Recognizing Meaning in the Crowd: Building Word Sense Inventories on Amazon Mechanical Turk

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dc.contributor.advisor Rumshisky, Anna
dc.contributor.author Botchan, Nicholas
dc.date.accessioned 2012-06-06T19:11:41Z
dc.date.available 2012-06-06T19:11:41Z
dc.date.issued 2012
dc.identifier.uri http://hdl.handle.net/10192/50
dc.description.abstract This thesis explores different strategies for constructing robust, inexpensive and empirically-derived word sense inventories and the corresponding sense-annotated corpus. All strategies explored rely on non-expert linguistic annotations collected through the use of the Amazon Mechanical Turk crowdsourcing marketplace. Experiments using implementation strategies with different quality control mechanisms are reported on in detail. Described herein are multiple best practices discovered through extensive system testing that are required to obtain high quality data given the challenge of using non-expert annotations. Results indicate that it is possible to obtain sense inventories that correlate with the gold standard, extending it in ways that may prove useful in a variety of other Natural Language Processing tasks.
dc.description.sponsorship Brandeis University, Graduate School of Arts and Sciences
dc.format.mimetype application/pdf
dc.language English
dc.language.iso eng
dc.publisher Brandeis University
dc.relation.ispartofseries Brandeis University Theses and Dissertations
dc.rights Copyright by Nicholas S. F. Botchan 2012
dc.subject crowdsourcing
dc.subject WSD
dc.subject word sense
dc.subject inventory
dc.title Recognizing Meaning in the Crowd: Building Word Sense Inventories on Amazon Mechanical Turk
dc.type Thesis
dc.contributor.department Department of Computational Linguistics
dc.degree.name MA
dc.degree.level Masters
dc.degree.discipline Computational Linguistics
dc.degree.grantor Brandeis University, Graduate School of Arts and Sciences


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