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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Acknowledgments
The research work has been partially funded by the Natural Science Foundation of China under Grant No. 61333018 and No. 61402478.
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Li, H., Zhang, J., Zong, C. (2015). Predicting Implicit Discourse Relations with Purely Distributed Representations. In: Sun, M., Liu, Z., Zhang, M., Liu, Y. (eds) Chinese Computational Linguistics and Natural Language Processing Based on Naturally Annotated Big Data. CCL NLP-NABD 2015 2015. Lecture Notes in Computer Science(), vol 9427. Springer, Cham. https://doi.org/10.1007/978-3-319-25816-4_24
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DOI: https://doi.org/10.1007/978-3-319-25816-4_24
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