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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
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[no author supplied]
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