NAACL HLT Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk 2010 — accepted papers
NAACL HLT Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk 2010 is the 2010 edition of the NAACL HLT Workshop on Creating Speech and Language Data with Amazon's Mechanical Turk, held in Los Angeles, June 2010 (publisher: Association for Computational Linguistics). OpenAlex lists 35 papers of it; our library holds 2 with our summaries. Below: the most cited papers, every accepted paper, and ours with their topics, authors and institutions.
35papers in the proceedings 2works in our library 655citations of these works Los Angeleswhere it was held June 2010when
All accepted papers (35)
- 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.
Papers with our summary in our library (2)
- Collecting Image Annotations Using Amazon's Mechanical TurkNAACL HLT Workshop on Creating Speech and Language Data with Amazon's Mechanical TurkCyrus Rashtchian, Peter Young, Micah Hodosh, et al.2010 · 569 citations
- Creating Speech and Language Data With Amazon's Mechanical TurkNAACL HLT Workshop on Creating Speech and Language Data with Amazon's Mechanical TurkChris Callison-Burch, Mark Dredze2010 · 86 citations
Fields
Computer Science (2)Arts and Humanities (1)
Top authors
🧑🔬Julia Hockenmaier1 work here · 569 citations🧑🔬Chris Callison-Burch1 work here · 86 citations🧑🔬Mark Dredze1 work here · 86 citations
Top institutions
This page in Markdown → https://md.research.elamur.ai/free/en/venues/naacl-hlt-workshop-on-creating-speech-and-language-data-with-amazon-s-mechanical/2010/