The spy saw a cop with a telescope: Who has the telescope?
An attempt to understand the basic building blocks of ambiguous PP-attachment sequences
Keywords:PP-attachment, Python, ambiguous, parsing
This paper explores the problem of ambiguous PP-attachment by extracting information from a PP-attachment corpus using Python. Cases of ambiguous PP-attachment involve sequences of the head words of the following type: verb > noun > preposition > noun. The head nouns of ambiguous PP-attachment sentences, as well as aspects beyond head words, are investigated by testing a number of hypotheses using a corpus of thousands of real-world examples. The hypotheses are partially based on theory and partially on empirical evidence. The results support some theoretical claims while discarding others. For instance, one finding that supports an existing claim is that of-PPs always attach to NPs whose heads are classifiers. This kind of knowledge can be put into practice when parsing natural language.
Boland, J. E., & Blodgett, A. (2006). Argument status and PP-attachment. Journal of psycholinguistic research, 35(5), 385-403. doi: 10.1007/s10936-006-9021-z
Dahlgren, K., & McDowell, J. P. (1986). Using Commonsense Knowledge to Disambiguate Prepositional Phrase Modifiers. In Fifth National Conference on Artificial Intelligence (AAAI-86) (pp. 589-593). Menlo Park, California, USA: The AAAI Press.
Hindle, D., & Rooth, M. (1993). Structural ambiguity and lexical relations. Computational linguistics, 19(1), 103-120.
Kawahara, D., & Kurohashi, S. (2005). PP-attachment disambiguation boosted by a gigantic volume of unambiguous examples. In Dale, R., Wong, K.-F., Su, J. & Kwong, O.Y. (eds.) Natural Language Processing – IJCNLP 2005 (pp. 188-198). Berlin, Heidelberg: Springer. doi:10.1007/11562214
Lehrer, A. (1986). English classifier constructions. Lingua, 68(2-3), 109-148.
Loper, E., & Bird, S. (2002). NLTK: The natural language toolkit. In Proceedings of the ACL-02 Workshop on Effective tools and methodologies for teaching natural language processing and computational linguistics-Volume 1 (pp. 63-70). Philadelphia, Pennsylvania, USA: Association for Computational Linguistics. https://arxiv.org/pdf/cs/0205028.pdf
Macleod, C., Grishman, R., Meyers, A., Barrett, L., & Reeves, R. (1998). Nomlex: A lexicon of nominalizations. In Proceedings of EURALEX (Vol. 98, pp. 187-193), Liège, Belgium. (http://nlp.cs.nyu.edu/nomlex/ - accessed at 5th of January 2015).
Merlo, P., & Ferrer, E. E. (2006). The notion of argument in prepositional phrase attachment. Computational linguistics, 32(3), 341-378.
Chomsky, N. (1965). Aspects of the Theory of Syntax. Cambridge, MA: MIT Press.
Python Software Foundation. Python Language Reference, version 2.7. Available at http://www.python.org
Roth, D. (1998). Learning to resolve natural language ambiguities: A unified approach. In Proceedings of the Fifteenth National on Artificial Intelligence (pp. 806–813). Menlo Park, California, USA: The AAAI Press.
Schütze, C. T., & Gibson, E. (1999). Argumenthood and English prepositional phrase attachment. Journal of Memory and Language, 40(3), 409-431.
How to Cite
The author/the authors hold the rigths to articles presented in the journal. The author/the authors are granted the right to reproduce their article as they see fit, if they mention LWorks as the original publisher of the article.