TLATR: Automatic Topic Labeling using Automatic (Domain-Specific) Term Recognition

Topic models are widely used in natural language processing, allowing researchers to estimate the underlying themes in a collection of documents. Most topic models require the additional step of attaching meaningful labels to estimated topics, a process that is not scalable, suffers from human bias, and is difficult to replicate. We present a transfer topic labeling method that seeks to remedy these problems, using domain-specific codebooks as the knowledge base to automatically label estimated topics. We demonstrate our approach with a large-scale topic model analysis of the complete corpus of UK House of Commons speeches from 1935 to 2014, using the coding instructions of the Comparative Agendas Project to label topics. We evaluated our results using human expert coding and compared our approach with more current state-of-the-art neural methods. Our approach was simple to implement, compared favorably to expert judgments, and outperformed the neural networks model for a majority of the topics we estimated.

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Scientific Problem Solving in a Virtual Laboratory: A Comparison Between Individuals and Pairs

Swiss Journal of Psychology ◽

10.1024/1421-0185.67.2.71 ◽

2008 ◽

Vol 67 (2) ◽

pp. 71-83 ◽

Cited By ~ 5

Author(s):

Yolanda A. Métrailler ◽

Ester Reijnen ◽

Cornelia Kneser ◽

Klaus Opwis

Keyword(s):

Visual Search ◽

Problem Solving ◽

Process Data ◽

Scientific Problem ◽

Domain Specific ◽

Scientific Problem Solving ◽

Before And After ◽

Verbal Data ◽

Psychological Knowledge ◽

Problem Solving Task

This study compared individuals with pairs in a scientific problem-solving task. Participants interacted with a virtual psychological laboratory called Virtue to reason about a visual search theory. To this end, they created hypotheses, designed experiments, and analyzed and interpreted the results of their experiments in order to discover which of five possible factors affected the visual search process. Before and after their interaction with Virtue, participants took a test measuring theoretical and methodological knowledge. In addition, process data reflecting participants’ experimental activities and verbal data were collected. The results showed a significant but equal increase in knowledge for both groups. We found differences between individuals and pairs in the evaluation of hypotheses in the process data, and in descriptive and explanatory statements in the verbal data. Interacting with Virtue helped all students improve their domain-specific and domain-general psychological knowledge.

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