22:
136:. Researchers, frustrated by the problems with using the classical method of asking research subjects to describe words as either simple or complex, have discovered that they can get a higher consistency in more levels of complexity if they ask labelers to sort words presented to them in order of complexity.
106:
Text simplification is illustrated with an example used by
Siddharthan (2006). The first sentence contains two relative clauses and one conjoined verb phrase. A text simplification system aims to change the first sentence into a group of simpler sentences, as seen just below the first sentence.
93:
remain the same. Text simplification is an important area of research because of communication needs in an increasingly complex and interconnected world more dominated by science, technology, and new media. But natural human languages pose huge problems because they ordinarily contain large
118:
Also contributing to the firmness in copper, the analyst noted, was a report by
Chicago purchasing agents. The Chicago report precedes the full purchasing agents report. The Chicago report gives an indication of what the full report might hold. The full report is due out
112:
Also contributing to the firmness in copper, the analyst noted, was a report by
Chicago purchasing agents, which precedes the full purchasing agents report that is due out today and gives an indication of what the full report might
288:
Siddhartha
Jonnalagadda, Luis Tari, Joerg Hakenberg, Chitta Baral and Graciela Gonzalez. Towards Effective Sentence Simplification for Automatic Processing of Biomedical Text. In Proc. of the NAACL-HLT 2009, Boulder, USA, June.
132:, a two-step process of first identifying complex words and then replacing them with simpler synonyms. A key challenge here is identifying complex words, which is performed by a machine learning classifier trained on
94:
vocabularies and complex constructions that machines, no matter how fast and well-programmed, cannot easily process. However, researchers have discovered that, to reduce linguistic diversity, they can use methods of
335:
495:
85:
to change, enhance, classify, or otherwise process an existing body of human-readable text so its grammar and structure is greatly simplified while the underlying
1099:
473:
884:
328:
290:
1053:
1094:
794:
485:
321:
43:
1048:
1084:
655:
809:
640:
65:
580:
997:
650:
645:
390:
914:
635:
285:". In Research on Language and Computation, Volume 4, Issue 1, Jun 2006, Pages 77–109, Springer Science, the Netherlands.
607:
180:
1089:
952:
937:
909:
774:
769:
344:
150:
82:
36:
30:
689:
660:
438:
301:
532:
385:
47:
1058:
982:
714:
670:
555:
453:
962:
932:
599:
819:
512:
490:
480:
448:
423:
278:". In Transactions of the Association for Computational Linguistics (TACL), Volume 3, 2015, Pages 283–297.
160:
145:
125:
679:
275:
1032:
708:
684:
537:
170:
165:
129:
95:
86:
242:"Comparative judgments are more consistent than binary classification for labelling word complexity"
1012:
942:
899:
855:
627:
612:
500:
1022:
894:
759:
522:
505:
363:
222:
175:
1027:
739:
547:
458:
904:
789:
764:
565:
468:
249:
214:
1016:
977:
972:
840:
570:
443:
418:
400:
155:
282:
724:
704:
428:
1078:
987:
799:
779:
560:
185:
226:
205:
Siddharthan, Advaith (28 March 2006). "Syntactic
Simplification and Text Cohesion".
967:
133:
307:
240:
Gooding, Sian; Kochmar, Ekaterina; Sarkar, Advait; Blackwell, Alan (August 2019).
924:
804:
517:
433:
410:
358:
90:
527:
313:
218:
241:
395:
254:
870:
850:
835:
814:
784:
729:
694:
575:
1007:
865:
845:
719:
463:
378:
373:
368:
1063:
699:
585:
317:
860:
15:
98:
to limit and simplify a set of words used in given texts.
308:
246:
Proceedings of the 13th
Linguistic Annotation Workshop
302:
Automatic
Induction of Rules for Text Simplification
274:
Wei Xu, Chris
Callison-Burch and Courtney Napoles. "
1041:
996:
951:
923:
883:
828:
750:
738:
669:
626:
598:
546:
409:
351:
276:Problems in Current Text Simplification Research
329:
8:
747:
543:
336:
322:
314:
283:Syntactic Simplification and Text Cohesion
253:
66:Learn how and when to remove this message
29:This article includes a list of general
197:
124:One approach to text simplification is
7:
1100:Tasks of natural language processing
795:Simple Knowledge Organization System
207:Research on Language and Computation
35:it lacks sufficient corresponding
14:
810:Thesaurus (information retrieval)
20:
391:Natural language understanding
1:
915:Optical character recognition
608:Multi-document summarization
1095:Natural language processing
938:Latent Dirichlet allocation
910:Natural language generation
775:Machine-readable dictionary
770:Linguistic Linked Open Data
345:Natural language processing
151:Controlled natural language
83:natural language processing
1116:
690:Explicit semantic analysis
439:Deep linguistic processing
1085:Computational linguistics
533:Word-sense disambiguation
386:Computational linguistics
219:10.1007/s11168-006-9011-1
1059:Natural Language Toolkit
983:Pronunciation assessment
885:Automatic identification
715:Latent semantic analysis
671:Distributional semantics
556:Compound-term processing
454:Named-entity recognition
81:is an operation used in
963:Automated essay scoring
933:Document classification
600:Automatic summarization
50:more precise citations.
820:Universal Dependencies
513:Terminology extraction
496:Semantic decomposition
491:Semantic role labeling
481:Part-of-speech tagging
449:Information extraction
434:Coreference resolution
424:Collocation extraction
281:Advaith Siddharthan. "
161:Lexical simplification
146:Automated paraphrasing
126:lexical simplification
581:Sentence segmentation
1033:Voice user interface
744:datasets and corpora
685:Document-term matrix
538:Word-sense induction
255:10.18653/v1/W19-4024
171:Semantic compression
166:Lexical substitution
130:lexical substitution
96:semantic compression
1013:Interactive fiction
943:Pachinko allocation
900:Speech segmentation
856:Google Ngram Viewer
628:Machine translation
618:Text simplification
613:Sentence extraction
501:Semantic similarity
79:Text simplification
1090:Speech recognition
1023:Question answering
895:Speech recognition
760:Corpus linguistics
740:Language resources
523:Textual entailment
506:Sentiment analysis
181:Simplified English
176:Text normalization
1072:
1071:
1028:Virtual assistant
953:Computer-assisted
879:
878:
636:Computer-assisted
594:
593:
586:Word segmentation
548:Text segmentation
486:Semantic analysis
474:Syntactic parsing
459:Ontology learning
76:
75:
68:
1107:
1049:Formal semantics
998:Natural language
905:Speech synthesis
887:and data capture
790:Semantic network
765:Lexical resource
748:
566:Lexical analysis
544:
469:Semantic parsing
338:
331:
324:
315:
267:
266:
264:
262:
257:
237:
231:
230:
202:
71:
64:
60:
57:
51:
46:this article by
37:inline citations
24:
23:
16:
1115:
1114:
1110:
1109:
1108:
1106:
1105:
1104:
1075:
1074:
1073:
1068:
1037:
1017:Syntax guessing
999:
992:
978:Predictive text
973:Grammar checker
954:
947:
919:
886:
875:
841:Bank of English
824:
752:
743:
734:
665:
622:
590:
542:
444:Distant reading
419:Argument mining
405:
401:Text processing
347:
342:
298:
271:
270:
260:
258:
239:
238:
234:
204:
203:
199:
194:
156:Language reform
142:
104:
72:
61:
55:
52:
42:Please help to
41:
25:
21:
12:
11:
5:
1113:
1111:
1103:
1102:
1097:
1092:
1087:
1077:
1076:
1070:
1069:
1067:
1066:
1061:
1056:
1051:
1045:
1043:
1039:
1038:
1036:
1035:
1030:
1025:
1020:
1010:
1004:
1002:
1000:user interface
994:
993:
991:
990:
985:
980:
975:
970:
965:
959:
957:
949:
948:
946:
945:
940:
935:
929:
927:
921:
920:
918:
917:
912:
907:
902:
897:
891:
889:
881:
880:
877:
876:
874:
873:
868:
863:
858:
853:
848:
843:
838:
832:
830:
826:
825:
823:
822:
817:
812:
807:
802:
797:
792:
787:
782:
777:
772:
767:
762:
756:
754:
745:
736:
735:
733:
732:
727:
725:Word embedding
722:
717:
712:
705:Language model
702:
697:
692:
687:
682:
676:
674:
667:
666:
664:
663:
658:
656:Transfer-based
653:
648:
643:
638:
632:
630:
624:
623:
621:
620:
615:
610:
604:
602:
596:
595:
592:
591:
589:
588:
583:
578:
573:
568:
563:
558:
552:
550:
541:
540:
535:
530:
525:
520:
515:
509:
508:
503:
498:
493:
488:
483:
478:
477:
476:
471:
461:
456:
451:
446:
441:
436:
431:
429:Concept mining
426:
421:
415:
413:
407:
406:
404:
403:
398:
393:
388:
383:
382:
381:
376:
366:
361:
355:
353:
349:
348:
343:
341:
340:
333:
326:
318:
312:
311:
305:
297:
296:External links
294:
293:
292:
286:
279:
269:
268:
232:
196:
195:
193:
190:
189:
188:
183:
178:
173:
168:
163:
158:
153:
148:
141:
138:
122:
121:
115:
103:
100:
74:
73:
28:
26:
19:
13:
10:
9:
6:
4:
3:
2:
1112:
1101:
1098:
1096:
1093:
1091:
1088:
1086:
1083:
1082:
1080:
1065:
1062:
1060:
1057:
1055:
1054:Hallucination
1052:
1050:
1047:
1046:
1044:
1040:
1034:
1031:
1029:
1026:
1024:
1021:
1018:
1014:
1011:
1009:
1006:
1005:
1003:
1001:
995:
989:
988:Spell checker
986:
984:
981:
979:
976:
974:
971:
969:
966:
964:
961:
960:
958:
956:
950:
944:
941:
939:
936:
934:
931:
930:
928:
926:
922:
916:
913:
911:
908:
906:
903:
901:
898:
896:
893:
892:
890:
888:
882:
872:
869:
867:
864:
862:
859:
857:
854:
852:
849:
847:
844:
842:
839:
837:
834:
833:
831:
827:
821:
818:
816:
813:
811:
808:
806:
803:
801:
800:Speech corpus
798:
796:
793:
791:
788:
786:
783:
781:
780:Parallel text
778:
776:
773:
771:
768:
766:
763:
761:
758:
757:
755:
749:
746:
741:
737:
731:
728:
726:
723:
721:
718:
716:
713:
710:
706:
703:
701:
698:
696:
693:
691:
688:
686:
683:
681:
678:
677:
675:
672:
668:
662:
659:
657:
654:
652:
649:
647:
644:
642:
641:Example-based
639:
637:
634:
633:
631:
629:
625:
619:
616:
614:
611:
609:
606:
605:
603:
601:
597:
587:
584:
582:
579:
577:
574:
572:
571:Text chunking
569:
567:
564:
562:
561:Lemmatisation
559:
557:
554:
553:
551:
549:
545:
539:
536:
534:
531:
529:
526:
524:
521:
519:
516:
514:
511:
510:
507:
504:
502:
499:
497:
494:
492:
489:
487:
484:
482:
479:
475:
472:
470:
467:
466:
465:
462:
460:
457:
455:
452:
450:
447:
445:
442:
440:
437:
435:
432:
430:
427:
425:
422:
420:
417:
416:
414:
412:
411:Text analysis
408:
402:
399:
397:
394:
392:
389:
387:
384:
380:
377:
375:
372:
371:
370:
367:
365:
362:
360:
357:
356:
354:
352:General terms
350:
346:
339:
334:
332:
327:
325:
320:
319:
316:
309:
306:
303:
300:
299:
295:
291:
287:
284:
280:
277:
273:
272:
256:
251:
247:
243:
236:
233:
228:
224:
220:
216:
213:(1): 77–109.
212:
208:
201:
198:
191:
187:
186:Basic English
184:
182:
179:
177:
174:
172:
169:
167:
164:
162:
159:
157:
154:
152:
149:
147:
144:
143:
139:
137:
135:
131:
127:
120:
116:
114:
110:
109:
108:
101:
99:
97:
92:
88:
84:
80:
70:
67:
59:
49:
45:
39:
38:
32:
27:
18:
17:
968:Concordancer
617:
364:Bag-of-words
259:. Retrieved
245:
235:
210:
206:
200:
134:labeled data
123:
117:
111:
105:
78:
77:
62:
53:
34:
925:Topic model
805:Text corpus
651:Statistical
518:Text mining
359:AI-complete
261:22 November
248:: 208–214.
91:information
48:introducing
1079:Categories
646:Rule-based
528:Truecasing
396:Stop words
192:References
31:references
955:reviewing
753:standards
751:Types and
56:June 2012
871:Wikidata
851:FrameNet
836:BabelNet
815:Treebank
785:PropBank
730:Word2vec
695:fastText
576:Stemming
227:14619244
140:See also
1042:Related
1008:Chatbot
866:WordNet
846:DBpedia
720:Seq2seq
464:Parsing
379:Trigram
102:Example
87:meaning
44:improve
1015:(c.f.
673:models
661:Neural
374:Bigram
369:n-gram
225:
119:today.
33:, but
1064:spaCy
709:large
700:GloVe
223:S2CID
113:hold.
829:Data
680:BERT
310:2004
304:1996
263:2019
128:via
89:and
861:UBY
250:doi
215:doi
1081::
244:.
221:.
209:.
1019:)
742:,
711:)
707:(
337:e
330:t
323:v
265:.
252::
229:.
217::
211:4
69:)
63:(
58:)
54:(
40:.
Text is available under the Creative Commons Attribution-ShareAlike License. Additional terms may apply.