Paper Info
Title
Can Crowdsourcing be used for Effective Annotation of Arabic?
Abstract
Crowdsourcing has been used recently as an alternative to traditional costly annotation by many natural language processing groups. In this paper, we explore the use of Amazon Mechanical Turk (AMT) in order to assess the feasibility of using AMT workers (also known as Turkers) to perform linguistic annotation of Arabic. We used a gold standard data set taken from the Quran corpus project annotated with part-of-speech and morphological information. An Arabic language qualification test was used to filter out potential non-qualified participants. Two experiments were performed, a part-of-speech tagging task in where the annotators were asked to choose a correct word-category from a multiple choice list and case ending identification task. The results obtained so far showed that annotating Arabic grammatical case is harder than POS tagging, and crowdsourcing for Arabic linguistic annotation requiring expert annotators could be not as effective as other crowdsourcing experiments requiring less expertise and qualifications.
Year
Venue
Keywords
2014
LREC 2014 - NINTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION
Crowdsourcing,Annotation,Arabic
Field
DocType
Citations 
Annotation,Arabic,Information retrieval,Computer science,Crowdsourcing,Speech recognition,Artificial intelligence,Natural language processing,Grammatical case,Multiple choice
Conference
0
PageRank 
References 
Authors
0.34
11
2
Name
Order
Citations
PageRank
Wajdi Zaghouani119721.27
Kais Dukes2746.42