Jointly Extracting Japanese Predicate-Argument Relation with Markov Logic
Jointly Extracting Japanese Predicate-Argument Relation with Markov Logic
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发表时间：
2011-11
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通讯作者：
Katsumasa Yoshikawa;Masayuki Asahara;Yuji Matsumoto
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作者：
Katsumasa Yoshikawa;Masayuki Asahara;Yuji Matsumoto
本文描述了一种用于日语谓词-论证 (PA) 关系提取的新马尔可夫逻辑方法。之前的大多数工作都构建了与每个案例角色相对应的单独分类器，并独立识别 PA 关系，忽略了两个或多个 PA 关系之间的依赖关系（约束）。我们提出了一种通过优化句子中所有参数候选来共同提取 PA 关系的方法。我们的方法可以联合考虑多个 PA 关系之间的依赖关系，并找到句子中谓词及其参数的最可能组合。此外，我们的模型还涉及新的约束，以避免考虑不合适的参数候选者并有效地识别正确的 PA 关系。与最先进的方法相比，我们的方法无需大规模数据即可实现有竞争力的结果。
This paper describes a new Markov Logic approach for Japanese Predicate-Argument (PA) relation extraction. Most previous work built separated classifiers corresponding to each case role and independently identified the PA relations, neglecting dependencies (constraints) between two or more PA relations. We propose a method which collectively extracts PA relations by optimizing all argument candidates in a sentence. Our method can jointly consider dependency between multiple PA relations and find the most probable combination of predicates and their arguments in a sentence. In addition, our model involves new constraints to avoid considering inappropriate candidates for arguments and identify correct PA relations effectively. Compared to the state-of-the-art, our method achieves competitive results without largescale data.