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Identifying intention posts in discussion forums using multi-instance learning and multiple sources transfer learning

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Abstract

This paper proposes a novel method for identifying intention posts in discussion forums. The main problem of identifying intention posts in discussion forums is that there exist a few intention sentences even in a post expressing an intention. That is, an intention post consists of a few intention sentences and a number of non-intention sentences, while non-intention posts have only non-intention sentences. Therefore, multi-instance learning which regards a post as a bag and the sentences in the post as instances of the bag is adopted as a solution to this problem. One distinct characteristic of the posts is that the ways of expressing an intention are similar across domains. Thus, we incorporate a multiple sources transfer learning into the multi-instance learning. As a result, the multi-instance learning is enhanced by leveraging knowledge of expressing intentions from multiple source domains. Through a set of experiments, it is proven that the proposed method is effective at identifying intention posts in discussion forums.

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Notes

  1. According to Chen et al. (2013), they collected 1000 posts for each domain. However, one post in Electronics has no content.

  2. http://nlp.stanford.edu/software/corenlp.shtml.

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Acknowledgements

This research was supported by Basic Science Research Program through the National Research Foundation (NRF) of Korea funded by the Ministry of Education (No. 2016R1D1A1B04935678).

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Correspondence to Seong-Bae Park.

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Communicated by V. Loia.

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Song, HJ., Park, SB. Identifying intention posts in discussion forums using multi-instance learning and multiple sources transfer learning. Soft Comput 22, 8107–8118 (2018). https://doi.org/10.1007/s00500-017-2755-8

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