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Towards Context-Aware Syntax Parsing and Tagging

Authors Alaa Mohasseb, Mohamed Bader-El-Den, Mihaela Cocea

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ACM Subject Classification
  • Computing methodologies → Natural language processing
Keywords
  • Information Retrieval
  • Natural Language Processing
  • PoS Tagging
  • PoS Parsing
  • Machine Learning

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Abstract

Information retrieval (IR) has become one of the most popular Natural Language Processing (NLP) applications. Part of speech (PoS) parsing and tagging plays an important role in IR systems. A broad range of PoS parsers and taggers tools have been proposed with the aim of helping to find a solution for the information retrieval problems, but most of these are tools based on generic NLP tags which do not capture domain-related information. In this research, we present a domain-specific parsing and tagging approach that uses not only generic PoS tags but also domain-specific PoS tags, grammatical rules, and domain knowledge. Experimental results show that our approach has a good level of accuracy when applying it to different domains.

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Alaa Mohasseb, Mohamed Bader-El-Den, and Mihaela Cocea. Towards Context-Aware Syntax Parsing and Tagging. In 2018 Imperial College Computing Student Workshop (ICCSW 2018). Open Access Series in Informatics (OASIcs), Volume 66, pp. 5:1-5:9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019) https://doi.org/10.4230/OASIcs.ICCSW.2018.5

Author Details

Alaa Mohasseb
  • School of Computing, University of Portsmouth, United Kingdom
Mohamed Bader-El-Den
  • School of Computing, University of Portsmouth, United Kingdom
Mihaela Cocea
  • School of Computing, University of Portsmouth, United Kingdom

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