Summary of the paper

Title Semi-Supervised Technical Term Tagging With Minimal User Feedback
Authors Behrang QasemiZadeh, Paul Buitelaar, Tianqi Chen and 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.
Topics Information Extraction, Information Retrieval, Statistical and machine learning methods, Tools, systems, applications
Full paper Semi-Supervised Technical Term Tagging With Minimal User Feedback
Bibtex @InProceedings{QASEMIZADEH12.342,
  author = {Behrang QasemiZadeh and Paul Buitelaar and Tianqi Chen and Georgeta Bordea},
  title = {Semi-Supervised Technical Term Tagging With Minimal User Feedback},
  booktitle = {Proceedings of the Eight International Conference on Language Resources and Evaluation (LREC'12)},
  year = {2012},
  month = {may},
  date = {23-25},
  address = {Istanbul, Turkey},
  editor = {Nicoletta Calzolari (Conference Chair) and Khalid Choukri and Thierry Declerck and Mehmet Uğur Doğan and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
  publisher = {European Language Resources Association (ELRA)},
  isbn = {978-2-9517408-7-7},
  language = {english}
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