期刊名称：International Journal of Computer Science and Network Solutions
印刷版ISSN：2345-3397
出版年度：2015
卷号：3
期号：11
页码：1-14
出版社：International Journal of Computer Science and Network Solutions
摘要：Natural language processing is a highly important subcategory in the wide area of artificial intelligence.Employing appropriate computational algorithms on sophisticated linguistic operations is the aim of naturallanguage processing to extract and create computational theories from languages. In order to achieve thisgoal, the knowledge of linguists is needed in addition to computer science. In the field of linguistics, thesyntactic and semantic relation of words and phrases and the extraction of causation is very significantwhich the latter is an information retrieval challenge.Recently, there is an increased attention towards the automatic extraction of causation from textual datasets. Although, previous research extracted the casual relations from uninterrupted data sets by usingknowledge-based inference technologies and manual coding. Recently, finding comprehensive approachesfor detection and extractions of causal arguments is a research area in the field of natural languageprocessing.In this paper, a three-stepped approach is established through which, the position of words with syntax treesis obtained by extracting causation from causal and non-causal sentences of Web text. The arguments ofevents were extracted according to the dependency tree of phrases implemented by Python packages. Thenpotential causal relations were extracted by the extraction of specific nodes of the tree. In the final step, astatistical model is introduced for measuring the potential causal relations. Experimental results andevaluations with Recall, Precision and F-measure metrics show the accuracy and efficiency of thesuggested model.
关键词：Causal relationship extracting; Causal extraction modeling; Natural language processing;Text mining.
