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Reducing the Effect of Name Explosion.

Paper i proceeding
Författare Dimitrios Kokkinakis
Publicerad i Proceedings of the LREC Workshop: Beyond Named Entity Recognition, Semantic labelling for NLP tasks. ourth Language Resources and Evaluation Conference (LREC)
Publiceringsår 2004
Publicerad vid Institutionen för svenska språket
Språk en
Ämneskategorier Datorlingvistik


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.

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