Topical Phrase Extraction from Clinical Reports by Incorporating both Local and Global Context

Gabriele Pergola, Yulan He, David Lowe

Research output: Chapter in Book/Report/Conference proceedingConference publication

Abstract

Making sense of words often requires to simultaneously
examine the surrounding context of a term as well as the
global themes characterizing the overall corpus. Several
topic models have already exploited word embeddings
to recognize local context, however, it has been weakly
combined with the global context during the topic inference.
This paper proposes to extract topical phrases
corroborating the word embedding information with the
global context detected by Latent Semantic Analysis,
and then combine them by means of the Polya urn ´
model. To highlight the effectiveness of this combined
approach the model was assessed analyzing clinical reports,
a challenging scenario characterized by technical
jargon and a limited word statistics available. Results
show it outperforms the state-of-the-art approaches in
terms of both topic coherence and computational cost.
Original languageEnglish
Title of host publicationThe 2nd AAAI Workshop on Health Intelligence
Pages499-506
Number of pages8
Publication statusE-pub ahead of print - 20 Jun 2018
Event2018 Workshop on Health Intelligence (W3PHIAI 2018) - New Orleans, United States
Duration: 2 Feb 20183 Feb 2018

Conference

Conference2018 Workshop on Health Intelligence (W3PHIAI 2018)
CountryUnited States
CityNew Orleans
Period2/02/183/02/18

Bibliographical note

Copyright
© 2018, Association for the Advancement of Artificial
Intelligence (www.aaai.org). All rights reserved.

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  • Cite this

    Pergola, G., He, Y., & Lowe, D. (2018). Topical Phrase Extraction from Clinical Reports by Incorporating both Local and Global Context. In The 2nd AAAI Workshop on Health Intelligence (pp. 499-506)