Building resources and a model for computing paraphrase based on lexical semantics

构建基于词汇语义的释义计算资源和模型

基本信息

批准号：
17300047
负责人：
INUI Kentaro
金额：
$ 10.15万
依托单位：
Nara Institute of Science and Technology
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
2005
资助国家：
日本
起止时间：
2005 至 2007
项目状态：
已结题

项目摘要

Aiming at building a computational model and computational recourses for computing paraphrase at the level of predicate-argument structure, this research project gained the following results:(i) For paraphrase knowledge, a large-scale hierarchical lexicon of predicate-argument structure was built. The lexicon organizes about 4,000 Japanese basic verbs (about 7,000 senses in total) with predicate-argument structure information in a fine-grained semantic hierarchy so that lexical entries in a semantic class can be regarded as near synonyms. For augmenting this knowledge base, additional knowledge about event relations are extracted from glosses found in a human-use dictionary of Japanese. Over 35,000 relations are extracted and classified into 8 relation types, all of which are considered useful for recognizing paraphrase or textual entailment.(ii) For scaling the basic paraphrase knowledge above, automatic acquisition of semantic relations between events from a large corpus was also exp … More lored. We proposed several extensions to a state-of-the-art method originally designed for entity relation extraction, reporting on the present results of our experiments on a Japanese Web corpus. The results show that (a) there are indeed specific cooccurrence patterns useful for event relation acquisition, (b) the use of cooccurrence samples involving verbal nouns has positive impacts on both re-call and precision, and (c) over five thousand relation instances are acquired from a 500M-sentence Web corpus with a precision of about 66% for action-effect relations.(iii) For building a computational model of paraphrase, we explore the regularity underlying these classes of paraphrases, focusing on the paraphrasing of Japanese light-verb constructions (LVCs). We propose a paraphrasing model for LVCs that is based on transforming the Lexical Conceptual Structures (LCSs) of verbal elements. We also propose a refinement of an existing LCS dictionary. Experimental results show that our LCS-based paraphrasing model characterizes some of the semantic features of those verbs required for generating paraphrases, such as the direction of an action and the relationship between arguments and surface cases. Less

旨在建立一个计算模型和计算重新解释在谓词 - 题材结构拱门项目的水平上获得了以下结果：（i）词汇知识的词汇量。可以将班级视为增强这一知识基础的近乎同义词。。特定的协同性模式对事件的审判很有帮助，（b）使用辅助样品样品言语名词对重新呼叫和精确的措辞都有积极的影响，并且（（c）超过五千个关系的实例是从500m ensence sentence web获取的对于释义的计算模型，我们探索了释义类别的规律性，探索了释义的措施，该措辞的精度约为66％。对于基于毒性的LVC。产生释义。