WordNet: a lexical database for English
Communications of the ACM
DIRT @SBT@discovery of inference rules from text
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
A maximum-entropy-inspired parser
NAACL 2000 Proceedings of the 1st North American chapter of the Association for Computational Linguistics conference
Disambiguating Nouns, Verbs, and Adjectives Using Automatically Acquired Selectional Preferences
Computational Linguistics
Verb-particle constructions and lexical resources
MWE '03 Proceedings of the ACL 2003 workshop on Multiword expressions: analysis, acquisition and treatment - Volume 18
Dependency-Based Construction of Semantic Space Models
Computational Linguistics
Introduction to Information Retrieval
Introduction to Information Retrieval
A structured vector space model for word meaning in context
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Detecting compositionality in multi-word expressions
ACLShort '09 Proceedings of the ACL-IJCNLP 2009 Conference Short Papers
SVD feature selection for probabilistic taxonomy learning
GEMS '09 Proceedings of the Workshop on Geometrical Models of Natural Language Semantics
Distributed structures and distributional meaning
DiSCo '11 Proceedings of the Workshop on Distributional Semantics and Compositionality
An unsupervised ranking model for noun-noun compositionality
SemEval '12 Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the main conference and the shared task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation
Semantic compositionality through recursive matrix-vector spaces
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
Parallels between machine and brain decoding
BI'12 Proceedings of the 2012 international conference on Brain Informatics
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In distributional semantics studies, there is a growing attention in compositionally determining the distributional meaning of word sequences. Yet, compositional distributional models depend on a large set of parameters that have not been explored. In this paper we propose a novel approach to estimate parameters for a class of compositional distributional models: the additive models. Our approach leverages on two main ideas. Firstly, a novel idea for extracting compositional distributional semantics examples. Secondly, an estimation method based on regression models for multiple dependent variables. Experiments demonstrate that our approach outperforms existing methods for determining a good model for compositional distributional semantics.