Hostname: page-component-78c5997874-v9fdk Total loading time: 0 Render date: 2024-11-05T13:28:13.642Z Has data issue: false hasContentIssue false
  • English
  • Français

General and specific paraphrases of semantic relations between nouns

Published online by Cambridge University Press:  20 May 2013

PAUL NULTY  and
FINTAN COSTELLO
PAUL NULTY
Affiliation:
Department of Methodology, London School of Economics, London, UK e-mail: [email protected]
FINTAN COSTELLO
Affiliation:
School of Computer Science and Informatics, University College Dublin, Dublin, Ireland. e-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Many English noun pairs suggest an almost limitless array of semantic interpretation. A fruit bowl might be described as a bowl for fruit, a bowl that contains fruit, a bowl for holding fruit, or even (perhaps in a modern sculpture class), a bowl made out of fruit. These interpretations vary in syntax, semantic denotation, plausibility, and level of semantic detail. For example, a headache pill is usually a pill for preventing headaches, but might, perhaps in the context of a list of side effects, be a pill that can cause headaches (Levi, J. N. 1978. The Syntax and Semantics of Complex Nominals. New York: Academic Press.). In addition to lexical ambiguity, both relational ambiguity and relational vagueness make automatic semantic interpretation of these combinations difficult. While humans parse these possibilities with ease, computational systems are only recently gaining the ability to deal with the complexity of lexical expressions of semantic relations. In this paper, we describe techniques for paraphrasing the semantic relations that can hold between nouns in a noun compound, using a semi-supervised probabilistic method to rank candidate paraphrases of semantic relations, and describing a new method for selecting plausible relational paraphrases at arbitrary levels of semantic specification. These methods are motivated by the observation that existing semantic relation classification schemes often exhibit a highly skewed class distribution, and that lexical paraphrases of semantic relations vary widely in semantic precision.

Type
Articles
Information
Natural Language Engineering , Volume 19 , Special Issue 3: On the semantics of noun compounds , July 2013 , pp. 357 - 384
Copyright
Copyright © Cambridge University Press 2013 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Baker, M. C. 2003. Lexical Categories: Verbs, Nouns, and Adjectives. Cambridge Studies in Linguistics. Cambridge, UK: Cambridge University Press.CrossRefGoogle Scholar
Balota, D. A. and Chumbley, J. I. 1984. Are lexical decisions a good measure of lexical access? The role of word frequency in the neglected decision stage. Journal of Experimental Science Psychology: Human Perception and Performance 10 (3): 340–57.Google ScholarPubMed
Banko, M., Cafarella, M. J., Soderland, S., Broadhead, M. and Etzioni, O. 2007. Open information extraction from the web. In Proceedings of the 20th International Joint Conference on Artificial Intelligence, Hyderabad, India, pp. 2670–76.Google Scholar
Baroni, M. and Lenci, A. 2010. Distributional memory: a general framework for corpus-based semantics. Computational Linguistics 36 (4): 673–721.CrossRefGoogle Scholar
Bollacker, K., Evans, C., Paritosh, P., Sturge, T., and Taylor, J. 2008. Freebase: a collaboratively created graph database for structuring human knowledge. In Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data (SIGMOD '08), pp. 1247–50. New York, NY: ACM.CrossRefGoogle Scholar
Brants, T. and Franz, A. 2006. Web 1T 5-gram Version 1. California: Google Research, Google Inc.Google Scholar
Butnariu, C., Kim, S. N., Nakov, P., Séaghdha, Diarmuid Ó., Szpakowicz, S., and Veale, T. 2010. Semeval-2010 task 9: the interpretation of noun compounds using paraphrasing verbs and prepositions. In Proceedings of the 5th International Workshop on Semantic Evaluation (SemEval '10), pp. 39–44. Uppsala, Sweden: Association for Computational Linguistics.Google Scholar
Butnariu, C. and Veale, T. 2008. A concept-centered approach to noun-compound interpretation. In Proceedings of the 22nd International Conference on Computational Linguistics – vol. 1 (COLING '08), pp. 81–8. Stroudsburg, PA: Association for Computational Linguistics.CrossRefGoogle Scholar
Daelemans, W., Zavrel, J., van der Sloot, K., and van den Bosch, A. 1999. TiMBL: Tilburg Memory Based Learner – Version 2.0 – Reference Guide. Tilburg, Netherlands: Tilburg University. Available at: http://ilk.uvt.nl/downloads/pub/papers/ilk.0707.pdf.Google Scholar
Essen, U. and Steinbiss, V. 1992. Cooccurrence smoothing for stochastic language modeling. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, 1992 (ICASSP-92), vol. 1, pp. 161–4. New York: IEEE.Google Scholar
Ferrucci, D. A., Brown, E. W., Chu-Carroll, J., Fan, J., Gondek, D., Kalyanpur, A., Lally, A., Murdock, J. W., Nyberg, E., Prager, J. M., Schlaefer, N., and Welty, C. A. 2010. Building Watson: an overview of the DeepQA project. AI Magazine 31 (3): 59–79.CrossRefGoogle Scholar
Geffet, M. and Dagan, I. 2004. The distributional inclusion hypothesis and lexical entailment. In Proceedings of the 42rd Annual Meeting of the ACL, pp. 107–14. Ann Arbor, MI: Association for Computational Linguistics.Google Scholar
Girju, R., Moldovan, D., Tatu, M. and Antohe, D. 2005. On the semantics of noun compounds. Computer Speech and Language 19 (4): 479–96 (Special issue on Multiword Expression).CrossRefGoogle Scholar
Grice, H. P. 1975. Logic and conversation. In Cole, P., and Morgan, J. L. (eds.), Syntax and Semantics, vol. 3, Speech Acts, pp. 41–58. San Diego, CA: Academic Press.Google Scholar
Hawker, T. (2006). Using Contexts of One Trillion Words for WSD. In Proceedings of the 10th Conference of the Pacific Association for Computational Linguistics, pp. 85–93.Google Scholar
Jackendoff, R. 2010. Meaning and the Lexicon: The Parallel Architecture, 1975-2010. Oxford, UK: Oxford University Press.Google Scholar
Johnston, M. and Busa, F. 1996. Qualia structure and the compositional interpretation of compounds. In Proceedings of the ACL SIGLEX Workshop on Breadth and Depth of Semantic Lexicons, Santa Cruz, CA, pp. 77–88. Stroudsburg, PA: ACL.Google Scholar
Kim, S. N. and Baldwin, T. 2005. Automatic interpretation of compound nouns using wordnet similarity. In Proceedings of 2nd International Joint Conference on Natural Language Processing, Jeju Island, Korea, pp. 945–56. Berlin, Germany: Springer.Google Scholar
Kim, S. N. and Baldwin, T. 2006. Interpreting semantic relations in noun compounds via verb semantics. In Proceedings of the COLING/ACL Main Conference Poster Sessions (COLING-ACL '06), pp. 491–8. Stroudsburg, PA: Association for Computational Linguistics.CrossRefGoogle Scholar
Kim, S. N. and Nakov, P. 2011. Large-scale noun compound interpretation using bootstrapping and the web as a corpus. In EMNLP, pp. 648–58. Stroudsburg, PA: ACL.Google Scholar
Kotlerman, L., Dagan, I., Szpektor, I. and Zhitomirsky-Geffet, M. 2010. Directional distributional similarity for lexical inference. Natural Language Engineering, 16 (4): 359–89.CrossRefGoogle Scholar
Lauer, M. 1995. Designing Statistical Language Learners: Experiments on Compound Nouns. PhD thesis, Macquarie University, NSW, Australia.Google Scholar
Levi, J. N. 1978. The Syntax and Semantics of Complex Nominals. New York: Academic Press.Google Scholar
Li, W. 1992. Random texts exhibit Zipf's-law-like word frequency distribution. IEEE Transactions on Information Theory 38 (6): 1842–5.CrossRefGoogle Scholar
Li, G., Lopez-Fernandez, A., and Veale, T. 2010. UCD-Goggle: a hybrid system for noun compound paraphrasing. In Proceedings of the 5th International Workshop on Semantic Evaluation (SemEval '10), pp. 230–33. Stroudsburg, PA: Association for Computational Linguistics.Google Scholar
Manin, D. Y. 2008. Zipf's law and avoidance of excessive synonymy. Cognitive Science 32 (7): 1075–98.CrossRefGoogle ScholarPubMed
McCarthy, D., Koeling, R., Weeds, J., and Carroll, J. 2004. Finding predominant word senses in untagged text. In Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics, pp 279–87. Stroudsburg, PA: Association for Computational Linguistics.Google Scholar
McCarthy, D., and Navigli, R. 2007. SemEval-2007 task 10: English lexical substitution task. In Proceedings of the 4th International Workshop on Semantic Evaluations (SemEval '07), pp. 48–53. Stroudsburg, PA: Association for Computational Linguistics.CrossRefGoogle Scholar
Mohamed, T., Hruschka, E. and Mitchell, T. 2011. Discovering relations between noun categories. In Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing, pp. 1447–55. Edinburgh, Scotland: Association for Computational Linguistics.Google Scholar
Nakov, P. 2008. Noun compound interpretation using paraphrasing verbs: feasibility study. In Dochev, D., Pistore, M., and Traverso, P. (eds.), Artificial Intelligence: Methodology, Systems, and Applications, pp. 103–17. Lecture Notes in Computer Science, vol. 5253. Berlin, Germany: Springer.CrossRefGoogle Scholar
Nakov, P. and Hearst, M. 2006. Using verbs to characterize noun-noun relations. In Euzenat, J. and Domingue, J. (eds.), Artificial Intelligence: Methodology, Systems, and Applications, pp. 233–244. Lecture Notes in Computer Science, vol. 4183. Berlin, Germany: Springer.CrossRefGoogle Scholar
Nastase, V. and Szpakowicz, S. 2003. Exploring noun-modifier semantic relations. In Fifth International Workshop on Computational Semantics (IWCS-5), Tilburg, Netherlands, pp. 285–301. Berlin, Germany: Springer/Kluwer.Google Scholar
Nulty, P. and Costello, F. 2010. A comparison of word similarity measures for noun compound disambiguation. In Coyle, L. and Freyne, J. (eds), Artificial Intelligence and Cognitive Science, pp 231–40. Lecture Notes in Computer Science, vol. 6206. Berlin, Germany: Springer.CrossRefGoogle Scholar
Ó Séaghdha, D. 2007. Designing and evaluating a semantic annotation scheme for compound nouns. In Proceedings of the 4th Corpus Linguistics Conference, July 27–30, University of Birmingham, UK.Google Scholar
Ó Séaghdha, D. 2008. Learning Compound Noun Semantics. PhD thesis, Computer Laboratory, University of Cambridge, Cambridge, UK. Published as Computer Laboratory Technical Report 735, University of Cambridge.Google Scholar
Pantel, P., Bhagat, R., Coppola, B., Chklovski, T., & Hovy, E. H. 2007. ISP: learning inferential selectional preferences. In Sidner, C. L., Schultz, T., Stone, M., and Zhai, Cheng Xiang (eds.), HLT-NAACL, pp. 564–71. Edinburgh, Scotland: Association for Computational Linguistics.Google Scholar
Pantel, P. and Pennacchiotti, M. 2006. Espresso: leveraging generic patterns for automatically harvesting semantic relations. In Proceedings of the 21st International Conference on Computational Linguistics and the 44th Annual Meeting of the Association for Computational Linguistics, pp. 113–20. Stroudsburg, PA: Association for Computational Linguistics.Google Scholar
Schubert, L. 2002. Can we derive general world knowledge from texts? In Proceedings of the Second International Conference on Human Language Technology Research, pp. 94–7. San Francisco, CA: Morgan Kaufmann.CrossRefGoogle Scholar
Sinha, R., McCarthy, D., and Mihalcea, R. 2010. Semeval-2010 task 2: crosslingual lexical substitution. In Proceedings of the 5th International Workshop on Semantic Evaluation, July 15–16, Uppsala, Sweden.Google Scholar
Stokoe, C., Oakes, M. P. and Tait, J. 2003. Word sense disambiguation in information retrieval revisited. In Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Informaion Retrieval (SIGIR '03), pp. 159–66. New York, NY: ACM.Google Scholar
Tratz, S. and Hovy, E. 2010. A taxonomy, dataset, and classifier for automatic noun compound interpretation. In Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics (ACL '10), pp. 678–87. Stroudsburg, PA: Association for Computational Linguistics.Google Scholar
Turney, P. D. 2006. Similarity of semantic relations. Compututational Linguistics 32(September): 379–416.CrossRefGoogle Scholar
Turney, P. D. and Pantel, P. 2010. From frequency to meaning: vector space models of semantics. Journal of Artificial Intelligence Research (JAIR) 37: 141–88.CrossRefGoogle Scholar
Vanderwende, L. 1994. Algorithm for automatic interpretation of noun sequences. In Proceedings of the 15th Conference on Computational Linguistics, vol. 2 (COLING '94), pp. 782–8. Stroudsburg, PA: Association for Computational Linguistics.CrossRefGoogle Scholar
Weeds, J. and Weir, D. 2005. Co-occurrence retrieval: a flexible framework for lexical distributional similarity. Computational Linguistics 31 (4): 439–75.CrossRefGoogle Scholar
Weeds, J., Weir, D. and McCarthy, D. 2004. Characterising measures of lexical distributional similarity. In Proceedings of COLING 2004, pp. 1015–21. Geneva, Switzerland: COLING.Google Scholar
Welty, C., Fan, J., Gondek, D. and Schlaikjer, A. 2010. Large-scale relation detection. In Proceedings of the NAACL HLT 2010 First International Workshop on Formalisms and Methodology for Learning by Reading, (FAM-LbR '10), pp. 24–33. Stroudsburg, PA: Association for Computational Linguistics.Google Scholar
Wubben, S. 2010. UvT: memory-based pairwise ranking of paraphrasing verbs. In Proceedings of the 5th International Workshop on Semantic Evaluation (SemEval '10), pp. 260–63. Stroudsburg, PA: Association for Computational Linguistics.Google Scholar
Zipf, G. K. 1935. The Psychobiology of Language. New York, NY: Houghton-Mifflin.Google Scholar
Zipf, G. K. 1945. The meaning-frequency relationship of words. Journal of General Psychology 1945 (33): 251–6.CrossRefGoogle Scholar