USING CONCEPT RELATIONSHIPS TO IMPROVE DOCUMENT CATEGORIZATION

In the information age we much depend on our ability to find information hidden in mostly unstructured and textual documents. This article proposes a simple method in which (as an addition to existing systems) categorization accuracy can be improved, compared to traditional techniques, without requiring any time-consuming or language-dependent computation. That result is achieved by exploiting properties observed in the entire document collection as opposed to individual documents, which may also be regarded as a construction of an approximate concept network (measuring semanticdistances). Thesepropertiesaresufficientlysimpletoavoidentailingmassivecomputations; however, they try to capture the most fundamental relationships between words or concepts. Experiments performed on the Reuters-21578 news article collections were evaluated using a set of simple measurements estimating clustering efficiency, and in addition by Cluto, a widely used document clustering software. Results show a 5–10% improvement in clustering quality over traditional tf (term frequency) or tf× idf (term frequency-inverse document frequency) based clustering.

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