Combining Minimally-supervised Methods for Arabic Named Entity Recognition
- Submitting institution
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Queen Mary University of London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 495
- Type
- D - Journal article
- DOI
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10.1162/tacl_a_00136
- Title of journal
- Transactions of the Association for Computational Linguistics
- Article number
- -
- First page
- 243
- Volume
- 3
- Issue
- -
- ISSN
- 2307-387X
- Open access status
- Out of scope for open access requirements
- Month of publication
- May
- Year of publication
- 2015
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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2
- Research group(s)
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-
- Citation count
- -
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper in the top NLP journal is the most high-profile output of our work on Arabic NLP, and made a decisive contribution towards us securing two projects in this area: first the KTP with Minority Rights Group on civilian-led monitoring of human rights violations in Arabic social media (KTP 9488), then ESRC’s Human Rights, Big Data and Technology project (https://www.hrbdt.ac.uk). The evidence of our lab’s expertise in Bayesian inference provided by this paper was also key to our successful ERC proposal (ERC 695662, 2016/21, €2.5 million, http://www.dalil-ambiguity.org).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -