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        <title>Context-Sensitive Inference Rule Discovery: A Graph-Based Method</title>
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            <title>Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers</title>
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        <name type="personal">
            <namePart type="given">Yuji</namePart>
            <namePart type="family">Matsumoto</namePart>
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    <abstract>Inference rule discovery aims to identify entailment relations between predicates, e.g., ‘X acquire Y –\textgreater X purchase Y’ and ‘X is author of Y –\textgreater X write Y’. Traditional methods dis-cover inference rules by computing distributional similarities between predicates, with each predicate is represented as one or more feature vectors of its instantiations. These methods, however, have two main drawbacks. Firstly, these methods are mostly context-insensitive, cannot accurately measure the similarity between two predicates in a specific context. Secondly, traditional methods usually model predicates independently, ignore the rich inter-dependencies between predicates. To address the above two issues, this pa-per proposes a graph-based method, which can discover inference rules by effectively modelling and exploiting both the context and the inter-dependencies between predicates. Specifically, we propose a graph-based representation—Predicate Graph, which can capture the semantic relevance between predicates using both the predicate-feature co-occurrence statistics and the inter-dependencies between predicates. Based on the predicate graph, we propose a context-sensitive random walk algorithm, which can learn con-text-specific predicate representations by distinguishing context-relevant information from context-irrelevant information. Experimental results show that our method significantly outperforms traditional inference rule discovery methods.</abstract>
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%0 Conference Proceedings
%T Context-Sensitive Inference Rule Discovery: A Graph-Based Method
%A Han, Xianpei
%A Sun, Le
%Y Matsumoto, Yuji
%Y Prasad, Rashmi
%S Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
%D 2016
%8 December
%I The COLING 2016 Organizing Committee
%C Osaka, Japan
%F han-sun-2016-context
%X Inference rule discovery aims to identify entailment relations between predicates, e.g., ‘X acquire Y –\textgreater X purchase Y’ and ‘X is author of Y –\textgreater X write Y’. Traditional methods dis-cover inference rules by computing distributional similarities between predicates, with each predicate is represented as one or more feature vectors of its instantiations. These methods, however, have two main drawbacks. Firstly, these methods are mostly context-insensitive, cannot accurately measure the similarity between two predicates in a specific context. Secondly, traditional methods usually model predicates independently, ignore the rich inter-dependencies between predicates. To address the above two issues, this pa-per proposes a graph-based method, which can discover inference rules by effectively modelling and exploiting both the context and the inter-dependencies between predicates. Specifically, we propose a graph-based representation—Predicate Graph, which can capture the semantic relevance between predicates using both the predicate-feature co-occurrence statistics and the inter-dependencies between predicates. Based on the predicate graph, we propose a context-sensitive random walk algorithm, which can learn con-text-specific predicate representations by distinguishing context-relevant information from context-irrelevant information. Experimental results show that our method significantly outperforms traditional inference rule discovery methods.
%U https://aclanthology.org/C16-1273
%P 2902-2911

Markdown (Informal)

[Context-Sensitive Inference Rule Discovery: A Graph-Based Method](https://aclanthology.org/C16-1273) (Han & Sun, COLING 2016)

• Context-Sensitive Inference Rule Discovery: A Graph-Based Method (Han & Sun, COLING 2016)

ACL

• Xianpei Han and Le Sun. 2016. Context-Sensitive Inference Rule Discovery: A Graph-Based Method. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers, pages 2902–2911, Osaka, Japan. The COLING 2016 Organizing Committee.