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Cited by 9Computational and Text Analysis MethodsTopic ModelingSentiment Analysis and Opinion Mining

Identification, Interpretability, and

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Lauretig, Adam·2019·Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science
DOI 10.18653/v1/w19-2102
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Abstract

Social scientists have recently turned to analyzing text using tools from natural language processing like word embeddings to measure concepts like ideology, bias, and affinity. However, word embeddings are difficult to use in the regression framework familiar to social scientists: embeddings are are neither identified, nor directly interpretable. I offer two advances on standard embedding models to remedy these problems. First, I develop Bayesian Word Embeddings with Automatic Relevance Determination priors, relaxing the assumption that all embedding dimensions have equal weight. Second, I apply work identifying latent variable models to anchor embeddings, identifying them, and making them interpretable and usable in a regression. I then apply this model and anchoring approach to two cases, the shift in internationalist rhetoric in the American presidents' inaugural addresses, and the relationship between bellicosity in American foreign policy decision-makers' deliberations. I find that inaugural addresses became less internationalist after 1945, which goes against the conventional wisdom, and that an increase in bellicosity is associated with an increase in hostile actions by the United States, showing that elite deliberations are not cheap talk, and helping confirm the validity of the model.

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  • Existing efforts to address inference in embeddings are typically data intensive and computationally intensive.

    “Although there have been efforts to address inference in embeddings (see, e.g, Kulkarni et al. 2015;Lauretig 2019), they are typically data intensive and computationally intensive.”

    From Embedding Regression: Models for Context-Specific Description and Inference (2023) · cites this paper as Lauretig 2019

    The passages available from our copy identify Lauretig’s paper, but none states that efforts to address inference in embeddings are data intensive or computationally intensive. Searches for those terms and for computational cost returned no relevant passage from the paper’s body, so the resource claim cannot be checked against the available text. (LLM, conf=0.69)

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Authors
Lauretig, A.
Corresponding author
Lauretig, Adam
Venue
Proceedings of the Third Workshop on Natural Language Processing and Computational Social Science
Publisher
Association for Computational Linguistics
Published
2019
Pages
7-17
DOI
10.18653/v1/w19-2102
OpenAlex
W2964163176
Cited by
9
References
42
Citations by year
2026: 1 · 2024: 2 · 2023: 2 · 2021: 2 · 2020: 1 · 2019: 1
FWCI
2.82
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
Workshop on Natural Language Processing and Computational Social Science (NLP+CSS) (publishedVersion, cc-by)
Topics
  • Social Sciences › General Social Sciences › Computational and Text Analysis Methods
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
Keywords
word embeddings, automatic relevance determination, latent variable models, interpretability, identification, text analysis, ideology, inaugural address, bellicosity, cheap talk

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