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Mapping structures onto tags

Hendrik Feddes-2003-01-01-Deutscher Universitätsverlag eBooks
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TL;DRAbstract

Named Entity Recognition (NEE) and baseNP chunking are two examples of NLP tasks that annotate chunks, i.e. non-embedding, non-overlapping token sequences. Many approaches to these tasks can be greatly simplified by viewing chunking as a tagging problem (Ramshaw and Marcus 1995). This perspective, however, raises the question of data representation, i. e. how chunk structures can be mapped onto tags, and whether the choice of chunk-tag mapping affects the system performance (cf. Tjong Kim Sang and Veenstra 1999).

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Named Entity Recognition (NEE) and baseNP chunking are two examples of NLP tasks that annotate chunks, i.e. non-embedding, non-overlapping token sequences. Many approaches to these tasks can be greatly simplified by viewing chunking as a tagging problem (Ramshaw and Marcus 1995). This perspective, however, raises the question of data representation, i. e. how chunk structures can be mapped onto tags, and whether the choice of chunk-tag mapping affects the system performance (cf. Tjong Kim Sang and Veenstra 1999).

Keywords

Chunking (psychology)Security tokenComputer scienceEmbeddingNatural language processingPerspective (graphical)Representation (politics)Artificial intelligence

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