Earticle

초록

영어

Named Entity Recognition (NER) is responsible for extracting and classifying some designators in the given specified text which can be name, location, organization etc. Since the last decade or so, researchers are greatly involved in this area as far as their interests are concerned. It is important procedure to extract the entities in a specified text based on a language which is termed as Natural Language. This language consists of various entities and the collection of such entities is called entity set. These entity sets are maintained in a uniform database called as gazetteer. In this paper we present a methodology called maximum entropy to retrieve the entity sets from the database. The machine is trained in such a way that it will retrieve the words which has the maximum entropy amongst all and has proved to be fastest method to extract and classify the entity sets from the database. The advantages of proposed method include sequence tagging which means this method has increased the freedom of choosing features to represent observations.

목차

Abstract
 1. Introduction
 2. Related Work
 3. Proposed Work and Implementation
  3.1. Raw Text
  3.2. Sentence Segmentation
  3.3. Tokenization
  3.4. Parts of Speech Tagging
  3.5. Entity Recognition
 4. Result and Analysis
  4.1. Tools Required
  4.2 Performance Evaluation
 5. Conclusion and Future Work
 References

키워드

저자

• Imran Ahmed [ SCSE, VIT University, Vellore-632014 ]
• Sathyaraj R [ SCSE, VIT University, Vellore-632014 ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Database Theory and Application

• 간기

  격월간

• pISSN

  2005-4270

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605