“Not not bad” is not “bad”: A distributional account of negation

With the increasing empirical success of distributional models of compositional semantics, it is timely to consider the types of textual logic that such models are capable of capturing. In this paper, we address shortcomings in the ability of current models to capture logical operations such as negation. As a solution we propose a tripartite formulation for a continuous vector space representation of semantics and subsequently use this representation to develop a formal compositional notion of negation within such models.

[1]  J. R. Firth,et al.  A Synopsis of Linguistic Theory, 1930-1955 , 1957 .

[2]  Stephen Clark,et al.  Mathematical Foundations for a Compositional Distributional Model of Meaning , 2010, ArXiv.

[3]  Edward Grefenstette,et al.  Towards a Formal Distributional Semantics: Simulating Logical Calculi with Tensors , 2013, *SEMEVAL.

[4]  Hinrich Schütze,et al.  Automatic Word Sense Discrimination , 1998, Comput. Linguistics.

[5]  Andrew Y. Ng,et al.  Semantic Compositionality through Recursive Matrix-Vector Spaces , 2012, EMNLP.

[6]  Jeffrey Pennington,et al.  Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection , 2011, NIPS.

[7]  Mehrnoosh Sadrzadeh,et al.  Experimental Support for a Categorical Compositional Distributional Model of Meaning , 2011, EMNLP.

[8]  Matthew Richardson,et al.  Markov logic networks , 2006, Machine Learning.

[9]  Dimitri Kartsaklis,et al.  A Unified Sentence Space for Categorical Distributional-Compositional Semantics: Theory and Experiments , 2012, COLING.

[10]  Richard Montague,et al.  ENGLISH AS A FORMAL LANGUAGE , 1975 .

[11]  S. Peters,et al.  Word Vectors and Quantum Logic Experiments with negation and disjunction , 2003 .

[12]  Mirella Lapata,et al.  Vector-based Models of Semantic Composition , 2008, ACL.

[13]  Roser Morante,et al.  *SEM 2012 Shared Task: Resolving the Scope and Focus of Negation , 2012, *SEMEVAL.

[14]  Marco Baroni,et al.  Nouns are Vectors, Adjectives are Matrices: Representing Adjective-Noun Constructions in Semantic Space , 2010, EMNLP.

[15]  Ioannis Korkontzelos,et al.  Estimating Linear Models for Compositional Distributional Semantics , 2010, COLING.

[16]  Johan Bos,et al.  Wide-Coverage Semantic Analysis with Boxer , 2008, STEP.

[17]  Cuong Chau,et al.  Montague Meets Markov: Deep Semantics with Probabilistic Logical Form , 2013, *SEMEVAL.

[18]  Gregory Grefenstette,et al.  Explorations in automatic thesaurus discovery , 1994 .

[19]  Mehrnoosh Sadrzadeh,et al.  Multi-Step Regression Learning for Compositional Distributional Semantics , 2013, IWCS.

[20]  Mirella Lapata,et al.  Composition in Distributional Models of Semantics , 2010, Cogn. Sci..

[21]  Mirella Lapata,et al.  A Comparison of Vector-based Representations for Semantic Composition , 2012, EMNLP.

[22]  Katrin Erk,et al.  A Structured Vector Space Model for Word Meaning in Context , 2008, EMNLP.

[23]  Katrin Erk,et al.  Integrating Logical Representations with Probabilistic Information using Markov Logic , 2011, IWCS.

[24]  Peter D. Turney.  Domain and Function: A Dual-Space Model of Semantic Relations and Compositions , 2012, J. Artif. Intell. Res..

[25]  James Richard Curran,et al.  From distributional to semantic similarity , 2004 .

[26]  Phil Blunsom,et al.  The Role of Syntax in Vector Space Models of Compositional Semantics , 2013, ACL.

[27]  T. Landauer,et al.  A Solution to Plato's Problem: The Latent Semantic Analysis Theory of Acquisition, Induction, and Representation of Knowledge. , 1997 .

[28]  Hinrich Schütze,et al.  Cutting Recursive Autoencoder Trees , 2013, ICLR.