1887
  1. Home
  2. e-Journals
  3. Constructions and Frames
  4. Online First Articles
  5. Online First Article
image of The computational learning of construction grammars*
PDF
EPUB

Abstract

Abstract

This paper documents and reviews the state of the art concerning computational models of construction grammar learning. It brings together prior work on the computational learning of form-meaning pairings, which has so far been studied in several distinct areas of research. The goal of this paper is threefold. First of all, it aims to synthesise the variety of methodologies that have been proposed to date and the results that have been obtained. Second, it aims to identify those parts of the challenge that have been successfully tackled and reveal those that require further research. Finally, it aims to provide a roadmap which can help to boost and streamline future research efforts on the computational learning of large-scale, usage-based construction grammars.

© John Benjamins Publishing Company
Available under the CC BY 4.0 license.
Loading

Article metrics loading...

/content/journals/10.1075/cf.23026.dou
2024-12-16
2025-01-13

  • From This Site

    /content/journals/10.1075/cf.23026.dou
    dcterms_title,dcterms_subject,pub_keyword
    -contentType:Journal -contentType:Contributor -contentType:Concept -contentType:Institution
    10
    5
Loading full text...

Full text loading...

/deliver/fulltext/10.1075/cf.23026.dou/cf.23026.dou.html?itemId=/content/journals/10.1075/cf.23026.dou&mimeType=html&fmt=ahah

References

  1. Abend, O., Kwiatkowski, T., Smith, N. J., Goldwater, S., & Steedman, M.
    (2017) Bootstrapping language acquisition. Cognition, , –. 10.1016/j.cognition.2017.02.009
     https://doi.org/10.1016/j.cognition.2017.02.009 [Google Scholar]
  2. Alishahi, A. & Stevenson, S.
    (2008) A computational model of early argument structure acquisition. Cognitive Science, (), –. 10.1080/03640210801929287
     https://doi.org/10.1080/03640210801929287 [Google Scholar]
  3. Artzi, Y., & Zettlemoyer, L.
    (2013) Weakly supervised learning of semantic parsers for mapping instructions to actions. Transactions of the Association for Computational Linguistics, , –. 10.1162/tacl_a_00209
     https://doi.org/10.1162/tacl_a_00209 [Google Scholar]
  4. Beekhuizen, B.
    (2015) Constructions Emerging [Doctoral dissertation]. LOT — Netherlands Graduate School of Linguistics.
  5. Beekhuizen, B., & Bod, R.
    (2014) Automating construction work: Data-oriented parsing and constructivist accounts of language acquisition. InR. Boogart, T. Colleman & G. Rutten (Eds.), Extending the scope of Construction Grammar (pp.–). Mouton de Gruyter. 10.1515/9783110366273.47
     https://doi.org/10.1515/9783110366273.47 [Google Scholar]
  6. Beekhuizen, B., Bod, R., Fazly, A., Stevenson, S., & Verhagen, A.
    (2014) A usage-based model of early grammatical development. InV. Demberg & T. O’Donnell (Eds.), Proceedings of the Fifth Workshop on Cognitive Modeling and Computational Linguistics (pp.–). Association for Computational Linguistics. 10.3115/v1/W14‑2006
     https://doi.org/10.3115/v1/W14-2006 [Google Scholar]
  7. Bender, E. M.
    (2008) Grammar engineering for linguistic hypothesis testing. InN. Gaylord, A. Palmer & E. Ponvert (Eds.), Proceedings of the Texas Linguistics Society X Conference: Computational linguistics for less-studied languages (pp.–). CSLI.
     [Google Scholar]
  8. Beuls, K., Gerasymova, K., & van Trijp, R.
    (2010) Situated learning through the use of language games. Proceedings of the 19th Annual Machine Learning Conference of Belgium and The Netherlands (BeNeLearn) (pp.–).
     [Google Scholar]
  9. Beuls, K., & Höfer, S.
    (2011) Simulating the emergence of grammatical agreement in multi-agent language games. InT. Welsh (Ed.), Proceedings of the Twenty-Second International Joint Conference on Artificial Intelligence (pp.–). AAAI Press.
     [Google Scholar]
  10. Beuls, K. & Steels, L.
    (2013) Agent-based models of strategies for the emergence and evolution of grammatical agreement. PLOS ONE, (), e58960. 10.1371/journal.pone.0058960
     https://doi.org/10.1371/journal.pone.0058960 [Google Scholar]
  11. Beuls, K. & Van Eecke, P.
    (2023) Fluid Construction Grammar: State of the art and future outlook. InC. Bonial & H. Tayyar Madabushi (Eds.), Proceedings of the First International Workshop on Construction Grammars and NLP (CxGs+NLP, GURT/SyntaxFest 2023) (pp.–). Association for Computational Linguistics.
     [Google Scholar]
  12. (2025) Construction grammar and artificial intelligence. InM. Fried & K. Nikiforidou (Eds.), The Cambridge handbook of Construction Grammar. Cambridge University Press.
     [Google Scholar]
  13. Beuls, K., Van Eecke, P., & Cangalovic, V. S.
    (2021) A computational construction grammar approach to semantic frame extraction. Linguistics Vanguard, (), 20180015. 10.1515/lingvan‑2018‑0015
     https://doi.org/10.1515/lingvan-2018-0015 [Google Scholar]
  14. Brown, R.
    (1973) A first language: The early stages. Harvard University Press. 10.4159/harvard.9780674732469
     https://doi.org/10.4159/harvard.9780674732469 [Google Scholar]
  15. Bybee, J.
    (2006) From usage to grammar: The mind’s response to repetition. Language, (), –. 10.1353/lan.2006.0186
     https://doi.org/10.1353/lan.2006.0186 [Google Scholar]
  16. Chang, N.
    (2008) Constructing grammar: A computational model of the emergence of early constructions [Doctoral dissertation]. University of California.
     [Google Scholar]
  17. Chen, D. L., & Mooney, R. J.
    (2008) Learning to sportscast: A test of grounded language acquisition. InA. McCallum & S. Roweis (Eds.), Proceedings of the 25th International Conference on Machine Learning (pp.–). Association for Computing Machinery. 10.1145/1390156.1390173
     https://doi.org/10.1145/1390156.1390173 [Google Scholar]
  18. Croft, W.
    (1998) Event structure in argument linking. InM. Butt & W. Geuder (Eds.), The projection of arguments: Lexical and compositional factors (pp.–). CSLI.
     [Google Scholar]
  19. Dominey, P. F.
    (2005a) Emergence of grammatical constructions: Evidence from simulation and grounded agent experiments. Connection Science, (), –. 10.1080/09540090500270714
     https://doi.org/10.1080/09540090500270714 [Google Scholar]
  20. (2005b) From sensorimotor sequence to grammatical construction: Evidence from simulation and neurophysiology. Adaptive Behavior, (), –. 10.1177/105971230501300401
     https://doi.org/10.1177/105971230501300401 [Google Scholar]
  21. (2006) From holophrases to abstract grammatical constructions: Insights from simulation studies. InE. Clark & B. Kelly (Eds.), Constructions in acquisition (pp.–). CSLI.
     [Google Scholar]
  22. Dominey, P. F., & Boucher, J.-D.
    (2005) Learning to talk about events from narrated video in a construction grammar framework. Artificial Intelligence, (), –. 10.1016/j.artint.2005.06.007
     https://doi.org/10.1016/j.artint.2005.06.007 [Google Scholar]
  23. Doumen, J., Beuls, K., & Van Eecke, P.
    (2023) Modelling language acquisition through syntactico-semantic pattern finding. InA. Vlachos & I. Augenstein (Eds.), Findings of the Association for Computational Linguistics: EACL 2023 (pp.–). Association for Computational Linguistics. 10.18653/v1/2023.findings‑eacl.99
     https://doi.org/10.18653/v1/2023.findings-eacl.99 [Google Scholar]
  24. Dunn, J.
    (2017) Computational learning of construction grammars. Language and Cognition, (), –. 10.1017/langcog.2016.7
     https://doi.org/10.1017/langcog.2016.7 [Google Scholar]
  25. (2018) Modeling the complexity and descriptive adequacy of construction grammars. Proceedings of the Society for Computation in Linguistics (SCiL), , –.
     [Google Scholar]
  26. (2019) Frequency vs. association for constraint selection in usage-based construction grammar. InE. Chersoni, N. Hollenstein, C. Jacobs, Y. Oseki, L. Prévot & E. Santus (Eds.), Proceedings of the Workshop on Cognitive Modeling and Computational Linguistics (pp.–). Association for Computational Linguistics. 10.18653/v1/W19‑2913
     https://doi.org/10.18653/v1/W19-2913 [Google Scholar]
  27. (2022) Exposure and emergence in usage-based grammar: Computational experiments in 35 languages. Cognitive Linguistics, (), –. 10.1515/cog‑2021‑0106
     https://doi.org/10.1515/cog-2021-0106 [Google Scholar]
  28. (2023) Exploring the constructicon: Linguistic analysis of a computational CxG. InC. Bonial & H. Tayyar Madabushi (Eds.), Proceedings of the First International Workshop on Construction Grammars and NLP (CxGs+NLP, GURT/SyntaxFest 2023) (pp.–). Association for Computational Linguistics.
     [Google Scholar]
  29. Dunn, J., & Tayyar Madabushi, H.
    (2021) Learned construction grammars converge across registers given increased exposure. InA. Bisazza & O. Abend (Eds.), Proceedings of the 25th Conference on Computational Natural Language Learning (pp.–). Association for Computational Linguistics. 10.18653/v1/2021.conll‑1.21
     https://doi.org/10.18653/v1/2021.conll-1.21 [Google Scholar]
  30. EHAI
    EHAI (2023) SemBrowse: Semantics-driven corpus exploration. https://ehai.ai.vub.ac.be/sembrowse/
  31. Ellis, N. C.
    (2006) Language acquisition as rational contingency learning. Applied Linguistics, (), –. 10.1093/applin/ami038
     https://doi.org/10.1093/applin/ami038 [Google Scholar]
  32. Fazly, A., Alishahi, A., & Stevenson, S.
    (2010) A probabilistic computational model of cross-situational word learning. Cognitive Science, (), –. 10.1111/j.1551‑6709.2010.01104.x
     https://doi.org/10.1111/j.1551-6709.2010.01104.x [Google Scholar]
  33. Garcia Casademont, E.
    (2018) Origins of recursive phrase structure through cultural self-organisation and selection [Doctoral dissertation]. Universitat Pompeu Fabra.
     [Google Scholar]
  34. Garcia Casademont, E., & Steels, L.
    (2015) Usage-based grammar learning as insight problem solving. InG. Airenti, B. G. Bara & G. Sandini (Eds.), Proceedings of the EuroAsianPacific Joint Conference on Cognitive Science (pp.–). CEUR Workshop Proceedings.
     [Google Scholar]
  35. (2016) Insight grammar learning. Journal of Cognitive Science, (), –. 10.17791/jcs.2016.17.1.27
     https://doi.org/10.17791/jcs.2016.17.1.27 [Google Scholar]
  36. Gaspers, J., & Cimiano, P.
    (2012) A usage-based model for the online induction of constructions from phoneme sequences. 2012 IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob), –. 10.1109/DevLrn.2012.6400825
     https://doi.org/10.1109/DevLrn.2012.6400825 [Google Scholar]
  37. (2014) A computational model for the item-based induction of construction networks. Cognitive Science, (), –. 10.1111/cogs.12114
     https://doi.org/10.1111/cogs.12114 [Google Scholar]
  38. Gaspers, J., Cimiano, P., Griffiths, S. S., & Wrede, B.
    (2011) An unsupervised algorithm for the induction of constructions. 2011 IEEE International Conference on Development and Learning (ICDL), –. 10.1109/DEVLRN.2011.6037371
     https://doi.org/10.1109/DEVLRN.2011.6037371 [Google Scholar]
  39. Gaspers, J., Cimiano, P., Rohlfing, K., & Wrede, B.
    (2016) Constructing a language from scratch: Combining bottom-up and top-down learning processes in a computational model of language acquisition. IEEE Transactions on Cognitive and Developmental Systems, (), –. 10.1109/TCDS.2016.2614958
     https://doi.org/10.1109/TCDS.2016.2614958 [Google Scholar]
  40. Gerasymova, K., & Spranger, M.
    (2010) Acquisition of grammar in autonomous artificial systems. InH. Coelho, R. Studer & M. Woolridge (Eds.), Proceedings of the 19th European Conference on Artificial Intelligence (ECAI-2010) (pp.–). IOS Press.
     [Google Scholar]
  41. (2012) An experiment in temporal language learning. InL. Steels & M. Hild (Eds.), Language grounding in robots (pp.–). Springer. 10.1007/978‑1‑4614‑3064‑3_12
     https://doi.org/10.1007/978-1-4614-3064-3_12 [Google Scholar]
  42. Goldberg, A. E.
    (2003) Constructions: A new theoretical approach to language. Trends in Cognitive Sciences, (), –. 10.1016/S1364‑6613(03)00080‑9
     https://doi.org/10.1016/S1364-6613(03)00080-9 [Google Scholar]
  43. Hemphill, C. T., Godfrey, J. J., & Doddington, G. R.
    (1990) The ATIS spoken language systems pilot corpus. Speech and Natural Language: Proceedings of a Workshop held at Hidden Valley (pp.-). 10.3115/116580.116613
     https://doi.org/10.3115/116580.116613 [Google Scholar]
  44. Johnson, J., Hariharan, B., van der Maaten, L., Fei-Fei, L., Lawrence Zitnick, C., & Girshick, R.
    (2017) CLEVR: A diagnostic dataset for compositional language and elementary visual reasoning. In2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp.–). IEEE. 10.1109/CVPR.2017.215
     https://doi.org/10.1109/CVPR.2017.215 [Google Scholar]
  45. Krenn, B., Sadeghi, S., Neubarth, F., Gross, S., Trapp, M., & Scheutz, M.
    (2020) Models of cross-situational and crossmodal word learning in task-oriented scenarios. IEEE Transactions on Cognitive and Developmental Systems, (), –. 10.1109/TCDS.2020.2995045
     https://doi.org/10.1109/TCDS.2020.2995045 [Google Scholar]
  46. Kwiatkowski, T., Goldwater, S., Zettlemoyer, L., & Steedman, M.
    (2012) A probabilistic model of syntactic and semantic acquisition from child-directed utterances and their meanings. InW. Daelemans (Ed.), Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics (pp.–). Association for Computational Linguistics.
     [Google Scholar]
  47. Kwiatkowski, T., Zettlemoyer, L., Goldwater, S., & Steedman, M.
    (2010) Inducing probabilistic CCG grammars from logical form with higher-order unification. InH. Li & L. Màrquez (Eds.), Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing (pp.–). Association for Computational Linguistics.
     [Google Scholar]
  48. (2011) Lexical generalization in CCG grammar induction for semantic parsing. InR. Barzilay & M. Johnson (Eds.), Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing (pp.–). Association for Computational Linguistics.
     [Google Scholar]
  49. MacWhinney, B.
    (2000) The CHILDES project: Tools for analyzing talk (3rd edition). Lawrence Erlbaum Associates.
     [Google Scholar]
  50. Martí, M. A., Taulé, M., Kovatchev, V., & Salamó, M.
    (2021) DISCOver: DIStributional approach based on syntactic dependencies for discovering COnstructions. Corpus Linguistics and Linguistic Theory, (), –. 10.1515/cllt‑2018‑0028
     https://doi.org/10.1515/cllt-2018-0028 [Google Scholar]
  51. Müller, S.
    (2015) The coregram project: Theoretical linguistics, theory development, and verification. Journal of Language Modelling, (), –. 10.15398/jlm.v3i1.91
     https://doi.org/10.15398/jlm.v3i1.91 [Google Scholar]
  52. Nevens, J., Doumen, J., Van Eecke, P., & Beuls, K.
    (2022) Language acquisition through intention reading and pattern finding. InN. Calzolari & C.-R. Huang (Eds), Proceedings of the 29th International Conference on Computational Linguistics (pp.–). International Committee on Computational Linguistics.
     [Google Scholar]
  53. Ons, B., Gemmeke, J. F., & Van hamme, H.
    (2014) Fast vocabulary acquisition in an NMF-based self-learning vocal user interface. Computer Speech & Language, (), –. 10.1016/j.csl.2014.03.004
     https://doi.org/10.1016/j.csl.2014.03.004 [Google Scholar]
  54. Pauw, S.
    (2013) Size matters: Grounding quantifiers in spatial perception [Doctoral dissertation]. University of Amsterdam.
     [Google Scholar]
  55. Renkens, V., & Van hamme, H.
    (2017) Automatic relevance determination for nonnegative dictionary learning in the gamma-poisson model. Signal Processing, , –. 10.1016/j.sigpro.2016.09.009
     https://doi.org/10.1016/j.sigpro.2016.09.009 [Google Scholar]
  56. Spranger, M.
    (2015) Incremental grounded language learning in robot-robot interactions: Examples from spatial language. In2015 Joint IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL-EpiRob) (pp.–). IEEE. 10.1109/DEVLRN.2015.7346140
     https://doi.org/10.1109/DEVLRN.2015.7346140 [Google Scholar]
  57. (2017) Usage-based grounded construction learning: A computational model. InThe 2017 AAAI Spring Symposium Series [Technical report] (pp.–). AAAI Press.
     [Google Scholar]
  58. Spranger, M., Pauw, S., Loetzsch, M., & Steels, L.
    (2012) Open-ended procedural semantics. InL. Steels, & M. Hild (Eds.), Language grounding in robots (pp.–). Springer. 10.1007/978‑1‑4614‑3064‑3_8
     https://doi.org/10.1007/978-1-4614-3064-3_8 [Google Scholar]
  59. Spranger, M., & Steels, L.
    (2015) Co-acquisition of syntax and semantics: an investigation in spatial language. InQ. Yang, & M. Wooldridge (Eds.), Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence (pp.–). AAAI Press.
     [Google Scholar]
  60. Steels, L.
    (1998) The origins of syntax in visually grounded robotic agents. Artificial Intelligence, (), –. 10.1016/S0004‑3702(98)00066‑6
     https://doi.org/10.1016/S0004-3702(98)00066-6 [Google Scholar]
  61. (2001) Language games for autonomous robots. IEEE Intelligent Systems, , –. 10.1109/MIS.2001.956077
     https://doi.org/10.1109/MIS.2001.956077 [Google Scholar]
  62. (2004) Constructivist development of grounded construction grammar. Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04) (pp.–). 10.3115/1218955.1218957
     https://doi.org/10.3115/1218955.1218957 [Google Scholar]
  63. Steels, L., & De Beule, J.
    (2006) Unify and merge in Fluid Construction Grammar. InP. Vogt, Y. Sugita, E. Tuci & C. L. Nehaniv (Eds), Symbol grounding and beyond, International Workshop on Emergence and Evolution of Linguistic Communication (EELC 2006) (pp.–). Springer. 10.1007/11880172_16
     https://doi.org/10.1007/11880172_16 [Google Scholar]
  64. Tayyar Madabushi, H., Romain, L., Divjak, D., & Milin, P.
    (2020) CxGBERT: BERT meets Construction Grammar. InD. Scott, N. Bel & C. Zong (Eds.), Proceedings of the 28th International Conference on Computational Linguistics (pp.–). International Committee on Computational Linguistics. 10.18653/v1/2020.coling‑main.355
     https://doi.org/10.18653/v1/2020.coling-main.355 [Google Scholar]
  65. Tayyar Madabushi, H., Romain, L., Milin, P., and Divjak, D.
    (2025) Construction Grammar and language models. InM. Fried, & K. Nikiforidou (Eds.), The Cambridge handbook of Construction Grammar. Cambridge University Press.
     [Google Scholar]
  66. ten Bosch, L., Boves, L., Van hamme, H., & Moore, R. K.
    (2009) A computational model of language acquisition: The emergence of words. Fundamenta Informaticae, (), –. 10.3233/FI‑2009‑0016
     https://doi.org/10.3233/FI-2009-0016 [Google Scholar]
  67. Tomasello, M.
    (2003) Constructing a language: A usage-based theory of language acquisition. Harvard University Press.
     [Google Scholar]
  68. Van Eecke, P.
    (2018) Generalisation and specialisation operators for computational construction grammar and their application in evolutionary linguistics research [Doctoral dissertation]. Vrije Universiteit Brussel, VUB Press.
     [Google Scholar]
  69. van Trijp, R.
    (2008) Analogy and multi-level selection in the formation of a Case Grammar. A case study in Fluid Construction Grammar [Doctoral dissertation]. University of Antwerp.
     [Google Scholar]
  70. (2016) The evolution of case grammar. Language Science Press. 10.26530/OAPEN_611694
     https://doi.org/10.26530/OAPEN_611694 [Google Scholar]
  71. van Trijp, R., Beuls, K., & Van Eecke, P.
    (2022) The FCG Editor: An innovative environment for engineering computational construction grammars. PLOS ONE, (), e0269708. 10.1371/journal.pone.0269708
     https://doi.org/10.1371/journal.pone.0269708 [Google Scholar]
  72. van Trijp, R., & Steels, L.
    (2012) Multilevel alignment maintains language systematicity. Advances in Complex Systems, (), 1250039. 10.1142/S0219525912500397
     https://doi.org/10.1142/S0219525912500397 [Google Scholar]
  73. Verheyen, L., Botoko Ekila, J., Nevens, J., Van Eecke, P., & Beuls, K.
    (2023) Neuro-symbolic procedural semantics for reasoning-intensive visual dialogue tasks. InK. Gal, A. Nowé, G. J. Nalepa, R. Fairstein, & R. Rădulescu (Eds.), Proceedings of the 26th European Conference on Artificial Intelligence (ECAI 2023) (pp.–). IOS Press. 10.3233/FAIA230544
     https://doi.org/10.3233/FAIA230544 [Google Scholar]
  74. Wang, P., & Van hamme, H.
    (2022) Bottleneck low-rank transformers for low-resource spoken language understanding. Interspeech 2022, –. 10.21437/Interspeech.2022‑10801
     https://doi.org/10.21437/Interspeech.2022-10801 [Google Scholar]
  75. Weissweiler, L., He, T., Otani, N., R. Mortensen, D., Levin, L., & Schütze, H.
    (2023) Construction grammar provides unique insight into neural language models. InC. Bonial, & H. Tayyar Madabushi (Eds.), Proceedings of the First International Workshop on Construction Grammars and NLP (CxGs+NLP, GURT/SyntaxFest 2023) (pp.–). Association for Computational Linguistics.
     [Google Scholar]
  76. 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. InY. Goldberg, Z. Kozareva & Y. Zhang (Eds.), Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing (pp.–). Association for Computational Linguistics. 10.18653/v1/2022.emnlp‑main.746
     https://doi.org/10.18653/v1/2022.emnlp-main.746 [Google Scholar]
  77. Willaert, T., Van Eecke, P., Beuls, K., & Steels, L.
    (2020) Building social media observatories for monitoring online opinion dynamics. Social Media + Society, (). 10.1177/2056305119898778
     https://doi.org/10.1177/2056305119898778 [Google Scholar]
  78. Zelle, J. M., & Mooney, R. J.
    (1996) Learning to parse database queries using inductive logic programming. Proceedings of the Thirteenth National Conference on Artificial Intelligence — Volume 2 (pp.–).
     [Google Scholar]
/content/journals/10.1075/cf.23026.dou
Loading
The computational learning of construction grammars*
John Benjamins ; https://doi.org/10.1075/cf.23026.dou
/content/journals/10.1075/cf.23026.dou
/content/journals/10.1075/cf.23026.dou
Loading

Data & Media loading...

  • Article Type: Research Article
Keywords: computational modelling ; construction grammar ; learning construction grammars ; usage-based linguistics ; computational construction grammar

Most Cited

This is a required field
Please enter a valid email address
Approval was successful
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error