Term-weighting approaches in automatic text retrieval
Information Processing and Management: an International Journal
Language independent NER using a maximum entropy tagger
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
The utility of information extraction in the classification of books
ECIR'07 Proceedings of the 29th European conference on IR research
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In this paper, we describe some experiments in large-scale Information Extraction (IE) focusing on book texts. We investigate the scalability of IE techniques to full-sized books, and the utility of IE techniques in extracting useful information from fiction. In particular, we evaluate a variety of Named Entity Recognition (NER) techniques in identifying the central characters in works of fiction. First, we describe the creation of a gold standard for evaluation, which contains ordered lists of characters for a corpus of classic book texts in Project Gutenberg. Second, we describe several approaches to the task of character identification, where our best model achieves an average coverage score of 78.4% across all central characters. Finally, we propose a number of approaches for future work.