Strudel: A Corpus-Based Semantic Model Based on Properties and Types

Computational models of meaning trained on naturally occurring text successfully model human performance on tasks involving simple similarity measures, but they characterize meaning in terms of undifferentiated bags of words or topical dimensions. This has led some to question their psychological plausibility (Murphy, 2002; Schunn, 1999). We present here a fully automatic method for extracting a structured and comprehensive set of concept descriptions directly from an English part-of-speech-tagged corpus. Concepts are characterized by weighted properties, enriched with concept

• Type: Journal
• Year: 2010
• Where: COGSCI
• Authors: Marco Baroni, Brian Murphy, Eduard Barbu, Massimo Poesio
• Keywords: COGSCI 2010, Computational Models, English Part-of-speech-tagged Corpus, Simple Similarity Measures