Semi-Supervised Technical Term Tagging With Minimal User Feedback

Behrang QasemiZadeh, Paul Buitelaar, Tianqi Chen, Georgeta Bordea


Abstract
In this paper, we address the problem of extracting technical terms automatically from an unannotated corpus. We introduce a technology term tagger that is based on Liblinear Support Vector Machines and employs linguistic features including Part of Speech tags and Dependency Structures, in addition to user feedback to perform the task of identification of technology related terms. Our experiments show the applicability of our approach as witnessed by acceptable results on precision and recall.
Anthology ID:
L12-1165
Volume:
Proceedings of the Eighth International Conference on Language Resources and Evaluation (LREC'12)
Month:
May
Year:
2012
Address:
Istanbul, Turkey
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
617–621
Language:
URL:
http://www.lrec-conf.org/proceedings/lrec2012/pdf/342_Paper.pdf
DOI:
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PDF:
http://www.lrec-conf.org/proceedings/lrec2012/pdf/342_Paper.pdf