Textual context analysis for information retrieval

We &.9dx? b WPbUkSU3ofQUESCOT, a program whkh analyacs and quantifies textual amtexta in doaunenta with reference to the WordNet databae. and hence awemina the dominance oftopics inadocument. 0uranaly3ia ia baaedon previous work in lexical cobeaim, a feature of texts which contributes to theif functioning aa a cuhaent unit. The applications arc diverse, but all pertain to infurmatian retrieval. Whilatour resulta auggeat that QuEXO’ria notwellauitedto word aenae diaambiguatkm and text segmentation, our ~~~_u@3Q~~ =~* Lxqonent produces premising results. We also used QUESM representationsto automaticallygenerate a resourceto aupplememt WerdNet, baaed on collocatkal relations between conceptsin a document collection. We conclude that QLrEsxrr is auitcd to applications based on document-level descriptions, where the degreeof granularityallowsinaccuraciesto be smoothedout.

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