% % 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 : Author: Tilman_Jaeger % @InProceedings{Buitelaar_et_al:2001, AUTHOR = {Buitelaar, Paul and Alexandersson, Jan and Jaeger, Tilman and Lesch, Stephan and Pfleger, Norbert and Raileanu, Diana and von den Berg, Tanja and Klöckner, Kerstin and Neis, Holger and Schlarb, Hubert}, TITLE = {An Unsupervised Semantic Tagger Applied to German}, YEAR = {2001}, BOOKTITLE = {Proceedings of the 3rd Conference on Recent Advances in Natural Language Processing (RANLP'01), September 5-7}, ADDRESS = {Tzigov Chark, Bulgaria}, URL = {ftp://lt-ftp.dfki.uni-sb.de/pub/papers/local/ranlp01.ps ftp://lt-ftp.dfki.uni-sb.de/pub/papers/local/ranlp01.pdf http://dfki.de/~paulb/ranlp01.pdf}, ABSTRACT = {We describe an unsupervised semantic tagger, applied to German, but which could be used with any language for which a corresponding XNet (WordNet, GermaNet, e tc.), POS tagger and morphological analyzer are available. Disambiguation is per formed by comparing co-occurrence weights on pairs of semantic classes (synsets from GermaNet). Precision is around 67% at a recall of around 65% (for all ambig uous words -- 81% for all words at a recall of 80%). Our results show the influe nce of context size and of semantic class frequency in the training corpus.}, ANNOTE = {COLIURL : Buitelaar:2001:UST.pdf Buitelaar:2001:UST.ps} } @InProceedings{Piskorski_et_al:2002_1, AUTHOR = {Piskorski, Jakub and Jaeger, Tilman and Xu, Feiyu}, TITLE = {A Framework for Domain and Task Adaptive Named-Entity Recognition}, YEAR = {2002}, BOOKTITLE = {Proceedings of the 5th International Baltic Conference on Databases and Information Systems, June 3-6}, ADDRESS = {Tallinn, Estonia}, URL = {http://www.dfki.de/~feiyu/balt.tar.gz}, ABSTRACT = {Robust Named--Entity Recognition software is an essential preprocessing tool for performing more complex text processing tasks in business information systems. In this paper we present a Framework for Domain and Task Adaptive Named--Entity Recognition. It consists of several clear--cut subcomponents which can be flexibly and variably combined together in order to construct a task--specific NE--Recognition tool. Additionally, a diagnostic tool for automatic prediction of best system configuration is provided, which speeds up the development cycle.}, ANNOTE = {COLIURL : Piskorski:2002:FDT.tar} }