Paper: Automatic Story Segmentation using a Bayesian Decision Framework for Statistical Models of Lexical Chain Features

ACL ID P09-2067
Title Automatic Story Segmentation using a Bayesian Decision Framework for Statistical Models of Lexical Chain Features
Venue Annual Meeting of the Association of Computational Linguistics
Session Short Paper
Year 2009
Authors

This paper presents a Bayesian decision framework that performs automatic story segmentation based on statistical model- ing of one or more lexical chain features. Automatic story segmentation aims to lo- cate the instances in time where a story ends and another begins. A lexical chain is formed by linking coherent lexical items chronologically. A story boundary is often associated with a significant number of lexical chains ending before it, starting after it, as well as a low count of chains continuing through it. We devise a Bayesian framework to capture such be- havior, using the lexical chain features of start, continuation and end. In the scoring criteria, lexical chain starts/ends are modeled statistically with the Weibull and uniform distributions at story boun- dar...