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Principles of Natural Language, Logic, and Tensor Semantics (Invited Paper)

Author Mehrnoosh Sadrzadeh

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LIPIcs.CALCO.2019.3.pdf
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Author Details

Mehrnoosh Sadrzadeh
  • School of Electronic Engineering and Computer Science, Queen Mary University of London, UK

Funding

Royal Academy of Engineering Industrial Fellowship Scheme IFS1718\63, Royal Society International Exchange Award IE161631

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Mehrnoosh Sadrzadeh. Principles of Natural Language, Logic, and Tensor Semantics (Invited Paper). In 8th Conference on Algebra and Coalgebra in Computer Science (CALCO 2019). Leibniz International Proceedings in Informatics (LIPIcs), Volume 139, pp. 3:1-3:4, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019) https://doi.org/10.4230/LIPIcs.CALCO.2019.3

Abstract

Residuated monoids model the structure of sentences. Vectors provide meaning representations for words. A functorial mapping between the two is obtained by lifting the vectors to tensors. The resulting sentence representations solve similarity, disambiguation and entailment tasks.

Subject Classification

ACM Subject Classification
  • Theory of computation → Categorical semantics
  • Computing methodologies → Natural language processing
  • General and reference → Experimentation
Keywords
  • Residuated Monoids
  • Vector Space Semantics
  • Corpora of Textual Data
  • Sentence Similarity and Disambiguation

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References

  1. K. Ajdukiewicz. Die syntaktische Konnexitat. Studia Philosophica, 1:1-27, 1935. Google Scholar
  2. Y. Bar-Hillel. A quasi-arithmetical notation for syntactic description. Language, 29:47-58, 1953. Google Scholar
  3. M. Baroni and R. Zamparelli. Nouns are vectors, adjectives are matrices: Representing adjective-noun constructions in semantic space. In Conference on Empirical Methods in Natural Language Processing, Cambridge, MA, 2010. Google Scholar
  4. Noam Chomsky. Three models for the description of language. IRE Transactions on Information Theory, 2:113-124, 1956. Google Scholar
  5. Stephen Clark and Stephen Pulman. Combining Symbolic and Distributional Models of Meaning. In Proceedings of the AAAI Spring Symposium on Quantum Interaction, pages 52-55, 2007. Google Scholar
  6. B. Coecke, M. Sadrzadeh, and S. Clark. Mathematical Foundations for Distributed Compositional Model of Meaning. Lambek Festschrift. Linguistic Analysis, 36:345-384, 2010. Google Scholar
  7. Bob Coecke, Edward Grefenstette, and Mehrnoosh Sadrzadeh. Lambek vs. Lambek: Functorial vector space semantics and string diagrams for Lambek calculus. Annals of Pure and Applied Logic, 164(11):1079-1100, 2013. Special issue on Seventh Workshop on Games for Logic and Programming Languages. Google Scholar
  8. J.R. Firth. A Synopsis of Linguistic Theory 1930-1955. In Studies in Linguistic Analysis. Longmans, 1957. Google Scholar
  9. E. Grefenstette, G. Dinu, Y. Zhang, M. Sadrzadeh, and M. Baroni. Multi-Step Regression Learning for Compositional Distributional Semantics. In 10th International Conference on Computational Semantics, Postdam, 2013. Google Scholar
  10. E. Grefenstette and M. Sadrzadeh. Experimental Support for a Categorical Compositional Distributional Model of Meaning. In Proceedings of Conference on Empirical Methods in Natural Language Processing, pages 1394-1404, 2011. Google Scholar
  11. Edward Grefenstette and Mehrnoosh Sadrzadeh. Concrete Models and Empirical Evaluations for the Categorical Compositional Distributional Model of Meaning. Computational Linguistics, 41:71-118, 2015. Google Scholar
  12. Z.S. Harris. Distributional structure. Word, 1954. Google Scholar
  13. D. Kartsaklis and M. Sadrzadeh. Prior Disambiguation of Word Tensors for Constructing Sentence Vectors. In Proceedings of Conference on Empirical Methods in Natural Language Processing, 2013. Google Scholar
  14. Dimitri Kartsaklis, Nal Kalchbrenner, and Mehrnoosh Sadrzadeh. Resolving Lexical Ambiguity in Tensor Regression Models of Meaning. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics, Baltimore, MD, USA, Volume 2: Short Papers, pages 212-217, 2014. Google Scholar
  15. J. Lambek. The mathematics of sentence structure. American Mathematics Monthly, 65, 1958. Google Scholar
  16. J. Lambek. Type grammars revisited. In proceedings of LACL 97, volume 1582 of Lecture Notes in Artificial Intelligence. Springer Verlag, 1997. Google Scholar
  17. Jean Maillard and Stephen Clark. Learning adjective meanings with a tensor-based skip-gram model. In Proceedings of the Nineteenth Conference on Computational Natural Language Learning, pages 327-331, 2015. Google Scholar
  18. Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems, pages 3111-3119, 2013. Google Scholar
  19. Dmitrijs Milajevs, Dimitri Kartsaklis, Mehrnoosh Sadrzadeh, and Matthew Purver. Evaluating Neural Word Representations in Tensor-Based Compositional Settings. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing, pages 708-719. Association for Computational Linguistics, 2014. Google Scholar
  20. Mati Pentus. Lambek Grammars Are Context Free. In In Proceedings of the Eighth Annual IEEE Symposium on Logic in Computer Science, pages 429-433. IEEE Computer Society Press, 1993. Google Scholar
  21. Tamara Polajnar, Luana Fagarasan, and Stephen Clark. Reducing dimensions of tensors in type-driven distributional semantics. In Proceedings of the Conference on Empirical Methods in Natural Language Processing, pages 1036-1046, 2014. Google Scholar
  22. A. Preller and M. Sadrzadeh. Bell States and Negative Sentences in the Distributed Model of Meaning. In P. Selinger B. Coecke, P. Panangaden, editor, Electronic Notes in Theoretical Computer Science, Proceedings of the 6th Workshop on Quantum Physics and Logic, volume 270, pages 141-153, 2010. Google Scholar
  23. Anne Preller and Joachim Lambek. Free compact 2-categories. Mathematical Structures in Computer Science, 17:309-340, 2007. Google Scholar
  24. H. Rubenstein and J.B. Goodenough. Contextual Correlates of Synonymy. Communications of the ACM, 8(10):627-633, 1965. Google Scholar
  25. M. Sadrzadeh. Unifying the Mathematics of Natural Language Grammar and Data. London Mathematical Society News Letter, pages 25-31, 2018. Google Scholar
  26. Mehrnoosh Sadrzadeh, Dimitri Kartsaklis, and Esma Balkır. Sentence entailment in compositional distributional semantics. Annals of Mathematics and Artificial Intelligence, 82(4):189-218, 2018. Google Scholar
  27. G. Salton, A. Wong, and C. S. Yang. A Vector Space Model for Automatic Indexing. Commun. ACM, 18:613-620, 1975. Google Scholar
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