2026-10-19T07:23:17Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:10186 2024-03-06T09:30:28Z ddc:004 open_access

Towards Context-Aware Syntax Parsing and Tagging Mohasseb, Alaa Bader-El-Den, Mohamed Cocea, Mihaela Information Retrieval Natural Language Processing PoS Tagging PoS Parsing Machine Learning 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. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Alaa Mohasseb and Mohamed Bader-El-Den and Mihaela Cocea 2019 Is Part Of OASIcs, Volume 66, 2018 Imperial College Computing Student Workshop (ICCSW 2018) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/OASIcs.ICCSW.2018.5 urn:nbn:de:0030-drops-101866 https://drops.dagstuhl.de/entities/document/10.4230/OASIcs.ICCSW.2018.5 eng https://creativecommons.org/licenses/by/3.0/legalcode