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Behavior Research Methods

, Volume 51, Issue 2, pp 480–492 | Cite as

Measuring the importance of context when modeling language comprehension

  • Justin Garten
  • Brendan Kennedy
  • Kenji Sagae
  • Morteza DehghaniEmail author
Article

Abstract

It is widely accepted that language requires context in order to function as communication between speakers and listeners. As listeners, we make use of background knowledge — about the speaker, about entities and concepts, about previous utterances — in order to infer the speaker’s intended meaning. But even if there is consensus that these sources of information are a necessary component of linguistic communication, it is another matter entirely to provide a thorough, quantitative accounting for context’s interaction with language. When does context matter? What kinds of context matter in which kinds of domains? The empirical investigation of these questions is inhibited by a number of factors: the challenge of quantifying language, the boundless combinations of domains and types of context to be measured, and the challenge of selecting and applying a given construct to natural language data. In response to these factors, we introduce and demonstrate a methodological framework for testing the importance of contextual information in inferring speaker intentions from text. We apply Long Short-term Memory (LSTM) networks, a standard for representing language in its natural, sequential state, and conduct a set of experiments for predicting the persuasive intentions of speakers in political debates using different combinations of text and background information about the speaker. We show, in our modeling and discussion, that the proposed framework is suitable for empirically evaluating the manner and magnitude of context’s relevance for any number of domains and constructs.

Keywords

Methodological innovation Text analysis Continuous representations Intent recognition 

Notes

Supplementary material

13428_2019_1200_MOESM1_ESM.pdf (71 kb)
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Copyright information

© The Psychonomic Society, Inc. 2019

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of Southern CaliforniaLos AngelesUSA
  2. 2.Department of LinguisticsUniversity of CaliforniaDavisUSA
  3. 3.Department of Psychology and Computer ScienceUniversity of Southern CaliforniaLos AngelesUSA

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