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2015 | OriginalPaper | Buchkapitel

Predicting Implicit Discourse Relations with Purely Distributed Representations

verfasst von : Haoran Li, Jiajun Zhang, Chengqing Zong

Erschienen in: Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data

Verlag: Springer International Publishing

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Abstract

Discourse relations between two consecutive segments play an important role in many natural language processing (NLP) tasks. However, a large portion of the discourse relations are implicit and difficult to detect due to the absence of connectives. Traditional detection approaches utilize discrete features, such as words, clusters and syntactic production rules, which not only depend strongly on the linguistic resources, but also lead to severe data sparseness. In this paper, we instead propose a novel method to predict the implicit discourse relations based on the purely distributed representations of words, sentences and syntactic features. Furthermore, we learn distributed representations for different kinds of features. The experiments show that our proposed method can achieve the best performance in most cases on the standard data sets.

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Vorheriges Kapitel Exploring Recurrent Neural Networks to Detect Named Entities from Biomedical Text

Nächstes Kapitel Answer Quality Assessment in CQA Based on Similar Support Sets

Pitler, E., Raghupathy, M., Mehta, H., Nenkova, A., Lee, A., Joshi, A.K.: Easily identifiable discourse relations. In: COLING (2008)

Pitler, E., Louis, A., Nenkova, A.: Automatic sense prediction for implicit discourse relations in text. 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, vol. 2, pp. 683–691. Association for Computational Linguistics (2009)

Lin, Z., Kan, M.-Y., Ng, H.T.: Recognizing implicit discourse relations in the penn discourse treebank. In: Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing, vol. 1, pp. 343–351. Association for Computational Linguistics (2009)

Zhou, Z.-M., Xu, Y., Niu, Z.-Y., Lan, M., Su, J., Tan, C.L.: Predicting discourse connectives for implicit discourse relation recognition. In: Proceedings of the 23rd International Conference on Computational Linguistics: Posters. Association for Computational Linguistics, pp. 1507–1514 (2010)

Louis, A., Joshi, A., Prasad, R., Nenkova, A.: Using entity features to classify implicit discourse relations. In: Proceedings of the 11th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pp. 59–62. Association for Computational Linguistics (2010)

Biran, O., McKeown, K.: Aggregated word pair features for implicit discourse relation disambiguation. In: Proceedings of the Conference ACL, p. 69 (2013)

Park, J., Cardie, C.: Improving implicit discourse relation recognition through feature set optimization. In: Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue, pp. 108–112. Association for Computational Linguistics (2012)

Li, J.J., Nenkova, A.: Reducing sparsity improves the recognition of implicit discourse relations. In: 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue, p. 199 (2014)

Rutherford, A.T., Xue, N.: Discovering implicit discourse relations through brown cluster pair representation and coreference patterns. EACL 2014, 645 (2014)

10.

Marcu, D., Echihabi, A.: An unsupervised approach to recognizing discourse relations. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, pp. 368–375. Association for Computational Linguistics (2002)

11.

Saito, M., Yamamoto, K., Sekine, S.: Using phrasal patterns to identify discourse relations. In: Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers, pp. 133–136. Association for Computational Linguistics (2006)

12.

Wang, W., Su, J., Tan, C.L.: Kernel based discourse relation recognition with temporal ordering information. In: Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pp. 710–719. Association for Computational Linguistics (2010)

13.

Xu, Y., Lan, M., Lu, Y., Niu, Z.Y., Tan, C.L.: Connective prediction using machine learning for implicit discourse relation classification. In: The 2012 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2012)

14.

Rutherford, A., Xue, N.: Improving the inference of implicit discourse relations via classifying explicit discourse connectives. In: Proceedings of the 2015 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Denver, Colorado: Association for Computational Linguistics, pp. 799–808, May-June 2015. http://www.aclweb.org/anthology/N15-1081

15.

Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, pp. 3111–3119 (2013)

16.

Kim, Y.: Convolutional neural networks for sentence classification (2014). arXiv preprint arXiv:1408.5882

17.

Le, Q.V., Mikolov, T.: Distributed representations of sentences and documents (2014) arXiv preprint. arXiv:1405.4053

18.

Kalchbrenner, N., Grefenstette, E., Blunsom, P.: A convolutional neural network for modelling sentences (2014) arXiv preprint. arXiv:1404.2188

19.

Li, J.J., Nenkova, A.: Addressing class imbalance for improved recognition of implicit discourse relations. In: 15th Annual Meeting of the Special Interest Group on Discourse and Dialogue, p. 142 (2014)

20.

Levy, O., Goldberg, Y., Dagan, I.: Improving distributional similarity with lessons learned from word embeddings. Trans. Assoc. Comput. Linguist. 3, 211–225 (2015)CrossRef

Titel: Predicting Implicit Discourse Relations with Purely Distributed Representations
verfasst von: Haoran Li
Jiajun Zhang
Chengqing Zong
Verlag: Springer International Publishing
Buch: Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data
Print ISBN: 978-3-319-25815-7

Electronic ISBN: 978-3-319-25816-4

Copyright-Jahr: 2015
DOI: https://doi.org/10.1007/978-3-319-25816-4_24

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