NAACL HLT Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk 2010: all accepted papers
- Amazon Mechanical Turk for Subjectivity Word Sense Disambiguation ↗Cem Akkaya, Alexander Conrad, Janyce Wiebe et al.
- An Enriched MT Grammar for Under \$100 ↗Omar F. Zaidan, Juri Ganitkevitch
- Annotating Large Email Datasets for Named Entity Recognition with Mechanical Turk ↗Nolan Lawson, Kevin Eustice, Mike Perkowitz et al.
- Annotating Named Entities in Twitter Data with Crowdsourcing ↗Tim Finin, William Murnane, Anand Karandikar et al.
- Can Crowds Build parallel corpora for Machine Translation Systems? ↗Vamshi Ambati, Stephan Vogel
- Cheap Facts and Counter-Facts ↗Rui Wang, Chris Callison-Burch
- Clustering dictionary definitions using Amazon Mechanical Turk ↗Gabriel Parent, Maxine Eskenazi
- Collecting Image Annotations Using Amazon's Mechanical TurkCyrus Rashtchian, Peter Young, Micah Hodosh et al.our summary
- Corpus Creation for New Genres: A Crowdsourced Approach to PP Attachment ↗Mukund Jha, Jacob Andreas, Kapil Thadani et al.
- Creating Speech and Language Data With Amazon's Mechanical TurkChris Callison-Burch, Mark Dredzeour summary
- Creating a Bi-lingual Entailment Corpus through Translations with Mechanical Turk: \$100 for a 10-day Rush ↗Matteo Negri, Yashar Mehdad
- Crowdsourced Accessibility: Elicitation of Wikipedia Articles ↗Scott Novotney, Chris Callison-Burch
- Crowdsourcing Document Relevance Assessment with Mechanical Turk ↗Catherine Grady, Matthew Lease
- Crowdsourcing and language studies: the new generation of linguistic data ↗Robert Munro, Steven Bethard, Victor Kuperman et al.
- Document Image Collection Using Amazon's Mechanical Turk ↗Audrey Le, Jerome Ajot, Mark Przybocki et al.
- Error Driven Paraphrase Annotation using Mechanical Turk ↗Olivia Buzek, Philip Resnik, Ben Bederson
- Evaluation of Commonsense Knowledge with Mechanical Turk ↗Jonathan Gordon, Benjamin Van Durme, Lenhart Schubert
- MTurk Crowdsourcing: A Viable Method for Rapid Discovery of Arabic Nicknames? ↗Chiara Higgins, Elizabeth McGrath, Laila Moretto
- Measuring Transitivity Using Untrained Annotators ↗Nitin Madnani, Jordan Boyd-Graber, Philip Resnik
- Non-Expert Correction of Automatically Generated Relation Annotations ↗Matthew R. Gormley, Adam Gerber, Mary Harper et al.
- Non-Expert Evaluation of Summarization Systems is Risky ↗Dan Gillick, Yang Liu
- Opinion Mining of Spanish Customer Comments with Non-Expert Annotations on Mechanical Turk ↗Bart Mellebeek, Francesc Benavent, Jens Grivolla et al.
- Preliminary Experiments with Amazon's Mechanical Turk for Annotating Medical Named Entities ↗Meliha Yetisgen-Yildiz, Imre Solti, Fei Xia et al.
- Rating Computer-Generated Questions with Mechanical Turk ↗Michael Heilman, Noah A. Smith
- Semi-supervised Word Alignment with Mechanical Turk ↗Qin Gao, Stephan Vogel
- Shedding (a Thousand Points of) Light on Biased Language ↗Tae Yano, Philip Resnik, Noah A. Smith
- The Wisdom of the Crowd's Ear: Speech Accent Rating and Annotation with Amazon Mechanical Turk ↗Stephen Kunath, Steven Weinberger
- Tools for Collecting Speech Corpora via Mechanical-Turk ↗Ian Lane, Matthias Eck, Kay Rottmann et al.
- Turker-Assisted Paraphrasing for English-Arabic Machine Translation ↗Michael Denkowski, Hassan Al-Haj, Alon Lavie
- Using Amazon Mechanical Turk for Transcription of Non-Native Speech ↗Keelan Evanini, Derrick Higgins, Klaus Zechner
- Using Mechanical Turk to Annotate Lexicons for Less Commonly Used Languages ↗Ann Irvine, Alexandre Klementiev
- Using Mechanical Turk to Build Machine Translation Evaluation Sets ↗Michael Bloodgood, Chris Callison-Burch
- Using the Amazon Mechanical Turk to Transcribe and Annotate Meeting Speech for Extractive Summarization ↗Matthew Marge, Satanjeev Banerjee, Alexander Rudnicky
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