BibTeX

@inProceedings{kokkinakis-2004-reducing-33928,
	title        = {Reducing the Effect of Name Explosion.},
	abstract     = {The problem of new vocabulary is particularly frustrating once one begins to work with large corpora of real texts. The identification
of unknown proper nouns, chains of non-proper nouns and even common words that function as names (i.e. named entities) in
unrestricted text, and their subsequent classification into some sort of semantic type is a challenging and difficult problem in Natural
Language Processing (NLP). Systems that perform Information Extraction, Information Retrieval, Question-Answering, Topic
Detection, Text Mining, Machine Translation and annotation for the Semantic Web have highlighted the need for the automatic
recognition of such entities, since their constant introduction in any domain, however narrow, is very common and needs special
attention. Proper names are usually not listed in defining or other common types of dictionaries, they may appear in many alias forms
and abbreviated variations, which makes their listing infeasible. This paper deals with some extensions to the “traditional” named
entity recognition approaches. It puts emphasis on more name classes and their further subclassification into finer sets. An operative
system that can be tested and evaluated on-line implements the ideas described in this paper.},
	booktitle    = {Proceedings of the LREC Workshop: Beyond Named Entity Recognition, Semantic labelling for NLP tasks. ourth Language Resources and Evaluation Conference (LREC)},
	author       = {Kokkinakis, Dimitrios},
	year         = {2004},
}