Domain specific syntax based approach for text classification in machine learning context

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Abstract

Due to the vast amount of data, searching and obtaining relevant information on the web is a challenging task. Despite that a broad range of classification techniques have been proposed to improve the information retrieval methods, many difficulties are still present because of the continuous increase in the amount of web contents, as well as its diversity. In this paper, we propose a method that automatically identifies and classifies user queries by using a domain specific grammar approach – this approach is based on the grammatical pattern of each type of search query. A framework is developed to test the performance of the proposed method. Experimental results show that our approach leads to accurate identification of different query types.
Original languageEnglish
Title of host publication2017 International Conference on Machine Learning and Cybernetics (ICMLC)
PublisherIEEE
ISBN (Electronic)978-1538604083
ISBN (Print)978-1538604069
DOIs
Publication statusPublished - 16 Nov 2017
EventThe 16th International Conference on Machine Learning and Cybernetics (ICMLC) - Ningbo, China
Duration: 9 Jul 201712 Jul 2017

Publication series

NameIEEE ICMLC Proceedings Series
PublisherIEEE
ISSN (Electronic)2160-1348

Conference

ConferenceThe 16th International Conference on Machine Learning and Cybernetics (ICMLC)
CountryChina
CityNingbo
Period9/07/1712/07/17

Keywords

  • query classification
  • machine learning text mining
  • text classification
  • natural language processing

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