_num": null
},
{
"text": "Now for the sentence S, we have its parsing tree T, the annotated opinion trios Tri(S) on it, and its dependency relations Rdep(S). The next step is to mark the op(r) on Rdep(S) according to its corresponding Tri(S). For each trio tri, if any descendent of its left node o left and any descendent of its right node o right together build a relation ) (S Rdep r \uf0ce , the opinion judgment of gop(r) of the relation r is set to true. Otherwise, gop(r) is set to false. Now we have gop(r) for each r in Rdep(S), our goal is to find good methods to generate op(r) so that it can predict gop(r) as precisely as possible. We propose methods to achieve this goal in Section 3.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "true.",
"sec_num": null
},
{
"text": "As mentioned, our goal is to predict opinion dependency relations as precisely as possible. However, to use more readable materials, opinion trios are first annotated on Chinese Treebank 5.1, and then they are mapped to the corresponding dependency relations. Before the aligning process, we use the annotated trios for training to predict the opinion trios in Section 3.1. Using these predict trios for opinion analysis shall show the performance before the aligning process. After that, the aligned depen-dency relations, i.e., the gold opinion dependency relations are adopted for training to predict the opinion dependency relations in Section 3.2. Because the parsing tree and the dependency tree are generated by the same parser, we can always align them by the provided word ID numbers.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Methods",
"sec_num": "3"
},
{
"text": "After prediction, the opinion dependency relations are available, and they can provide necessary information for many applications. However, we go one step further to test whether they benefit the opinion analysis. To fulfill this purpose, a basic method which uses the opinion dependency relations to extract opinionated sentences and determine their polarities is proposed in Section 3.3.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Methods",
"sec_num": "3"
},
{
"text": "We predict the opinion trios by the sequential labeling model Conditional Random Field (CRF, Lafferty et al., 2001) . In a parsing tree, the tag of the internal node is the syntactic structure of its sub-tree, and the tag of the leaf node contains its part of speech and the content word. For each node, tags of its first four children (the first level), first four children of them (the second level), and their three children are used as features of this node. Features of its siblings (the window size is five) are considered, too.",
"cite_spans": [
{
"start": 87,
"end": 115,
"text": "(CRF, Lafferty et al., 2001)",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Predicting Opinion Trios",
"sec_num": "3.1"
},
{
"text": "The labels l we would like the CRF to predict labels for each node, which are N or labels of the form t-C, where",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Predicting Opinion Trios",
"sec_num": "3.1"
},
{
"text": "Rpt t \uf0ce , } , { R L C \uf03d , L",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Predicting Opinion Trios",
"sec_num": "3.1"
},
{
"text": "indicates that the current node is o left in some opinion trio and R indicates o right . The label N indicates that the current node does not belong to any Tri tri \uf0ce . The cardinality of the set Rpt is five, so that a total of 11 labels are used in CRF. CRF++ 1 is selected for experiments.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Predicting Opinion Trios",
"sec_num": "3.1"
},
{
"text": "After aligning the opinion trios to the dependency relations, we will have gop(r) for each one of them. In the previous research, usually only some relations were selected for opinion analysis. No statistical numbers showed the connection between the dependency relations and the opinions. We believe that it is because the opinion annotation on dependency relations is more difficult than on words, sentences, or documents. However, because of this alignment, we are able to see the distribution of different dependency relations in opinion sentences and opinion segments (opinion trios).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Predicting Opinion Dependency Relations",
"sec_num": "3.2"
},
{
"text": "We then predict opinion dependency relations based on these distributions: the op(r) of the relation r is set to true when its corresponding gop(r) appears massively frequently to be true in opinion sentences. To make this method reasonable, the assumption that there are no opinion trios in non-opinionated sentences must hold. A similar assumption that there are no opinion segments in non-opinionated sentences was made when annotating the NTCIR MOAT corpus, too (Seki et al., 2008) . Under this assumption, the relation that is in most case opinionated in opinion sentences is also in most case opinionated in all sentences. We believe that this assumption holds because intuitively if there is an opinion segment in one sentence, this sentence should be opinionated.",
"cite_spans": [
{
"start": 466,
"end": 485,
"text": "(Seki et al., 2008)",
"ref_id": "BIBREF15"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Predicting Opinion Dependency Relations",
"sec_num": "3.2"
},
{
"text": "In the previous research, relations were usually extracted automatically and then were used in various applications. As these relations are available after the prediction (or alignment) and as our purpose is to provide easy to use opinion dependency relations for further applications, we simply design rules for these relations in opinion analysis to show the baseline enhancement of using them.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Using Syntactic Information for Opinion Extraction",
"sec_num": "3.3"
},
{
"text": "In the past, Ku et al. (2009) have conducted rule based experiments for opinion trios. They designed formula for trios of each",
"cite_spans": [
{
"start": 13,
"end": 29,
"text": "Ku et al. (2009)",
"ref_id": "BIBREF7"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Using opinion trios",
"sec_num": "3.3.1"
},
{
"text": ". Therefore, we adopted their rules on our augmentative experiment materials. We define the opinion scoring function S(.), and its output opinion score varies with the input variables. These rules are shown by trio types as follows. ",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Rpt t \uf0ce",
"sec_num": null
},
{
"text": "o S o S o o S o S SIGN o S SIGN o S o o S o S o S \uf02b \uf03d \uf0b4 \uf0b4 \uf03d \uf0b9 \uf0b9 (3)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Rpt t \uf0ce",
"sec_num": null
},
{
"text": "\uf06c Verb-Complement Type: The scoring function for trios of this type is defined the same as that of a Subjective-Predicate type in Formula (2). The complement node is the deciding factor of the opinion score.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Rpt t \uf0ce",
"sec_num": null
},
{
"text": "The usages of opinion dependency relations were seen in several researches (Bikel and Castelli, 2008) . In these researches, rules for a small number of major dependency relations were proposed in different papers but they were not listed together for a better utilization. Some rules were not ever mentioned in pervious researches. Instead, all relations are analyzed in this paper. For each relation r of which gop(r) equals true (when gold opinion relations are used for opinion analysis) or op(r) equals true (when predicted opinion relations are used for opinion analysis), we calculate its opinion score ops(r). Let RM(w) be a function to return the dependency relations of word w's modifiers one at a time, n is the total number of relations RM(w) returns, and S(.) is also the defined opinion scoring function then ops(r) is defined as in Formula (4).",
"cite_spans": [
{
"start": 75,
"end": 101,
"text": "(Bikel and Castelli, 2008)",
"ref_id": "BIBREF1"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Using opinion dependency relations",
"sec_num": "3.3.2"
},
{
"text": "\uf0e5 \uf02b \uf03d )) ( ( 1 ) , , ( ) ( m m h w RM ops n w w rel S r ops (4)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Using opinion dependency relations",
"sec_num": "3.3.2"
},
{
"text": "That is, the opinion score of a dependency relation is an average of the aggregate scores of its descendent dependency relations. In practice, we design different rules for calculating opinion scores by the current relation type rel in S(.). Here to simplify the problem, we adopted Formula (1) and treated w m as o left and w h as o right in it.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Using opinion dependency relations",
"sec_num": "3.3.2"
},
{
"text": "Though there were researches which predicted opinion dependency relations, they did not predict directly from the parsing results. Instead, they predicted from documents or sentences according to the context and a large quantity of training instances were needed. They did not predict on all dependency relations either. Therefore, there is no existing dataset containing correct opinion labels on dependency relations. In this section, we describe how to generate opinionated syntactic dataset on parsing trees, and align the annotated labels to dependency trees. After that, qualitative and quantitative analyses of opinion dependency relations are provided. At the end, we discuss the evaluation results of the proposed methods.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Experiments",
"sec_num": "4"
},
{
"text": "To use the Stanford parser as our tool to generate dependency tree for experimental sentences and to avoid errors as possible, we adopted Chinese Treebank 5.1 as experiment materials. Sentences in Chinese Treebank are already segmented and part of speech tagged, and its tagging set is the same with the one Stanford parser uses. Therefore, the Stanford parser can take the data from the Chinese Treebank to generate more accurate dependency trees.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Data Set and Preprocessing",
"sec_num": "4.1"
},
{
"text": "The dataset Chinese Treebank 5.1 contains 507,222 words, 824,983 Hanzi, 18,782 sentences, and 890 data files. For the opinion analysis experiments, opinionated labels, i.e., opinionated, non-opinionated, positive, neutral, negative, were annotated on all sentences in Chinese Opinion Treebank. Afterward 57,706 trios were annotated on the parsing trees of gold opinion sentences, i.e., sentences which were annotated as opinionated. Methods for generating the gold opinion sentences proposed by were adopted.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Data Set and Preprocessing",
"sec_num": "4.1"
},
{
"text": "Next, the Stanford parser took all sentences in Chinese Treebank as input to generate their dependency trees. A total of 416,581 dependency relations were generated, and 284,590 of them were in opinion sentences. Then the annotated trios were aligned to their corresponding dependency relations, and because trios were only annotated on opinionated sentences, the gop(r) of these aligned relations were set to true. At the end, a total of 54,753 relations gop(r) were set to true. Table 2 . Statistics of structural trios. Table 1 shows the distribution of the opinion and polarity labels. Table 2 shows the statistics of trios. Trios of the Substantive-Modifier and Verb-Object types are the majority in opinion sentences, while trios of the Verb-Complement type are few. Table 3 further shows the distribution of dependency relations. It shows that previously the most adopted dependency relations for opinion analysis, e.g., amod (adjective modifier) or advmod (adverb modifier), do not certainly bear opinions or appear in opinion sentences. In Section 4.3, we will further test the performance of finding the opinionated relations with the help of the opinion word dictionary, which was also widely adopted by previous work (Feng et al., 2009) .",
"cite_spans": [
{
"start": 1229,
"end": 1248,
"text": "(Feng et al., 2009)",
"ref_id": "BIBREF5"
}
],
"ref_spans": [
{
"start": 481,
"end": 488,
"text": "Table 2",
"ref_id": null
},
{
"start": 523,
"end": 530,
"text": "Table 1",
"ref_id": null
},
{
"start": 590,
"end": 597,
"text": "Table 2",
"ref_id": null
},
{
"start": 773,
"end": 780,
"text": "Table 3",
"ref_id": null
}
],
"eq_spans": [],
"section": "Data Set and Preprocessing",
"sec_num": "4.1"
},
{
"text": "In this section, results of predicting opinion trios by CRF mentioned in Section 3.1 are shown. We first predicted the appearance of o left and o right in trios, and then predicted the trio type",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Evaluation of Opinion Trio Prediction",
"sec_num": "4.2"
},
{
"text": "for each trio. The performance in Table 4 is not promising. Therefore, we consider the structure of trios, that is, o left and o right should appear as an ordered pair, and otherwise the label was viewed as illegal. The performance is shown in Table 5 . Table 5 shows that all predicted trios were opinionated, and this tells that some opinion trios are of certain structures, but not all of them. We observed that the precisions 1.00 came from the collocations of specific words and structures, while the low recalls were from other trios which were not identified. However, these results still confirmed that we can find opinion trios by phrase structures and they may benefit in the opinion analysis process. Table 3 . Distributions of dependency relations and opinion dependency relations.",
"cite_spans": [],
"ref_spans": [
{
"start": 34,
"end": 41,
"text": "Table 4",
"ref_id": null
},
{
"start": 244,
"end": 252,
"text": "Table 5",
"ref_id": null
},
{
"start": 255,
"end": 262,
"text": "Table 5",
"ref_id": null
},
{
"start": 713,
"end": 720,
"text": "Table 3",
"ref_id": null
}
],
"eq_spans": [],
"section": "Rpt t \uf0ce",
"sec_num": null
},
{
"text": "(A: type of dependency relations (rel); B: total occurrences in generated dependency trees; C: total occurrence in generated dependency trees of opinions; D: total occurrence in generated dependency trees of opinions when it bears opinions (gop(r) equals true); E: percentage that this relation appears in generated dependency trees of opinions; F: percentage that this relation appears in generated dependency trees of opinions when it bears opinions (gop(r) equals true).)",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Rpt t \uf0ce",
"sec_num": null
},
{
"text": "To predict which dependency relations are opinionated, we start with analyzing the distribution of them. Table 3 presented the distribution of dependency relations. The percentage of a relation appearing in dependency trees of opinion sentences when bearing opinions (gop(r) equals true), i.e., the value in F column, is taken as the support value. The support value indicates that in what degree this relation bears opinions. If the support value is high, it is confident to say that the relation is opinionated; otherwise, considering the content words is necessary. This idea conforms to the previous observation in Section 4.2: some of the opinions are structural, but not all of them. According to the support value, dependency relations were divided into four categories. The Chinese opinion word dictionary NTUSD is involved to help identify opinion dependency relations when the support value is not high. The selecting criteria are listed as follows. \uf06c Very supportive: with the support value above 0.8, e.g., dvpmod. Relations in this category are viewed as opinionated and their gop(r) are automatically set to true. \uf06c Supportive: with the support value above 0.35 but lower than 0. 8, e.g., pass, dobj, npsubj, ba, top, nsubj, neg, amod, rcmod Table 6 . Performance of predicting opinion dependency relations.",
"cite_spans": [
{
"start": 1194,
"end": 1255,
"text": "8, e.g., pass, dobj, npsubj, ba, top, nsubj, neg, amod, rcmod",
"ref_id": null
}
],
"ref_spans": [
{
"start": 105,
"end": 112,
"text": "Table 3",
"ref_id": null
},
{
"start": 1256,
"end": 1263,
"text": "Table 6",
"ref_id": null
}
],
"eq_spans": [],
"section": "Evaluation of Opinion Dependency Relation Prediction",
"sec_num": "4.3"
},
{
"text": "The results of two experiment settings are listed: prediction performed on all dependency relations and on only modification-related dependency relations (in the form of lex-mod, e.g., amod, rcmod, etc.) The later are the relations adopted in many previous researches. Table 6 shows the performance of predicting opinion dependency relations. It indicates that if only modification related relations were considered, the f-score dropped nearly half because more than half of the opinion dependency relations were expelled in this case. In other word, results show that predicting on all relations instead of taking only modificationrelated dependency relations as clues can capture more opinion relations, and hence the prediction of opinion relations is necessary.",
"cite_spans": [],
"ref_spans": [
{
"start": 269,
"end": 276,
"text": "Table 6",
"ref_id": null
}
],
"eq_spans": [],
"section": "Evaluation of Opinion Dependency Relation Prediction",
"sec_num": "4.3"
},
{
"text": "In this section, predicted opinion trios and predicted opinion dependency relations were utilized in an opinion extraction system. In order to make use of these structural cues, opinion analysis methods proposed by were selected. Their methods calculated opinion scores of sentences from characters and words accumulatively, so syntactic cues can be added in and function jointly. Five settings for opinion analysis were experimented: \uf06c C+W+N: characters, words, and negations were used as cues for calculating opinion scores. It was the original method proposed by Ku et al. \uf06c C+W+N+goldTrio: annotated opinion trios were utilized additionally. \uf06c C+W+N+Trio: predicted opinion trios were utilized additionally. \uf06c C+W+N+goldDep: opinion dependency relations aligned from the annotated trios were utilized additionally. \uf06c C+W+N+Dep: predicted opinion dependency relations were utilized additionally. The results were shown in Table 7 . The performance of the opinion extraction improves 10.40% (0.7162->0.7993) when utilizing opinion trios and 8.66% (0.7162->0.7782) when utilizing opinion dependency relations. These results clearly indicate that the syntactic information benefit opinion analysis. Because of the possible information loss in the automatic alignment process, that the performance of using trios is a little better than using dependency relations matches our expectation. Table 7 . Performance of using syntactic information for opinion analysis.",
"cite_spans": [],
"ref_spans": [
{
"start": 925,
"end": 932,
"text": "Table 7",
"ref_id": null
},
{
"start": 1388,
"end": 1395,
"text": "Table 7",
"ref_id": null
}
],
"eq_spans": [],
"section": "Evaluation of Opinion Extraction Using Predicted Opinion Trios and Dependency Relations",
"sec_num": "4.4"
},
{
"text": "For all we know, no previous work has annotated opinion information on all dependency relations, or mapped annotated opinionated structures to dependency relations on a large quantity of documents or sentences. Therefore, to the best of our knowledge, no statistically analysis of opinion dependency relations involving manually annotations has been conducted. Researchers designed ruled or extracted dependency relations as features for opinion analysis based on their linguistic knowledge (Qiu et al., 2011 ). Yet there are still several lines of related work, including (i) opinion analysis (ii) opinion corpora (iii) syntactic information. Several dozen papers have been published on the topic of opinion analysis. Two general approaches have been proposed previously. They are machine learning approaches and heuristicrule approaches. For both approaches, syntactic structures could be utilized. For the former, they can be used as features (Abbasi et al., 2008) ; for the later, rules can be designed according to them (Ku et al., 2009) . We can see from the previous work that syntactic structures can help to enhance the performance.",
"cite_spans": [
{
"start": 491,
"end": 508,
"text": "(Qiu et al., 2011",
"ref_id": "BIBREF14"
},
{
"start": 946,
"end": 967,
"text": "(Abbasi et al., 2008)",
"ref_id": "BIBREF0"
},
{
"start": 1025,
"end": 1042,
"text": "(Ku et al., 2009)",
"ref_id": "BIBREF7"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "5"
},
{
"text": "As to the experimental corpora, some researchers managed to generate annotated materials and gold standards under constraints. Somasundaran (2007) annotated discourse information from meeting dialogs to train a sentiment model. MPQA annotated opinions and their sources (Wiebe et al., 2002) . NTCIR annotated opinions, polarities, sources, and targets for its multilingual opinion analysis task (MOAT, Seki et al., 2008) . However, none of them were annotated on materials with syntac-tic structures, and it caused the lack of analysis of opinion syntactic structures.",
"cite_spans": [
{
"start": 127,
"end": 146,
"text": "Somasundaran (2007)",
"ref_id": "BIBREF16"
},
{
"start": 270,
"end": 290,
"text": "(Wiebe et al., 2002)",
"ref_id": "BIBREF17"
},
{
"start": 395,
"end": 420,
"text": "(MOAT, Seki et al., 2008)",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "5"
},
{
"text": "Researchers have acquired syntactic structures (Zhou, 2008) , but few of them have tried to associate syntactic structures with opinions. The most similar previous work to ours was proposed by Ku et al. (2009) . Compared to it, the proposed process made the development of opinion dependency parser feasible. As dependency relations and the predicted opinion dependency relations are of the same form, no extra knowledge or integration is needed for the use of them.",
"cite_spans": [
{
"start": 47,
"end": 59,
"text": "(Zhou, 2008)",
"ref_id": "BIBREF18"
},
{
"start": 193,
"end": 209,
"text": "Ku et al. (2009)",
"ref_id": "BIBREF7"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "5"
},
{
"text": "The proposed new process is the main contribution of this paper. It annotated opinion syntactic structures on phrase structure trees, which are more readable for annotators, and aligned these structures to grammatical structures, which facilitates their usage. Chinese Treebank was selected as the source of phrase structure trees, and dependency relations as the grammatical structures. They are both widely used in natural language processing.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusions and Future Work",
"sec_num": "6"
},
{
"text": "Though the experiments were implemented on Chinese materials, this process is language independent. It can be applied to materials in different languages without modifications.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusions and Future Work",
"sec_num": "6"
},
{
"text": "By predicting opinion dependency relations, we can say that a basic opinion dependency parser has been developed. Experiments have shown that the predicted opinion dependency relations are beneficial for opinion extraction. Although we still need a parser to generate syntactic structures, parsing is relatively a mature technique in natural language processing. For a comparably new research problem like opinion analysis, it is common that tools are not handy. The best of the proposed method is that it can function in a multilingual environment by incorporating a domain or language specific resources (here, NTUSD for Chinese).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusions and Future Work",
"sec_num": "6"
},
{
"text": "Through the alignment, we made a large quantity of opinion dependency relations available. According to their distributions shown in this paper, researchers can select suitable relations to use according to their diverse needs, such as extracting evaluative features in product reviews or comments, opinions or their polarities.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusions and Future Work",
"sec_num": "6"
},
{
"text": "http://crfpp.sourceforge.net/",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "",
"sec_num": null
}
],
"back_matter": [],
"bib_entries": {
"BIBREF0": {
"ref_id": "b0",
"title": "Sentiment analysis in multiple languages: Feature selection for opinion classification in Web forums",
"authors": [
{
"first": "A",
"middle": [],
"last": "Abbasi",
"suffix": ""
},
{
"first": "H",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "A",
"middle": [],
"last": "Salem",
"suffix": ""
}
],
"year": 2008,
"venue": "ACM Trans. Inf. Syst",
"volume": "26",
"issue": "",
"pages": "1--34",
"other_ids": {
"DOI": [
"10.1145/1361684"
]
},
"num": null,
"urls": [],
"raw_text": "Abbasi, A., Chen, H., and Salem, A. 2008. Senti- ment analysis in multiple languages: Feature se- lection for opinion classification in Web forums. ACM Trans. Inf. Syst. 26, 3 (Jun. 2008), 1-34. DOI= http://doi.acm.org/10.1145/1361684.",
"links": null
},
"BIBREF1": {
"ref_id": "b1",
"title": "Event Matching Using the Transitive Closure of Dependency Relations",
"authors": [
{
"first": "D",
"middle": [
"M"
],
"last": "Bikel",
"suffix": ""
},
{
"first": "V",
"middle": [],
"last": "Castelli",
"suffix": ""
}
],
"year": 2008,
"venue": "Proceedings of the 46th Annual Meeting on Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "145--148",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Bikel, D. M. and Castelli, V. 2008. Event Matching Using the Transitive Closure of Dependency Re- lations. Proceedings of the 46th Annual Meeting on Association for Computational Linguistics, pages 145-148.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "Identifying Expressions of Opinion in Context",
"authors": [
{
"first": "E",
"middle": [],
"last": "Breck",
"suffix": ""
},
{
"first": "Y",
"middle": [],
"last": "Choi",
"suffix": ""
},
{
"first": "C",
"middle": [],
"last": "Cardie",
"suffix": ""
}
],
"year": 2007,
"venue": "Proceedings of the 20th International Joint Conferences on Artificial Intelligence",
"volume": "",
"issue": "",
"pages": "2683--2688",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Breck, E., Choi, Y. and Cardie, C. 2007. Identify- ing Expressions of Opinion in Context. Proceed- ings of the 20th International Joint Conferences on Artificial Intelligence, pages 2683-2688.",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "Discriminative Reordering with Chinese Grammatical Relations Features",
"authors": [
{
"first": "P.-C",
"middle": [],
"last": "Chang",
"suffix": ""
},
{
"first": "H",
"middle": [],
"last": "Tseng",
"suffix": ""
},
{
"first": "D",
"middle": [],
"last": "Jurafsky",
"suffix": ""
},
{
"first": "C",
"middle": [
"D"
],
"last": "Manning",
"suffix": ""
}
],
"year": 2009,
"venue": "Proceedings of the Third Workshop on Syntax and Structure in Statistical Translation",
"volume": "",
"issue": "",
"pages": "51--59",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Chang, P.-C., Tseng, H., Jurafsky, D. and Manning C. D. 2009. Discriminative Reordering with Chinese Grammatical Relations Features. Pro- ceedings of the Third Workshop on Syntax and Structure in Statistical Translation, pages 51-59.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "Searching Question by Identifying Question Topic and Question Focus",
"authors": [
{
"first": "H",
"middle": [],
"last": "Doan",
"suffix": ""
},
{
"first": "Y",
"middle": [],
"last": "Cao",
"suffix": ""
},
{
"first": "C.-Y",
"middle": [],
"last": "Lin",
"suffix": ""
},
{
"first": "Y",
"middle": [],
"last": "Yu",
"suffix": ""
}
],
"year": 2008,
"venue": "Proceedings of the 46th Annual Meeting on Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "156--164",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Doan, H., Cao, Y., Lin, C.-Y. and Yu, Y. 2008. Searching Question by Identifying Question Topic and Question Focus. Proceedings of the 46th Annual Meeting on Association for Compu- tational Linguistics, pages 156-164.",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "Chinese Blog Clustering by Hidden Sentiment Factors. ADMA",
"authors": [
{
"first": "S",
"middle": [],
"last": "Feng",
"suffix": ""
},
{
"first": "D",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "G",
"middle": [],
"last": "Yu",
"suffix": ""
},
{
"first": "C",
"middle": [],
"last": "Yang",
"suffix": ""
},
{
"first": "N",
"middle": [],
"last": "Yang",
"suffix": ""
}
],
"year": 2009,
"venue": "",
"volume": "5678",
"issue": "",
"pages": "140--151",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Feng, S., Wang, D., Yu, G., Yang, C. and Yang, N. 2009. Chinese Blog Clustering by Hidden Sen- timent Factors. ADMA, Vol. 5678, Springer (2009), pages 140-151.",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "Mining Opinions from the Web: Beyond Relevance Retrieval",
"authors": [
{
"first": "L.-W",
"middle": [],
"last": "Ku",
"suffix": ""
},
{
"first": "H.-H",
"middle": [],
"last": "Chen",
"suffix": ""
}
],
"year": 2007,
"venue": "Journal of American Society for Information Science and Technology, Special Issue on Mining Web Resources for Enhancing Information Retrieval",
"volume": "58",
"issue": "12",
"pages": "1838--1850",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ku, L.-W. and Chen, H.-H. 2007. Mining Opinions from the Web: Beyond Relevance Retrieval. Journal of American Society for Information Science and Technology, Special Issue on Min- ing Web Resources for Enhancing Information Retrieval, 58(12), 1838-1850.",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "Using Morphological and Syntactic Structures for Chinese Opinion Analysis",
"authors": [
{
"first": "L.-W",
"middle": [],
"last": "Ku",
"suffix": ""
},
{
"first": "T.-H",
"middle": [],
"last": "Huang",
"suffix": ""
},
{
"first": "H.-H",
"middle": [],
"last": "Chen",
"suffix": ""
}
],
"year": 2009,
"venue": "Proceedings of Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "1260--1269",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ku, L.-W., Huang, T.-H. and Chen, H.-H. 2009. Using Morphological and Syntactic Structures for Chinese Opinion Analysis. Proceedings of Conference on Empirical Methods in Natural Language Processing, pages 1260-1269.",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "Test Collection Selection and Gold Standard Generation for a Multiply-Annotated Opinion Corpus",
"authors": [
{
"first": "L.-W",
"middle": [],
"last": "Ku",
"suffix": ""
},
{
"first": "Y.-S",
"middle": [],
"last": "Lo",
"suffix": ""
},
{
"first": "Chen H.-H",
"middle": [],
"last": "",
"suffix": ""
}
],
"year": 2007,
"venue": "Proceedings of the 45th Annual Meeting on Association for Computational Linguistics",
"volume": "",
"issue": "",
"pages": "89--92",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Ku, L.-W., Lo, Y.-S. and Chen H.-H. 2007. Test Collection Selection and Gold Standard Genera- tion for a Multiply-Annotated Opinion Corpus. Proceedings of the 45th Annual Meeting on As- sociation for Computational Linguistics, pages 89-92.",
"links": null
},
"BIBREF9": {
"ref_id": "b9",
"title": "Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data",
"authors": [
{
"first": "J",
"middle": [],
"last": "Lafferty",
"suffix": ""
},
{
"first": "A",
"middle": [],
"last": "Mccallum",
"suffix": ""
},
{
"first": "F",
"middle": [],
"last": "Pereira",
"suffix": ""
}
],
"year": 2001,
"venue": "Proceedings of International Conference on Machine Learning",
"volume": "",
"issue": "",
"pages": "282--289",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Lafferty, J., McCallum, A. and Pereira, F. 2001. Conditional Random Fields: Probabilistic Mod- els for Segmenting and Labeling Sequence Data. Proceedings of International Conference on Ma- chine Learning, pages 282-289.",
"links": null
},
"BIBREF10": {
"ref_id": "b10",
"title": "Rated Aspect Summarization of Short Comments",
"authors": [
{
"first": "Y",
"middle": [],
"last": "Lu",
"suffix": ""
},
{
"first": "C",
"middle": [
"X"
],
"last": "Zhai",
"suffix": ""
},
{
"first": "N",
"middle": [],
"last": "Sundaresan",
"suffix": ""
}
],
"year": 2009,
"venue": "Proceedings of 18 th International World Wide Web Conference",
"volume": "",
"issue": "",
"pages": "131--140",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Lu, Y., Zhai, C.X. and Sundaresan, N. 2009. Rated Aspect Summarization of Short Comments. Pro- ceedings of 18 th International World Wide Web Conference, pages 131-140.",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "Stanford typed dependencies manual",
"authors": [
{
"first": ", M.-C",
"middle": [],
"last": "De Marneffe",
"suffix": ""
},
{
"first": "C",
"middle": [
"D"
],
"last": "Manning",
"suffix": ""
}
],
"year": 2008,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "de Marneffe, M.-C. and Manning, C. D. 2008. Stan- ford typed dependencies manual. Technichal re- port. http://nlp.stanford.edu/software/depend encies_manual.pdf",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "Thumbs up? Sentiment classification using machine learning techniques",
"authors": [
{
"first": "B",
"middle": [],
"last": "Pang",
"suffix": ""
},
{
"first": "L",
"middle": [],
"last": "Lee",
"suffix": ""
},
{
"first": "S",
"middle": [],
"last": "Vaithyanathan",
"suffix": ""
}
],
"year": 2002,
"venue": "Proceedings of the 2002 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "79--86",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Pang, B., Lee, L. and Vaithyanathan, S. 2002. Thumbs up? Sentiment classification using ma- chine learning techniques. Proceedings of the 2002 Conference on Empirical Methods in Natu- ral Language Processing, pages 79-86.",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "Incorporate the Syntactic. Knowledge in Opinion Mining in User-generated Content",
"authors": [
{
"first": "G",
"middle": [],
"last": "Qiu",
"suffix": ""
},
{
"first": "C",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "J",
"middle": [],
"last": "Bu",
"suffix": ""
},
{
"first": "K",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "C",
"middle": [],
"last": "Chen",
"suffix": ""
}
],
"year": 2008,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Qiu, G., Wang, C., Bu, J., Liu, K. and Chen, C. 2008. Incorporate the Syntactic. Knowledge in Opinion Mining in User-generated Content. Pro- ceedings of NLPIX'08.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "Opinion Word Expansion and Target Extraction through Double Propagation",
"authors": [
{
"first": "G",
"middle": [],
"last": "Qiu",
"suffix": ""
},
{
"first": "B",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "J",
"middle": [],
"last": "Bu",
"suffix": ""
},
{
"first": "C",
"middle": [],
"last": "Chen",
"suffix": ""
}
],
"year": 2011,
"venue": "Computational Linguistics",
"volume": "37",
"issue": "1",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Qiu, G., Liu, B., Bu, J. and Chen, C. 2011. Opinion Word Expansion and Target Extraction through Double Propagation. Computational Linguistics, March 2011, Vol. 37, No. 1: 9.27",
"links": null
},
"BIBREF15": {
"ref_id": "b15",
"title": "Overview of Multilingual Opinion Analysis Task at NTCIR-7",
"authors": [
{
"first": "Y",
"middle": [],
"last": "Seki",
"suffix": ""
},
{
"first": "D",
"middle": [
"K"
],
"last": "Evans",
"suffix": ""
},
{
"first": "L.-W",
"middle": [],
"last": "Ku",
"suffix": ""
},
{
"first": "L",
"middle": [],
"last": "Sun",
"suffix": ""
},
{
"first": "H.-H",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "N",
"middle": [],
"last": "Kando",
"suffix": ""
}
],
"year": 2008,
"venue": "Proceedings of the 7th NTCIR Workshop Meeting on Evaluation of Information Access Technologies:Information Retrieval, Question Answering, and Cross-Lingual Information Access",
"volume": "",
"issue": "",
"pages": "185--203",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Seki, Y., D. K. Evans, L.-W. Ku, L. Sun, H.-H. Chen and N. Kando. 2008. Overview of Multi- lingual Opinion Analysis Task at NTCIR-7. Proceedings of the 7th NTCIR Workshop Meet- ing on Evaluation of Information Access Tech- nologies:Information Retrieval, Question Ans- wering, and Cross-Lingual Information Access, pages 185-203.",
"links": null
},
"BIBREF16": {
"ref_id": "b16",
"title": "Detecting arguing and sentiment in meetings",
"authors": [
{
"first": "S",
"middle": [],
"last": "Somasundaran",
"suffix": ""
},
{
"first": "J",
"middle": [],
"last": "Ruppenhofer",
"suffix": ""
},
{
"first": "J",
"middle": [],
"last": "Wiebe",
"suffix": ""
}
],
"year": 2007,
"venue": "Proceedings of the SIGdial Workshop on Discourse and Dialogue",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Somasundaran, S., J. Ruppenhofer and J. Wiebe. 2007. Detecting arguing and sentiment in meet- ings. Proceedings of the SIGdial Workshop on Discourse and Dialogue 2007.",
"links": null
},
"BIBREF17": {
"ref_id": "b17",
"title": "NRRC summer workshop on multi-perspective question answering, final report. ARDA NRRC Summer",
"authors": [
{
"first": "J",
"middle": [],
"last": "Wiebe",
"suffix": ""
},
{
"first": "E",
"middle": [],
"last": "Breck",
"suffix": ""
},
{
"first": "C",
"middle": [],
"last": "Buckly",
"suffix": ""
},
{
"first": "C",
"middle": [],
"last": "Cardie",
"suffix": ""
},
{
"first": "P",
"middle": [],
"last": "Davis",
"suffix": ""
},
{
"first": "B",
"middle": [],
"last": "Fraser",
"suffix": ""
},
{
"first": "D",
"middle": [],
"last": "Litman",
"suffix": ""
},
{
"first": "D",
"middle": [],
"last": "Pierce",
"suffix": ""
},
{
"first": "E",
"middle": [],
"last": "Riloff",
"suffix": ""
},
{
"first": "T",
"middle": [],
"last": "Wilson",
"suffix": ""
}
],
"year": 2002,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Wiebe, J., E. Breck, C. Buckly, C. Cardie, P. Davis, B. Fraser, D. Litman, D. Pierce, E. Riloff and T. Wilson. 2002. NRRC summer workshop on mul- ti-perspective question answering, final report. ARDA NRRC Summer 2002 Workshop.",
"links": null
},
"BIBREF18": {
"ref_id": "b18",
"title": "Automatic rule acquisition for Chinese intra-chunk relations",
"authors": [
{
"first": "Q",
"middle": [],
"last": "Zhou",
"suffix": ""
}
],
"year": 2008,
"venue": "Proceedings of International Joint Conference of Natural Language Processing",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Zhou, Q. 2008. Automatic rule acquisition for Chi- nese intra-chunk relations. Proceedings of Inter- national Joint Conference of Natural Language Processing (IJCNLP-2008).",
"links": null
}
},
"ref_entries": {
"FIGREF0": {
"text": "A sample dependency tree with three aligned opinion dependency relations.",
"uris": null,
"type_str": "figure",
"num": null
},
"FIGREF1": {
"text": "A sample parsing tree and opinion trios within the sentence inFigure 1are shown inFigure 2. The literal output of opinion trios are shown in",
"uris": null,
"type_str": "figure",
"num": null
},
"FIGREF2": {
"text": "In the trio tri = (3, NP-OBJ, \u5706\u6ee1, \u6210\u529f, Substantive-Modifier), triID = 3, o parent = NP-OBJ, o left =\u5706\u6ee1 (perfect), o right =\u6210\u529f(success), and t = Substantive-Modifier.",
"uris": null,
"type_str": "figure",
"num": null
},
"FIGREF3": {
"text": "A sample parsing tree with trios. 1, IP, \u6d3b\u52a8, VP, Subjective-Predicate 2, VP, \u53d6\u5f97, NP-OBJ, Verb-Object 3, NP-OBJ, \u5706\u6ee1, \u6210\u529f, Substantive-Modifier",
"uris": null,
"type_str": "figure",
"num": null
},
"FIGREF4": {
"text": "Figure 3. Opinion trios",
"uris": null,
"type_str": "figure",
"num": null
},
"FIGREF5": {
"text": "\uf06c",
"uris": null,
"type_str": "figure",
"num": null
},
"TABREF1": {
"type_str": "table",
"html": null,
"num": null,
"text": "o left of this trio type modifies o right , so that the trio's opinion weight comes from the absolute opinion score of o left , while the opinion polarity is determined by the occurrence of negative o left or o right . If at least one of them is negative, the trio is negative, else it is positive. Predicate Type: o left of this trio type is a subject and o right is the action it performs, so that the action decides the opinion score of the trio. If the action is not an opinion or it is neutral, the subject determines the opinion score of this trio.",
"content": "<table><tr><td>if</td><td>(</td><td colspan=\"2\">S</td><td>(</td><td colspan=\"4\">left o</td><td>)</td><td colspan=\"2\">\uf0b9</td><td>0</td><td/><td colspan=\"5\">and</td><td colspan=\"2\">S</td><td>(</td><td>o</td><td>right</td><td>)</td><td>\uf0b9</td><td>0</td><td>)</td><td>then</td></tr><tr><td/><td/><td colspan=\"11\">( o left left ) o S ( S else ( if</td><td colspan=\"5\">0 right o \uf03e</td><td colspan=\"3\">-and ) \uf03d</td><td>1 S \uf0b4 (</td><td>( right left ) o S o</td><td>) \uf03e</td><td>0</td><td>)</td><td>then</td><td>S</td><td>(</td><td>left o</td><td>right o</td><td>)</td><td>\uf03d</td><td>S</td><td>(</td><td>left o</td><td>)</td><td>(1)</td></tr><tr><td colspan=\"3\">else</td><td colspan=\"3\">S</td><td>(</td><td colspan=\"3\">left o</td><td colspan=\"4\">right o</td><td>)</td><td colspan=\"2\">\uf03d</td><td/><td>S</td><td>(</td><td>left o</td><td>)</td><td>\uf02b</td><td>S</td><td>(</td><td>o</td><td>right</td><td>)</td></tr><tr><td/><td/><td colspan=\"9\">else ( if S S (</td><td colspan=\"5\">( o o right left</td><td colspan=\"2\">) o</td><td colspan=\"3\">) ) 0 right \uf0b9</td><td>( then S \uf03d</td><td>( left S o</td><td>) o</td><td>left</td><td>o</td><td>right</td><td>)</td><td>\uf03d</td><td>S</td><td>(</td><td>o</td><td>right</td><td>)</td><td>(2)</td></tr><tr><td>if</td><td>(</td><td/><td>(</td><td/><td/><td colspan=\"2\">left</td><td>)</td><td/><td/><td colspan=\"2\">0</td><td colspan=\"5\">and</td><td/><td/><td>(</td><td>right</td><td>)</td><td>0</td><td>)</td></tr><tr><td/><td colspan=\"5\">then</td><td/><td/><td colspan=\"2\">(</td><td/><td colspan=\"2\">left</td><td/><td colspan=\"3\">right</td><td colspan=\"2\">)</td><td/><td>(</td><td>left</td><td>)</td><td>(</td><td>(</td><td>left</td><td>))</td><td>(</td><td>(</td><td>right</td><td>))</td></tr><tr><td/><td colspan=\"4\">else</td><td/><td/><td colspan=\"2\">(</td><td colspan=\"3\">left</td><td colspan=\"3\">right</td><td colspan=\"2\">)</td><td/><td/><td/><td>(</td><td>left</td><td>)</td><td>(</td><td>right</td><td>)</td></tr></table>"
}
}
}
} |