Year: 2006
Type: conference-paper
Abstract: In this paper we address the issue of automatically assigning information status to discourse entities. Using an annotated corpus of conversational English and exploiting morpho-syntactic and lexical features, we train a decision tree to classify entities introduced by noun phrases as old, mediated, or new. We compare its performance with hand-crafted rules that are mainly based on morpho-syntactic features and closely relate to the guidelines that had been used for the manual annotation. The decision tree model achieves an overall accuracy of 79.5%, significantly outperforming the hand-crafted algorithm (64.4%). We also experiment with binary classifications by collapsing in turn two of the three target classes into one and retraining the model. The highest accuracy achieved on binary classification is 93.1%.
Author: Malvina Nissim
Institution: National Research Council
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W1982246600