File size: 75,584 Bytes
6fa4bc9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563 564 565 566 567 568 569 570 571 572 573 574 575 576 577 578 579 580 581 582 583 584 585 586 587 588 589 590 591 592 593 594 595 596 597 598 599 600 601 602 603 604 605 606 607 608 609 610 611 612 613 614 615 616 617 618 619 620 621 622 623 624 625 626 627 628 629 630 631 632 633 634 635 636 637 638 639 640 641 642 643 644 645 646 647 648 649 650 651 652 653 654 655 656 657 658 659 660 661 662 663 664 665 666 667 668 669 670 671 672 673 674 675 676 677 678 679 680 681 682 683 684 685 686 687 688 689 690 691 692 693 694 695 696 697 698 699 700 701 702 703 704 705 706 707 708 709 710 711 712 713 714 715 716 717 718 719 720 721 722 723 724 725 726 727 728 729 730 731 732 733 734 735 736 737 738 739 740 741 742 743 744 745 746 747 748 749 750 751 752 753 754 755 756 757 758 759 760 761 762 763 764 765 766 767 768 769 770 771 772 773 774 775 776 777 778 779 780 781 782 783 784 785 786 787 788 789 790 791 792 793 794 795 796 797 798 799 800 801 802 803 804 805 806 807 808 809 810 811 812 813 814 815 816 817 818 819 820 821 822 823 824 825 826 827 828 829 830 831 832 833 834 835 836 837 838 839 840 841 842 843 844 845 846 847 848 849 850 851 852 853 854 855 856 857 858 859 860 861 862 863 864 865 866 867 868 869 870 871 872 873 874 875 876 877 878 879 880 881 882 883 884 885 886 887 888 889 890 891 892 893 894 895 896 897 898 899 900 901 902 903 904 905 906 907 908 909 910 911 912 913 914 915 916 917 918 919 920 921 922 923 924 925 926 927 928 929 930 931 932 933 934 935 936 937 938 939 940 941 942 943 944 945 946 947 948 949 950 951 952 953 954 955 956 957 958 959 960 961 962 963 964 965 966 967 968 969 970 971 972 973 974 975 976 977 978 979 980 981 982 983 984 985 986 987 988 989 990 991 992 993 994 995 996 997 998 999 1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097 1098 1099 1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197 1198 1199 1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297 1298 1299 1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397 1398 1399 1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497 1498 1499 1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597 1598 1599 1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627 1628 1629 1630 1631 1632 1633 | {
"paper_id": "2020",
"header": {
"generated_with": "S2ORC 1.0.0",
"date_generated": "2023-01-19T07:58:03.186224Z"
},
"title": "Graph-based Syntactic Word Embeddings",
"authors": [
{
"first": "Ragheb",
"middle": [],
"last": "Al-Ghezi",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Aalto University",
"location": {}
},
"email": "ragheb.al-ghezi@aalto.fi"
},
{
"first": "Mikko",
"middle": [],
"last": "Kurimo",
"suffix": "",
"affiliation": {
"laboratory": "",
"institution": "Aalto University",
"location": {}
},
"email": "mikko.kurimo@aalto.fi"
}
],
"year": "",
"venue": null,
"identifiers": {},
"abstract": "We propose a simple and efficient framework to learn syntactic embeddings based on information derived from constituency parse trees. Using biased random walk methods, our embeddings not only encode syntactic information about words, but they also capture contextual information. We also propose a method to train the embeddings on multiple constituency parse trees to ensure the encoding of global syntactic representation. Quantitative evaluation of the embeddings shows competitive performance on POS tagging task when compared to other types of embeddings, and qualitative evaluation reveals interesting facts about the syntactic typology learned by these embeddings.",
"pdf_parse": {
"paper_id": "2020",
"_pdf_hash": "",
"abstract": [
{
"text": "We propose a simple and efficient framework to learn syntactic embeddings based on information derived from constituency parse trees. Using biased random walk methods, our embeddings not only encode syntactic information about words, but they also capture contextual information. We also propose a method to train the embeddings on multiple constituency parse trees to ensure the encoding of global syntactic representation. Quantitative evaluation of the embeddings shows competitive performance on POS tagging task when compared to other types of embeddings, and qualitative evaluation reveals interesting facts about the syntactic typology learned by these embeddings.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Abstract",
"sec_num": null
}
],
"body_text": [
{
"text": "Distributional similarity methods have been the standard learning representation in NLP. Word representations methods such as Word2vec, GloVe, and FastText [1, 2, 3] aim to create vector representation to words from other words or characters that mutually appear in the same context. The underlying premise is that \"a word can be defined by its company\" [4] . For example, in the sentences, \"I eat an apple every day\" and \"I eat an orange every day\", the words 'orange' and 'apple' are similar as they share similar contexts.",
"cite_spans": [
{
"start": 156,
"end": 159,
"text": "[1,",
"ref_id": "BIBREF0"
},
{
"start": 160,
"end": 162,
"text": "2,",
"ref_id": "BIBREF1"
},
{
"start": 163,
"end": 165,
"text": "3]",
"ref_id": "BIBREF2"
},
{
"start": 354,
"end": 357,
"text": "[4]",
"ref_id": "BIBREF3"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Recent approaches have proposed a syntax-based extension to distributional word embeddings to include functional similarity in the word vectors by leveraging the power of dependency parsing [5] [6] . Syntactic word embeddings have been shown to be advantageous in specific NLP tasks such as question type classification [7] , semantic role labeling [8] , part-of-speech tagging [6] , biomedical event trigger identification [9] , and predicting brain activation patterns [10] . One limitation of these methods is that they do not encode the hierarchical syntactic structure of which a word is a part due to its reliance on nonconstituency parsing such as dependency parsing. While the latter analyzes the grammatical structure of a sentence by establishing a directed binary head-dependent relation among its words, constituency parsing analyzes the syntactic structure of a sentence according to a phrase structure grammar.",
"cite_spans": [
{
"start": 190,
"end": 193,
"text": "[5]",
"ref_id": "BIBREF4"
},
{
"start": 194,
"end": 197,
"text": "[6]",
"ref_id": "BIBREF5"
},
{
"start": 320,
"end": 323,
"text": "[7]",
"ref_id": "BIBREF6"
},
{
"start": 349,
"end": 352,
"text": "[8]",
"ref_id": "BIBREF7"
},
{
"start": 378,
"end": 381,
"text": "[6]",
"ref_id": "BIBREF5"
},
{
"start": 424,
"end": 427,
"text": "[9]",
"ref_id": "BIBREF8"
},
{
"start": 471,
"end": 475,
"text": "[10]",
"ref_id": "BIBREF9"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "Syntactic hierarchy has advantages in tasks such as grammar checking, question answering, and information extraction [11] . It has also been encoded in neural models such as Recursive Neural Tensor Network and has proved it can predict the compositional semantic effects of sentiment in language [12] . Moreover, it can uniquely disambiguate the functional role of some words and therefore the overall semantic meaning. Figure 1 shows the modal verb should in the following sentences: (1) Let me know should you have any question. and (2) I should study harder for the next exam. Even though the word should is a modal verb (MD) in both sentences, it exhibits two different grammatical functions: conditionality and necessity respectively. Similarly, the word is in (3) The king is at home. and (4) Is the king at home? has a similar semantic meaning in both sentences, yet it exhibits two different syntactic roles (statement-forming and question-forming). Traditional word embeddings methods give a contextual, semantic representation to words like is and should, but they make no distinction of their grammatical function due to the absence of information on syntactic hierarchy. On the other hand, constituency parse trees provide a syntactic representation that can easily capture such distinction. Figures (a) and (b) show constituency parse tree of sentences (1) and (2) respectively. The difference in the position of the modal verb should in both sentences indicates a difference in the grammatical function, especially if it is compared to words with similar grammatical function in other sentences such as the one in (figure (c)). Comparing figures (a) and (c), we can note that should hold the same sense of conditionality the word if has. To this end, we propose a simple, graph-based framework to build syntactic word embeddings that can be flexibly customized to capture syntactic as well as contextual information by leveraging information derived from either manually or automatically constituency-parsed trees.",
"cite_spans": [
{
"start": 117,
"end": 121,
"text": "[11]",
"ref_id": "BIBREF10"
},
{
"start": 296,
"end": 300,
"text": "[12]",
"ref_id": "BIBREF11"
}
],
"ref_spans": [
{
"start": 420,
"end": 428,
"text": "Figure 1",
"ref_id": "FIGREF1"
},
{
"start": 1304,
"end": 1315,
"text": "Figures (a)",
"ref_id": null
}
],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "While recent transformer-based models such as BERT [13] have proved to be more sophisticated than word embeddings, the latter remains a popular choice due to its simplicity and efficiency. Thus, the contribution of this work is two-fold: (1) bridge the research gap in the literature of word embedding by introducing hierarchical syntactic embeddings based on constituency parsing (2) propose a graphtheoretic training method that cluster words according to their syntactic and constituent role without sacrificing the original context in which a word appears. ",
"cite_spans": [
{
"start": 51,
"end": 55,
"text": "[13]",
"ref_id": "BIBREF12"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Introduction",
"sec_num": "1"
},
{
"text": "The NLP literature is rich with studies suggesting an improvement to original word embeddings models by incorporating external semantic resources like lexicons and ontologies [14, 15, 16, 17, 18] . However, very few studies were dedicated to syntactic embeddings. One of the earliest methods was dependency-based word embeddings [5] , which generalizes the Skip-gram algorithm to include arbitrary word context. Instead of using bag-of-word context, they use context derived automatically from dependency parse trees. Specifically, for a word w with modifiers m 1 , ..., m k and head h, the contexts",
"cite_spans": [
{
"start": 175,
"end": 179,
"text": "[14,",
"ref_id": "BIBREF13"
},
{
"start": 180,
"end": 183,
"text": "15,",
"ref_id": "BIBREF14"
},
{
"start": 184,
"end": 187,
"text": "16,",
"ref_id": "BIBREF15"
},
{
"start": 188,
"end": 191,
"text": "17,",
"ref_id": "BIBREF16"
},
{
"start": 192,
"end": 195,
"text": "18]",
"ref_id": "BIBREF17"
},
{
"start": 329,
"end": 332,
"text": "[5]",
"ref_id": "BIBREF4"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "(m1, lbl 1 ), ..., (m k , lbl k ), (h, lbl 1 h ),",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "where lbl is a type of dependency relation between the head. and the modifier (e.g. nsub, dobj, etc). For example, the context for the word scientist in \"Australian scientist discovers star with telescope\" is Australian/amod and discovers/nsubj 1 .",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "Another modification to word2vec model was proposed by [6] to improve the word embeddings to syntax-based tasks by making it sensitive to the positioning of the words, and thereby accounting for its lack of order-dependence. The modification does not involve incorporating external parsing information, but it includes using 2 output predictors for every word in the window context each of which is dedicated to predicting position-specific value. Results on syntax-based tasks such as POS tagging and parsing show an improvement over classic word2vec embeddings.",
"cite_spans": [
{
"start": 55,
"end": 58,
"text": "[6]",
"ref_id": "BIBREF5"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "More recently, a new approach, named SynGCN, for learning dependency-based syntactic embeddings is introduced by [19] . SynGCN builds syntactic word representation by using Graph Convolution Network (GCN). Using GCN allows SynGCN to capture global information from the graph on which it was trained while remaining efficient at training due to parallelization. Experiments show that SynGCN obtains improvement over state-of-the-art approaches when used with methods such as ELMo [20] .",
"cite_spans": [
{
"start": 113,
"end": 117,
"text": "[19]",
"ref_id": "BIBREF18"
},
{
"start": 479,
"end": 483,
"text": "[20]",
"ref_id": "BIBREF19"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "Most syntactic word embeddings methods rely on dependency parsing, and to the best of our knowledge that our work is the first utilizing constituency parsing to build syntactic representation.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Related Work",
"sec_num": "2"
},
{
"text": "Our goal is to learn word embeddings that not only capture the sentence-level syntactic hierarchy encoded by the constituency parse tree, but also capture a global (suprasentential) syntactic representation, and because the constituency parse tree only provides sentence-level syntactic representations, we need a method to combine multiple constituency parse trees. We also need a flexible algorithm to learn the embeddings from those combined trees. In this section, we present a method of parse tree combination (namely graph unionization) as well as the Node2vec algorithm.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Method",
"sec_num": "3"
},
{
"text": "Graph Unionization Given a training dataset of constituency parse trees, we compose one graph (henceforth supergraph, Figure 2 ) by unionizing all the sentence trees in the training dataset. Formally, let G(V, E) be a graph in the training corpus, where V represents a lexical or a non-lexical vertex in a constituency parse tree and E is the edge between them, and let H be",
"cite_spans": [],
"ref_spans": [
{
"start": 118,
"end": 126,
"text": "Figure 2",
"ref_id": "FIGREF3"
}
],
"eq_spans": [],
"section": "Method",
"sec_num": "3"
},
{
"text": "n i=1 G i (V i , E i )",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Method",
"sec_num": "3"
},
{
"text": "where is a non-disjoint union operator and n is the number of sentences in the training corpus. The vertices and edges of the supergraph V H and E H are n i=1 V i and n i=1 E i respectively [21, 22] .",
"cite_spans": [
{
"start": 190,
"end": 194,
"text": "[21,",
"ref_id": "BIBREF20"
},
{
"start": 195,
"end": 198,
"text": "22]",
"ref_id": "BIBREF21"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Method",
"sec_num": "3"
},
{
"text": "Node2vec For learning syntactic embeddings from the supergraph, we use a variant of skip-gram algorithm, called node2vec algorithm [23] . Node2vec adapts Word2vec algorithm to graphs in which a node is defined by an arbitrary set of other nodes in the same graph sampled using a biased random walk. Using tunable parameters p and q, the biased random walk offers BFS and DFS search behavior in which more diverse neighborhoods are explored, and therefore richer representation may be learned [23] . As shown in Figure 2 , nodes tagged with certain labels, such as adjectives (JJ) or nouns (NN), will be linked together in the supergraph while remaining children of a noun phrase (NP). Similarly, sentences with similar grammatical structures such as interrogative sentences or questions (SQ) will be clustered together in the supergraph. It can be noted that the supergraph can cluster words of similar syntactic functions together while simultaneously enforcing/preserving the global syntactic hierarchy of the training corpus. The supergraph with the aid of the biased sampling strategy Node2vec offers the flexibility of learning customizable syntactic representation. A breadth-first search strategy, for instance, would favor the selection of words of similar POS tags and thereby yielding word-class-specific representation while a depth-first strategy would yield more hierarchical or contextual representation.",
"cite_spans": [
{
"start": 131,
"end": 135,
"text": "[23]",
"ref_id": "BIBREF22"
},
{
"start": 492,
"end": 496,
"text": "[23]",
"ref_id": "BIBREF22"
}
],
"ref_spans": [
{
"start": 511,
"end": 519,
"text": "Figure 2",
"ref_id": "FIGREF3"
}
],
"eq_spans": [],
"section": "Method",
"sec_num": "3"
},
{
"text": "For the purposes of training the syntactic embeddings, we use the Penn Treebank corpus [24] , which contains over 43,000 constituency parse trees to sentences collected from the Wall Street Journal (WSJ). Next, we unionize all the parses trees into one supergraph. The supergraph has 51071 vertices and 65895 edges, and it has an average degree of 2.5805 and a density of 5.05 \u00d7 10 \u22125 . We chose to unionize all the trees in the training corpus for simplicity, but we certainly could have grouped the sentences into clusters of thematic or semantic identity prior to applying unionization. After that, we train the embeddings with node2vec algorithm using SGD of 10,000 epochs and a learning rate of 0.025 with a weight decay of 0.005. In terms of node2vec hyperparameters, we chose a random walk of length 200 and a batch size of 100, and the return parameter p and the in-out parameter q are both set to 10 \u2212 6. Lower p values keep the walk close to the starting node, and lower q values encourage the walk to behave in a DFS manner [23] . The training took 51 seconds on 1 Tesla K80 GPU using the Graphvite Python package [25] . We initialize the word vectors randomly for simplicity, but initialization with other types of distributional embeddings such as word2vec or GloVe is possible. We decide to explore the latter in future work.",
"cite_spans": [
{
"start": 87,
"end": 91,
"text": "[24]",
"ref_id": "BIBREF23"
},
{
"start": 1035,
"end": 1039,
"text": "[23]",
"ref_id": "BIBREF22"
},
{
"start": 1125,
"end": 1129,
"text": "[25]",
"ref_id": "BIBREF24"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Data and Experiment",
"sec_num": "4"
},
{
"text": "We conduct two types of evaluations: qualitative and quantitative. In the qualitative evaluation, we examine the extent to which the learned embeddings can encode grammatical information about the words using words analogies and word arithmetics. In addition, we compare its performance against GloVe vectors [2] and SynGCN [19] on one stream task, POS tagging.",
"cite_spans": [
{
"start": 309,
"end": 312,
"text": "[2]",
"ref_id": "BIBREF1"
},
{
"start": 324,
"end": 328,
"text": "[19]",
"ref_id": "BIBREF18"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Results",
"sec_num": "5"
},
{
"text": "One common method to evaluate word embeddings is examining word vectors by their top k nearest neighbors in the latent vector space. From table 5.1, we observe that the top 3 neighbors for the words complicate, failed, earthquakes are all of the similar syntactic category: a present verb attracts similar present verbs; a plural noun attracts plural nouns; and so on. Similarly, the adjective responsible and the adverb handsomely maintain a very close distance to words of the same part-of-speech. In contrast, neither GloVe vectors nor SynGCN exhibit similar neighborhood typology. This confirms that our constituency-based embeddings have consistently preserved syntactic information about words.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Qualitative Evaluation",
"sec_num": "5.1"
},
{
"text": "Another way to evaluate word embeddings is by explaining word analogies by the means of word vector arithmetics [26] . The famous example used in [1] is woman is to queen as man is to king, or (w q + w k ) \u2212 w m \u2248 w w . When we apply the same method to our constituency-based syntactic vectors, we assert that the vector arithmetic sense strongly matches the syntactic analogies. For example, in table 5.1, if we subtract the sum of word vectors in the prepositional phrase (PP) in an industrial from the PP of any clearly domestic, the top 3 nearest neighbors in our embeddings are all adverbs (ADV) to compensate for the missing adverb in the second PP. We also note the case is not true for the other types of embeddings where the top nearest neighbors are affected by words in the PP. Similarly, applying the same arithmetic operations to the phrases his state-of-the-art plan and her plan would results in adjective vectors, unlike the other embeddings. [19] , GloVe [2] , and our embeddings. Words in bold belong to the same POS tag/ grammatical category.",
"cite_spans": [
{
"start": 112,
"end": 116,
"text": "[26]",
"ref_id": "BIBREF25"
},
{
"start": 146,
"end": 149,
"text": "[1]",
"ref_id": "BIBREF0"
},
{
"start": 959,
"end": 963,
"text": "[19]",
"ref_id": "BIBREF18"
},
{
"start": 972,
"end": 975,
"text": "[2]",
"ref_id": "BIBREF1"
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Qualitative Evaluation",
"sec_num": "5.1"
},
{
"text": "We also test the performance of our constituency-based embeddings on a mainstream task, parts-ofspeech tagging. Our goal is not to achieve state-of-the-art results in POS tagging, but we want to demonstrate the grammatical potential of our embeddings. For this purpose, we treat POS tagging as SVM-F1 Score CRF-F1 Score Glove [2] 0.731 0.894 SynGCN [19] 0.892 0.898 Ours 0.881 0.910 Table 2 : Evaluation on POS tagging using SVM and CRF classifiers. Scores represent a mean F1 score of 5-fold cross-validation.",
"cite_spans": [
{
"start": 326,
"end": 329,
"text": "[2]",
"ref_id": "BIBREF1"
},
{
"start": 349,
"end": 353,
"text": "[19]",
"ref_id": "BIBREF18"
}
],
"ref_spans": [
{
"start": 383,
"end": 390,
"text": "Table 2",
"ref_id": null
}
],
"eq_spans": [],
"section": "Intrinsic Evaluation",
"sec_num": "5.2"
},
{
"text": "an independent classification task (as opposed to structured prediction one) in which a non-sequential classifier support vector machine (SVM) is used to predict a POS tag for a word acontextually. The use of non-neural, non-sequential classifier ensures that the grammatical generalizability comes strictly from the embeddings and not from the neural network or the context. Nevertheless, we also treat POS tagging as a structured prediction task in which we use a sequential classifier like conditional random field (CRF) for the purposes of comparison. Performance is also reported for two other word embeddings: GloVe and SynGCN under the same settings. We test the performance using the trained vectors on the first 2000 sentences of the Brown corpus [27] . In table 2, we report the mean F1 score of 5-fold cross-validation in which we can observe that our vectors are competitive in performance to SynGCN and far better than GloVe when used with SVM classifier. In addition, our embeddings outperform both of the competing embeddings when used with CRF.",
"cite_spans": [
{
"start": 756,
"end": 760,
"text": "[27]",
"ref_id": null
}
],
"ref_spans": [],
"eq_spans": [],
"section": "Intrinsic Evaluation",
"sec_num": "5.2"
},
{
"text": "Even though the performance of the constituency-based embeddings slightly lags behind SynGCN in the case of independent classification, the size of the corpus upon which our embeddings were trained (Penn Treebanks 1 million tokens) is much smaller compared to the one upon which SynGCN was trained (Wikipedia 1.1 billion tokens). In addition, the flexibility of learning customizable syntactic word embeddings as well as the training efficiency make constituency-based word embeddings a powerful and promising research direction that can be applied to other graph-based tasks.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Intrinsic Evaluation",
"sec_num": "5.2"
},
{
"text": "We presented a simple and efficient framework to learn syntactic embeddings from constituency parse trees using a combination of multiple graph unionization and biased random walk. Our framework can be flexibly customized to learn purely contextual and non-contextual syntactic embeddings, and it can be also used as a post-hoc method for other kinds of (distributional) word embeddings. Thus, for future studies, we would like to investigate training constituency-based vectors on a larger corpus and examine the effect of different initialization on more mainstream tasks such as machine translation and automatic speech recognition.",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Conclusion and Future Work",
"sec_num": "6"
}
],
"back_matter": [
{
"text": "We would like to thank Sandeep Suntwal, Yaroslav Getman, and Katja Voskoboinik for their helpful discussion and insightful comments. We also thank the anonymous reviewers for their constructive comments. This work was funded by the Academy of Finland (grant number 322625).",
"cite_spans": [],
"ref_spans": [],
"eq_spans": [],
"section": "Acknowledgments",
"sec_num": "7"
}
],
"bib_entries": {
"BIBREF0": {
"ref_id": "b0",
"title": "Distributed representations of words and phrases and their compositionality",
"authors": [
{
"first": "Tomas",
"middle": [],
"last": "Mikolov",
"suffix": ""
},
{
"first": "Ilya",
"middle": [],
"last": "Sutskever",
"suffix": ""
},
{
"first": "Kai",
"middle": [],
"last": "Chen",
"suffix": ""
},
{
"first": "Greg",
"middle": [
"S"
],
"last": "Corrado",
"suffix": ""
},
{
"first": "Jeff",
"middle": [],
"last": "Dean",
"suffix": ""
}
],
"year": 2013,
"venue": "Advances in neural information processing systems",
"volume": "",
"issue": "",
"pages": "3111--3119",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed representa- tions of words and phrases and their compositionality. In Advances in neural information processing systems, pages 3111-3119, 2013.",
"links": null
},
"BIBREF1": {
"ref_id": "b1",
"title": "Glove: Global vectors for word representation",
"authors": [
{
"first": "Jeffrey",
"middle": [],
"last": "Pennington",
"suffix": ""
},
{
"first": "Richard",
"middle": [],
"last": "Socher",
"suffix": ""
},
{
"first": "Christopher D",
"middle": [],
"last": "Manning",
"suffix": ""
}
],
"year": 2014,
"venue": "Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP)",
"volume": "",
"issue": "",
"pages": "1532--1543",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jeffrey Pennington, Richard Socher, and Christopher D Manning. Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP), pages 1532-1543, 2014.",
"links": null
},
"BIBREF2": {
"ref_id": "b2",
"title": "Enriching word vectors with subword information",
"authors": [
{
"first": "Piotr",
"middle": [],
"last": "Bojanowski",
"suffix": ""
},
{
"first": "Edouard",
"middle": [],
"last": "Grave",
"suffix": ""
},
{
"first": "Armand",
"middle": [],
"last": "Joulin",
"suffix": ""
},
{
"first": "Tomas",
"middle": [],
"last": "Mikolov",
"suffix": ""
}
],
"year": 2017,
"venue": "Transactions of the Association for Computational Linguistics",
"volume": "5",
"issue": "",
"pages": "135--146",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. Enriching word vectors with subword information. Transactions of the Association for Computational Linguistics, 5:135- 146, 2017.",
"links": null
},
"BIBREF3": {
"ref_id": "b3",
"title": "Indiana University Studies in the History and Theory of Linguistics. Longmans",
"authors": [
{
"first": "J",
"middle": [
"R"
],
"last": "Firth",
"suffix": ""
},
{
"first": "F",
"middle": [
"R"
],
"last": "Palmer",
"suffix": ""
}
],
"year": 1968,
"venue": "",
"volume": "",
"issue": "",
"pages": "1952--1959",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "J.R. Firth and F.R. Palmer. Selected Papers of J.R. Firth, 1952-1959. Indiana University Studies in the History and Theory of Linguistics. Longmans, 1968.",
"links": null
},
"BIBREF4": {
"ref_id": "b4",
"title": "Dependency-based word embeddings",
"authors": [
{
"first": "Omer",
"middle": [],
"last": "Levy",
"suffix": ""
},
{
"first": "Yoav",
"middle": [],
"last": "Goldberg",
"suffix": ""
}
],
"year": 2014,
"venue": "Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics",
"volume": "2",
"issue": "",
"pages": "302--308",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Omer Levy and Yoav Goldberg. Dependency-based word embeddings. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 302-308, 2014.",
"links": null
},
"BIBREF5": {
"ref_id": "b5",
"title": "Two/too simple adaptations of word2vec for syntax problems",
"authors": [
{
"first": "Wang",
"middle": [],
"last": "Ling",
"suffix": ""
},
{
"first": "Chris",
"middle": [],
"last": "Dyer",
"suffix": ""
},
{
"first": "Alan",
"middle": [
"W"
],
"last": "Black",
"suffix": ""
},
{
"first": "Isabel",
"middle": [],
"last": "Trancoso",
"suffix": ""
}
],
"year": 2015,
"venue": "Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
"volume": "",
"issue": "",
"pages": "1299--1304",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Wang Ling, Chris Dyer, Alan W Black, and Isabel Trancoso. Two/too simple adaptations of word2vec for syntax problems. In Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1299-1304, 2015.",
"links": null
},
"BIBREF6": {
"ref_id": "b6",
"title": "Dependency based embeddings for sentence classification tasks",
"authors": [
{
"first": "Alexandros",
"middle": [],
"last": "Komninos",
"suffix": ""
},
{
"first": "Suresh",
"middle": [],
"last": "Manandhar",
"suffix": ""
}
],
"year": 2016,
"venue": "Proceedings of the 2016 conference of the North American chapter of the association for computational linguistics: human language technologies",
"volume": "",
"issue": "",
"pages": "1490--1500",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Alexandros Komninos and Suresh Manandhar. Dependency based embeddings for sentence classi- fication tasks. In Proceedings of the 2016 conference of the North American chapter of the associ- ation for computational linguistics: human language technologies, pages 1490-1500, 2016.",
"links": null
},
"BIBREF7": {
"ref_id": "b7",
"title": "Neural semantic role labeling with dependency path embeddings",
"authors": [
{
"first": "Michael",
"middle": [],
"last": "Roth",
"suffix": ""
},
{
"first": "Mirella",
"middle": [],
"last": "Lapata",
"suffix": ""
}
],
"year": 2016,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1605.07515"
]
},
"num": null,
"urls": [],
"raw_text": "Michael Roth and Mirella Lapata. Neural semantic role labeling with dependency path embeddings. arXiv preprint arXiv:1605.07515, 2016.",
"links": null
},
"BIBREF8": {
"ref_id": "b8",
"title": "Biomedical event trigger detection by dependency-based word embedding",
"authors": [
{
"first": "Jian",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Jianhai",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Yuan",
"middle": [],
"last": "An",
"suffix": ""
},
{
"first": "Hongfei",
"middle": [],
"last": "Lin",
"suffix": ""
},
{
"first": "Zhihao",
"middle": [],
"last": "Yang",
"suffix": ""
},
{
"first": "Yijia",
"middle": [],
"last": "Zhang",
"suffix": ""
},
{
"first": "Yuanyuan",
"middle": [],
"last": "Sun",
"suffix": ""
}
],
"year": 2016,
"venue": "BMC medical genomics",
"volume": "9",
"issue": "2",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jian Wang, Jianhai Zhang, Yuan An, Hongfei Lin, Zhihao Yang, Yijia Zhang, and Yuanyuan Sun. Biomedical event trigger detection by dependency-based word embedding. BMC medical genomics, 9(2):45, 2016.",
"links": null
},
"BIBREF9": {
"ref_id": "b9",
"title": "Experiential, distributional and dependency-based word embeddings have complementary roles in decoding brain activity",
"authors": [
{
"first": "Samira",
"middle": [],
"last": "Abnar",
"suffix": ""
},
{
"first": "Rasyan",
"middle": [],
"last": "Ahmed",
"suffix": ""
},
{
"first": "Max",
"middle": [],
"last": "Mijnheer",
"suffix": ""
},
{
"first": "Willem",
"middle": [],
"last": "Zuidema",
"suffix": ""
}
],
"year": 2017,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1711.09285"
]
},
"num": null,
"urls": [],
"raw_text": "Samira Abnar, Rasyan Ahmed, Max Mijnheer, and Willem Zuidema. Experiential, distributional and dependency-based word embeddings have complementary roles in decoding brain activity. arXiv preprint arXiv:1711.09285, 2017.",
"links": null
},
"BIBREF10": {
"ref_id": "b10",
"title": "Speech and language processing",
"authors": [
{
"first": "Dan",
"middle": [],
"last": "Jurafsky",
"suffix": ""
},
{
"first": "H",
"middle": [],
"last": "James",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Martin",
"suffix": ""
}
],
"year": 2014,
"venue": "",
"volume": "3",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Dan Jurafsky and James H Martin. Speech and language processing. vol. 3, 2014.",
"links": null
},
"BIBREF11": {
"ref_id": "b11",
"title": "Recursive deep models for semantic compositionality over a sentiment treebank",
"authors": [
{
"first": "Richard",
"middle": [],
"last": "Socher",
"suffix": ""
},
{
"first": "Alex",
"middle": [],
"last": "Perelygin",
"suffix": ""
},
{
"first": "Jean",
"middle": [],
"last": "Wu",
"suffix": ""
},
{
"first": "Jason",
"middle": [],
"last": "Chuang",
"suffix": ""
},
{
"first": "D",
"middle": [],
"last": "Christopher",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Manning",
"suffix": ""
},
{
"first": "Y",
"middle": [],
"last": "Andrew",
"suffix": ""
},
{
"first": "Christopher",
"middle": [],
"last": "Ng",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Potts",
"suffix": ""
}
],
"year": 2013,
"venue": "Proceedings of the 2013 conference on empirical methods in natural language processing",
"volume": "",
"issue": "",
"pages": "1631--1642",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Richard Socher, Alex Perelygin, Jean Wu, Jason Chuang, Christopher D Manning, Andrew Y Ng, and Christopher Potts. Recursive deep models for semantic compositionality over a sentiment tree- bank. In Proceedings of the 2013 conference on empirical methods in natural language processing, pages 1631-1642, 2013.",
"links": null
},
"BIBREF12": {
"ref_id": "b12",
"title": "Pre-training of deep bidirectional transformers for language understanding",
"authors": [
{
"first": "Jacob",
"middle": [],
"last": "Devlin",
"suffix": ""
},
{
"first": "Ming-Wei",
"middle": [],
"last": "Chang",
"suffix": ""
},
{
"first": "Kenton",
"middle": [],
"last": "Lee",
"suffix": ""
},
{
"first": "Kristina",
"middle": [
"Toutanova"
],
"last": "Bert",
"suffix": ""
}
],
"year": 2018,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1810.04805"
]
},
"num": null,
"urls": [],
"raw_text": "Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805, 2018.",
"links": null
},
"BIBREF13": {
"ref_id": "b13",
"title": "Retrofitting word vectors to semantic lexicons",
"authors": [
{
"first": "Manaal",
"middle": [],
"last": "Faruqui",
"suffix": ""
},
{
"first": "Jesse",
"middle": [],
"last": "Dodge",
"suffix": ""
},
{
"first": "K",
"middle": [],
"last": "Sujay",
"suffix": ""
},
{
"first": "Chris",
"middle": [],
"last": "Jauhar",
"suffix": ""
},
{
"first": "Eduard",
"middle": [],
"last": "Dyer",
"suffix": ""
},
{
"first": "Noah A",
"middle": [],
"last": "Hovy",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Smith",
"suffix": ""
}
],
"year": 2014,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1411.4166"
]
},
"num": null,
"urls": [],
"raw_text": "Manaal Faruqui, Jesse Dodge, Sujay K Jauhar, Chris Dyer, Eduard Hovy, and Noah A Smith. Retrofitting word vectors to semantic lexicons. arXiv preprint arXiv:1411.4166, 2014.",
"links": null
},
"BIBREF14": {
"ref_id": "b14",
"title": "Specializing word embeddings for similarity or relatedness",
"authors": [
{
"first": "Douwe",
"middle": [],
"last": "Kiela",
"suffix": ""
},
{
"first": "Felix",
"middle": [],
"last": "Hill",
"suffix": ""
},
{
"first": "Stephen",
"middle": [],
"last": "Clark",
"suffix": ""
}
],
"year": 2015,
"venue": "Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "2044--2048",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Douwe Kiela, Felix Hill, and Stephen Clark. Specializing word embeddings for similarity or re- latedness. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 2044-2048, 2015.",
"links": null
},
"BIBREF15": {
"ref_id": "b15",
"title": "Dict2vec: Learning word embeddings using lexical dictionaries",
"authors": [
{
"first": "Julien",
"middle": [],
"last": "Tissier",
"suffix": ""
},
{
"first": "Christophe",
"middle": [],
"last": "Gravier",
"suffix": ""
},
{
"first": "Amaury",
"middle": [],
"last": "Habrard",
"suffix": ""
}
],
"year": 2017,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Julien Tissier, Christophe Gravier, and Amaury Habrard. Dict2vec: Learning word embeddings using lexical dictionaries. 2017.",
"links": null
},
"BIBREF16": {
"ref_id": "b16",
"title": "Rc-net: A general framework for incorporating knowledge into word representations",
"authors": [
{
"first": "Chang",
"middle": [],
"last": "Xu",
"suffix": ""
},
{
"first": "Yalong",
"middle": [],
"last": "Bai",
"suffix": ""
},
{
"first": "Jiang",
"middle": [],
"last": "Bian",
"suffix": ""
},
{
"first": "Bin",
"middle": [],
"last": "Gao",
"suffix": ""
},
{
"first": "Gang",
"middle": [],
"last": "Wang",
"suffix": ""
},
{
"first": "Xiaoguang",
"middle": [],
"last": "Liu",
"suffix": ""
},
{
"first": "Tie-Yan",
"middle": [],
"last": "Liu",
"suffix": ""
}
],
"year": 2014,
"venue": "Proceedings of the 23rd ACM international conference on conference on information and knowledge management",
"volume": "",
"issue": "",
"pages": "1219--1228",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Chang Xu, Yalong Bai, Jiang Bian, Bin Gao, Gang Wang, Xiaoguang Liu, and Tie-Yan Liu. Rc-net: A general framework for incorporating knowledge into word representations. In Proceedings of the 23rd ACM international conference on conference on information and knowledge management, pages 1219-1228, 2014.",
"links": null
},
"BIBREF17": {
"ref_id": "b17",
"title": "Auto-encoding dictionary definitions into consistent word embeddings",
"authors": [
{
"first": "Tom",
"middle": [],
"last": "Bosc",
"suffix": ""
},
{
"first": "Pascal",
"middle": [],
"last": "Vincent",
"suffix": ""
}
],
"year": 2018,
"venue": "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing",
"volume": "",
"issue": "",
"pages": "1522--1532",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Tom Bosc and Pascal Vincent. Auto-encoding dictionary definitions into consistent word embed- dings. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Process- ing, pages 1522-1532, 2018.",
"links": null
},
"BIBREF18": {
"ref_id": "b18",
"title": "Incorporating syntactic and semantic information in word embeddings using graph convolutional networks",
"authors": [
{
"first": "Shikhar",
"middle": [],
"last": "Vashishth",
"suffix": ""
},
{
"first": "Manik",
"middle": [],
"last": "Bhandari",
"suffix": ""
},
{
"first": "Prateek",
"middle": [],
"last": "Yadav",
"suffix": ""
},
{
"first": "Piyush",
"middle": [],
"last": "Rai",
"suffix": ""
},
{
"first": "Chiranjib",
"middle": [],
"last": "Bhattacharyya",
"suffix": ""
},
{
"first": "Partha",
"middle": [],
"last": "Talukdar",
"suffix": ""
}
],
"year": 2018,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1809.04283"
]
},
"num": null,
"urls": [],
"raw_text": "Shikhar Vashishth, Manik Bhandari, Prateek Yadav, Piyush Rai, Chiranjib Bhattacharyya, and Partha Talukdar. Incorporating syntactic and semantic information in word embeddings using graph convolutional networks. arXiv preprint arXiv:1809.04283, 2018.",
"links": null
},
"BIBREF19": {
"ref_id": "b19",
"title": "Deep contextualized word representations",
"authors": [
{
"first": "E",
"middle": [],
"last": "Matthew",
"suffix": ""
},
{
"first": "Mark",
"middle": [],
"last": "Peters",
"suffix": ""
},
{
"first": "Mohit",
"middle": [],
"last": "Neumann",
"suffix": ""
},
{
"first": "Matt",
"middle": [],
"last": "Iyyer",
"suffix": ""
},
{
"first": "Christopher",
"middle": [],
"last": "Gardner",
"suffix": ""
},
{
"first": "Kenton",
"middle": [],
"last": "Clark",
"suffix": ""
},
{
"first": "Luke",
"middle": [],
"last": "Lee",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Zettlemoyer",
"suffix": ""
}
],
"year": 2018,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1802.05365"
]
},
"num": null,
"urls": [],
"raw_text": "Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. Deep contextualized word representations. arXiv preprint arXiv:1802.05365, 2018.",
"links": null
},
"BIBREF20": {
"ref_id": "b20",
"title": "Graph Theory",
"authors": [
{
"first": "Frank",
"middle": [],
"last": "Harary",
"suffix": ""
}
],
"year": 1969,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Frank Harary. Graph Theory. Addison Wesley Publishing Company. 1969.",
"links": null
},
"BIBREF21": {
"ref_id": "b21",
"title": "Graph theory and its applications",
"authors": [
{
"first": "L",
"middle": [],
"last": "Jonathan",
"suffix": ""
},
{
"first": "Jay",
"middle": [],
"last": "Gross",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Yellen",
"suffix": ""
}
],
"year": 2005,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Jonathan L Gross and Jay Yellen. Graph theory and its applications. CRC press, 2005.",
"links": null
},
"BIBREF22": {
"ref_id": "b22",
"title": "node2vec: Scalable feature learning for networks",
"authors": [
{
"first": "Aditya",
"middle": [],
"last": "Grover",
"suffix": ""
},
{
"first": "Jure",
"middle": [],
"last": "Leskovec",
"suffix": ""
}
],
"year": 2016,
"venue": "Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining",
"volume": "",
"issue": "",
"pages": "855--864",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Aditya Grover and Jure Leskovec. node2vec: Scalable feature learning for networks. In Proceed- ings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, pages 855-864, 2016.",
"links": null
},
"BIBREF23": {
"ref_id": "b23",
"title": "Treebank-3. Linguistic Data Consortium",
"authors": [
{
"first": "P",
"middle": [],
"last": "Mitchell",
"suffix": ""
},
{
"first": "Beatrice",
"middle": [],
"last": "Marcus",
"suffix": ""
},
{
"first": "Mary",
"middle": [
"Ann"
],
"last": "Santorini",
"suffix": ""
},
{
"first": "Ann",
"middle": [],
"last": "Marcinkiewicz",
"suffix": ""
},
{
"first": "",
"middle": [],
"last": "Taylor",
"suffix": ""
}
],
"year": 1999,
"venue": "",
"volume": "14",
"issue": "",
"pages": "",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Mitchell P Marcus, Beatrice Santorini, Mary Ann Marcinkiewicz, and Ann Taylor. Treebank-3. Linguistic Data Consortium, Philadelphia, 14, 1999.",
"links": null
},
"BIBREF24": {
"ref_id": "b24",
"title": "Graphvite: A high-performance cpu-gpu hybrid system for node embedding",
"authors": [
{
"first": "Zhaocheng",
"middle": [],
"last": "Zhu",
"suffix": ""
},
{
"first": "Shizhen",
"middle": [],
"last": "Xu",
"suffix": ""
},
{
"first": "Meng",
"middle": [],
"last": "Qu",
"suffix": ""
},
{
"first": "Jian",
"middle": [],
"last": "Tang",
"suffix": ""
}
],
"year": 2019,
"venue": "The World Wide Web Conference",
"volume": "",
"issue": "",
"pages": "2494--2504",
"other_ids": {},
"num": null,
"urls": [],
"raw_text": "Zhaocheng Zhu, Shizhen Xu, Meng Qu, and Jian Tang. Graphvite: A high-performance cpu-gpu hybrid system for node embedding. In The World Wide Web Conference, pages 2494-2504. ACM, 2019.",
"links": null
},
"BIBREF25": {
"ref_id": "b25",
"title": "Analogies explained: Towards understanding word embeddings",
"authors": [
{
"first": "Carl",
"middle": [],
"last": "Allen",
"suffix": ""
},
{
"first": "Timothy",
"middle": [],
"last": "Hospedales",
"suffix": ""
}
],
"year": 2019,
"venue": "",
"volume": "",
"issue": "",
"pages": "",
"other_ids": {
"arXiv": [
"arXiv:1901.09813"
]
},
"num": null,
"urls": [],
"raw_text": "Carl Allen and Timothy Hospedales. Analogies explained: Towards understanding word embed- dings. arXiv preprint arXiv:1901.09813, 2019.",
"links": null
}
},
"ref_entries": {
"FIGREF1": {
"type_str": "figure",
"uris": null,
"text": "Trees (a), (b), (d) and (e) show different positions of the words should and is respectively indicating differences in syntactic functions. Tree (c) is analogous to (a) suggesting similarity in syntactic function between should and if.",
"num": null
},
"FIGREF3": {
"type_str": "figure",
"uris": null,
"text": "Trees (a), (b) and (c) are sample parse trees in the training corpus while (d) represents their unionized supergraph.",
"num": null
},
"TABREF1": {
"html": null,
"content": "<table/>",
"text": "Comparison of top 3 KNN with cosine similarity produced by SynGCN",
"num": null,
"type_str": "table"
}
}
}
} |