nach oben

Erschienen in:

2018 | OriginalPaper | Buchkapitel

Knowledge-lean Paraphrase Identification Using Character-Based Features

verfasst von : Asli Eyecioglu, Bill Keller

Erschienen in: Artificial Intelligence and Natural Language

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

The paraphrase identification task has practical importance in the NLP community because of the need to deal with the pervasive problem of linguistic variation. Accurate methods should help improve the performance of NLP applications, including machine translation, information retrieval, question answering, text summarization, document clustering and plagiarism detection, amongst others. We consider an approach to paraphrase identification that may be considered “knowledge-lean”. Our approach minimizes the need for data transformation and avoids the use of knowledge-based tools and resources. Candidate paraphrase pairs are represented using combinations of word- and character-based features. We show that SVM classifiers may be trained to distinguish paraphrase and non-paraphrase pairs across a number of different paraphrase corpora with good results. Analysis shows that features derived from character bigrams are particularly informative. We also describe recent experiments in identifying paraphrase for Russian, a language with rich morphology and free word order that presents a particularly interesting challenge for our knowledge-lean approach. We are able to report good results on a three-way paraphrase classification task.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel RuThes Thesaurus in Detecting Russian Paraphrases

Nächstes Kapitel Paraphrase Detection Using Machine Translation and Textual Similarity Algorithms

In order to download TuPC: https://osf.io/wp83a/.

A debatable pair arises where the decisions of the four annotators are equally divided between “paraphrase” and “non-paraphrase”.

http://ainlconf.ru/paraphraser.

http://matplotlib.org/.

Agirre, E., et al.: Semeval-2012 task 6: A pilot on semantic textual similarity. In: Proceedings of the 6th International Workshop on Semantic Evaluation, in Conjunction with the First Joint Conference on Lexical and Computational Semantics, pp. 385–393 (2012)

Androutsopoulos, I., Malakasiotis, P.: A survey of paraphrasing and textual entailment methods. Artif. Intell. Res. 38(1), 135–187 (2010)MATH

Bannard, C., Callison-Burch, C.: Paraphrasing with bilingual parallel corpora. In: Proceedings of the 43th Annual Meeting on Association for Computational Linguistics, pp. 597–604 (2005)

Barron-Cedeno, A., et al.: Plagiarism meets paraphrasing: insights for the next generation in automatic plagiarism detection. Comput. Linguist. 39(4), 917–947 (2013)CrossRef

Barzilay, R., et al.: Information fusion in the context of multi-document summarization. In: Proceedings of ACL, pp. 550–557 (1999)

Barzilay, R., Lee, L.: Learning to paraphrase: an unsupervised approach using multiple-sequence alignment. In: Naacl-2003, pp. 16–23 (2003)

Blacoe, W., Lapata, M.: A comparison of vector-based representations for semantic composition. In: Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL 2012), pp. 546–556 (2012)

Callison-Burch, C., et al.: Improved statistical machine translation using paraphrases. In: Proceedings of the Main Conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics (HLT-NAACL 2006), pp. 17–24 (2006)

Chang, C., Lin, C.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 1–27 (2011)CrossRef

10.

Culicover, P.W.: Paraphrase generation and information retrieval from stored text. Mech. Transl. Comput. Linguist. 11(1–2), 78–88 (1968)

11.

Das, D., Smith, N.A.: Paraphrase identification as probabilistic quasi-synchronous recognition. In: Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: ACL-IJCNLP 2009, pp. 468–476 (2009)

12.

Demir, S., et al.: Turkish paraphrase corpus. In: Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC 2012), pp. 4087–4091 (2012)

13.

Dolan, W.B., et al.: Unsupervised construction of large paraphrase corpora: exploiting massively parallel news sources. In: Proceedings of the 20th International Conference on Computational Linguistics, COLING 2004. Association for Computational Linguistics, Geneva (2004)

14.

Dolan, W.B., Brockett, C.: Automatically constructing a corpus of sentential paraphrases. In: Proceedings of IWP, pp. 9–16. Asia Federation of Natural Language Processing (2005)

15.

Duclaye, F., et al.: Using the web as a linguistic resource for learning reformulations automatically. In: Proceedings of the Third International Conference on Language Resources and Evaluation (LREC 2002), Las Palmas, Canary Islands, Spain, pp. 390–396 (2002)

16.

Eyecioglu, A., Keller, B.: ASOBEK: Twitter paraphrase identification with simple overlap features and SVMs. In: Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015), Denver, Colorado, pp. 64–69 (2015)

17.

Eyecioglu, A., Keller, B.: Constructing a Turkish corpus for paraphrase identification and semantic similarity. In: Proceedings of the 17th International Conference on Intelligent Text Processing and Computational Linguistics. LNCS, pp. 562–574 (2016)

18.

Fellbaum, C.: WordNet. An Electronic Lexical Database. MIT Press, Cambridge (1998)MATH

19.

Fernando, S., Stevenson, M.: A semantic similarity approach to paraphrase detection. In: Proceedings of the 11th Annual Research Colloquium of the UK Special Interest Group for Computational Linguistics, pp. 45–52 (2008)

20.

Finch, A., et al.: Using Machine translation evaluation techniques to determine sentence-level semantic equivalence. In: Proceedings of the Third International Workshop on Paraphrasing (IWP 2005), pp. 17–24 (2005)

21.

Fleiss, J.L.: Measuring nominal scale agreement among many raters. Psychol. Bull. 76, 378–382 (1971)CrossRef

22.

Ganitkevitch, J., et al.: Learning sentential paraphrases from bilingual parallel corpora for text-to-text generation, pp. 1168–1179. Computational Linguistics (2011)

23.

He, W., et al.: Enriching SMT training data via paraphrasing. In: Proceedings of the 5th International Joint Conference on Natural Language Processing, IJCNLP 2011, pp. 803–810. Asian Federation of Natural Language Processing (2011)

24.

Hearst, M.A., Grefenstette, G.: Refining automatically-discovered lexical relations: combining weak techniques for stronger results. In: Statistically-Based Natural Language Programming Techniques, Papers from the 1992 AAAI Workshop, Menlo Park, CA, pp. 64–72 (1992)

25.

Hsu, C.-W., et al.: A practical guide to support vector classification. BJU Int. 101(1), 1396–1400 (2008)

26.

Hunter, J.D.: Matplotlib: a 2D graphics environment. Comput. Sci. Eng. 9(3), 90–95 (2007)CrossRef

27.

Ji, Y., Eisenstein, J.: Discriminative improvements to distributional sentence similarity. In: Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pp. 891–896. Association for Computational Linguistics, Seattle (2013)

28.

Kim, Y., et al.: Character-aware neural language models. CoRR 1508.06615 (2015)

29.

Kozareva, Z., Montoyo, A.: Paraphrase identification on the basis of supervised machine learning techniques. In: Salakoski, T., Ginter, F., Pyysalo, S., Pahikkala, T. (eds.) FinTAL 2006. LNCS (LNAI), vol. 4139, pp. 524–533. Springer, Heidelberg (2006). https://doi.org/10.1007/11816508_52CrossRef

30.

Ling, W., et al.: Finding function in form: compositional character models for open vocabulary word representation. CoRR 1508.02096 (2015)

31.

Lintean, M., Rus, V.: Dissimilarity kernels for paraphrase identification. In: Proceedings of the 24th International Florida Artificial Intelligence Research Society Conference, Palm Beach, FL, pp. 263–268 (2011)

32.

Madnani, N., et al.: Re-examining machine translation metrics for paraphrase identification. In: Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT 2012), PA, USA, pp. 182–190 (2012)

33.

Madnani, N., et al.: Using paraphrases for parameter tuning in statistical machine translation. In: Proceedings of the Second Workshop on Statistical Machine Translation (WMT 2007), Prague, Czech Republic (2007)

34.

Malakasiotis, P.: Paraphrase recognition using machine learning to combine similarity measures. In: Proceedings of the ACL-IJCNLP 2009 Student Research Workshop, Suntec, Singapore, pp. 27–35 (2009)

35.

Marton, Y., et al.: Filtering antonymous, trend-contrasting, and polarity-dissimilar distributional paraphrases for improving statistical machine translation. In: Proceedings of the Sixth Workshop on Statistical Machine Translation, pp. 237–249. Association for Computational Linguistics, Edingburgh, Scotland (2011)

36.

Marton, Y., et al.: Improved statistical machine translation using monolingually-derived paraphrases. In: Conference on Empirical Methods in Natural Language Processing (EMNLP), Singapore (2009)

37.

Mckeown, K.R.: Paraphrasing questions using given and new information. Comput. Linguist. 9(1), 1–10 (1983)MathSciNet

38.

Mihalcea, R., et al.: Corpus-based and knowledge-based measures of text semantic similarity. In: Proceedings of the 21st National Conference on Artificial Intelligence, vol. 1, pp. 775–780. AAAI Press (2006)

39.

Owczarzak, K., et al.: Contextual bitext-derived paraphrases in automatic MT evaluation. In: StatMT 2006, Stroudsburg, PA, USA, pp. 86–93 (2006)

40.

Pedersen, T., Bruce, R.: Knowledge lean word-sense disambiguation. In: Proceedings of the Fifteenth National Conference on Artificial Intelligence, pp. 800–805. AAAI Press (1998)

41.

Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., et al.: Scikit-learn: Machine Learning in Python. http://scikit-learn.org/stable/

42.

Pivovarova, L., et al.: ParaPhraser: Russian paraphrase corpus and shared task. In: Filchenkov, A., et al. (eds.) AINL 2017, CCIS, vol. 789, pp. 211–225. Springer, Cham (2018)

43.

Power, R., Scott, D.: Automatic generation of large-scale paraphrases. In: Proceedings of the 3rd International Workshop on Paraphrasing (IWP2005), Jeju, Republic of Korea, pp. 33–40 (2005)

44.

Pronoza, E., Yagunova, E.: Comparison of sentence similarity measures for Russian paraphrase identification. In: Proceedings of the AINL-ISMW FRUCT 2015, pp. 74–82. IEEE (2015)

45.

Ravichandran, D., Hovy, E.: Learning surface text patterns for a question answering system. In: Proceedings of the 40th Annual Meeting of the Association for Computational Linguistics. (2002)

46.

Rus, V., et al.: On paraphrase identification corpora. In: Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC 2014). European Language Resources Association (ELRA), Reykjavik, Iceland (2014)

47.

Shinyama, Y., et al.: Automatic paraphrase acquisition from news articles. In: Proceedings of the Second International Conference on Human Language Technology Research, pp. 313–318 (2002)

48.

Shinyama, Y., Sekine, S.: Paraphrase acquisition for information extraction. In: Proceedings of the second International Workshop on Paraphrasing - Volume 16 (PARAPHRASE 2003), vol. 16, pp. 65–71. Association for Computational Linguistics, Stroudsburg (2003)

49.

Socher, R., et al.: Dynamic pooling and unfolding recursive autoencoders for paraphrase detection. In: Advances in Neural Information Processing Systems, pp. 801–809 (2011)

50.

Wan, S., et al.: Using dependency-based features to take the ‘Para-farce’ out of paraphrase. In: Proceedings of the Australasian Language Technology Workshop, Sydney, Australia, pp. 131–138 (2006)

51.

Xu, W.: Data-driven approaches for paraphrasing across language variations. New York University (2014)

52.

Zhang, Y., Patrick, J.: Paraphrase identification by text canonicalization. In: proceedings of the Australasian Language Technology Workshop, Sydney, Australia, pp. 160–166 (2005)

Titel: Knowledge-lean Paraphrase Identification Using Character-Based Features
verfasst von: Asli Eyecioglu
Bill Keller
Verlag: Springer International Publishing
Buch: Artificial Intelligence and Natural Language
Print ISBN: 978-3-319-71745-6

Electronic ISBN: 978-3-319-71746-3

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-319-71746-3_21

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner