Sentence Extraction for Machine Comprehension
Hemavati Sabu1, Meghana Nagori2
1Hemavati Sabu, Department of Computer Science and Engineering, Government College of Engineering, Aurangabad, India.
2Dr. Meghana Nagori, Department of Computer Science and Engineering, Government College of Engineering, Aurangabad, India.
Manuscript received on 13 March 2019 | Revised Manuscript received on 18 March 2019 | Manuscript published on 30 July 2019 | PP: 5511-5514 | Volume-8 Issue-2, July 2019 | Retrieval Number: B3095078219/19©BEIESP | DOI: 10.35940/ijrte.B3095.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Machine comprehension is a broad research area from Natural Language Processing domain, which deals with making a computerised system understand the given natural language text. Question answering system is one such variant used to find the correct ‘answer’ for a ‘query’ using the supplied ‘context’. Using a sentence instead of the whole context paragraph to determine the ‘answer’ is quite useful in terms of computation as well as accuracy. Sentence selection can, therefore, be considered as a first step to get the answer. This work devises a method for sentence selection that uses cosine similarity and common word count between each sentence of context and question. This removes the extensive training overhead associated with other available approaches, while still giving comparable results. The SQuAD dataset is used for accuracy-based performance comparison.
Index Terms: Machine Comprehension, Cosine Similarity, Word Embedding, NLP, SQuAD.
Scope of the Article: Machine Learning