Accès libre

Automatic Keyphrase Extraction from Scientific Chinese Medical Abstracts Based on Character-Level Sequence Labeling

À propos de cet article

Citez

Augenstein, I., Das, M., Riedel, S., Vikraman, L., & McCallum, A. (2017). Semeval 2017 task 10: Scienceie-extracting keyphrases and relations from scientific publications. ArXiv Preprint ArXiv:1704.02853. AugensteinI. DasM. RiedelS. VikramanL. McCallumA. 2017 Semeval 2017 task 10: Scienceie-extracting keyphrases and relations from scientific publications ArXiv Preprint ArXiv:1704.02853. Search in Google Scholar

Barker, K., & Cornacchia, N. (2000). Using noun phrase heads to extract document keyphrases. Conference of the Canadian Society for Computational Studies of Intelligence, 40–52. BarkerK. CornacchiaN. 2000 Using noun phrase heads to extract document keyphrases Conference of the Canadian Society for Computational Studies of Intelligence 40 52 10.1007/3-540-45486-1_4 Search in Google Scholar

Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007). Greedy layer-wise training of deep networks. Advances in Neural Information Processing Systems, 153–160. BengioY. LamblinP. PopoviciD. LarochelleH. 2007 Greedy layer-wise training of deep networks Advances in Neural Information Processing Systems 153 160 Search in Google Scholar

Berend, G. (2011). Opinion expression mining by exploiting keyphrase extraction. In Proceedings of the 5th International Joint Conference on Natural Language Processing, 1162–1170, Chiang Mai, Thailand. BerendG. 2011 Opinion expression mining by exploiting keyphrase extraction In Proceedings of the 5th International Joint Conference on Natural Language Processing 1162 1170 Chiang Mai, Thailand Search in Google Scholar

Bougouin, A., Boudin, F., & Daille, B. (2013). Topicrank: Graph-based topic ranking for keyphrase extraction. In Proceedings of International Joint Conference on Natural Language, 543–551, Nagoya, Japan. BougouinA. BoudinF. DailleB. 2013 Topicrank: Graph-based topic ranking for keyphrase extraction In Proceedings of International Joint Conference on Natural Language 543 551 Nagoya, Japan Search in Google Scholar

Campos, R., Mangaravite, V., Pasquali, A., Jorge, A.M., Nunes, C., & Jatowt, A. (2018). A text feature based automatic keyword extraction method for single documents. European Conference on Information Retrieval, 684–691. CamposR. MangaraviteV. PasqualiA. JorgeA.M. NunesC. JatowtA. 2018 A text feature based automatic keyword extraction method for single documents European Conference on Information Retrieval 684 691 10.1007/978-3-319-76941-7_63 Search in Google Scholar

Caragea, C., Bulgarov, F.A., Godea, A., & Gollapalli, S.D. (2014). Citation-enhanced keyphrase extraction from research papers: A supervised approach. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, 1435–1446. CarageaC. BulgarovF.A. GodeaA. GollapalliS.D. 2014 Citation-enhanced keyphrase extraction from research papers: A supervised approach In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing 1435 1446 10.3115/v1/D14-1150 Search in Google Scholar

Carpena, P., Bernaola-Galván, P., Hackenberg, M., Coronado, A., & Oliver, J. (2009). Level statistics of words: Finding keywords in literary texts and symbolic sequences. Physical Review E, 79(3), 035102. CarpenaP. Bernaola-GalvánP. HackenbergM. CoronadoA. OliverJ. 2009 Level statistics of words: Finding keywords in literary texts and symbolic sequences Physical Review E 79 3 035102 10.1103/PhysRevE.79.035102 Search in Google Scholar

Chien, L.F. (1997). PAT-tree-based keyword extraction for Chinese information retrieval. In Proceedings of the 20th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 50–58. ChienL.F. 1997 PAT-tree-based keyword extraction for Chinese information retrieval In Proceedings of the 20th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 50 58 10.1145/258525.258534 Search in Google Scholar

Cohen, J.D. (1995). Highlights: Language-and domain-independent automatic indexing terms for abstracting. Journal of the American Society for Information Science, 46(3), 162–174. CohenJ.D. 1995 Highlights: Language-and domain-independent automatic indexing terms for abstracting Journal of the American Society for Information Science 46 3 162 174 10.1002/(SICI)1097-4571(199504)46:3<162::AID-ASI2>3.0.CO;2-6 Search in Google Scholar

Dai, A.M., & Le, Q.V. (2015). Semi-supervised sequence learning. Advances in Neural Information Processing Systems, 3079–3087. DaiA.M. LeQ.V. 2015 Semi-supervised sequence learning Advances in Neural Information Processing Systems 3079 3087 Search in Google Scholar

Danesh, S., Sumner, T., & Martin, J.H. (2015). Sgrank: Combining statistical and graphical methods to improve the state of the art in unsupervised keyphrase extraction. Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics, 117–126. DaneshS. SumnerT. MartinJ.H. 2015 Sgrank: Combining statistical and graphical methods to improve the state of the art in unsupervised keyphrase extraction Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics 117 126 10.18653/v1/S15-1013 Search in Google Scholar

Devlin, J., Chang, M.W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. ArXiv:1810.04805 [Cs]. http://arxiv.org/abs/1810.04805 DevlinJ. ChangM.W. LeeK. ToutanovaK. 2019 BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding ArXiv:1810.04805 [Cs]. http://arxiv.org/abs/1810.04805 Search in Google Scholar

Erhan, D., Bengio, Y., Courville, A., Manzagol, P.A., Vincent, P., & Bengio, S. (2010). Why does unsupervised pre-training help deep learning? Journal of Machine Learning Research, 11(Feb), 625–660. ErhanD. BengioY. CourvilleA. ManzagolP.A. VincentP. BengioS. 2010 Why does unsupervised pre-training help deep learning? Journal of Machine Learning Research 11 Feb 625 660 Search in Google Scholar

Erkan, G., & Radev, D.R. (2004). Lexrank: Graph-based lexical centrality as salience in text summarization. Journal of Artificial Intelligence Research, 22, 457–479. ErkanG. RadevD.R. 2004 Lexrank: Graph-based lexical centrality as salience in text summarization Journal of Artificial Intelligence Research 22 457 479 10.1613/jair.1523 Search in Google Scholar

Frank, E., Paynter, G., Witten, I., Gutwin, C., & Nevill-Manning, C. (1999). Domain-Specific Keyphrase Extraction. In Proceeding of 16th International Joint Conference on Artificial Intelligence, Stockholm, Sweden, 668–673. FrankE. PaynterG. WittenI. GutwinC. Nevill-ManningC. 1999 Domain-Specific Keyphrase Extraction In Proceeding of 16th International Joint Conference on Artificial Intelligence Stockholm, Sweden 668 673 Search in Google Scholar

Giorgi, J.M., & Bader, G.D. (2018). Transfer learning for biomedical named entity recognition with neural networks. Bioinformatics, 34(23), 4087–4094. GiorgiJ.M. BaderG.D. 2018 Transfer learning for biomedical named entity recognition with neural networks Bioinformatics 34 23 4087 4094 10.1093/bioinformatics/bty449 Search in Google Scholar

Grineva, M., Grinev, M., & Lizorkin, D. (2009). Extracting key terms from noisy and multitheme documents. In Proceedings of the 18th International Conference on World Wide Web, 661–670. GrinevaM. GrinevM. LizorkinD. 2009 Extracting key terms from noisy and multitheme documents In Proceedings of the 18th International Conference on World Wide Web 661 670 10.1145/1526709.1526798 Search in Google Scholar

Habibi, M., Weber, L., Neves, M., Wiegandt, D.L., & Leser, U. (2017). Deep learning with word embeddings improves biomedical named entity recognition. Bioinformatics, 33(14), i37–i48. HabibiM. WeberL. NevesM. WiegandtD.L. LeserU. 2017 Deep learning with word embeddings improves biomedical named entity recognition Bioinformatics 33 14 i37 i48 10.1093/bioinformatics/btx228587072928881963 Search in Google Scholar

Hasan, K.S., & Ng, V. (2014). Automatic keyphrase extraction: A survey of the state of the art. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 1262–1273. https://doi.org/10.3115/v1/P14-1119 HasanK.S. NgV. 2014 Automatic keyphrase extraction: A survey of the state of the art In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) 1262 1273 https://doi.org/10.3115/v1/P14-1119 10.3115/v1/P14-1119 Search in Google Scholar

Hinton, G.E., Osindero, S., & Teh, Y.W. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18(7), 1527–1554. https://doi.org/10.1162/neco.2006.18.7.1527 HintonG.E. OsinderoS. TehY.W. 2006 A fast learning algorithm for deep belief nets Neural Computation 18 7 1527 1554 https://doi.org/10.1162/neco.2006.18.7.1527 10.1162/neco.2006.18.7.152716764513 Search in Google Scholar

Howard, J., & Ruder, S. (2018). Universal language model fine-tuning for text classification. ArXiv:1801.06146 [Cs, Stat]. http://arxiv.org/abs/1801.06146 HowardJ. RuderS. 2018 Universal language model fine-tuning for text classification ArXiv:1801.06146 [Cs, Stat]. http://arxiv.org/abs/1801.06146 10.18653/v1/P18-1031 Search in Google Scholar

Hulth, A. (2003). Improved automatic keyword extraction given more linguistic knowledge. In Proceedings of the 2003 Conference on Empirical Methods in Natural Language, 216–223. HulthA. 2003 Improved automatic keyword extraction given more linguistic knowledge In Proceedings of the 2003 Conference on Empirical Methods in Natural Language 216 223 10.3115/1119355.1119383 Search in Google Scholar

Hulth, A., Karlgren, J., Jonsson, A., Boström, H., & Asker, L. (2001). Automatic keyword extraction using domain knowledge. International Conference on Intelligent Text Processing and Computational Linguistics, 472–482. HulthA. KarlgrenJ. JonssonA. BoströmH. AskerL. 2001 Automatic keyword extraction using domain knowledge International Conference on Intelligent Text Processing and Computational Linguistics 472 482 10.1007/3-540-44686-9_47 Search in Google Scholar

Hulth, A., & Megyesi, B.B. (2006). A study on automatically extracted keywords in text categorization. In Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics, 537–544. HulthA. MegyesiB.B. 2006 A study on automatically extracted keywords in text categorization In Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics 537 544 10.3115/1220175.1220243 Search in Google Scholar

Jones, S., & Staveley, M.S. (1999). Phrasier: A system for interactive document retrieval using keyphrases. In Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, 160–167. JonesS. StaveleyM.S. 1999 Phrasier: A system for interactive document retrieval using keyphrases In Proceedings of the 22nd Annual International ACM SIGIR Conference on Research and Development in Information Retrieval 160 167 10.1145/312624.312671 Search in Google Scholar

Kelleher, D., & Luz, S. (2005). Automatic hypertext keyphrase detection. IJCAI, 5, Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence, Edinburgh, Scotland, UK, 1608–1609. KelleherD. LuzS. 2005 Automatic hypertext keyphrase detection IJCAI, 5, Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence Edinburgh, Scotland, UK 1608 1609 Search in Google Scholar

Kim, S.N., & Kan, M.Y. (2009). Re-examining automatic keyphrase extraction approaches in scientific articles. In Proceedings of the Workshop on Multiword Expressions: Identification, Interpretation, Disambiguation and Applications, 9–16. KimS.N. KanM.Y. 2009 Re-examining automatic keyphrase extraction approaches in scientific articles In Proceedings of the Workshop on Multiword Expressions: Identification, Interpretation, Disambiguation and Applications 9 16 10.3115/1698239.1698242 Search in Google Scholar

Kim, S.N., Medelyan, O., Kan, M.Y., & Baldwin, T. (2010). Semeval-2010 task 5: Automatic keyphrase extraction from scientific articles. In Proceedings of the 5th International Workshop on Semantic Evaluation, 21–26. KimS.N. MedelyanO. KanM.Y. BaldwinT. 2010 Semeval-2010 task 5: Automatic keyphrase extraction from scientific articles In Proceedings of the 5th International Workshop on Semantic Evaluation 21 26 Search in Google Scholar

Le, T.T.N., Le Nguyen, M., & Shimazu, A. (2016). Unsupervised keyphrase extraction: Introducing new kinds of words to keyphrases. Australasian Joint Conference on Artificial Intelligence, 665–671. LeT.T.N. Le NguyenM. ShimazuA. 2016 Unsupervised keyphrase extraction: Introducing new kinds of words to keyphrases Australasian Joint Conference on Artificial Intelligence 665 671 10.1007/978-3-319-50127-7_58 Search in Google Scholar

Lee, J., Yoon, W., Kim, S., Kim, D., Kim, S., So, C.H., & Kang, J. (2019). BioBERT: A pre-trained biomedical language representation model for biomedical text mining. Bioinformatics, btz682. https://doi.org/10.1093/bioinformatics/btz682 LeeJ. YoonW. KimS. KimD. KimS. SoC.H. KangJ. 2019 BioBERT: A pre-trained biomedical language representation model for biomedical text mining Bioinformatics btz682. https://doi.org/10.1093/bioinformatics/btz682 10.1093/bioinformatics/btz682 Search in Google Scholar

Liu, Z.Y., Huang, W.Y., Zheng, Y.B., & Sun, M.S. (2010). Automatic keyphrase extraction via topic decomposition. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, 366–376. LiuZ.Y. HuangW.Y. ZhengY.B. SunM.S. 2010 Automatic keyphrase extraction via topic decomposition In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing 366 376 Search in Google Scholar

Liu, Z.Y., Li, P., Zheng, Y.B., & Sun, M.S. (2009). Clustering to find exemplar terms for keyphrase extraction. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1-Volume 1, 257–266. LiuZ.Y. LiP. ZhengY.B. SunM.S. 2009 Clustering to find exemplar terms for keyphrase extraction In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1-Volume 1 257 266 10.3115/1699510.1699544 Search in Google Scholar

Luhn, H.P. (1957). A statistical approach to mechanized encoding and searching of literary information. IBM Journal of Research and Development, 1(4), 309–317. LuhnH.P. 1957 A statistical approach to mechanized encoding and searching of literary informatio IBM Journal of Research and Development 1 4 309 317 10.1147/rd.14.0309 Search in Google Scholar

Matsuo, Y., & Ishizuka, M. (2004). Keyword extraction from a single document using word co-occurrence statistical information. International Journal on Artificial Intelligence Tools, 13(01), 157–169. MatsuoY. IshizukaM. 2004 Keyword extraction from a single document using word co-occurrence statistical informatio International Journal on Artificial Intelligence Tools 13 01 157 169 10.1142/S0218213004001466 Search in Google Scholar

Medelyan, O., Frank, E., & Witten, I.H. (2009). Human-competitive tagging using automatic keyphrase extraction. In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3-Volume 3, 1318–1327. MedelyanO. FrankE. WittenI.H. 2009 Human-competitive tagging using automatic keyphrase extraction In Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 3-Volume 3 1318 1327 10.3115/1699648.1699678 Search in Google Scholar

Mihalcea, R., & Tarau, P. (2004). Textrank: Bringing order into text. In Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, 404–411. MihalceaR. TarauP. 2004 Textrank: Bringing order into text In Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing 404 411 Search in Google Scholar

Papagiannopoulou, E., & Tsoumakas, G. (2019). A review of keyphrase extraction. ArXiv:1905. 05044 [Cs]. http://arxiv.org/abs/1905.05044 PapagiannopoulouE. TsoumakasG. 2019 A review of keyphrase extraction ArXiv:1905. 05044 [Cs]. http://arxiv.org/abs/1905.05044 Search in Google Scholar

Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018). Deep Contextualized Word Representations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), 2227–2237. https://doi.org/10.18653/v1/N18-1202 PetersM. NeumannM. IyyerM. GardnerM. ClarkC. LeeK. ZettlemoyerL. 2018 Deep Contextualized Word Representations In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) 2227 2237 https://doi.org/10.18653/v1/N18-1202 10.18653/v1/N18-1202 Search in Google Scholar

Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding with unsupervised learning. Technical Report, OpenAI. RadfordA. NarasimhanK. SalimansT. SutskeverI. 2018 Improving language understanding with unsupervised learning Technical Report, OpenAI. Search in Google Scholar

Sahrawat, D., Mahata, D., Kulkarni, M., Zhang, H., Gosangi, R., Stent, A., Sharma, A., Kumar, Y., Shah, R.R., & Zimmermann, R. (2019). Keyphrase Extraction from Scholarly Articles as Sequence Labeling using Contextualized Embeddings. ArXiv Preprint ArXiv:1910.08840. SahrawatD. MahataD. KulkarniM. ZhangH. GosangiR. StentA. SharmaA. KumarY. ShahR.R. ZimmermannR. 2019 Keyphrase Extraction from Scholarly Articles as Sequence Labeling using Contextualized Embeddings ArXiv Preprint ArXiv:1910.08840. Search in Google Scholar

Salton, G., & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing & Management, 24(5), 513–523. SaltonG. BuckleyC. 1988 Term-weighting approaches in automatic text retrieva Information Processing & Management 24 5 513 523 10.1016/0306-4573(88)90021-0 Search in Google Scholar

Salton, G., Yang, C.S., & Yu, C.T. (1975). A theory of term importance in automatic text analysis. Journal of the American Society for Information Science, 26(1), 33–44. SaltonG. YangC.S. YuC.T. 1975 A theory of term importance in automatic text analysi Journal of the American Society for Information Science 26 1 33 44 10.1002/asi.4630260106 Search in Google Scholar

Tomokiyo, T., & Hurst, M. (2003). A language model approach to keyphrase extraction. In Proceedings of the ACL 2003 Workshop on Multiword Expressions: Analysis, Acquisition and Treatment, 33–40. TomokiyoT. HurstM. 2003 A language model approach to keyphrase extraction In Proceedings of the ACL 2003 Workshop on Multiword Expressions: Analysis, Acquisition and Treatment 33 40 10.3115/1119282.1119287 Search in Google Scholar

Turney, P.D. (2000). Learning algorithms for keyphrase extraction. Information Retrieval, 2(4), 303–336. TurneyP.D. 2000 Learning algorithms for keyphrase extractio Information Retrieval 2 4 303 336 10.1023/A:1009976227802 Search in Google Scholar

Turney, P.D. (2002). Learning to extract keyphrases from text. ArXiv Preprint Cs/0212013. TurneyP.D. 2002 Learning to extract keyphrases from text ArXiv Preprint Cs/0212013. Search in Google Scholar

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 5998–6008. VaswaniA. ShazeerN. ParmarN. UszkoreitJ. JonesL. GomezA.N. KaiserŁ. PolosukhinI. 2017 Attention is all you need Advances in Neural Information Processing Systems 5998 6008 Search in Google Scholar

Wan, X., & Xiao, J. (2008). Single document keyphrase extraction using neighborhood knowledge. AAAI, 8, 855–860. WanX. XiaoJ. 2008 Single document keyphrase extraction using neighborhood knowledge AAAI 8 855 860 Search in Google Scholar

Wang, M., Zhao, B., & Huang, Y. (2016). Ptr: Phrase-based topical ranking for automatic key-phrase extraction in scientific publications. International Conference on Neural Information Processing, 120–128. WangM. ZhaoB. HuangY. 2016 Ptr: Phrase-based topical ranking for automatic key-phrase extraction in scientific publications International Conference on Neural Information Processing 120 128 10.1007/978-3-319-46681-1_15 Search in Google Scholar

Wang, X., Zhang, Y., Ren, X., Zhang, Y.H., Zitnik, M., Shang, J.B., Langlotz, C., & Han, J.W. (2019). Cross-type biomedical named entity recognition with deep multi-task learning. Bioinformatics, 35(10), 1745–1752. WangX. ZhangY. RenX. ZhangY.H. ZitnikM. ShangJ.B. LanglotzC. HanJ.W. 2019 Cross-type biomedical named entity recognition with deep multi-task learnin Bioinformatics 35 10 1745 1752 10.1093/bioinformatics/bty86930307536 Search in Google Scholar

Witten, I.H., Paynter, G.W., Frank, E., Gutwin, C., & Nevill-Manning, C.G. (2005). Kea: Practical automated keyphrase extraction. In Design and Usability of Digital Libraries: Case Studies in the Asia Pacific (pp. 129–152). IGI global. WittenI.H. PaynterG.W. FrankE. GutwinC. Nevill-ManningC.G. 2005 Kea: Practical automated keyphrase extraction In Design and Usability of Digital Libraries: Case Studies in the Asia Pacific 129 152 IGI global 10.4018/978-1-59140-441-5.ch008 Search in Google Scholar

Wu, Y., Schuster, M., Chen, Z., Le, Q.V., Norouzi, M., Macherey, W., Krikun, M., Cao, Y., Gao, Q., & Macherey, K. (2016). Google’s neural machine translation system: Bridging the gap between human and machine translation. ArXiv Preprint ArXiv:1609.08144. WuY. SchusterM. ChenZ. LeQ.V. NorouziM. MachereyW. KrikunM. CaoY. GaoQ. MachereyK. 2016 Google’s neural machine translation system: Bridging the gap between human and machine translation ArXiv Preprint ArXiv:1609.08144. Search in Google Scholar

Zhang C.Z., Wang H.L., Liu Y., Wu D., Liao Y., & Wang B. (2008). Automatic keyword extraction from documents using conditional random fields. Journal of Computational Information Systems, 4(3), 1169–1180. ZhangC.Z. WangH.L. LiuY. WuD. LiaoY. WangB. 2008 Automatic keyword extraction from documents using conditional random field Journal of Computational Information Systems 4 3 1169 1180 Search in Google Scholar

Zhang, Q., Wang, Y., Gong, Y., & Huang, X.J. (2016). Keyphrase extraction using deep recurrent neural networks on twitter. In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, 836–845. ZhangQ. WangY. GongY. HuangX.J. 2016 Keyphrase extraction using deep recurrent neural networks on twitter In Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing 836 845 10.18653/v1/D16-1080 Search in Google Scholar

Zhang, Y., Zincir-Heywood, N., & Milios, E. (2004). World wide web site summarization. Web Intelligence and Agent Systems: An International Journal, 2(1), 39–53. ZhangY. Zincir-HeywoodN. MiliosE. 2004 World wide web site summarizatio Web Intelligence and Agent Systems: An International Journal 2 1 39 53 Search in Google Scholar

Zhao, W.X., Jiang, J., He, J., Song, Y., Achananuparp, P., Lim, E.P., & Li, X. (2011). Topical keyphrase extraction from twitter. In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies-Volume 1, 379–388. ZhaoW.X. JiangJ. HeJ. SongY. AchananuparpP. LimE.P. LiX. 2011 Topical keyphrase extraction from twitter In Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies 1 379 388 Search in Google Scholar

Li, L.S., Dang, Y.Z., Zhang, J., & Li, D. (2013). Term extraction in the automotive field based on conditional random fields. Journal of Dalian University of Technology, 53(2), 267–272. LiL.S. DangY.Z. ZhangJ. LiD. 2013 Term extraction in the automotive field based on conditional random field Journal of Dalian University of Technology 53 2 267 272 Search in Google Scholar

Li, S.J., Wang, H.F., Yu, S.W., & Xin, C.S. (2004). Application research of maximum entropy model for keyword automatic indexing. Chinese Journal of Computers, 27(9), 1192–1197. LiS.J. WangH.F. YuS.W. XinC.S. 2004 Application research of maximum entropy model for keyword automatic indexin Chinese Journal of Computers 27 9 1192 1197 Search in Google Scholar

eISSN:
2543-683X
Langue:
Anglais