Cover not available
Part of
Computational Phraseology
Edited by Gloria Corpas Pastor and Jean-Pierre Colson
[IVITRA Research in Linguistics and Literature 24] 2020
► pp. 83–110

Computational extraction of formulaic sequences from corpora

Two case studies of a new extraction algorithm

| Donders Institute for Brain, Cognition and Behaviour, Radboud University
| University of California Santa Barbara | Justus Liebig University

We describe a new algorithm for the extraction of formulaic language from corpora. Entitled MERGE (Multi-word Expressions from the Recursive Grouping of Elements), it iteratively combines adjacent bigrams into progressively longer sequences based on lexical association strengths. We then provide empirical evidence for this approach via two case studies. First, we compare the performance of MERGE to that of another algorithm by examining the outputs of the approaches compared with manually annotated formulaic sequences from the spoken component of the British National Corpus. Second, we employ two child language corpora to examine whether MERGE can predict the formulas that the children learn based on caregiver input. Ultimately, we show that MERGE indeed performs well, offering a powerful approach for the extraction of formulas.

Keywords: formulaic sequences, collocation extraction, lexical association, child language, MERGE, adjusted frequency list
Article outline
  • 1.Introduction
    • 1.1Counting co-occurrences
    • 1.2N-Gram sizes/configurations and the problem of redundancy
    • 1.3Recent approaches
  • 2.The MERGE algorithm
  • 3.Case study 1: MERGE vs. AFL
    • 3.1Materials
    • 3.2Results
    • 3.3Interim conclusions
  • 4.Case study 2: Exploring MERGE in the context of L1 acquisition
    • 4.1Materials and methods
    • 4.2Results
    • 4.3Discussion
  • 5.Conclusion
  • Notes
  • References
Published online: 8 May 2020
DOI logo
https://doi.org/10.1075/ivitra.24.05wah
References
Altenberg, B.
(1998) On the phraseology of spoken English: The evidence of recurrent word-combinations. In A. P. Cowie (Ed.), Phraseology: Theory, analysis, and applications (pp. 101–102). Oxford: Oxford University Press.Google Scholar
Arnon, I., & Snider, N.
(2010) More than words: Frequency effects for multi-word phrases. Journal of Memory and Language 6, 67–82. DOI logoGoogle Scholar
Bannard, C., Lieven, E., & Tomasello, M.
(2009) Modeling children’s early grammatical knowledge. Proceedings of the National Academy of Science 106(41), 17284–17289. DOI logoGoogle Scholar
Biber, D., Conrad, S., & Cortes, V.
(2004) If you look at …: Lexical bundles in university teaching and textbooks. Applied Linguistics, 25(3), 371–405. DOI logoGoogle Scholar
Bod, R.
(2009) From exemplar to grammar: A probabilistic analogy-based model of language learning. Cognitive Science, 33(5), 752–793. DOI logoGoogle Scholar
Bolinger, D.
(1976) Meaning and memory. Forum Linguisticum 1, 1–14.Google Scholar
Bybee, J.
(2010) Language, usage, and cognition. Cambridge: Cambridge University Press. DOI logoGoogle Scholar
Daudaraviĉius, V., & Marcinkeviĉienė, R.
(2004) Gravity counts for the boundaries of collocations. International Journal of Corpus Linguistics, 9(2), 321–348. DOI logoGoogle Scholar
Dunning, T.
(1993) Accurate methods for the statistics of surprise and coincidence. Computational Linguistics, 19(1), 61–74.Google Scholar
Ellis, N. C., Simpson-Vlach, R., & Maynard, C.
(2008) Formulaic language in native and second language speakers: Psycholinguistics, corpus linguistics, and TESOL. TESOL Quarterly, 42(3), 375–396. DOI logoGoogle Scholar
Erman, B., & Warren, B.
(2000) The idiom principle and the open choice principle. Text, 20(1), 29–62. DOI logoGoogle Scholar
Evert, S.
(2004) The statistics of word co-occurrences: Word pairs and collocations. (PhD Thesis, Universität Stuttgart).Google Scholar
(2009) Corpora and collocations. In A. Lüdeling, & M. Kytö (Eds.), Corpus linguistics: an international handbook, Vol. 2 (pp. 1212–1248). Berlin/New York: Mouton de Gruyter.Google Scholar
Goldberg, A. E.
(1995) Constructions: a construction grammar approach to argument structure. Chicago: University of Chicago Press.Google Scholar
(2006) Constructions at work. Oxford: Oxford University Press.Google Scholar
Granger, S., & Meunier, F.
(Eds) (2008) Phraseology. An interdisciplinary perspective. Amsterdam/Philadelphia: John Benjamins. DOI logoGoogle Scholar
Gries, S. Th.
(2008) Dispersions and adjusted frequencies in corpora. International Journal of Corpus Linguistics, 13(4), 403–437. DOI logoGoogle Scholar
Gries, S. Th., & Mukherjee, J.
(2010) Lexical gravity across varieties of English: An ICE-based study of n-grams in Asian Englishes. International Journal of Corpus Linguistics, 15(4), 520–548. DOI logoGoogle Scholar
Gries, S. Th.
(2015) Some current quantitative problems in corpus linguistics and a sketch of some solutions. Language and Linguistics, 16(1), 93–117. DOI logoGoogle Scholar
Langacker, R. W.
(1987) Foundations of cognitive grammar: Vol. 1: Theoretical prerequisites. Stanford: Stanford University Press.Google Scholar
Lieven, E., Salomo D., & Tomasello, M.
(2009) Two-year-old children’s production of multiword utterances: A usage-based analysis. Cognitive Linguistics, 20(3), 481–507. DOI logoGoogle Scholar
MacWhinney, B.
(2000) The CHILDES project. Tools for analyzing talk. Third edition. Mahwah, NJ: Lawrence Erlbaum Associates.Google Scholar
McEnery, T.
(2006) Swearing in English: Bad language, purity and power from 1586 to the present. Abington: Routledge.Google Scholar
O’Donnell, M. B.
(2011) The adjusted frequency list: A method to produce cluster-sensitive frequency lists. ICAME Journal, 35, 135–169.Google Scholar
Pecina, P.
(2009) Lexical association measures: Collocation extraction. Prague: Charles University.Google Scholar
Rowland, C. F., & Fletcher, S. L.
(2006) The effect of sampling on estimates of lexical specificity and error rates. The Journal of Child Language 33(4), 859–877. DOI logoGoogle Scholar
Swingley, D.
(2005) Statistical clustering and the contents of the infant vocabulary. Cognitive Psychology, 50, 86–132. DOI logoGoogle Scholar
Tomasello, M.
(2005) Constructing a language: a usage-based theory of language acquisition. Cambridge, MA: Harvard University Press.Google Scholar
Wahl, A., & Gries, S. Th.
(2018) Multi-word expressions: A novel computational approach to their bottom-up statistical extraction. In P. L. Cantos-Gómez and M. Almela-Sánchez (Eds.), Lexical collocation analysis: advances and applications (pp. 85–109). Berlin/New York: Springer. DOI logoGoogle Scholar
Wible, D., Kuo, C., Chen, M., Tsao, N., & Hung, T.
(2006) A computational approach to the discovery and representation of lexical chunks. TALN 2006 Leuven, Belgium.Google Scholar
Wray, A.
(2002) Formulaic language and the lexicon. Cambridge: Cambridge University Press. DOI logoGoogle Scholar
Cited by

Cited by 3 other publications

Kranich, Svenja & Tine Breban
2021. Lost in Change. In Lost in Change [Studies in Language Companion Series, 218], ► pp. 1 ff. DOI logo
Tichý, Ondřej
2021. Corpus driven identification of lexical bundle obsolescence in Late Modern English. In Lost in Change [Studies in Language Companion Series, 218], ► pp. 101 ff. DOI logo
[no author supplied]
2021. Lost in Change. In Lost in Change [Studies in Language Companion Series, 218], DOI logo

This list is based on CrossRef data as of 23 march 2024. Please note that it may not be complete. Sources presented here have been supplied by the respective publishers. Any errors therein should be reported to them.