Abstract
Research in Computational Linguistics (CL) has been growing rapidly in recent years in terms of novel scientific challenges and commercial application opportunities. This is due to the fact that a very large part of the Web content is textual and written in many languages. A part from linguistic resources (e.g., WordNet), the research trend is moving towards the automatic extraction of semantic information from large corpora to support on-line understanding of textual data. An example of direct outcome is represented by common-sense semantic resources. The main example is ConceptNet, the final result of the Open Mind Common Sense project developed by MIT, which collected unstructured common-sense knowledge by asking people to contribute over the Web. In spite of being promising for its size and broad semantic coverage, few applications appeared in the literature so far, due to a number of issues such as inconsistency and sparseness. In this paper, we present the results of the application of this type of knowledge in two different (supervised and unsupervised) scenarios: the computation of semantic similarity (the keystone of most Computational Linguistics tasks), and the automatic identification of word meanings (Word Sense Induction) in simple syntactic structures.
Access provided by Autonomous University of Puebla. Download to read the full chapter text
Chapter PDF
Similar content being viewed by others
Keywords
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.
References
Padó, S., Lapata, M.: Dependency-based construction of semantic space models. Computational Linguistics 33(2), 161–199 (2007)
Navigli, R., Velardi, P., Faralli, S.: A graph-based algorithm for inducing lexical taxonomies from scratch. In: Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence-Volume Volume Three, pp. 1872–1877. AAAI Press (2011)
Lenci, A.: Carving verb classes from corpora. In: Simone, R., Masini, F. (eds.) Word Classes, p. 7. John Benjamins, Amsterdam, Philadelphia (2010)
Speer, R., Havasi, C.: Representing general relational knowledge in ConceptNet 5. In: LREC, pp. 3679–3686 (2012)
Miller, G.A.: Wordnet: a lexical database for english. Communications of the ACM 38(11), 39–41 (1995)
Stevenson, M., Wilks, Y.: Word-sense disambiguation. The Oxford Handbook of Comp. Linguistics, 249–265 (2003)
Manning, C.D., Schütze, H.: Foundations of statistical natural language processing. MIT press (1999)
Gibson, J.J.: The Theory of Affordances. Lawrence Erlbaum (1977)
Baroni, M., Lenci, A.: Distributional memory: A general framework for corpus-based semantics. Computational Linguistics 36(4), 673–721 (2010)
Hill, F., Reichart, R., Korhonen, A.: Simlex-999: Evaluating semantic models with (genuine) similarity estimation (2014). arXiv preprint arXiv:1408.3456
Denkowski, M.: A survey of techniques for unsupervised word sense induction. Language & Statistics II Literature Review (2009)
Schutze, H.: Dimensions of meaning. In: Supercomputing 1992., Proceedings, pp. 787–796. IEEE (1992)
Borgelt, C.: Frequent item set mining. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 2(6), 437–456 (2012)
Zaki, M.J., Hsiao, C.J.: Charm: an efficient algorithm for closed itemset mining. In: SDM, Vol. 2, pp. 457–473. SIAM (2002)
Karlgren, J., Holst, A., Sahlgren, M.: Filaments of meaning in word space. In: Macdonald, C., Ounis, I., Plachouras, V., Ruthven, I., White, R.W. (eds.) ECIR 2008. LNCS, vol. 4956, pp. 531–538. Springer, Heidelberg (2008)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Di Caro, L., Ruggeri, A., Cupi, L., Boella, G. (2015). Common-Sense Knowledge for Natural Language Understanding: Experiments in Unsupervised and Supervised Settings. In: Gavanelli, M., Lamma, E., Riguzzi, F. (eds) AI*IA 2015 Advances in Artificial Intelligence. AI*IA 2015. Lecture Notes in Computer Science(), vol 9336. Springer, Cham. https://doi.org/10.1007/978-3-319-24309-2_18
Download citation
DOI: https://doi.org/10.1007/978-3-319-24309-2_18
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-24308-5
Online ISBN: 978-3-319-24309-2
eBook Packages: Computer ScienceComputer Science (R0)