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Semantic Question Classification


Author(s):

Daniyal Parveez , BNM Institute of Technology; Amber Ramesh, BNM Institute of Technology

Keywords:

Text Classification, NLP, Deep Learning, Neural Network, Language Model

Abstract:

Text classification is a classical natural language processing (NLP) problem. The task is to classify a word, phrase, sentence or document into one of several categories, based on things like sentiment or hidden topics. Question and Answer (Q&A) websites are community-oriented platforms where text classification can be applied to ensure better organization of user-generated content. These social platforms allow users to post questions and answers to these questions. Questions can be tagged on the basis of the topics they represent, they can be classified as duplicates based on pre-existing questions, they can be classified on the basis of question quality and relevance, or they can be classified on the basis of sentiment. These tasks are usually performed by human moderators that are prone to bias, or a lapse in judgement. With the emergence of deep learning as a hot research area, with the democratization of deep learning frameworks, and with the emergence of more powerful hardware, models like neural networks and language models can be explored to see how well they fare on the task of classifying questions. It is important not just to look at syntax during question classification and tagging, but also semantics as well. Semantics refers to the meaning behind words. It is important to take context into account when classifying text and deep learning approaches offer an efficient way to do that.


Other Details:

Manuscript Id :IJSTEV5I11022
Published in :Volume : 5, Issue : 11
Publication Date: 01/06/2019
Page(s): 63-65
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