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Combining Formal Logic and Machine Learning for Sentiment Analysis

  • Niklas Christoffer Petersen
  • Jørgen Villadsen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8502)

Abstract

This paper presents a formal logical method for deep structural analysis of the syntactical properties of texts using machine learning techniques for efficient syntactical tagging. To evaluate the method it is used for entity level sentiment analysis as an alternative to pure machine learning methods for sentiment analysis, which often work on sentence or word level, and are argued to have difficulties in capturing long distance dependencies.

Keywords

Noun Phrase Relative Clause Sentiment Analysis Lexical Unit Lexical Category 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Barendregt, H., Dekkers, W., Statman, R.: Lambda Calculus with Types. Cambridge University Press (2013)Google Scholar
  2. 2.
    Blitzer, J., Dredze, M., Pereira, F.: Biographies, Bollywood, Boomboxes and Blenders: Domain adaptation for sentiment classification. In: Annual Meeting of the Association of Computational Linguistics, pp. 440–447 (2007)Google Scholar
  3. 3.
    Bresnan, J., Kaplan, R.M., Peters, S., Zaenen, A.: Cross-serial dependencies in Dutch. Linguistic Inquiry 13(4), 613–635 (1982)Google Scholar
  4. 4.
    Cambria, E., Schuller, B., Liu, B., Wang, H., Havasi, C.: Statistical approaches to concept-level sentiment analysis. IEEE Intelligent Systems 28(3), 6–9 (2013)CrossRefGoogle Scholar
  5. 5.
    Clark, S.: A supertagger for combinatory categorial grammar. In: International Workshop on Tree Adjoining Grammars and Related Frameworks, Venice, Italy, pp. 19–24 (2002)Google Scholar
  6. 6.
    Clark, S., Curran, J.R.: Wide-coverage efficient statistical parsing with CCG and log-linear models. Computational Linguistics 33(4), 493–552 (2007)CrossRefzbMATHGoogle Scholar
  7. 7.
    Feldman, R.: Techniques and applications for sentiment analysis. Commun. ACM 56(4), 82–89 (2013)CrossRefGoogle Scholar
  8. 8.
    Ganesan, K., Zhai, C.X., Han, J.: Opinosis: A graph based approach to abstractive summarization of highly redundant opinions. In: International Conference on Computational Linguistics, pp. 340–348 (2010)Google Scholar
  9. 9.
    Hockenmaier, J.: Data and Models for Statistical Parsing with Combinatory Categorial Grammar. PhD thesis, University of Edinburgh (2003)Google Scholar
  10. 10.
    Hockenmaier, J., Steedman, M.: CCGbank: A corpus of CCG derivations and dependency structures extracted from the Penn treebank. Computational Linguistics 33(3), 355–396 (2007)CrossRefzbMATHGoogle Scholar
  11. 11.
    Joshi, A.K., Levy, L.S., Takahashi, M.: Tree adjunct grammars. Journal of Computer and System Sciences 10(1), 136–163 (1975)CrossRefzbMATHMathSciNetGoogle Scholar
  12. 12.
    Joshi, A.K., Shanker, K.V., Weir, D.: The convergence of mildly context-sensitive grammar formalisms. Technical Report, Department of Computer and Information Science, University of Pennsylvania (1990)Google Scholar
  13. 13.
    Liu, B.: Web Data Mining: Exploring Hyperlinks, Contents, and Usage Data. Springer (2007)Google Scholar
  14. 14.
    Marcus, M.P., Marcinkiewicz, M.A., Santorini, B.: Building a large annotated corpus of English: The Penn treebank. Computational Linguistics 19(2), 313–330 (1993)Google Scholar
  15. 15.
    Montague, R.: Formal Philosophy: Selected Papers of Richard Montague. Yale University Press (1974)Google Scholar
  16. 16.
    Pang, B., Lee, L.: Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2(1-2), 1–135 (2008)CrossRefGoogle Scholar
  17. 17.
    Pollard, C.: Generalized Context-Free Grammars, Head Grammars and Natural Language. PhD thesis, Stanford University (1984)Google Scholar
  18. 18.
    Shieber, M.S.: Evidence against the context-freeness of natural language. Linguistics and Philosophy 8(3), 333–343 (1985)CrossRefGoogle Scholar
  19. 19.
    Steedman, M.: Categorial grammar. In: The MIT Encyclopedia of Cognitive Sciences. The MIT Press (1999)Google Scholar
  20. 20.
    Steedman, M.: The Syntactic Process. The MIT Press (2000)Google Scholar
  21. 21.
    Vijay-Shanker, K., Weir, D.J.: The equivalence of four extensions of context-free grammars. Mathematical Systems Theory 27, 27–511 (1994)CrossRefMathSciNetGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Niklas Christoffer Petersen
    • 1
  • Jørgen Villadsen
    • 1
  1. 1.Algorithms, Logic and Graphs Section, Department of Applied Mathematics and Computer ScienceTechnical University of DenmarkKongens LyngbyDenmark

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