Automatic discovery of person-related named-entity in news articles based on verb analysis

  • Goh H
  • Soon L
  • Haw S
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Abstract

Verb is the most important word in a sentence as it asserts an action, events, feeling about the subject and object discussed in the sentence. For news articles, it is observable that there is always at least a verb attached to the person(s) mentioned in the news. As such, a hypothesis has been formed such that there must exist some verbs that specifically describe human being conducts within a news article. In this paper, we propose an approach which aims to identify named-entity (NE) that performs human activity automatically. More specifically, our approach attempts to identify person-related NE generally and "person name" predefined type specifically by studying the nature of verb that associated with human activity via TreeTagger, Stanford packages and WordNet. The experimental results show that it is viable to use verb in identifying "person name"entity type. In addition, our empirical study proves that the approach is applicable to small text size articles. Another significant contribution of our approach is that it does not require training data set and anaphora resolution.

Author-supplied keywords

  • Named-entity
  • Pattern extraction
  • Semantic content
  • Text mining
  • Verb

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Authors

  • Hui Ngo Goh

  • Lay Ki Soon

  • Su Cheng Haw

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