@inproceedings{81c7fcf9fb1941d0b1266e5faf1d6664,
title = "Semi-supervised technical term taggingwith minimal user feedback",
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.",
keywords = "Information Extraction, Machine Learning, Technical Term Tagging",
author = "Behrang QasemiZadeh and Paul Buitelaar and Tianqi Chen and Georgeta Bordea",
year = "2012",
language = "English",
series = "Proceedings of the 8th International Conference on Language Resources and Evaluation, LREC 2012",
publisher = "European Language Resources Association (ELRA)",
pages = "617--621",
editor = "Dogan, \{Mehmet Ugur\} and Joseph Mariani and Asuncion Moreno and Sara Goggi and Khalid Choukri and Nicoletta Calzolari and Jan Odijk and Thierry Declerck and Bente Maegaard and Stelios Piperidis and Helene Mazo and Olivier Hamon",
booktitle = "Proceedings of the 8th International Conference on Language Resources and Evaluation, LREC 2012",
note = "8th International Conference on Language Resources and Evaluation, LREC 2012 ; Conference date: 21-05-2012 Through 27-05-2012",
}