SavedAdd your journal

Journals / MetaZihin: Yapay Zeka ve Zihin Felsefesi Dergisi / 2023 / Cilt: 6 Sayı: 1

The Role of Lexical Ambiguity in Natural Language Processing Systems

Sözcüksel Muğlaklığın Doğal Dil İşleme Sistemlerindeki Yeri

Tolgahan TOY
Pages
17–31
Publication Date
2023-08-27
DOI
—
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

In this work, with reference to contemporary literature, it is stated that lexical ambiguity, especially polysemy, is not a defect but an advantage for cognitive systems.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:What are the basic tenets of a system that understands natural languages?
📊
Key Findings & Data:What obstacles need to be overcome?
💡
Conclusion & Impact:In this work, with reference to contemporary literature, it is stated that lexical ambiguity, especially polysemy, is not a defect but an advantage for cognitive systems.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:muğlaklık · çokanlamlılık · doğal dil işleme

Abstract

What are the basic tenets of a system that understands natural languages? What obstacles need to be overcome? One of the problems related to natural language understanding processes is ambiguity. There are two types of ambiguity: Syntactic ambiguity and lexical ambiguity. In syntactic ambiguity, an expression can be decomposed into its constituents in more than one way. In lexical ambiguity, words have more than one meaning. Lexical ambiguity is one of the features that distinguish natural languages from formal languages. There is no lexical ambiguity in a programming language. Is lexical ambiguity an obstacle to modeling natural languages? In other words, does it have adverse effects on artificial intelligence studies? In this work, with reference to contemporary literature, it is stated that lexical ambiguity, especially polysemy, is not a defect but an advantage for cognitive systems.

Özet

Doğal dilleri anlayabilen bir sistemin temel özellikleri neler olmalıdır? Hangi engellerin aşılması gerekmektedir? Doğal dil anlama süreçleriyle ilgili önemli sorunlardan birisi de muğlaklık olarak görülmektedir. Muğlaklığın iki türünden bahsedilebilir: Sözdizimsel muğlaklık ve sözcüksel muğlaklık. Sözdizimsel muğlaklık durumunda verilen bir ifade bağlama göre birden fazla şekilde sözdizimsel parçalarına ayrılabilmektedir. Muğlaklığın diğer bir türü ise sözcüksel muğlaklıktır. Bu durumda, kelimeler birden fazla anlama sahiptirler. Sözcüksel muğlaklık doğal dilleri biçimsel dillerden ayıran özelliklerden birisidir. Bir programlama dilinin söz dağarcığındaki ifadeler için muğlaklıktan bahsetmek mümkün değildir. Sözcüksel muğlaklık doğal dillerin modellenmesinin önünde bir engel midir? Diğer bir deyişle doğal dillerdeki anlamsal muğlaklığın yapay zekâ çalışmalarını kötü yönde etkilediği söylenebilir mi? Bu çalışmada güncel literatür referans alınarak sözcüksel muğlaklığın özellikle de çokanlamlılığın doğal diller için bilişsel açıdan aslında bir avantaj olduğu ifade edilmektedir.

🔖

Cite This Article

TOY, T. (2023). The Role of Lexical Ambiguity in Natural Language Processing Systems. MetaZihin: Yapay Zeka ve Zihin Felsefesi Dergisi, Cilt: 6 Sayı: 1, 17–31. https://ajindex.com/articles/964655

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (12)

  1. Bar-Hillel, Y. (1964). “A demonstration of the nonfeasibility of fully automatic high quality machine translation." Language and Information: Selected Essays on Their Theory and Application içinde (s. 174-179).
  2. Reading, Mass.: Addison-Wesley. Baxter, D. L. (2016). "Hume on Space and Time." P. Russell (Der.), The Oxford Handbook of Hume içinde (s. 173-190). Oxford: Oxford University Press.
  3. Bonnen, C. A., ve Flage, D. E. (2000). "Descartes: The Matter of Time." International Studies in Philosophy, 32(4): 1-11.
  4. Brugman, C., ve Lakoff, G. (1988). “Cognitive Topology and Lexical Networks." G. W. Steven L. Small ve M. K. Tanenhaus (Der.), Lexical Ambiguity Resolution: Perspectives from Psycholinguistics, Neuropsychology, and Artificial Intelligence içinde (s. 477–508).
  5. San, Mateo, CA: Morgan Kaufmann. Chomsky, N. (2002). On Nature and Language. New York: Cambridge University Press.
  6. Clark, H. H. (1983). “Making Sense of Nonce Sense.” G. B. D'Arcais, ve R. J. Jarvella (Der.), The Process of Language Understanding içinde (s. 297–331). John Wiley & Sons Ltd. Cohen, J. (1985). “A problem about Ambiguity in Truth-Conditional Semantics.” Analysis, 45: 129–135. de Sola Pool, I., & Kochen, M. (1978). “Contacts and Influence.” Social Networks, 1: 5–51.
  7. Descartes, R. (1637/1968). “Discourse on Method and the Meditations.” Çev. F. E. Sutcliffe. London: Penguin Books Ltd. Descartes, R. (1985). Meditations on First Philosophy. J. Cottingham, R. Stoothoff, ve D. Murdoch (Düz.), The Philsophical Wrings of Descartes (Cilt II) içinde, (s. 1-63). Cambridge: Cambridge University Press.
  8. Falkum, I. L., ve Vicente, A. (2015). “Polysemy: Current Perspectives and Approaches.” Lingua, 157: 1-16.
  9. Fellbaum, C. (2013). “WordNet.” C. A. Chapelle (Der.), The Encyclopedia of Applied Linguistics içinde (s. 707-728). Wiley/Blackwell. Ferrer i Cancho, R., ve Solé, R. V. (2003). “Least Effort and the Origins of Scaling in Human Language.” Proceedings of the National Academy of Sciences of the United States of America, 100(3): 788–791.
  10. Garrett, D. (1981). “Hume's Self-Doubts about Personal Identity.” The Philosophical Review, 90(3): 337-358.
  11. Garrett, D. (2008). “Hume’s Theory of Ideas." E. S. Radcliffe (Der.), A Companion to Hume içinde (s. 41-57). Oxford: Blackwell Publishing. Giles, J. (1993). “The No-Self Theory: Hume, Buddhism, and Personal Identity.” Philosophy East and West, 43(2): 175-200.
  12. Goodman, N. (1968). Languages of Art: An Approach to a Theory of Symbols. Hackett publishing. Harris, C