Year: 2014
Type: conference-paper
Abstract: Although many NLP systems are moving toward entity-based processing, most still identify important phrases using classical keyword-based approaches.To bridge this gap, we introduce the task of entity salience: assigning a relevance score to each entity in a document.We demonstrate how a labeled corpus for the task can be automatically generated from a corpus of documents and accompanying abstracts.We then show how a classifier with features derived from a standard NLP pipeline outperforms a strong baseline by 34%.Finally, we outline initial experiments on further improving accuracy by leveraging background knowledge about the relationships between entities.
DOI: 10.3115/v1/e14-4040
Authors: Jesse Dunietz, Daniel Gillick
Institutions: Carnegie Mellon University, Google (United States)
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W2250818300