Palavras-chave
Como Citar
Resumo
Philosophy of language and computer science, despite being very distinct fields, share a great interest in natural language. However, while philosophy has traditionally opted for a formalist approach, computer science has been increasingly favoring probabilistic models. After presenting these two approaches in more detail, we discuss some of their main virtues and limitations. On the one hand, formalist models have trouble in acquiring semantic information from corpora and learning from large amounts of data. Probabilistic approaches, on the other hand, have difficulty in operating with compositionality, in dealing with contrast sets and hierarchical relations, and in distinguishing normative and descriptive views of meaning. We argue that a more fruitful dialogue between philosophers and computer scientists may help to produce a better approach to natural language and stimulate the integration of logical and probabilistic methods.
Referências
Augenstein, I.; Derczynski, L.; Bontcheva, K. “Generalisation in Named Entity Recognition: A Quantitative Analysis”. Computer Speech & Language, v. 44, pp. 61-83, 2017.
Baroni, M.; Dinu, G.; Kruszewski, G. “Don’t Count, Predict! A Systematic Comparison of Context-Counting vs. Context-Predicting Semantic Vectors”. In: Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pp. 238-247, 2014.
Boleda, G.; Herbelot, A. “Formal Distributional Semantics: Introduction to the Special Issue”. Computational Linguistics, v. 42, n. 4, pp. 619-635, 2016.
Bolukbasi, T. et al. Man is to computer programmer as woman is to homemaker? debiasing word embeddings. Advances in neural information processing systems, v. 29, p. 4349-4357, 2016.
Bos, Johan; Markert, Katja. Recognising textual entailment with logical inference. In: Proceedings of Human Language Technology Conference and Conference on Empirical Methods in Natural Language Processing, pp. 628-635, 2005.
Brandom, R. Making It Explicit: Reasoning, Representing, and Discursive Commitment. Harvard University Press, Cambridge, MA, 1995.
Cruse, D. A. Lexical Semantics. Cambridge University Press, 1986.
Davis, W. “Implicature”. In: E. N. Zalta (ed.), The Stanford Encyclopedia of Philosophy (Fall 2019 Edition), . Available at: https://plato.stanford.edu/archives/fall2019/entries/implicature/
Devlin, J. et al. “Bert: Pre-Training of Deep Bidirectional Transformers for Language Understanding”. arXiv preprint arXiv:1810.04805, 2018.
Dobler, T. “What Is Wrong with Hacker’s Wittgenstein? On Grammar, Context and Sense-Determination”. Philosophical Investigations, v. 36, n. 3, pp. 231-250, 2013.
Erk, K.; Padó, S. “A Structured Vector Space Model for Word Meaning in Context”. In: Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, pp. 897-906, 2008.
Frege, G. “Letter to Jourdain”. In: Philosophical and Mathematical Correspondence. University of Chicago Press, pp. 78-80, 1980. [1914]
Frege, G. “Begriffsschrift, a Formula Language, Modeled upon that of Arithmetic, for Pure Thought”. In J. van Heijenoort (Ed.), From Frege to Gödel: A Source Book in Mathematical Logic, 1879-1931. Harvard University Press, 2002. [1879]
Frege, G. “On Sense and Reference”. In: P. Geach & M. Black (Eds.), Translations from the Philosophical Writings of Gottlob Frege. Basil Blackwell: Oxford, 1980, p. 56-78. [1892]
Garcez, Artur D’avila; Lamb, Luís C. “Neurosymbolic AI: the 3rd Wave.” arXiv preprint arXiv: 2012.05876, 2020.
Glock, H-J. A Wittgenstein Dictionary. Wiley-Blackwell, Hoboken, New Jersey, 1996.
Grice, H. P. "Logic and Conversation". Syntax and Semantics: Vol. 3: Speech Acts. Academic Press, Cambridge, MA, 1975.
Grice, H. P. Studies in the Way of Words. Cambridge, MA, Harvard University Press, 1989.
Grimsley, et al. (2020). “Why attention is not explanation: Surgical intervention and causal reasoning about neural models.” Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020), pages 1780-1790.
Hacker, P. M. S.; Baker, P. Wittgenstein: Rules, Grammar and Necessity. Wiley-Blackwell, Hoboken, New Jersey , 2009.
Kaplan, D. “Demonstratives”. Themes from Kaplan. Oxford University Press, pp. 481-563, 1989.
Krahmer, E.; Van Deemter, K. “Computational Generation of Referring Expressions: A Survey”. Computational Linguistics , v. 38, n. 1, pp. 173-218, 2012.
Kratzer, A. “What ‘Must’ and ‘Can’ Must and Can Mean”. Linguistics and philosophy, v. 1, n. 3, pp. 337-355, 1977.
Kruszewski, G. et al. “There is no Logical Negation Here, But There Are Alternatives: Modeling Conversational Negation With Distributional Semantics”. Computational Linguistics , v. 42, n. 4, pp. 637-660, 2016.
Kuusela, O. “Do the Concepts of Grammar and Use in Wittgenstein Articulate a Theory of Language or Meaning?”. Philosophical Investigations , v. 29, n. 4, pp. 309-341, 2006.
Lenci, A. “Distributional Models of Word Meaning”. Annual Review of Linguistics, v. 4, pp. 151-171, 2018.
Lepore, E.; Ludwig, K. A. “What is Logical Form?”. In: G. Preyer (Ed.), Logical Form and Language. Oxford University Press, 2001.
Lewis, D. “General Semantics”. Montague Grammar. Academic Press, pp. 1-50, 1976.
Linzen, T. “Issues in Evaluating Semantic Spaces Using Word Analogies”. arXiv preprint arXiv:1606.07736, 2016.
Lipton, Z. C. (2016). “The Mythos of Model Interpretability”. CoRR, abs/1606.03490.
Liu, Y. et al. “Roberta: A Robustly Optimized Bert Pretraining Approach”. arXiv preprint arXiv:1907.11692, 2019.
Locke, J. Essay Concerning Human Understanding. Chicago, Encyclopaedia Britannica, 1955. [1690]
Manning, C.; Schutze, H. Foundations of Statistical Natural Language Processing. MIT press, 1999.
Meinong, A. “The Theory of Objects”. In R. M. Chisholm (Ed.), Realism and the Background of Phenomenology. Free Press, pp. 76-117, 1981. [1904]
Miller, T. (2017). “Explanation in Artificial Intelligence: Insights from the Social Sciences”. CoRR, abs/1706.07269.
Mikolov, T. et al. “Distributed Representations of Words and Phrases and Their Compositionality”. arXiv preprint arXiv: 1310.4546, 2013. [2013a]
Mikolov, T. et al. “Efficient Estimation of Word Representations in Vector Space”. arXiv preprint arXiv:1301.3781, 2013. [2013b]
Moot, R.; Retore, C. The Logic of Categorial Grammars: A Deductive Account of Natural Language Syntax and Semantics. Springer Verlag, 2012.
Radford, A. et al. “Improving Language Understanding by Generative Pre-Training”. 2018. Available at: https://www.cs.ubc.ca/~amuham01/LING530/papers/radford2018improving.pdf
Rudin, C. “Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead”. Nature Machine Intelligence, v. 1, n. 5, pp. 206-215, 2019.
Russell, B. “Our Knowledge of the External World”. Routledge: Abingdon, 2009. [1914]
Russell, B. “On Denoting”. Mind, v. 14, n. 56, pp. 479-493, 1905.
Spärck-Jones, K. Synonymy and Semantic Classification. Edinburgh University Press, 1986. [1964]
Strawson, P. F. “On Referring”. Mind , v. 59, n. 235, pp. 320-344, 1950.
Thater, S.; Fürstenau, H.; Pinkal, M. “Word Meaning in Context: A Simple and Effective Vector Model. Proceedings of 5th International Joint Conference on Natural Language Processing, pp. 1134-43, 2011.
Wittgenstein, W. Philosophical Investigations . Wiley-Blackwell: Hoboken, New Jersey, 2009. [1953]
Wittgenstein, W. Tractatus Logico-Philosophicus. Routledge, Abingdon, 2001. [1921]
Zhao, J. et al. “Men Also Like Shopping: Reducing Gender Bias Amplification Using Corpus-Level Constraints”. arXiv preprint arXiv:1707.09457, 2017.
Este trabalho está licenciado sob uma licença Creative Commons Attribution 4.0 International License.
Copyright (c) 2022 Manuscrito: Revista Internacional de Filosofia
Downloads
