Extending sparse text with induced domain-specific lexicons and embeddings: A case study on predicting donations. (January 2020)

• Record Type: Journal Article
• Authors: Dong, MeiXing; Mihalcea, Rada; Radev, Dragomir
• Is Part Of: Computer speech & language. Volume 59(2020)
• Page Start: 157
• Page End: 168
• Publication Date: 2020-01
• Subjects: Natural language processing -- Text expansion -- Sparse text; Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2019.06.007
• Languages: English
• ISSNs: 0885-2308

Abstract

Abstract: This paper addresses the problem of expanding sparse textual content to increase the accuracy of data-driven prediction tasks. We evaluate the use of word embeddings and lexicons within the context of a donation prediction task, where we classify potential donors as either likely or unlikely to donate. We perform several comparative experiments and analyses, and show that our methods to automatically enhance sparse textual data significantly improve the predictive performance on this task.