The identification of new versus given information within a text has been frequently investigated by researchers of language and discourse. Despite theoretical advances, an accurate computational method for assessing the degree to which a text contains new versus given information has not previously been implemented. This study discusses a variety of computational new/given systems and analyzes four typical expository and narrative texts against a widely accepted theory of new/given proposed by Prince (1981). Findings suggest that a latent semantic analysis (LSA) based measure called span outperforms standard LSA in detecting both new and given information in text. Further, the span measure outperforms standard LSA for distinguishing low versus high cohesion versions of text. Results suggest that span may be a useful variable in a wide array of discourse analyses.
|Original language||English (US)|
|Title of host publication||Cross-Disciplinary Advances in Applied Natural Language Processing|
|Subtitle of host publication||Issues and Approaches|
|Number of pages||23|
|State||Published - Dec 1 2011|
ASJC Scopus subject areas
- Computer Science(all)