Machine learning for constituency test of coordinating conjunctions in requirements specifications
Richa Sharma, Jaspreet Bhatia, K. K. Biswas · 2014
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.