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Extracting possessions from text: Experiments and error analysis

Published online by Cambridge University Press:  09 March 2021

Dhivya Chinnappa Open the ORCID record for Dhivya Chinnappa [Opens in a new window]  and
Eduardo Blanco
Dhivya Chinnappa*
Affiliation:
University of North Texas, Denton, TX 76203, USA
Eduardo Blanco
Affiliation:
University of North Texas, Denton, TX 76203, USA
*
*Corresponding author. E-mail: dhivyainfantchinnappa@my.unt.edu
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Abstract

This paper presents a corpus and experiments to mine possession relations from text. Specifically, we target alienable and control possessions and assign temporal anchors indicating when a possession relation holds between the possessor and possessee. We work with intra-sentential possessor and possessees that satisfy lexical and syntactic constraints. We experiment with traditional classifiers and neural networks to automate the task. In addition, we analyze the factors that help to determine possession existence and possession type and common errors made by the best performing classifiers. Experimental results show that determining possession existence relies on the entire sentence, whereas determining possession type primarily relies on the verb, possessor and possessee.

Keywords

PossessionTemporal ProcessingCorpus annotation

Information

Type
Article
Information
Natural Language Engineering , Volume 28 , Issue 3 , May 2022 , pp. 295 - 316
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Copyright
© The Author(s), 2021. Published by Cambridge University Press

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Footnotes

†

Currently at Thomson Reuters.

References

Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G.S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., Harp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mané, D., Monga, R., Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., ViÉgas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y. and Zheng, X. (2015). TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. Software available from tensorflow.org.Google Scholar
Aikhenvald, A.Y. (2013). Possession and ownership: a cross-linguistic perspective. In Aikhenvald A.Y. and Dixon R.M.W. (eds), Possession and Ownership: A Cross-Linguistic Typology, Chapter 1. Oxford: Oxford University Press, pp. 1–64.Google Scholar
Aikhenvald, A.Y. and Dixon, R.M.W. (2012). Possession and Ownership: A Cross-Linguistic Typology. Explorations in Linguistic Typology. Oxford: Oxford University Press.Google Scholar
Artstein, R. and Poesio, M. (2008). Inter-coder agreement for computational linguistics. Computational Linguistics 34(4), 555–596.CrossRefGoogle Scholar
Banea, C., Chen, X. and Mihalcea, R. (2016). Building a dataset for possessions identification in text. In Calzolar N., Choukri K., Declerck T., Goggi S., Grobelnik M., Maegaard B., Mariani J., Mazo H., Moreno A., Odijk J. and Piperidis S. (eds), Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016). Paris, France: European Language Resources Association (ELRA).Google Scholar
Banea, C. and Mihalcea, R. (2018). Possession identification in text. Natural Language Engineering 24(04), 1–22.CrossRefGoogle Scholar
Beavers, J. (2011). An aspectual analysis of ditransitive verbs of caused possession in English. Journal of Semantics 28(1), 1–54.CrossRefGoogle Scholar
Bender, E.M. and Koller, A. (2020). Climbing towards NLU: on meaning, form, and understanding in the age of data. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Online: Association for Computational Linguistics, pp. 5185–5198.Google Scholar
Chinnappa, D. and Blanco, E. (2018a). Mining possessions: existence, type and temporal anchors. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. New Orleans, Louisiana, USA: Association for Computational Linguistics, pp. 496–505.CrossRefGoogle Scholar
Chinnappa, D. and Blanco, E. (2018b). Possessors change over time: a case study with artworks. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Brussels, Belgium: Association for Computational Linguistics, pp. 2278–2287.Google Scholar
Chinnappa, D., Murugan, S. and Blanco, E. (2019). Extracting possessions from social media: images complement language. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). Hong Kong, China: Association for Computational Linguistics, pp. 663–672.CrossRefGoogle Scholar
Chinnappa, D., Murugan, S. and Blanco, E. (2020). Beyond possession existence: duration and co-possession. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Online: Association for Computational Linguistics, pp. 8332–8341.Google Scholar
Chinnappa, D., Palmer, A. and Blanco, E. (2020). WikiPossessions: possession timeline generation as an evaluation benchmark for machine reading comprehension of long texts. In Proceedings of The 12th Language Resources and Evaluation Conference. Marseille, France: European Language Resources Association, pp. 1110–1117.Google Scholar
Chollet, F., et al. (2015). Keras. Available at https://github.com/fchollet/keras.Google Scholar
Fancellu, F., Lopez, A. and Webber, B. (2016). Neural networks for negation scope detection. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Berlin, Germany: Association for Computational Linguistics, pp. 495–504.CrossRefGoogle Scholar
Gildea, D. and Jurafsky, D. (2002). Automatic labeling of semantic roles. Computational Linguistics 28(3), 245–288.CrossRefGoogle Scholar
Heine, B. (1997). Possession: Cognitive Sources, Forces, and Grammaticalization. Cambridge Studies in Linguistics. Cambridge: Cambridge University Press.Google Scholar
Hovy, E., Marcus, M., Palmer, M., Ramshaw, L. and Weischedel, R. (2006). OntoNotes: the 90% solution. In NAACL’06: Proceedings of the Human Language Technology Conference of the NAACL, Companion Volume: Short Papers on XX. Morristown, NJ, USA: Association for Computational Linguistics, pp. 57–60.CrossRefGoogle Scholar
Kingma, D.P. and Ba, J. (2014). Adam: A Method for Stochastic Optimization. CoRR, abs/1412.6980.Google Scholar
Liu, H. and Singh, P. (2004). ConceptNet—a practical commonsense reasoning tool-kit. BT Technology Journal 22(4), 211–226.CrossRefGoogle Scholar
Miller, G.A. (1995). WordNet: a lexical database for English. In Communications of the ACM, vol. 38, pp. 39–41.CrossRefGoogle Scholar
Nakov, P.I. and Hearst, M.A. (2013). Semantic interpretation of noun compounds using verbal and other paraphrases. ACM Transactions on Speech and Language 10(3), 13:1–13:51.Google Scholar
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M. and Duchesnay, E. (2011). Scikit-learn: machine learning in python. Journal of Machine Learning Research 12, 2825–2830.Google Scholar
Pennington, J., Socher, R. and Manning, C.D. (2014). GloVe: global vectors for word representation. In Empirical Methods in Natural Language Processing (EMNLP), pp. 1532–1543.CrossRefGoogle Scholar
Qin, G., Yao, J.-G., Wang, X., Wang, J. and Lin, C.-Y. (2018). Learning latent semantic annotations for grounding natural language to structured data. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Brussels, Belgium: Association for Computational Linguistics, pp. 3761–3771.Google Scholar
Sarabi, Z. and Blanco, E. (2017). If no media were allowed inside the venue, was anybody allowed? In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers. Valencia, Spain: Association for Computational Linguistics, pp. 860–869.Google Scholar
Sauri, R. and Pustejovsky, J. (2009). FactBank: a corpus annotated with event factuality. Language Resources and Evaluation 43(3), 227–268.CrossRefGoogle Scholar
Sohrab, M.G. and Miwa, M. (2018). Deep exhaustive model for nested named entity recognition. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. Brussels, Belgium: Association for Computational Linguistics, pp. 2843–2849.CrossRefGoogle Scholar
Stassen, L. (2009). Predicative Possession. Oxford Studies in Typology and Linguistic Theory. Oxford/New York: Oxford University Press.Google Scholar
Tham, S.W. (2004). Representing Possessive Predication: Semantic Dimensions and Pragmatic Bases. PhD Thesis, Stanford University.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. Stroudsburg, PA, USA: Association for Computational Linguistics, pp. 678–687.Google Scholar
Tratz, S. and Hovy, E.H. (2013). Automatic interpretation of the English possessive. In ACL (1). The Association for Computer Linguistics, pp. 372–381.Google Scholar
Viberg, A. (2010). Basic verbs of possession. CogniTextes 4.Google Scholar