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An Open Information Extraction For Question Answering System

机译:问题接听系统的开放信息提取

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An Open Information Extraction (OIE) is a process of extracting meaningful information in a structured format from a set of unstructured or semi-structured data. OIE is used for applications like question answering, bio-text mining, text comprehension, ontology learning and sentence similarity. The main focus of OIE is to extract maximum possible set of verb-based triples. However, it ignores extracting adverbial and adjective clauses that are necessary for efficient semantic applications like question answering and paraphrase identification. Our method deals with the use of OIE techniques for extracting clauses which includes the adverbial and the adjective clauses. We have evaluated our OIE approach to extract answers for the given questions. The answers for the questions are determined based on pattern matching. We have used New York Times data set to evaluated the performance of our question answering system using OIE approach. We have obtained precision, recall and F1-measure as 39.47%, 75% and 51.7% respectively.
机译:开放信息提取(OIE)是从一组非结构化或半结构数据中以结构化格式提取有意义信息的过程。 OIE用于问题应答,生物文本挖掘,文本理解,本体学习和句子相似性等应用程序。 OIE的主要焦点是提取最大可能的动词基于动词。然而,它忽略提取有效语义应用所必需的状语和形容词条款,如问题应答和解释识别。我们的方法涉及使用OIE技术来提取包括状语和形容词条款的子句。我们已经评估了我们的OIE方法来提取给定问题的答案。问题的答案是基于模式匹配确定的。我们使用了纽约时报数据集,以评估使用OIE方法的问题应答系统的性能。我们已经获得了精度,召回和F1 - 措施分别为39.47 %,75 %和51.7%。

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