Title Of Paper:
Extraction of Textual Causal Relationships based on Natural Language Processing
Author's Name :  Sepideh Jamshidi-Nejad, Fatemeh Ahmadi- Abkenari, Reza Ebrahimi-Atani
KeyWords:  Causal relationship extracting, Causal extraction modeling, Natural language processing, Text mining.
Pages:  1 -14
Volume: 3
Issue: 11
Year: 2015

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 natural language processing to extract and create computational theories from languages. In order to achieve this goal, the knowledge of linguists is needed in addition to computer science. In the field of linguistics, the syntactic and semantic relation of words and phrases and the extraction of causation is very significant which the latter is an information retrieval challenge. Recently, there is an increased attention towards the automatic extraction of causation from textual data sets. Although, previous research extracted the casual relations from uninterrupted data sets by using knowledge-based inference technologies and manual coding. Recently, finding comprehensive approaches for detection and extractions of causal arguments is a research area in the field of natural language processing.In this paper, a three-stepped approach is established through which, the position of words with syntax trees is obtained by extracting causation from causal and non-causal sentences of Web text. The arguments of events were extracted according to the dependency tree of phrases implemented by Python packages. Then potential causal relations were extracted by the extraction of specific nodes of the tree. In the final step, a statistical model is introduced for measuring the potential causal relations. Experimental results and evaluations with Recall, Precision and F-measure metrics show the accuracy and efficiency of the suggested model.




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