Topic modeling of research fields: An interdisciplinary perspective

Michael Paul, Roxana Girju

Research output: Contribution to journalConference article

Abstract

This paper addresses the problem of scientific research analysis. We use the topic model Latent Dirichlet Allocation [2] and a novel classifier to classify research papers based on topic and language. Moreover, we show various insightful statistics and correlations within and across three research fields: Linguistics, Computational Linguistics, and Education. In particular, we show how topics change over time within each field, what relations and influences exist between topics within and across fields, as well as what trends can be established for some of the world's natural languages. Finally, we talk about trend prediction and topic suggestion as future extensions of this research.

Original languageEnglish (US)
Pages (from-to)337-342
Number of pages6
JournalInternational Conference Recent Advances in Natural Language Processing, RANLP
StatePublished - Dec 1 2009
EventInternational Conference on Recent Advances in Natural Language Processing, RANLP-2009 - Borovets, Bulgaria
Duration: Sep 14 2009Sep 16 2009

Keywords

  • Scientific research analysis
  • Statistical approaches
  • Topic models

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Artificial Intelligence
  • Electrical and Electronic Engineering

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