A formal lexicon in the Meaning-Text Theory: (or how to do lexica with words)
Computational Linguistics - Special issue of the lexicon
CYC: a large-scale investment in knowledge infrastructure
Communications of the ACM
Foundations of statistical natural language processing
Foundations of statistical natural language processing
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Automatic labeling of semantic roles
Computational Linguistics
A hybrid approach for named entity and sub-type tagging
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
Assigning function tags to parsed text
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Automatic labeling of semantic roles
ACL '00 Proceedings of the 38th Annual Meeting on Association for Computational Linguistics
The Penn Treebank: annotating predicate argument structure
HLT '94 Proceedings of the workshop on Human Language Technology
Digraph analysis of dictionary preposition definitions
WSD '02 Proceedings of the ACL-02 workshop on Word sense disambiguation: recent successes and future directions - Volume 8
Preposition semantic classification via Penn Treebank and FrameNet
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
A characterization of wordnet features in Boolean models for text classification
AusDM '06 Proceedings of the fifth Australasian conference on Data mining and analystics - Volume 61
Exploiting semantic role resources for preposition disambiguation
Computational Linguistics
Discovering semantic relations using prepositional phrases
ISMIS'12 Proceedings of the 20th international conference on Foundations of Intelligent Systems
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This paper describes how to automatically classify the functional relations from the FACTOTUM knowledge base via a statistical machine learning algorithm. This incorporates a method for inferring prepositional relation indicators from corpus data. It also uses lexical collocations (i.e., word associations) and class-based collocations based on the WordNet hypernym relations (i.e., is-subset-of). The result shows substantial improvement over a baseline approach.