Latent Semantic Analysis for Text Segmentation

This paper describes a method for linear text segmentation that is more accurate or at least as accurate as state-of-the-art methods (Utiyama and Isahara, 200

Adding syntactic information to LSA

Much effort has been expended in the field of Natural Language Understanding in developing methods for deriving the syntactic structure of a text. It is still unclear, however, to what extent syntactic information actually matters for the representation of meaning. LSA (Latent Semantic Analysis) allows you to derive information about the meaning without paying attention even to the order of words within a sentence. This is consistent with the view that syntax plays a subordinate role for semantic processing of text. But LSA does not perform as well as humans do in discriminating meanings. Can synta

Telecommunications, and Information Systems

Although much effort has been directed towards creating theoretical models of coreference resolution and towards developing algorithms to perform coreference, no one has previously attempted to directly account for human judgements of coreference acceptability. Gordon and Hendrick (1997 and 1998) have collected human data and developed a formal theory of human coreference resolution which they call Discourse Prominence Theory (DPT). This paper describes our computational implementation of DPT and our evaluation of that implementation with respect to the human data