Please use this identifier to cite or link to this item:
|Title:||Predicting discourse connectives for implicit discourse relation recognition|
|Source:||Zhou, Z.-M.,Xu, Y.,Niu, Z.-Y.,Lan, M.,Su, J.,Tan, C.L. (2010). Predicting discourse connectives for implicit discourse relation recognition. Coling 2010 - 23rd International Conference on Computational Linguistics, Proceedings of the Conference 2 : 1507-1514. ScholarBank@NUS Repository.|
|Abstract:||Existing works indicate that the absence of explicit discourse connectives makes it difficult to recognize implicit discourse relations. In this paper we attempt to overcome this difficulty for implicit relation recognition by automatically inserting discourse connectives between arguments with the use of a language model. Then we propose two algorithms to leverage the information of these predicted connectives. One is to use these predicted implicit connectives as additional features in a supervised model. The other is to perform implicit relation recognition based only on these predicted connectives. Results on Penn Discourse Treebank 2.0 show that predicted discourse connectives help implicit relation recognition and the first algorithm can achieve an absolute average f-score improvement of 3% over a state of the art baseline system.|
|Source Title:||Coling 2010 - 23rd International Conference on Computational Linguistics, Proceedings of the Conference|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 9, 2017
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.