A vector space model for automatic indexing
Communications of the ACM
Explorations in Automatic Thesaurus Discovery
Explorations in Automatic Thesaurus Discovery
Accurate methods for the statistics of surprise and coincidence
Computational Linguistics - Special issue on using large corpora: I
Automatic word sense discrimination
Computational Linguistics - Special issue on word sense disambiguation
Contextual spelling correction using latent semantic analysis
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Automatic retrieval and clustering of similar words
COLING '98 Proceedings of the 17th international conference on Computational linguistics - Volume 2
Measures of distributional similarity
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
LaTaT: language and text analysis tools
HLT '01 Proceedings of the first international conference on Human language technology research
The distributional inclusion hypotheses and lexical entailment
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
A semantic approach to IE pattern induction
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
A bootstrapping approach to unsupervised detection of cue phrase variants
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Linguistic preprocessing for distributional classification of words
ElectricDict '04 Proceedings of the Workshop on Enhancing and Using Electronic Dictionaries
Co-occurrence contexts for noun compound interpretation
MWE '07 Proceedings of the Workshop on a Broader Perspective on Multiword Expressions
Comparison of similarity models for the relation discovery task
LD '06 Proceedings of the Workshop on Linguistic Distances
From frequency to meaning: vector space models of semantics
Journal of Artificial Intelligence Research
Class-based approach to disambiguating levin verbs
Natural Language Engineering
A tensor encoding model for semantic processing
Proceedings of the 21st ACM international conference on Information and knowledge management
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Traditional vector-based models use word co-occurrence counts from large corpora to represent lexical meaning. In this paper we present a novel approach for constructing semantic spaces that takes syntactic relations into account. We introduce a formalisation for this class of models and evaluate their adequacy on two modelling tasks: semantic priming and automatic discrimination of lexical relations.