Semantic interpretation and the resolution of ambiguity
Semantic interpretation and the resolution of ambiguity
An intelligent analyzer and understander of English
Communications of the ACM
Automatic labeling of semantic roles
Computational Linguistics
Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Distributional clustering of English words
ACL '93 Proceedings of the 31st annual meeting on Association for Computational Linguistics
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Explaining away ambiguity: learning verb selectional preference with Bayesian networks
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Class-based probability estimation using a semantic hierarchy
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Semantic classes and syntactic ambiguity
HLT '93 Proceedings of the workshop on Human Language Technology
Disambiguating Nouns, Verbs, and Adjectives Using Automatically Acquired Selectional Preferences
Computational Linguistics
Evaluating WordNet-based Measures of Lexical Semantic Relatedness
Computational Linguistics
Yago: a core of semantic knowledge
Proceedings of the 16th international conference on World Wide Web
Learning to link with wikipedia
Proceedings of the 17th ACM conference on Information and knowledge management
Frame Detection over the Semantic Web
ESWC 2009 Heraklion Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications
New features for FrameNet: WordNet mapping
CoNLL '09 Proceedings of the Thirteenth Conference on Computational Natural Language Learning
Discriminative learning of selectional preference from unlabeled text
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
WordNet::Similarity: measuring the relatedness of concepts
HLT-NAACL--Demonstrations '04 Demonstration Papers at HLT-NAACL 2004
Combining word sense and usage for modeling frame semantics
STEP '08 Proceedings of the 2008 Conference on Semantics in Text Processing
Learning Semantic Roles for Ontology Patterns
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Improving semantic role classification with Selectional Preferences
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Probabilistic frame-semantic parsing
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
BabelNet: building a very large multilingual semantic network
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
SemEval-2010 task 10: Linking events and their participants in discourse
SemEval '10 Proceedings of the 5th International Workshop on Semantic Evaluation
A flexible, corpus-driven model of regular and inverse selectional preferences
Computational Linguistics
DBpedia spotlight: shedding light on the web of documents
Proceedings of the 7th International Conference on Semantic Systems
A novel Framenet-based resource for the semantic web
Proceedings of the 27th Annual ACM Symposium on Applied Computing
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Compared to other existing semantic role repositories, FrameNet is characterized by an extremely high number of roles or Frame Elements (FEs), which amount to 8,884 in the last resource release. This represents an interesting issue to investigate both from a theoretical and a practical point of view. In this paper, we analyze the semantics of frame elements by automatically assigning them a set of synsets characterizing the typical FE fillers. We show that the synset repository created for each FE can adequately generalize over the fillers, while providing more informative sense labels than just one generic semantic type. We also evaluate the impact of the enriched FE information on a semantic role labeling task, showing that it can improve classification precision, though at the cost of lower recall.