Knowledge

Shallow parsing

Source 📝

36: 1146: 170:, etc.) can take contextual information into account and thus compose chunks in such a way that they better reflect the semantic relations between the basic constituents. That is, these more advanced methods get around the problem that combinations of elementary constituents can have different higher level meanings depending on the context of the sentence. 397: 557: 150:
which first identifies constituent parts of sentences (nouns, verbs, adjectives, etc.) and then links them to higher order units that have discrete grammatical meanings (
260: 158:, verb groups, etc.). While the most elementary chunking algorithms simply link constituent parts on the basis of elementary search patterns (e.g., as specified by 1211: 535: 946: 390: 330: 282: 1216: 1187: 1115: 856: 547: 383: 344: 1110: 717: 1206: 871: 702: 119: 642: 53: 1059: 712: 100: 707: 452: 57: 72: 976: 697: 1180: 669: 79: 1014: 999: 971: 836: 831: 406: 174: 751: 722: 500: 1153: 594: 447: 181:
for computer languages. Under the name "shallow structure hypothesis", it is also used as an explanation for why
86: 1120: 1044: 776: 732: 617: 515: 370: 1173: 1024: 994: 661: 68: 46: 278: 881: 574: 552: 542: 510: 485: 365: 147: 741: 254: 1094: 770: 746: 599: 17: 1074: 1004: 961: 917: 689: 679: 674: 562: 1084: 956: 821: 584: 567: 425: 242: 159: 1089: 801: 609: 520: 348: 93: 966: 851: 826: 627: 530: 234: 178: 163: 1078: 1039: 1034: 902: 505: 480: 462: 207: 182: 225:
Clahsen, Felser, Harald, Claudia (2006). "Grammatical Processing in Language Learners".
1157: 786: 766: 490: 167: 1200: 1049: 861: 841: 622: 246: 1029: 986: 866: 579: 495: 472: 420: 155: 35: 589: 375: 238: 457: 27:
Analysis of a sentence which first identifies constituent parts of sentences
1145: 932: 912: 897: 876: 846: 791: 756: 637: 318: 1069: 927: 907: 781: 525: 440: 321: 142: 297: 435: 430: 360: 339: 1125: 761: 647: 336: 151: 379: 922: 29: 185:
learners often fail to parse complex sentences correctly.
1161: 214:. Singapore: Pearson Education Inc. pp. 577–586. 327: 1103: 1058: 1013: 985: 945: 890: 812: 800: 731: 688: 660: 608: 471: 413: 60:. Unsourced material may be challenged and removed. 328:GATE General Architecture for Text Engineering 1181: 391: 298:"Parsing By Chunks | Principle-Based Parsing" 8: 259:: CS1 maint: multiple names: authors list ( 1188: 1174: 809: 605: 398: 384: 376: 283:Association for Computational Linguistics 120:Learn how and when to remove this message 199: 252: 7: 1212:Tasks of natural language processing 1142: 1140: 857:Simple Knowledge Organization System 58:adding citations to reliable sources 18:Chunking (computational linguistics) 177:. It is similar to the concept of 25: 872:Thesaurus (information retrieval) 173:It is a technique widely used in 1144: 279:"NP Chunking (State of the art)" 34: 1217:Computational linguistics stubs 45:needs additional citations for 453:Natural language understanding 212:Speech and Language Processing 1: 977:Optical character recognition 1160:. You can help Knowledge by 670:Multi-document summarization 1000:Latent Dirichlet allocation 972:Natural language generation 837:Machine-readable dictionary 832:Linguistic Linked Open Data 407:Natural language processing 210:; Martin, James H. (2000). 175:natural language processing 164:machine learning techniques 1233: 1139: 752:Explicit semantic analysis 501:Deep linguistic processing 1154:computational linguistics 595:Word-sense disambiguation 448:Computational linguistics 239:10.1017/S0142716406060024 227:Applied Psycholinguistics 1207:Natural language parsing 1121:Natural Language Toolkit 1045:Pronunciation assessment 947:Automatic identification 777:Latent semantic analysis 733:Distributional semantics 618:Compound-term processing 516:Named-entity recognition 371:Named entity recognition 1025:Automated essay scoring 995:Document classification 662:Automatic summarization 345:Illinois Shallow Parser 162:), approaches that use 1156:-related article is a 882:Universal Dependencies 575:Terminology extraction 558:Semantic decomposition 553:Semantic role labeling 543:Part-of-speech tagging 511:Information extraction 496:Coreference resolution 486:Collocation extraction 366:Semantic role labeling 296:Abney, Steven (1991). 146:) is an analysis of a 643:Sentence segmentation 1095:Voice user interface 806:datasets and corpora 747:Document-term matrix 600:Word-sense induction 54:improve this article 1075:Interactive fiction 1005:Pachinko allocation 962:Speech segmentation 918:Google Ngram Viewer 690:Machine translation 680:Text simplification 675:Sentence extraction 563:Semantic similarity 333:includes a chunker. 324:includes a chunker. 307:. pp. 257–278. 160:regular expressions 1085:Question answering 957:Speech recognition 822:Corpus linguistics 802:Language resources 585:Textual entailment 568:Sentiment analysis 1169: 1168: 1134: 1133: 1090:Virtual assistant 1015:Computer-assisted 941: 940: 698:Computer-assisted 656: 655: 648:Word segmentation 610:Text segmentation 548:Semantic analysis 536:Syntactic parsing 521:Ontology learning 130: 129: 122: 104: 69:"Shallow parsing" 16:(Redirected from 1224: 1190: 1183: 1176: 1148: 1141: 1111:Formal semantics 1060:Natural language 967:Speech synthesis 949:and data capture 852:Semantic network 827:Lexical resource 810: 628:Lexical analysis 606: 531:Semantic parsing 400: 393: 386: 377: 308: 305:www.vinartus.net 302: 292: 290: 289: 265: 264: 258: 250: 222: 216: 215: 208:Jurafsky, Daniel 204: 179:lexical analysis 125: 118: 114: 111: 105: 103: 62: 38: 30: 21: 1232: 1231: 1227: 1226: 1225: 1223: 1222: 1221: 1197: 1196: 1195: 1194: 1137: 1135: 1130: 1099: 1079:Syntax guessing 1061: 1054: 1040:Predictive text 1035:Grammar checker 1016: 1009: 981: 948: 937: 903:Bank of English 886: 814: 805: 796: 727: 684: 652: 604: 506:Distant reading 481:Argument mining 467: 463:Text processing 409: 404: 357: 347:Shallow Parser 315: 300: 295: 287: 285: 277: 274: 269: 268: 251: 224: 223: 219: 206: 205: 201: 196: 191: 183:second language 133:Shallow parsing 126: 115: 109: 106: 63: 61: 51: 39: 28: 23: 22: 15: 12: 11: 5: 1230: 1228: 1220: 1219: 1214: 1209: 1199: 1198: 1193: 1192: 1185: 1178: 1170: 1167: 1166: 1149: 1132: 1131: 1129: 1128: 1123: 1118: 1113: 1107: 1105: 1101: 1100: 1098: 1097: 1092: 1087: 1082: 1072: 1066: 1064: 1062:user interface 1056: 1055: 1053: 1052: 1047: 1042: 1037: 1032: 1027: 1021: 1019: 1011: 1010: 1008: 1007: 1002: 997: 991: 989: 983: 982: 980: 979: 974: 969: 964: 959: 953: 951: 943: 942: 939: 938: 936: 935: 930: 925: 920: 915: 910: 905: 900: 894: 892: 888: 887: 885: 884: 879: 874: 869: 864: 859: 854: 849: 844: 839: 834: 829: 824: 818: 816: 807: 798: 797: 795: 794: 789: 787:Word embedding 784: 779: 774: 767:Language model 764: 759: 754: 749: 744: 738: 736: 729: 728: 726: 725: 720: 718:Transfer-based 715: 710: 705: 700: 694: 692: 686: 685: 683: 682: 677: 672: 666: 664: 658: 657: 654: 653: 651: 650: 645: 640: 635: 630: 625: 620: 614: 612: 603: 602: 597: 592: 587: 582: 577: 571: 570: 565: 560: 555: 550: 545: 540: 539: 538: 533: 523: 518: 513: 508: 503: 498: 493: 491:Concept mining 488: 483: 477: 475: 469: 468: 466: 465: 460: 455: 450: 445: 444: 443: 438: 428: 423: 417: 415: 411: 410: 405: 403: 402: 395: 388: 380: 374: 373: 368: 363: 356: 353: 352: 351: 342: 334: 325: 319:Apache OpenNLP 314: 313:External links 311: 310: 309: 293: 273: 270: 267: 266: 217: 198: 197: 195: 192: 190: 187: 168:topic modeling 166:(classifiers, 128: 127: 42: 40: 33: 26: 24: 14: 13: 10: 9: 6: 4: 3: 2: 1229: 1218: 1215: 1213: 1210: 1208: 1205: 1204: 1202: 1191: 1186: 1184: 1179: 1177: 1172: 1171: 1165: 1163: 1159: 1155: 1150: 1147: 1143: 1138: 1127: 1124: 1122: 1119: 1117: 1116:Hallucination 1114: 1112: 1109: 1108: 1106: 1102: 1096: 1093: 1091: 1088: 1086: 1083: 1080: 1076: 1073: 1071: 1068: 1067: 1065: 1063: 1057: 1051: 1050:Spell checker 1048: 1046: 1043: 1041: 1038: 1036: 1033: 1031: 1028: 1026: 1023: 1022: 1020: 1018: 1012: 1006: 1003: 1001: 998: 996: 993: 992: 990: 988: 984: 978: 975: 973: 970: 968: 965: 963: 960: 958: 955: 954: 952: 950: 944: 934: 931: 929: 926: 924: 921: 919: 916: 914: 911: 909: 906: 904: 901: 899: 896: 895: 893: 889: 883: 880: 878: 875: 873: 870: 868: 865: 863: 862:Speech corpus 860: 858: 855: 853: 850: 848: 845: 843: 842:Parallel text 840: 838: 835: 833: 830: 828: 825: 823: 820: 819: 817: 811: 808: 803: 799: 793: 790: 788: 785: 783: 780: 778: 775: 772: 768: 765: 763: 760: 758: 755: 753: 750: 748: 745: 743: 740: 739: 737: 734: 730: 724: 721: 719: 716: 714: 711: 709: 706: 704: 703:Example-based 701: 699: 696: 695: 693: 691: 687: 681: 678: 676: 673: 671: 668: 667: 665: 663: 659: 649: 646: 644: 641: 639: 636: 634: 633:Text chunking 631: 629: 626: 624: 623:Lemmatisation 621: 619: 616: 615: 613: 611: 607: 601: 598: 596: 593: 591: 588: 586: 583: 581: 578: 576: 573: 572: 569: 566: 564: 561: 559: 556: 554: 551: 549: 546: 544: 541: 537: 534: 532: 529: 528: 527: 524: 522: 519: 517: 514: 512: 509: 507: 504: 502: 499: 497: 494: 492: 489: 487: 484: 482: 479: 478: 476: 474: 473:Text analysis 470: 464: 461: 459: 456: 454: 451: 449: 446: 442: 439: 437: 434: 433: 432: 429: 427: 424: 422: 419: 418: 416: 414:General terms 412: 408: 401: 396: 394: 389: 387: 382: 381: 378: 372: 369: 367: 364: 362: 359: 358: 354: 350: 346: 343: 341: 338: 335: 332: 329: 326: 323: 320: 317: 316: 312: 306: 299: 294: 284: 280: 276: 275: 271: 262: 256: 248: 244: 240: 236: 232: 228: 221: 218: 213: 209: 203: 200: 193: 188: 186: 184: 180: 176: 171: 169: 165: 161: 157: 153: 149: 145: 144: 138: 134: 124: 121: 113: 110:February 2016 102: 99: 95: 92: 88: 85: 81: 78: 74: 71: –  70: 66: 65:Find sources: 59: 55: 49: 48: 43:This article 41: 37: 32: 31: 19: 1162:expanding it 1151: 1136: 1030:Concordancer 632: 426:Bag-of-words 304: 286:. Retrieved 255:cite journal 230: 226: 220: 211: 202: 172: 140: 136: 132: 131: 116: 107: 97: 90: 83: 76: 64: 52:Please help 47:verification 44: 987:Topic model 867:Text corpus 713:Statistical 580:Text mining 421:AI-complete 1201:Categories 708:Rule-based 590:Truecasing 458:Stop words 288:2016-01-30 189:References 154:groups or 80:newspapers 1017:reviewing 815:standards 813:Types and 194:Citations 933:Wikidata 913:FrameNet 898:BabelNet 877:Treebank 847:PropBank 792:Word2vec 757:fastText 638:Stemming 355:See also 340:chunking 247:15990215 233:: 3–42. 148:sentence 137:chunking 1104:Related 1070:Chatbot 928:WordNet 908:DBpedia 782:Seq2seq 526:Parsing 441:Trigram 322:OpenNLP 272:Sources 156:phrases 143:parsing 94:scholar 1077:(c.f. 735:models 723:Neural 436:Bigram 431:n-gram 361:Parser 245:  141:light 135:(also 96:  89:  82:  75:  67:  1152:This 1126:spaCy 771:large 762:GloVe 301:(PDF) 243:S2CID 101:JSTOR 87:books 1158:stub 891:Data 742:BERT 349:Demo 337:NLTK 331:GATE 261:link 152:noun 73:news 923:UBY 235:doi 139:or 56:by 1203:: 303:. 281:. 257:}} 253:{{ 241:. 231:27 229:. 1189:e 1182:t 1175:v 1164:. 1081:) 804:, 773:) 769:( 399:e 392:t 385:v 291:. 263:) 249:. 237:: 123:) 117:( 112:) 108:( 98:· 91:· 84:· 77:· 50:. 20:)

Index

Chunking (computational linguistics)

verification
improve this article
adding citations to reliable sources
"Shallow parsing"
news
newspapers
books
scholar
JSTOR
Learn how and when to remove this message
parsing
sentence
noun
phrases
regular expressions
machine learning techniques
topic modeling
natural language processing
lexical analysis
second language
Jurafsky, Daniel
doi
10.1017/S0142716406060024
S2CID
15990215
cite journal
link
"NP Chunking (State of the art)"

Text is available under the Creative Commons Attribution-ShareAlike License. Additional terms may apply.