%
% GENERATED FROM https://www.coli.uni-saarland.de
%    by   : anonymous
%    IP   : coli2006.lst.uni-saarland.de
%    at   : Mon, 05 Feb 2024 15:43:04 +0100 GMT
%    
% Selection : Reference #1365
%




@InProceedings{Xu_et_al:2002_1,
      AUTHOR = {Xu, Feiyu and Kurz, Daniela and Piskorski, Jakub and Schmeier, Sven},
      TITLE = {A Domain Adaptive Approach to Automatic Acquisition of Domain Relevant Terms and their Relations with Bootstrapping},
      YEAR = {2002},
      BOOKTITLE = {Proceedings of the 3rd International Conference on Language Resources an Evaluation (LREC'02), May 29-31},
      ADDRESS = {Las Palmas, Canary Islands, Spain},
      URL = {http://www.dfki.uni-sb.de/~feiyu/LREC_TermExtraction_final.pdf},
      ABSTRACT = {In this paper, we present an unsupervised hybrid text-mining approach to automatic acquisition of domain relevant terms and their relations. We deploy the TFIDF-based term classification method to acquire domain relevant single-word terms. Further, we apply two strategies in order to learn lexico-syntatic patterns which indicate paradigmatic and domain relevant syntagmatic relations between the extracted terms. The first one uses an existing ontology as initial knowledge for learning lexico-syntactic patterns, while the second is based on different collocation acquisition methods to deal with the free-word order languages like German. This domain-adaptive method yields good results even when trained on relatively small training corpora. It can be applied to different real-world applications, which need domain-relevant ontology, for example, information extraction, information retrieval or text classification.},
      ANNOTE = {COLIURL : Xu:2002:DAA.pdf}
}