Jump to ContentJump to Main Navigation
Show Summary Details
More options …

Corpus Linguistics and Linguistic Theory

Founded by Gries, Stefan Th. / Stefanowitsch, Anatol

Ed. by Wulff, Stefanie

IMPACT FACTOR 2017: 1.200
5-year IMPACT FACTOR: 1.386

CiteScore 2017: 0.80

SCImago Journal Rank (SJR) 2017: 0.288
Source Normalized Impact per Paper (SNIP) 2017: 0.930

See all formats and pricing
More options …

The role of syntactic dependencies in compositional distributional semantics

Pablo Gamallo
  • Corresponding author
  • CiTIUS – Centro Singular de Investigación en Tecnoloxías da Información, University of Santiago de Compostela, Galiza, Spain
  • Email
  • Other articles by this author:
  • De Gruyter OnlineGoogle Scholar
Published Online: 2017-01-24 | DOI: https://doi.org/10.1515/cllt-2016-0038


This article provides a preliminary semantic framework for Dependency Grammar in which lexical words are semantically defined as contextual distributions (sets of contexts) while syntactic dependencies are compositional operations on word distributions. More precisely, any syntactic dependency uses the contextual distribution of the dependent word to restrict the distribution of the head, and makes use of the contextual distribution of the head to restrict that of the dependent word. The interpretation of composite expressions and sentences, which are analyzed as a tree of binary dependencies, is performed by restricting the contexts of words dependency by dependency in a left-to-right incremental way. Consequently, the meaning of the whole composite expression or sentence is not a single representation, but a list of contextualized senses, namely the restricted distributions of its constituent (lexical) words. We report the results of two large-scale corpus-based experiments on two different natural language processing applications: paraphrasing and compositional translation.

This article offers supplementary material which is provided at the end of the article.

Keywords: distributional similarity; compositional semantics; syntactic analysis; dependencies


  • Baroni, Marco. 2013. Composition in distributional semantics. Language and Linguistics Compass 7. 511–522.CrossrefGoogle Scholar

  • Baroni, Marco, Raffaella Bernardi & Roberto Zamparelli. 2014. Frege in space: A program for compositional distributional semantics. LiLT 9. 241–346.Google Scholar

  • Baroni, Marco, Silvia Bernardini, Adriano Ferraresi & Eros Zanchetta. 2009. The wacky wide web: A collection of very large linguistically processed webcrawled corpora. Language Resources and Evaluation 43(3). 209–226.CrossrefGoogle Scholar

  • Baroni, Marco & Roberto Zamparelli. 2010. Nouns are vectors, adjectives are matrices: Representing adjective-noun constructions in semantic space. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, EMNLP’10, 1183–1193. Stroudsburg, PA, USA.Google Scholar

  • Barwise, Jon. 1987. Recent developments in situation semantics. Language and Artificial Intelligence. Berlin: Springer-Verlag.Google Scholar

  • Coecke, B., M. Sadrzadeh & S. Clark. 2010. Mathematical foundations for a compositional distributional model of meaning. Linguistic Analysis 36(1–4). 345–384.Google Scholar

  • Copestake, Ann & Aurelie Herbelot. 2012. Lexicalised compositionality. In http://www.cl.cam.ac.uk/ah433/lc-semprag.pdf.

  • Costa, F., V. Lombardo, P. Frasconi & G. Soda. 2001. Wide coverage incremental parsing by learning attachment preferences. In Conference of the Italian Association for Artificial Intelligence (AIIA).

  • Davidson, Donald. 1969. The individuation of events, 216–234. Dordrecht: Springer Netherlands. ISBN 978-94-017-1466–2.Google Scholar

  • Delpech, Estelle, Béatrice Daille, Emmanuel Morin & Claire Lemaire. 2012. Extraction of domain-specific bilingual lexicon from comparable corpora: Compositional translation and ranking. In COLING 2012, 24th International Conference on Computational Linguistics, Mumbai, India, 745–762.Google Scholar

  • Dinu, G., N. Pham & M. Baroni. 2013a. Dissect: Distributional semantics composition toolkit. In ACL 2013 Workshop on Continuous Vector Space Models and their Compositionality (CVSC 2013), 31–36. East Stroudsburg, PA.

  • Dinu, G., N. Pham & M. Baroni. 2013b. General estimation and evaluation of compositional distributional semantic models. In ACL 2013 Workshop on Continuous Vector Space Models and their Compositionality (CVSC 2013), 50–58. East Stroudsburg, PA.

  • Erk, Katrin. 2013. Towards a semantics for distributional representations. In IWCS-2013.

  • Erk, Katrin & Sebastian Padó. 2008. A structured vector space model for word meaning in context. In Proceedings of EMNLP. Honolulu, HI.

  • Fellbaum, C. 1998. A semantic network of English: The mother of all WordNets. Computer and the Humanities 32. 209–220.CrossrefGoogle Scholar

  • Fung, Pascale & Lo Yuen Yee. 1998. An IR approach for translating new words from nonparallel, comparable texts. In Coling’98, 414–420. Montreal, Canada.Google Scholar

  • Gamallo, Pablo. 2003. Cognitive characterisation of basic grammatical structures. Pragmatics and Cognition 11(2). 209–240.CrossrefGoogle Scholar

  • Gamallo, Pablo. 2007. Learning bilingual lexicons from comparable English and Spanish Corpora. In Machine Translation SUMMIT XI. Copenhagen, Denmark.Google Scholar

  • Gamallo, Pablo. 2008. The meaning of syntactic dependencies. Linguistik OnLine 35(3). 33–53.Google Scholar

  • Gamallo, Pablo, Alexandre Agustini & Gabriel Lopes. 2005. Clustering syntactic positions with similar semantic requirements. Computational Linguistics 31(1). 107–146.CrossrefGoogle Scholar

  • Gamallo, Pablo & Isaac González. 2011. A grammatical formalism based on patterns of part-of-speech tags. International Journal of Corpus Linguistics 16(1). 45–71.CrossrefGoogle Scholar

  • Gamallo, Pablo & José Ramom Pichel. 2008. Learning Spanish-Galician translation equivalents using a comparable corpus and a bilingual dictionary. LNCS 4919. 413–423.Google Scholar

  • Grefenstette, Gregory. 1996. Evaluation techniques for automatic semantic extraction: Comparing syntactic and window based approaches. In B. Boguraev & J. Pustejovsky (eds.), Corpus processing for lexical acquisition, 205–216. Cambridge, MA: The MIT Press.

  • Grefenstette, Gregory. 1999. The World Wide Web as a resource for example-based machine translation tasks. In Translating and the Computer 21: Proceedings of the 21st International Conference on Translating and the Computer.

  • Grefenstette, Edward, Mehrnoosh Sadrzadeh, Stephen Clark, Bob Coecke & Stephen Pulman. 2011. Concrete sentence spaces for compositional distributional models of meaning. In Proceedings of the Ninth International Conference on Computational Semantics, IWCS ’11, 125–134.

  • Groenendijk, J. & M. Stokhof. 1991. Dynamic predicate logic. Linguistics and Philosophy 14. 39–100.CrossrefGoogle Scholar

  • Guevara, Emiliano. 2010. A regression model of adjective-noun compositionality in distributional semantics. In Proceedings of the 2010 Workshop on GEometrical Models of Natural Language Semantics, GEMS ’10.Google Scholar

  • Hanks, Patrick. 2013. Lexical analysis: Norms and exploitations. Cambridge, MA: MIT Press.Google Scholar

  • Harris, Zellig. 1954. Distributional structure. Word 10(23). 146–162.CrossrefGoogle Scholar

  • Hudson, Richard. 2003. The psychological reality of syntactic dependency relations. In MTT 2003. Paris.

  • Jezek, Elisabetta & Patrick Hanks. 2010. What lexical sets tell us about conceptual categories. Lexis [Online], 4 | 2010, Online since 14 April 2010. http://lexis.revues.org/555 (accessed 16 January 2017), DOI: 10.4000/lexis.555.

  • Kahane, Sylvain. 2003. Meaning-text theory. In V. Ágel et al. (eds.), Dependency and valency: An international handbook of contemporary research. Berlin: De Gruyter.Google Scholar

  • Kamp, H. & U. Reyle. 1993. From discourse to logic: Introduction to model-theoretic semantics of natural language. Formal logic and discourse representation theory. Dordrecht: Kluwer Academic Publisher.Google Scholar

  • Kempson, R., W. Meyer-Viol & D. Gabbay. 1997. Language understanding: A procedural perspective. In C. Retore (ed.), First international conference on logical aspects of computational linguistics, 228–247. Lecture Notes in Artificial Intelligence Vol. 1328. Springer Verlag.Google Scholar

  • Kempson, R., W. Meyer-Viol & D. Gabbay. 2001. Dynamic syntax: The flow of language understanding. Oxford: Blackwell.Google Scholar

  • Koehn, Philipp. 2009. Statistical machine translation. Cambridge: Cambridge University Press.Google Scholar

  • Krishnamurthy, Jayant & Tom Mitchell. 2013. Proceedings of the workshop on continuous vector space models and their compositionality, chap. Vector Space Semantic Parsing: A Framework for Compositional Vector Space Models, 1–10. Association for Computational Linguistics.

  • Langacker, Ronald W. 1991. Foundations of cognitive grammar: Descriptive applications, vol. 2. Stanford: Stanford University Press.Google Scholar

  • McRae, K., T.R. Ferreti & L. Amoyte. 1997. Thematic roles as verb-specific concepts. In M. MacDonald (ed.), Lexical representations and sentence processing, 137–176. Sussex, UK: Psychology Press.

  • Meillet, Antoine. 1921. Linguistique historique et linguistique générale. Paris: La Société Linguistique de Paris.Google Scholar

  • Milward, David. 1992. Dynamics, dependency grammar and incremental interpretation. In 14th Conference on Computational Linguistics (Coling92), 1095–1099. Nantes.Google Scholar

  • Mitchell, Jeff & Mirella Lapata. 2008. Vector-based models of semantic composition. In Proceedings of ACL-08: HLT, 236–244.Google Scholar

  • Mitchell, Jeff & Mirella Lapata. 2009. Language models based on semantic composition. In Proceedings of EMNLP, 430–439.

  • Mitchell, Jeff & Mirella Lapata. 2010. Composition in distributional models of semantics. Cognitive Science 34(8). 1388–1439.CrossrefPubMedGoogle Scholar

  • Montague, Richard. 1970. Universal grammar. theoria. Theoria 36. 373–398.Google Scholar

  • Morin, Emmanuel & Béatrice Daille. 2012. Revising the compositional method for terminology acquisition from comparable corpora. In COLING 2012, 24th International Conference on Computational Linguistics, Mumbai, India, 1797–1810.Google Scholar

  • Navigli, Roberto. 2009. Word sense disambiguation: A survey. ACM Computing Surveys 41(2). 1–69.CrossrefGoogle Scholar

  • Partee, Barbara. 2007. Private adjectives: Subsective plus coercion. In R. Bäuerle, U. Reyle & T. E. Zimmermann (eds.), Presuppositions and discourse. Amsterdam: Elsevier.Google Scholar

  • Pustejovsky, James. 1995. The generative lexicon. Cambridge: MIT Press.Google Scholar

  • Rapp, Reinhard. 1999. Automatic identification of word translations from unrelated English and German Corpora. In ACL’99, 519–526.

  • Schlesewsky, M. & I. Bornkessel. 2004. On incremental interpretation: Degrees of meaning accessed during sentence comprehension. Lingua 114. 1213–1234.CrossrefGoogle Scholar

  • Schütze, Hinrich. 1998. Automatic word sense discrimination. Computational Linguistics 24(1). 97–124.Google Scholar

  • Sperber, Dan & Deirdre Wilson. 1995. Relevance: Communication and cognition, 2nd edn. Oxford: Blackwell.

  • Steedman, Mark. 1996. Surface structure and interpretation. Cambridge, MA: The MIT Press.

  • Studtmann, Paul. 2014. Aristotle’s categories. In E. N. Zalta (ed.), The Stanford encyclopedia of philosophy. Summer 2014 edn.

  • Tanaka, Takaaki & Timothy Baldwin. 2003. Noun-noun compound machine translation a feasibility study on shallow processing. In Proceedings of the ACL 2003 Workshop on Multiword Expressions: Analysis, Acquisition and Treatment, 17–24. Sapporo, Japan.Google Scholar

  • Tanenhaus, M.K. & G.N. Carlson. 1989. Lexical structure and language comprehension. In W. Marslen-Wilson (ed.), Lexical representation and process, 530–561. Cambridge, MA: The MIT Press.

  • Tesniére, Lucien. 1959. Eléments de syntaxe structurale. Paris: Klincksieck.Google Scholar

  • Thater, Stefan, Hagen Fürstenau & Manfred Pinkal. 2010. Contextualizing semantic representations using syntactically enriched vector models. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, 948–957. Stroudsburg, PA, USA.

  • Truswell, J.C., M.K. Tanenhaus & S.M. Garnsey. 1994. Semantic influences on parsing: use of thematic role information in syntactic ambiguity resolution. Journal of Memory and Language 33. 285–318.CrossrefGoogle Scholar

  • Turney, Peter D. 2013. Domain and function: A dual-space model of semantic relations and compositions. Journal of Artificial Intelligence Research (JAIR) 44. 533–585.Google Scholar

  • Zanzotto, Fabio Massimo, Ioannis Korkontzelos, Francesca Fallucchi & Suresh Manandhar. 2010. Estimating linear models for compositional distributional semantics. In Proceedings of the 23rd International Conference on Computational Linguistics, COLING ’10, 1263–1271.Google Scholar

About the article

Published Online: 2017-01-24

Published in Print: 2017-09-26

This work is funded by Project TELPARES, Ministry of Economy and Competitiveness (FFI2014-51978-C2-1-R), and the program “Ayuda Fundación BBVA a Investigadores y Creadores Culturales 2016”.

Citation Information: Corpus Linguistics and Linguistic Theory, Volume 13, Issue 2, Pages 261–289, ISSN (Online) 1613-7035, ISSN (Print) 1613-7027, DOI: https://doi.org/10.1515/cllt-2016-0038.

Export Citation

© 2017 Walter de Gruyter GmbH, Berlin/Boston.Get Permission

Supplementary Article Materials

Comments (0)

Please log in or register to comment.
Log in