NDLI: Machine learning for constituency test of coordinating conjunctions in requirements specifications

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Machine learning for constituency test of coordinating conjunctions in requirements specifications

Content Provider	ACM Digital Library
Author	Biswas, K. K. Bhatia, Jaspreet Sharma, Richa
Abstract	Coordinating conjunctions have been a major source of ambiguity in Natural Language statements and the concern has been a major research focus in English Linguistics. Natural Language is also the most common form of expressing the requirements for an envisioned software system. These requirement documents also suffer from similar concern of coordination ambiguity. Presence of nocuous coordination ambiguity is a major concern for the requirements analysts. In this paper, we explore the applicability of constituency test for identifying coordinating conjunction instances in the requirements documents. We show through our study how identification of nocuous and innocuous coordinating conjunctions can be improved using semantic similarity heuristics and machine learning. Our study indicates that Naïve Bayes classifier outperforms other machine learning algorithms. .
Starting Page	25
Ending Page	31
Page Count	7
File Format	PDF
ISBN	9781450328463
DOI	10.1145/2593801.2593806
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2014-06-03
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Constituency test Coordination ambiguity Requirements specifications Machine learning Conjunctions
Content Type	Text
Resource Type	Article

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