Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Enriching the knowledge sources used in a maximum entropy part-of-speech tagger
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
Definition Extraction with Balanced Random Forests
GoTAL '08 Proceedings of the 6th international conference on Advances in Natural Language Processing
Towards the automatic extraction of definitions in Slavic
ACL '07 Proceedings of the Workshop on Balto-Slavonic Natural Language Processing: Information Extraction and Enabling Technologies
Automatic extraction of definitions from German court decisions
IEBeyondDoc '06 Proceedings of the Workshop on Information Extraction Beyond The Document
Language technology for elearning
EC-TEL'06 Proceedings of the First European conference on Technology Enhanced Learning: innovative Approaches for Learning and Knowledge Sharing
Learning word-class lattices for definition and hypernym extraction
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
An automatic definition extraction in Arabic language
NLDB'10 Proceedings of the Natural language processing and information systems, and 15th international conference on Applications of natural language to information systems
Extracting definitions from brazilian legal texts
ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part III
ACL '12 Proceedings of the ACL-2012 Special Workshop on Rediscovering 50 Years of Discoveries
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Books and other text-based learning material contain implicit information which can aid the learner but which usually can only be accessed through a semantic analysis of the text. Definitions of new concepts appearing in the text are one such instance. If extracted and presented to the learner in form of a glossary, they can provide an excellent reference for the study of the main text. One way of extracting definitions is by reading through the text and annotating definitions manually --- a tedious and boring job. In this paper, we explore the use of machine learning to extract definitions from nontechnical texts, reducing human expert input to a minimum. We report on experiments we have conducted on the use of genetic programming to learn the typical linguistic forms of definitions and a genetic algorithm to learn the relative importance of these forms. Results are very positive, showing the feasibility of exploring further the use of these techniques in definition extraction. The genetic program is able to learn similar rules derived by a human linguistic expert, and the genetic algorithm is able to rank candidate definitions in an order of confidence.