Boosting Formal Concept Analysis Based Definition Extraction via Named Entity Recognition

被引：0

作者：

Mahalakshmi, G. S. ^{[1
]}

Adline, A. L. Agasta ^{[2
]}

机构：

[1] Anna Univ, Dept CSE, Madras 600025, Tamil Nadu, India

[2] Easwari Engn Coll, Dept IT, Madras, Tamil Nadu, India

来源：

COMPUTATIONAL INTELLIGENCE IN DATA MINING, VOL 3 | 2015年 / 33卷

关键词：

Definition extraction; Named entities; Formal concept analysis; Machine learning; CONCEPT LATTICES;

D O I：

10.1007/978-81-322-2202-6_8

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

E-learning involves learning materials of different standards which might be difficult for the e-learner to ponder ideas for fast reading. Extracting important definitions and phrases from the learning material will help in representation of knowledge in a more useful and attractive manner. This paper discusses a formal conceptualization based definition extraction approach for theoretical learning materials. The experiments have been conducted on Abraham Silberschatz, Peter B Galvin and Greg Gagne's Operating Systems Concepts-e-book and the results have outperformed the Named Entity based approach for Definition Extraction.

引用

页数：12

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