Adapting SVM for data sparseness and imbalance: a case study in information extraction

Support Vector Machines (SVM) have been used successfully in many Natural Language Processing (NLP) tasks. The novel contribution of this paper is in investigating two techniques for making SVM more suitable for language learning tasks. Firstly, we propose an SVM with uneven margins (SVMUM) model to...

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Bibliographic Details
Published in:Natural language engineering 2009-04, Vol.15 (2), p.241-271
Main Authors: LI, YAOYONG, BONTCHEVA, KALINA, CUNNINGHAM, HAMISH
Format: Article
Language:English
Subjects:
Active learning
Algorithms
Case studies
Classification
Computer Applications
Data
Educational activities
Extraction
Imbalance
Information Retrieval
Language acquisition
Machine Learning
Machinery
Natural Language Processing
Seminars
Support vector machines
Tagging
Task performance
Training
Word sense disambiguation
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