Machine Learning
Learning to identify single-snippet answers to definition questions
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Dealing with Small, Noisy and Imbalanced Data
TSD '08 Proceedings of the 11th international conference on Text, Speech and Dialogue
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
Dealing with Small, Noisy and Imbalanced Data
TSD '08 Proceedings of the 11th international conference on Text, Speech and Dialogue
Evolutionary algorithms for definition extraction
WDE '09 Proceedings of the 1st Workshop on Definition Extraction
Language independent system for definition extraction: first results using learning algorithms
WDE '09 Proceedings of the 1st Workshop on Definition Extraction
Definition extraction using linguistic and structural features
WDE '09 Proceedings of the 1st Workshop on Definition Extraction
Exploring discrepancies in findings obtained with the KDD Cup '99 data set
Intelligent Data Analysis
Automatic extraction of prerequisites and learning outcome from learning material
International Journal of Metadata, Semantics and Ontologies
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We propose a novel machine learning approach to the task of identifying definitions in Polish documents. Specifics of the problem domain and characteristics of the available dataset have been taken into consideration, by carefully choosing and adapting a classification method to highly imbalanced and noisy data. We evaluate the performance of a Random Forest-based classifier in extracting definitional sentences from natural language text and give a comparison with previous work.