Instance-Based Learning Algorithms
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Neural networks: a systematic introduction
Neural networks: a systematic introduction
Internal and external evidence in the identification and semantic categorization of proper names
Corpus processing for lexical acquisition
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
Message Understanding Conference-6: a brief history
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
Machine Learning
Design of the MUC-6 evaluation
MUC6 '95 Proceedings of the 6th conference on Message understanding
Introduction to the CoNLL-2002 shared task: language-independent named entity recognition
COLING-02 proceedings of the 6th conference on Natural language learning - Volume 20
Introduction to the CoNLL-2003 shared task: language-independent named entity recognition
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Flexible text segmentation with structured multilabel classification
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
Key element summarisation: extracting information from company announcements
AI'04 Proceedings of the 17th Australian joint conference on Advances in Artificial Intelligence
Identifying non-elliptical entity mentions in a coordinated NP with ellipses
Journal of Biomedical Informatics
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Although the literature contains reports of very high accuracy figures for the recognition of named entities in text, there are still some named entity phenomena that remain problematic for existing text processing systems. One of these is the ambiguity of conjunctions in candidate named entity strings, an all-too-prevalent problem in corporate and legal documents. In this paper, we distinguish four uses of the conjunction in these strings, and explore the use of a supervised machine learning approach to conjunction disambiguation trained on a very limited set of `name internal' features that avoids the need for expensive lexical or semantic resources. We achieve 84% correctly classified examples using k-fold evaluation on a data set of 600 instances. Further improvements are likely to require the use of wider domain knowledge and name external features.