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2018 | OriginalPaper | Buchkapitel

Scientific Keyphrase Extraction: Extracting Candidates with Semi-supervised Data Augmentation

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Abstract

Keyphrase extraction can provide effective ways of organizing scientific documents. For this task, neural-based methods usually suffer from performance unstability due to data scarcity. In this paper, we adopt the pipeline two-step method including candidate extraction and keyphrase ranking, where candidate extraction is a key to influence the whole performance. In the candidate extraction step, to overcome the low-recall problem of traditional rule-based method, we propose a novel semi-supervised data augmentation method, where a neural-based tagging model and a discriminative classifier boost each other and get more confident phrases as candidates. With more reasonable candidates, keyphrase are identified with recall promoted. Experiments on SemEval 2017 Task 10 show that our model can achieve competitive results.

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Literatur
1.
Zurück zum Zitat Ammar, W., Peters, M.E., Bhagavatula, C., Power, R.: The AI2 system at SemEval-2017 Task 10 (ScienceIE): semi-supervised end-to-end entity and relation extraction. In: Proceedings of the 11th International Workshop on Semantic Evaluation, SemEval@ACL 2017, 3–4 August 2017, Vancouver, Canada, pp. 592–596 (2017) Ammar, W., Peters, M.E., Bhagavatula, C., Power, R.: The AI2 system at SemEval-2017 Task 10 (ScienceIE): semi-supervised end-to-end entity and relation extraction. In: Proceedings of the 11th International Workshop on Semantic Evaluation, SemEval@ACL 2017, 3–4 August 2017, Vancouver, Canada, pp. 592–596 (2017)
2.
Zurück zum Zitat Batista, G.E., Bazzan, A.L., Monard, M.C.: Balancing training data for automated annotation of keywords: a case study. In: WOB, pp. 10–18 (2003) Batista, G.E., Bazzan, A.L., Monard, M.C.: Balancing training data for automated annotation of keywords: a case study. In: WOB, pp. 10–18 (2003)
3.
Zurück zum Zitat Bird, S., Loper, E.: NLTK: the natural language toolkit. In: Proceedings of the ACL 2004 on Interactive Poster and Demonstration Sessions, p. 31. Association for Computational Linguistics (2004) Bird, S., Loper, E.: NLTK: the natural language toolkit. In: Proceedings of the ACL 2004 on Interactive Poster and Demonstration Sessions, p. 31. Association for Computational Linguistics (2004)
4.
Zurück zum Zitat Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)CrossRef Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: Smote: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)CrossRef
5.
Zurück zum Zitat Chiu, J.P., Nichols, E.: Named entity recognition with bidirectional LSTM-CNNs. Trans. Assoc. Comput. Linguist. 4, 357–370 (2016) Chiu, J.P., Nichols, E.: Named entity recognition with bidirectional LSTM-CNNs. Trans. Assoc. Comput. Linguist. 4, 357–370 (2016)
6.
Zurück zum Zitat Danesh, S., Sumner, T., Martin, J.H.: Sgrank: combining statistical and graphical methods to improve the state of the art in unsupervised keyphrase extraction. In: Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics, *SEM 2015, 4–5 June 2015, Denver, Colorado, USA, pp. 117–126 (2015) Danesh, S., Sumner, T., Martin, J.H.: Sgrank: combining statistical and graphical methods to improve the state of the art in unsupervised keyphrase extraction. In: Proceedings of the Fourth Joint Conference on Lexical and Computational Semantics, *SEM 2015, 4–5 June 2015, Denver, Colorado, USA, pp. 117–126 (2015)
7.
Zurück zum Zitat Dong, L., Mallinson, J., Reddy, S., Lapata, M.: Learning to paraphrase for question answering. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, 9–11 September 2017, Copenhagen, Denmark, pp. 875–886 (2017) Dong, L., Mallinson, J., Reddy, S., Lapata, M.: Learning to paraphrase for question answering. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, 9–11 September 2017, Copenhagen, Denmark, pp. 875–886 (2017)
8.
Zurück zum Zitat Hasan, K.S., Ng, V.: Automatic keyphrase extraction: a survey of the state of the art. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (vol. 1: Long Papers), pp. 1262–1273 (2014) Hasan, K.S., Ng, V.: Automatic keyphrase extraction: a survey of the state of the art. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (vol. 1: Long Papers), pp. 1262–1273 (2014)
9.
Zurück zum Zitat Hosseini, H., Kannan, S., Zhang, B., Poovendran, R.: Deceiving Google’s perspective API built for detecting toxic comments. arXiv preprint arXiv:1702.08138 (2017) Hosseini, H., Kannan, S., Zhang, B., Poovendran, R.: Deceiving Google’s perspective API built for detecting toxic comments. arXiv preprint arXiv:​1702.​08138 (2017)
10.
11.
Zurück zum Zitat Jia, R., Liang, P.: Adversarial examples for evaluating reading comprehension systems. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, 9–11 September 2017, Copenhagen, Denmark, pp. 2021–2031 (2017) Jia, R., Liang, P.: Adversarial examples for evaluating reading comprehension systems. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, 9–11 September 2017, Copenhagen, Denmark, pp. 2021–2031 (2017)
12.
Zurück zum Zitat Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR abs/1412.6980 (2014) Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR abs/1412.6980 (2014)
13.
Zurück zum Zitat Liu, Z., Huang, W., Zheng, Y., Sun, M.: Automatic keyphrase extraction via topic decomposition. In: Conference on Empirical Methods in Natural Language Processing, pp. 366–376 (2010) Liu, Z., Huang, W., Zheng, Y., Sun, M.: Automatic keyphrase extraction via topic decomposition. In: Conference on Empirical Methods in Natural Language Processing, pp. 366–376 (2010)
14.
Zurück zum Zitat Liu, Z., Li, P., Zheng, Y., Sun, M.: Clustering to find exemplar terms for keyphrase extraction. In: Conference on Empirical Methods in Natural Language Processing, pp. 257–266 (2009) Liu, Z., Li, P., Zheng, Y., Sun, M.: Clustering to find exemplar terms for keyphrase extraction. In: Conference on Empirical Methods in Natural Language Processing, pp. 257–266 (2009)
15.
Zurück zum Zitat Lopez, P., Romary, L.: HUMB: automatic key term extraction from scientific articles in grobid. In: Proceedings of the 5th International Workshop on Semantic Evaluation, SemEval 2010, pp. 248–251. Association for Computational Linguistics, Stroudsburg (2010) Lopez, P., Romary, L.: HUMB: automatic key term extraction from scientific articles in grobid. In: Proceedings of the 5th International Workshop on Semantic Evaluation, SemEval 2010, pp. 248–251. Association for Computational Linguistics, Stroudsburg (2010)
16.
Zurück zum Zitat Luan, Y., Ostendorf, M., Hajishirzi, H.: Scientific information extraction with semi-supervised neural tagging. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, 9–11 September 2017, Copenhagen, Denmark, pp. 2641–2651 (2017) Luan, Y., Ostendorf, M., Hajishirzi, H.: Scientific information extraction with semi-supervised neural tagging. In: Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, 9–11 September 2017, Copenhagen, Denmark, pp. 2641–2651 (2017)
17.
Zurück zum Zitat Ma, X., Hovy, E.H.: End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016, 7–12 August 2016, Berlin, Germany, vol. 1: Long Papers (2016) Ma, X., Hovy, E.H.: End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF. In: Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics, ACL 2016, 7–12 August 2016, Berlin, Germany, vol. 1: Long Papers (2016)
18.
Zurück zum Zitat Mihalcea, R., Tarau, P.: TextRank: bringing order into text. In: Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, EMNLP 2004, A meeting of SIGDAT, a Special Interest Group of the ACL, held in conjunction with ACL 2004, 25–26 July 2004, Barcelona, Spain, pp. 404–411 (2004) Mihalcea, R., Tarau, P.: TextRank: bringing order into text. In: Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing, EMNLP 2004, A meeting of SIGDAT, a Special Interest Group of the ACL, held in conjunction with ACL 2004, 25–26 July 2004, Barcelona, Spain, pp. 404–411 (2004)
19.
Zurück zum Zitat Paszke, A., et al.: Automatic differentiation in PyTorch. In: NIPS-W (2017) Paszke, A., et al.: Automatic differentiation in PyTorch. In: NIPS-W (2017)
20.
Zurück zum Zitat Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12(Oct), 2825–2830 (2011)MathSciNetMATH Pedregosa, F., et al.: Scikit-learn: machine learning in python. J. Mach. Learn. Res. 12(Oct), 2825–2830 (2011)MathSciNetMATH
21.
Zurück zum Zitat Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, 25–29 October 2014, Doha, Qatar, A meeting of SIGDAT, a Special Interest Group of the ACL, pp. 1532–1543 (2014) Pennington, J., Socher, R., Manning, C.D.: Glove: global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, EMNLP 2014, 25–29 October 2014, Doha, Qatar, A meeting of SIGDAT, a Special Interest Group of the ACL, pp. 1532–1543 (2014)
23.
Zurück zum Zitat Schwenker, F.: Ensemble methods: foundations and algorithms [book review]. IEEE Comput. Int. Mag. 8(1), 77–79 (2013)MathSciNetCrossRef Schwenker, F.: Ensemble methods: foundations and algorithms [book review]. IEEE Comput. Int. Mag. 8(1), 77–79 (2013)MathSciNetCrossRef
24.
Zurück zum Zitat Tomek, I.: An experiment with the edited nearest-neighbor rule. IEEE Trans. Syst. Man Cybern. SMC–6(6), 448–452 (1976)MathSciNetMATH Tomek, I.: An experiment with the edited nearest-neighbor rule. IEEE Trans. Syst. Man Cybern. SMC–6(6), 448–452 (1976)MathSciNetMATH
25.
Zurück zum Zitat Tomek, I.: Two modifications of CNN. IEEE Trans. Syst. Man Cybern. SMC–6(11), 769–772 (1976)MathSciNetMATH Tomek, I.: Two modifications of CNN. IEEE Trans. Syst. Man Cybern. SMC–6(11), 769–772 (1976)MathSciNetMATH
26.
Zurück zum Zitat Wang, C., Li, S.: CoRankBayes: Bayesian learning to rank under the co-training framework and its application in keyphrase extraction. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, CIKM 2011, pp. 2241–2244. ACM, New York (2011) Wang, C., Li, S.: CoRankBayes: Bayesian learning to rank under the co-training framework and its application in keyphrase extraction. In: Proceedings of the 20th ACM International Conference on Information and Knowledge Management, CIKM 2011, pp. 2241–2244. ACM, New York (2011)
28.
Zurück zum Zitat Wang, L., Li, S.: PKU\_ICL at SemEval-2017 Task 10: Keyphrase extraction with model ensemble and external knowledge. In: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pp. 934–937 (2017) Wang, L., Li, S.: PKU\_ICL at SemEval-2017 Task 10: Keyphrase extraction with model ensemble and external knowledge. In: Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pp. 934–937 (2017)
29.
Zurück zum Zitat Yasunaga, M., Kasai, J., Radev, D.: Robust multilingual part-of-speech tagging via adversarial training. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1 (Long Papers), pp. 976–986. Association for Computational Linguistics (2018) Yasunaga, M., Kasai, J., Radev, D.: Robust multilingual part-of-speech tagging via adversarial training. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1 (Long Papers), pp. 976–986. Association for Computational Linguistics (2018)
Metadaten
Titel
Scientific Keyphrase Extraction: Extracting Candidates with Semi-supervised Data Augmentation
verfasst von
Qianying Liu
Daisuke Kawahara
Sujian Li
Copyright-Jahr
2018
DOI
https://doi.org/10.1007/978-3-030-01716-3_16