Joint Learning for Coreference Resolution with Markov Logic
Pairwise coreference resolution models must merge pairwise coreference decisions to generate final outputs. Traditional merging methods adopt different strategies such as the best first method and enforcing the transitivity constraint, but most of these methods are used independently of the pairwise...
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| Main Authors: | , , , , |
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| Format: | text |
| Language: | English |
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Institutional Knowledge at Singapore Management University
2012
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| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/sis_research/1620 https://ink.library.smu.edu.sg/context/sis_research/article/2619/viewcontent/jingjiang.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | Pairwise coreference resolution models must merge pairwise coreference decisions to generate final outputs. Traditional merging methods adopt different strategies such as the best first method and enforcing the transitivity constraint, but most of these methods are used independently of the pairwise learning methods as an isolated inference procedure at the end. We propose a joint learning model which combines pairwise classification and mention clustering with Markov logic. Experimental results show that our joint learning system outperforms independent learning systems. Our system gives a better performance than all the learning-based systems from the CoNLL-2011 shared task on the same dataset. Compared with the best system from CoNLL- 2011, which employs a rule-based method, our system shows competitive performance. |
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