Element contents
Series: Elements in Construction Grammar
Copilots for Linguists
AI, Constructions, and Frames
Published online by Cambridge University Press: 20 December 2023
Summary
AI can assist the linguist in doing research on the structure of language. This Element illustrates this possibility by showing how a conversational AI based on a Large Language Model (AI LLM chatbot) can assist the Construction Grammarian, and especially the Frame Semanticist. An AI LLM chatbot is a text-generation system trained on vast amounts of text. To generate text, it must be able to find patterns in the data and mimic some linguistic capacity, at least in the eyes of a cooperative human user. The authors do not focus on whether AIs “understand” language. Rather, they investigate whether AI LLM chatbots are useful tools for linguists. They reframe the discussion from what AI LLM chatbots can do with language to what they can do for linguists. They find that a chatty LLM can labor usefully as an eliciting interlocutor, and present precise, scripted routines for prompting conversational LLMs.
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- Element
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- Series: Elements in Construction GrammarOnline ISBN: 9781009439190Publisher: Cambridge University PressPrint publication: 01 February 2024
References
Almeida, V. G. 2016. Identificação Automática de Construções de Estrutura Argumental. MA thesis, Department of Linguistics, Federal University of Juiz de Fora, Juiz de Fora, Brazil.Google Scholar
Almeida, V. G. 2022. Modelagem e Identificação Automática de Construções de Estrutura Argumental: Uma proposta para o Constructicon da FrameNet Brasil. Ph.D. dissertation, Department of Linguistics, Federal University of Juiz de Fora, Juiz de Fora, Brazil.Google Scholar
Anwar, S., Ustalov, D., Arefyev, N., Ponzetto, S. P., Biemann, C., & Panchenko, A. 2019. HHMM at SemEval-2019 Task 2: Unsupervised frame induction using contextualized word embeddings. In Proceedings of the 13th International Workshop on Semantic Evaluation, Minneapolis, Minnesota (pp. 125–129). Association for Computational Linguistics.Google Scholar
Arefyev, N., Sheludko, B., Davletov, A., Kharchev, D., Nevidomsky, A., & Panchenko, A. 2019. Neural granny at SemEval-2019 Task 2: A combined approach for better modeling of semantic relationships in semantic frame induction. In Proceedings of the 13th International Workshop on Semantic Evaluation (pp. 31–38).Google Scholar
Bender, E. M. & Koller, A. 2020, July. 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 (pp. 5185–5198).CrossRefGoogle Scholar
Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. 2021. On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (pp. 610–623). New York: Association for Computing Machinery.Google Scholar
Bergen, B. & Binsted, K. 2015. Embodied grammar and humor. In Brône, G., Feyaerts, K., & Veale, T. (eds.), Cognitive Linguistics and Humor Research (pp. 49–68). Berlin: De Gruyter Mouton.Google Scholar
Birhane, A., Kasirzadeh, A., Leslie, D., et al. (2023). Science in the age of large language models. Nature Reviews Physics, 5: 277–280. https://doi.org/10.1038/s42254-023-00581-4.Google Scholar
Birhane, A., Prabhu, V. U., & Kahembwe, E. 2021. Multimodal datasets: Misogyny, pornography, and malignant stereotypes. arXiv preprint. arXiv:2110.01963.Google Scholar
Boas, H. C. 2005. Determining the productivity of resultative constructions: A reply to Goldberg and Jackendoff. Language, 81.2: 448–464.Google Scholar
Boas, H. C. 2013. Cognitive construction grammar. In Hoffmann, T. & Trousdale, G. (eds.), The Oxford Handbook of Construction Grammar (pp. 233–252). Oxford: Oxford University Press.Google Scholar
Boas, H. C. & Ziem, A. 2018. Constructing a constructicon for German. In Lyngfelt, B., Borin, L., Ohara, K., & Torrent, T. T. (eds.), Constructicography: Constructicon Development across Languages (pp. 83–228). Amsterdam: John Benjamins.Google Scholar
Bommasani, R., Hudson, D. A., Adeli, E., et al. 2021. On the opportunities and risks of foundation models. arXiv preprint. arXiv:2108.07258.Google Scholar
Bond, F. & Foster, R. 2013. Linking and extending an open multilingual wordnet. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 1352–1362). Association for Computational Linguistics.Google Scholar
Brown, T., Mann, B., Ryder, N., et al. 2020. Language models are few-shot learners. Advances in Neural Information Processing Systems, 33: 1877–1901.Google Scholar
Chen, Brian X. 2023. We’re using A.I. chatbots wrong: Here’s how to direct them. The New York Times, July 20. www.nytimes.com/2023/07/20/technology/personaltech/ai-chatgpt-bing-directions.html.Google Scholar
Coulson, S. 2001. Semantic Leaps: Frame-Shifting and Conceptual Blending in Meaning Construction. Cambridge: Cambridge University Press.Google Scholar
Dannélls, D., Borin, L., & Heppin, K. H. 2021. The Swedish FrameNet++: Harmonization, Integration, Method Development and Practical Language Technology Applications. Amsterdam: John Benjamins Publishing Company.Google Scholar
Diessel, H. 2019. The Grammar Network: How Linguistic Structure Is Shaped by Language Use. Cambridge: Cambridge University Press.Google Scholar
Diniz da Costa, A., Gamonal, M. A., Paiva, V. M. R. L., et al. 2018. FrameNet-based modeling of the domains of tourism and sports for the development of a personal travel assistant application. In Torrent, T. T., Borin, L., & Baker, C. F. (eds.), Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan (pp. 6–12). Paris: ELRA.Google Scholar
Fauconnier, G. & Turner, M. 1996. Blending as a central process of grammar. In Goldberg, A. (ed.), Conceptual Structure, Discourse, and Language (pp. 113–130). Stanford: Center for the Study of Language and Information. [Expanded web version 1998, available at http://ssrn.com/author=1058129.]Google Scholar
Fauconnier, G. & Turner, M. 2002. The Way We Think: Conceptual Blending and the Mind’s Hidden Complexities. New York: Basic Books.Google Scholar
Fillmore, C. J. 1968. The case for case. In Bach, E. & Harms, R. T. (eds.), Universals in Linguistic Theory (pp. 1–88). New York: Holt, Rinehart and Winston.Google Scholar
Fillmore, C. J. 1976. Frame semantics and the nature of languages. In Annals of the New York Academy of Sciences: Conference on the Origin and Development of Language and Speech, 280: 20–32.CrossRefGoogle Scholar
Fillmore, C. J. 1977a. Scenes-and-frames semantics. In Zampolli, A. (ed.), Fundamental Studies in Computer Science, number 59 (pp. 55–88). Amsterdam: North Holland Publishing.Google Scholar
Fillmore, C. J. 1977b. The need for a frame semantics in linguistics. In Karlgren, H. (ed.), Statistical Methods in Linguistics, volume 12 (pp. 5–29). Stockholm: Scriptor.Google Scholar
Fillmore, C. J. 1982. Frame semantics. In Linguistics in the Morning Calm. Linguistic Society of Korea (pp. 111–137). Seoul: Hanshin Publishing Company.Google Scholar
Fillmore, C. J. 1985. Frames and the semantics of understanding. Quaderni di Semantica, 6.2: 222–254.Google Scholar
Fillmore, C. J. 2008. The merging of “frames”. In Rossini Favretti, R. (ed.), Frames, Corpora, and Knowledge Representation (pp. 1–12). Bologna: Bononia University Press.Google Scholar
Fillmore, C. J. 2013. Berkeley Construction Grammar. In Hoffmann, T. & Trousdale, G. (eds.), The Oxford Handbook of Construction Grammar (pp. 111–132). Oxford: Oxford University Press.Google Scholar
Fillmore, C. J. & Atkins, B. T. 1992. Towards a frame-based organization of the lexicon: The semantics of RISK and its neighbors. In Lehrer, A. & Kittay, E. (eds.), Frames, Fields, and Contrast: New Essays in Semantics and Lexical Organization (pp. 75–102). Hillsdale: Lawrence Erlbaum Associates, .Google Scholar
Fillmore, C. J. & Atkins, B. T. 1994. Starting where the dictionaries stop: The challenge for computational lexicography. In Atkins, B. T. S. & Zampolli, A. (eds.), Computational Approaches to the Lexicon (pp. 349–393). Oxford: Oxford University Press.Google Scholar
Fillmore, C. J., Kay, P., & O’Connor, M. C. 1988. Regularity and idiomaticity in grammatical constructions: The case of let alone. Language, 64.3: 501–538.Google Scholar
Fillmore, C. J., Johnson, C. R., & Petruck, M. R. 2003. Background to FrameNet. International Journal of Lexicography, 16.3: 235–250.Google Scholar
Goldberg, A. E. 1995. Constructions: A Construction Grammar Approach to Argument Structure Constructions. Chicago: University of Chicago Press.Google Scholar
Goldberg, A. E. 2019. Explain Me This: Creativity, Competition, and the Partial Productivity of Constructions. Princeton: Princeton University Press.Google Scholar
Gruzitis, N., Nespore-Berzkalne, G., & Saulite, B. 2018. Creation of Latvian FrameNet based on universal dependencies. In Torrent, T. T., Borin, L., & Baker, C. F. (eds.), Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Miyazaki, Japan (pp. 23–27). Paris: European Language Resources Association (ELRA).Google Scholar
Hahm, Y., Noh, Y., Han, J. Y., et al. 2020. Crowdsourcing in the development of a multilingual framenet: A case study of Korean FrameNet. In Proceedings of the 12th Language Resources and Evaluation Conference, Marseille, France (pp. 236–244). European Language Resources Association (ELRA).Google Scholar
Hartmann, S. & Gurevych, I. 2013. FrameNet on the way to Babel: Creating a bilingual FrameNet using Wiktionary as interlingual connection. In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Sofia, Bulgaria (pp. 1363–1373). Association for Computational Linguistics.Google Scholar
Herbst, T. 2018. Collo-creativity and blending: Recognizing creativity requires lexical storage in constructional slots. Zeitschrift für Anglistik und Amerikanistik, 66.3: 309–326.Google Scholar
Hilpert, M. 2019. Construction Grammar and Its Application to English. 2nd ed. Edinburgh: Edinburgh University Press.CrossRefGoogle Scholar
Hoffmann, T. 2022a. Construction Grammar: The Structure of English (Cambridge Textbooks in Linguistics). Cambridge: Cambridge University Press.Google Scholar
Hoffmann, T. 2022b. Constructionist approaches to creativity. Yearbook of the German Cognitive Linguistics Association, 10 1: 259–284.Google Scholar
Hoffmann, T. & Bergs, A. 2015. Are you a construction in disguise? Was Fußballgesänge uns über soziale und physische Kontexteigenschaften von Konstruktionen lehren. In Ziem, A. & Lasch, A. (eds.), Konstruktionsgrammatik IV: Konstruktionen als soziale Konventionen und kognitive Routinen (pp. 115–131). Tübingen: Stauffenburg.Google Scholar
Hoffmann, T. & Bergs, A. 2018. A Construction Grammar approach to genre. CogniTextes, 18: 1–27.Google Scholar
Hoffmann, T. & Bergs, A. 2024. Constructions all the way! Text types as constructions. In Hennemann, A. & Tacke, F. (eds.), Diskurstraditionen – Konstruktionen – Genres (Sprache in kulturellen Kontexten/Language in Cultural Contexts). Bonn: Bonn University Press.Google Scholar
Hoffmann, T. & Trousdale, G. (eds.) 2013. The Oxford Handbook of Construction Grammar. Oxford: Oxford University Press.Google Scholar
Hofstadter, D. R. 1995. Preface 4 the ineradicable Eliza effect and its dangers . In Fluid Concepts and Creative Analogies: Computer Models of the Fundamental Mechanisms of Thought (pp. 155–168). New York: Basic Books.Google Scholar
Kim, J., Hahm, Y., & Choi, K-S. 2016. Korean FrameNet expansion based on projection of Japanese FrameNet. In Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: System Demonstrations, Osaka, Japan (pp. 175–179). The COLING 2016 Organizing Committee.Google Scholar
Kirchenbauer, J., Geiping, J., Wen, Y., Katz, J., Miers, I., & Goldstein, T. 2023. A watermark for large language models. arXiv preprint. arXiv:2301.10226.Google Scholar
Lowe, J. B., Baker, C. F., & Fillmore, C. J. 1997. A frame-semantic approach to semantic annotation. In Proceedings of the ACL SIGLEX Workshop on Tagging Text with Lexical Semantics (pp. 18–24). ACL.Google Scholar
Mahowald, K., Ivanova, A. A., Blank, I. A., Kanwisher, N., Tenenbaum, J. B., & Fedorenko, E. 2023. Dissociating language and thought in large language models: A cognitive perspective. arXiv preprint. arXiv:2301.06627.Google Scholar
Matos, E. E., Torrent, T. T., Almeida, V. G., et al. 2017. Constructional analysis using constrained spreading activation in a FrameNet-based structured connectionist model. In The AAAI 2017 Spring Symposium on Computational Construction Grammar and Natural Language Understanding Technical Report SS-17-02 (pp. 222–229). Volume 17. Palo Alto, CA: AAAI Publications.Google Scholar
Ohara, K. H., Fujii, S., Ohori, T., Suzuki, R., Saito, H., & Ishizaki, S. 2004. The Japanese framenet project: An introduction. In Proceedings of LREC-04 Satellite Workshop “Building Lexical Resources from Semantically Annotated Corpora”(LREC 2004) (pp. 9–11). European Language Resources Association (ELRA).Google Scholar
Pavlick, E., Wolfe, T., Rastogi, P., Callison-Burch, C., Dredze, M., & Van Durme, B. 2015. FrameNet+: Fast paraphrastic tripling of FrameNet. In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), Beijing, China (pp. 408–413). Association for Computational Linguistics.Google Scholar
Pennacchiotti, M., De Cao, D., Basili, R., Croce, D., & Roth, M. 2008. Automatic induction of FrameNet lexical units. In Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing (pp. 457–465), Honolulu, HI. Association for Computational Linguistics.Google Scholar
Perek, F. & Lemmens, M. 2010. Getting at the meaning of the English at-construction: The case of a constructional split. CogniTextes 5. http://cognitextes.revues.org/331.Google Scholar
QasemiZadeh, B., Petruck, M. R., Stodden, R., Kallmeyer, L., & Candito, M. 2019. SemEval-2019 task 2: Unsupervised lexical frame induction. In Proceedings of the 13th International Workshop on Semantic Evaluation. Minneapolis, Minnesota (pp. 16–30). Association for Computational Linguistics.Google Scholar
Ribeiro, E., Mendonça, V., Ribeiro, R., et al. 2019, June. L2F/INESC-ID at SemEval-2019 task 2: Unsupervised lexical semantic frame induction using contextualized word representations. In Proceedings of the 13th International Workshop on Semantic Evaluation (pp. 130–136).Google Scholar
Rogers, A. 2021. Changing the world by changing the data. arXiv preprint. arXiv:2105.13947v1.Google Scholar
Rogers, A., Kovaleva, O., & Rumshisky, A. 2020. A primer in BERTology: What we know about how BERT works. In Transactions of the Association for Computational Linguistics (Vol. 8, pp. 842–866). ACL. https://doi.org/10.1162/tacl_a_00349.Google Scholar
Ruane, E., Birhane, A., & Ventresque, A. 2019. Conversational AI: Social and ethical considerations. In AICS (pp. 104–115).Google Scholar
Ruppenhofer, J., Ellsworth, M., Petruck, M. R. L., Johnson, C. R., Baker, C. F., & Scheffczyk, J. 2016. FrameNet II: Extended theory and practice. Berkeley, CA: ICSI.Google Scholar
Sampaio, T. F. 2010. A Família de Construções de Argumento Cindido no Português do Brasil. Ph.D. dissertation, Department of Linguistics, Federal University of Juiz de Fora, Juiz de Fora, Brazil.Google Scholar
Stefanowitsch, A. 2013. Collostructional analysis. In Hoffmann, T. & Trousdale, G. (eds.), The Oxford Handbook of Construction Grammar (pp. 290–306). Oxford: Oxford University Press.Google Scholar
Subirats, C. & Petruck, M. 2003. Surprise: Spanish FrameNet. In Proceedings of CIL, Prague (Vol. 17, p. 188).Google Scholar
Torrent, T. T., Salomão, M. M. M., da Silva Matos, E. E., et al. 2014. Multilingual lexicographic annotation for domain-specific electronic dictionaries: The Copa 2014 FrameNet Brasil project. Constructions and Frames, 6:1: 73–91.Google Scholar
Torrent, T. T., Matos, E. E. D. S., Belcavello, F., et al. 2022a. Representing context in FrameNet: A multidimensional, multimodal approach. Frontiers in Psychology, 13: 573.Google Scholar
Torrent, T. T., Almeida, A. L., Matos, E. E., Belcavello, F., Viridiano, M., & Gamonal, M. A. 2022b. Lutma: A frame-making tool for collaborative FrameNet development. In Proceedings of the 1st Workshop on Perspectivist Approaches to NLP, Marseille, France (pp. 100–107). European Language Resources Association (ELRA).Google Scholar
Touvron, H., Lavril, T., Izacard, G., et al. (2023). Llama: Open and efficient foundation language models. arXiv preprint. arXiv:2302.13971.Google Scholar
Turner, M. 1987. Death is the Mother of Beauty: Mind, Metaphor, Criticism. Chicago, IL: University of Chicago Press.Google Scholar
Turner, M. 1998. Figure. In Cacciari, C., Gibbs, R. Jr., & Katz, A. (eds.), Figurative Language and Thought (pp. 44–87). Oxford: Oxford University Press.Google Scholar
Turner, M. 2015. Blending in language and communication. In Dąbrowska, E. & Divjak, D. (eds.), Handbook of Cognitive Linguistics (pp. 211–232). Berlin: De Gruyter Mouton.Google Scholar
Turner, M. 2020. Constructions and creativity. Cognitive Semiotics, 13:1. https://doi.org/10.1515/cogsem-2020-2019.Google Scholar
Ungerer, T. & Hartmann, S. 2023. Constructionist Approaches: Past, Present, Future. (Cambridge Elements in Construction Grammar). Cambridge: Cambridge University Press.Google Scholar
van Dis, E. A. M., Bollen, J., Zuidema, W., van Rooij, R., & Bockting, C. L. 2023. ChatGPT: Five priorities for research. Conversational AI is a game-changer for science. Here’s how to respond. Nature, 614: 224–226.Google Scholar
Yamada, K., Sasano, R., & Takeda, K. 2021. Semantic frame induction using masked word embeddings and two-step clustering. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers) (pp. 811–816). Association for Computational Linguistics.Google Scholar
Yong, Z. X. & Torrent, T. T. 2020. Semi-supervised deep embedded clustering with anomaly detection for semantic frame induction. In Proceedings of the 12th Language Resources and Evaluation Conference, Marseille, France (pp. 3509–3519). European Language Resources Association (ELRA).Google Scholar
You, L. & Liu, K. 2005. Building Chinese FrameNet database. In 2005 International Conference on Natural Language Processing and Knowledge Engineering, Wuhan, China (pp 301–306). IEEE. https://doi.org/10.1109/NLPKE.2005.1598752.Google Scholar
Weissweiler, L., Hofmann, V., Köksal, A., & Schütze, H. 2022. The better your syntax, the better your semantics? Probing pretrained language models for the English comparative correlative. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, December 7–11, 2022, Abu Dhabi, United Arab Emirates (pp. 10859–10882). Association for Computational Linguistics. https://aclanthology.org/2022.emnlp-main.746.pdf.Google Scholar
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