Vorlesen

Research Seminar Series in Statistics and Mathematics

Wirtschaftsuniversität Wien , Departments 4 D4.4.008 09:00 - 10:30

Art Vortrag/Diskussion
SpracheEnglish
Vortragende/r Kenneth Benoit (Department of Methodology, London School of Economics and Political Science)
Veranstalter Institut für Statistik und Mathematik
Kontakt katrin.artner@wu.ac.at

Kenneth Benoit (Department of Methodology, London School of Economics and Political Science) about "More than Unigrams Can Say: Detecting Meaningful Multi-word Expressions from Political Texts"

The Institute for Statistics and Mathematics (Department of Finance, Accounting and Statistics) cordially invites everyone interested to attend the talks in our Research Seminar Series, where internationally renowned scholars from leading universities present and discuss their (working) papers.
No registration required.

The list of talks for the winter term 2019/20 is available via the following link: https://www.wu.ac.at/en/statmath/resseminar

Abstract:
Almost universal among existing approaches to text mining is the adoption of the bag of words approach, counting each word as a feature without regard to grammar or order. This approach remains extremely useful despite being an obviously inaccurate model of how observed words are generated in natural language. Many substantively meaningful textual features, however, occur not as unigram words but rather as multi-word expressions (MWEs): pairs of words or phrases that together form a single conceptual entity whose meaning is distinct from its individual elements. Here we present a new model for detecting meaningful multi-word expressions, based on the novel application of a statistical method for detecting variable-length term collocations. Combined with frequency and part-of-speech filtering, we show how to detect meaningful MWEs with an application to public policy, political economy, and law. We extract and validate a dictionary of meaningful collocations from three large corpora totalling over 1 billion words, drawn from political manifestos, legislative floor debates, and US federal and Supreme court briefs. Applying the collocations to replicate published studies using unigrams only applied to each field, we demonstrate that using collocations can improve accuracy and validity over the standard unigram bag of words model.



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