Year: 2022
Type: conference-paper
Abstract: We propose a probabilistic approach to select a subset of a target domain representative keywords from a candidate set, contrasting with a context domain.Such a task is crucial for many downstream tasks in natural language processing.To contrast the target domain and the context domain, we adapt the two-component mixture model concept to generate a distribution of candidate keywords.It provides more importance to the distinctive keywords of the target domain than common keywords contrasting with the context domain.To support the representativeness of the selected keywords towards the target domain, we introduce an optimization algorithm for selecting the subset from the generated candidate distribution.We have shown that the optimization algorithm can be efficiently implemented with a near-optimal approximation guarantee.Finally, extensive experiments on multiple domains demonstrate the superiority of our approach over other baselines for the tasks of keyword summary generation and trending keywords selection.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Awards: G2716193700, G3668672548, G5190785519, BIGDATA: F: Bringing Interactive Data Management to Scientists, Analysts, and the Masses: A Holistic Unification of Spreadsheets and Databases, III: Small: Social Discovery of Users and Content in Social Media Through Similarity-Based and Graph-Based Inference of Attributes and Queries
OpenAlex ID: W4221146498