Improving Automatic Indexing through Concept Combination and Term Enrichment

13 years 10 months ago

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Although indexes may overlap, the output of an automatic indexer is generally presented as a fiat and unstructured list of terms. Our purpose is to exploit term overlap and embedding so as to yield a substantial qualitative and quantitative improvement in automatic indexing through concept combination. The increase in the volume of indexing is 10.5% for free indexing and 52.3% for controlled indexing. The resulting structure of the indexed corpus is a partial conceptual analysis. 1 Overview The method, proposed here for improving automatic indexing, builds partial syntactic structures by combining overlapping indexes. It is complemented by a method for term acquisition which is described in (Jacquemin, 1996). The text, thus structured, is reindexed; new indexes are produced and new candidates are discovered. Most NLP approaches to automatic indexing concern free indexing and rely on large-scale shallow parsers with a particular concern for dependency relations (Strzalkowski, 1996). Fo...

Christian Jacquemin

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