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Language Resources and Evaluation

, Volume 47, Issue 1, pp 97–122 | Cite as

Creating a system for lexical substitutions from scratch using crowdsourcing

  • Chris Biemann
Original Paper

Abstract

This article describes the creation and application of the Turk Bootstrap Word Sense Inventory for 397 frequent nouns, which is a publicly available resource for lexical substitution. This resource was acquired using Amazon Mechanical Turk. In a bootstrapping process with massive collaborative input, substitutions for target words in context are elicited and clustered by sense; then, more contexts are collected. Contexts that cannot be assigned to a current target word’s sense inventory re-enter the bootstrapping loop and get a supply of substitutions. This process yields a sense inventory with its granularity determined by substitutions as opposed to psychologically motivated concepts. It comes with a large number of sense-annotated target word contexts. Evaluation on data quality shows that the process is robust against noise from the crowd, produces a less fine-grained inventory than WordNet and provides a rich body of high precision substitution data at low cost. Using the data to train a system for lexical substitutions, we show that amount and quality of the data is sufficient for producing high quality substitutions automatically. In this system, co-occurrence cluster features are employed as a means to cheaply model topicality.

Keywords

Amazon Turk Lexical substitution Word sense disambiguation Language resource creation Crowdsourcing 

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Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.Technische Universität DarmstadtDarmstadtGermany

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