A case for deep learning in semantics

Pater's target article builds a persuasive case for establishing stronger ties between theoretical linguistics and connectionism (deep learning). This commentary extends his arguments to semantics, focusing in particular on issues of learning, compositionality, and lexical meaning.

[1]  B. Partee Lexical semantics and compositionality. , 1995 .

[2]  Marco Baroni,et al.  Frege in Space: A Program of Compositional Distributional Semantics , 2014 .

[3]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[4]  J. Searle Chomsky's Revolution in Linguistics , 2016 .

[5]  Barbara H. Partee,et al.  Montague Grammar, Mental Representations, and Reality , 1980 .

[6]  A. Wierzbicka,et al.  Semantics and cognition. , 2006, Wiley interdisciplinary reviews. Cognitive science.

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

[8]  Christopher Potts,et al.  Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank , 2013, EMNLP.

[9]  Geoffrey E. Hinton,et al.  Distributed representations and nested compositional structure , 1994 .

[10]  Joe Pater Generative linguistics and neural networks at 60: Foundation, friction, and fusion , 2019, Language.

[11]  F. Newmeyer The Linguistic Wars , 1986 .

[12]  Arnim von Stechow,et al.  Semantics From Different Points of View , 1979 .

[13]  Jordan B. Pollack,et al.  Recursive Distributed Representations , 1990, Artif. Intell..

[14]  E. Doerr,et al.  General Semantics. , 1958, Science.

[15]  Barbara Hall-Partee Semantics — Mathematics or Psychology? , 1979 .

[16]  Michael Johnson,et al.  Compositionality , 2020, The Wiley Blackwell Companion to Semantics.

[17]  Roger Levy,et al.  Negotiating Lexical Uncertainty and Speaker Expertise with Disjunction , 2015 .

[18]  Geoffrey E. Hinton Tensor Product Variable Binding and the Representation of Symbolic Structures in Connectionist Systems , 1991 .

[19]  Josef Ruppenhofer,et al.  FrameNet II: Extended theory and practice , 2006 .

[20]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[21]  Christopher D. Manning Computational Linguistics and Deep Learning , 2015, Computational Linguistics.

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

[23]  Patrick Pantel,et al.  From Frequency to Meaning: Vector Space Models of Semantics , 2010, J. Artif. Intell. Res..

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

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

[26]  László Dezsö,et al.  Universal Grammar , 1981, Certainty in Action.

[27]  Gregory Norman Carlson,et al.  Reference to kinds in English , 1977 .

[28]  Deirdre Wilson,et al.  A unitary approach to lexical pragmatics: relevance, inference and ad hoc concepts. , 2007 .

[29]  Kurt Hornik,et al.  Approximation capabilities of multilayer feedforward networks , 1991, Neural Networks.

[30]  Christopher Potts,et al.  Bringing Machine Learning and Compositional Semantics Together , 2015 .

[31]  Jeffrey Pennington,et al.  Semi-Supervised Recursive Autoencoders for Predicting Sentiment Distributions , 2011, EMNLP.

[32]  Alessandro Lenci,et al.  Distributional Models of Word Meaning , 2018 .

[33]  Shalom Lappin,et al.  当代语义理论指南 = The Handbook of Contemporary Semantic Theory , 2015 .

[34]  George Cybenko,et al.  Approximation by superpositions of a sigmoidal function , 1992, Math. Control. Signals Syst..

[35]  R. Montague Formal philosophy; selected papers of Richard Montague , 1974 .

[36]  Michael C. Frank,et al.  PSYCHOLOGICAL SCIENCE Research Article Using Speakers ’ Referential Intentions to Model Early Cross-Situational Word Learning , 2022 .

[37]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[38]  Yoshua Bengio,et al.  Neural Machine Translation by Jointly Learning to Align and Translate , 2014, ICLR.

[39]  Vasilios K. Kimiskidis,et al.  Introduction , 2019, Int. J. Neural Syst..

[40]  Christoph Goller,et al.  Learning task-dependent distributed representations by backpropagation through structure , 1996, Proceedings of International Conference on Neural Networks (ICNN'96).

[41]  Ewan Klein,et al.  Type-driven translation , 1985 .

[42]  H. H. Clark Dogmas of understanding , 1997 .

[43]  Jeffrey L. Elman,et al.  Finding Structure in Time , 1990, Cogn. Sci..