Intelligent agent for information extraction from arabic text without machine translation

Tarek Helmy, Abdirahman Daud

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

Abstract

The process of classifying text into two opposing opinions is known as sentiment polarity classification. It has been shown in the literature that this problem cannot reach accuracy higher than 80-85%. This paper shows that a higher accuracy (96%) can be achieved without the need to translate text into English language. More specifically, our case study is: Islamic Hadith Narration. The problem is to tell whether a person is trustworthy or not based on his biographical data. With such high accuracy, the agent can be used to create new books in the area of Hadith automatically instead of manual classification done before. The results of our experiments encourage the use of an intelligent agent for information extraction using supervised learning, domain knowledge and number of natural language processing techniques.

Original languageEnglish
JournalCEUR Workshop Proceedings
Volume687
StatePublished - 2010

Keywords

  • Arabic
  • Information extraction
  • Machine learning
  • Machine translation
  • Natural language processing
  • Sentiment analysis
  • Supervised learning

ASJC Scopus subject areas

  • General Computer Science

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