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Article published In:
International Journal of Corpus Linguistics
Vol. 23:2 (2018) ► pp.183–215

Multi-unit association measures

Moving beyond pairs of words

| University of Canterbury
This paper formulates and evaluates a series of multi-unit measures of directional association, building on the pairwise ΔP measure, that are able to quantify association in sequences of varying length and type of representation. Multi-unit measures face an additional segmentation problem: once the implicit length constraint of pairwise measures is abandoned, association measures must also identify the borders of meaningful sequences. This paper takes a vector-based approach to the segmentation problem by using 18 unique measures to describe different aspects of multi-unit association. An examination of these measures across eight languages shows that they are stable across languages and that each provides a unique rank of associated sequences. Taken together, these measures expand corpus-based approaches to association by generalizing across varying lengths and types of representation.
Keywords: association strength, multi-unit association, sequences, ΔP, collocations
Article outline
  • 1.Introduction
  • 2.Direction of association and sequence length
  • 3.Data and methodology
  • 4.Analysis: Formulating multi-unit association measures
    • 4.1Mean ΔP and Sum ΔP
    • 4.2Minimum ΔP
    • 4.3Reduced ΔP
    • 4.4Divided ΔP
    • 4.5End-point ΔP
    • 4.6Changed ΔP
    • 4.7Summarizing the association measures
  • 5.Discussion: Empirical analysis of association measures
    • 5.1Relations between directions and measures
    • 5.2Stability across languages and representation types
  • 6.Using the measures together
  • 7.Conclusions
  • Acknowledgements
  • References
Published online: 5 October 2018
DOI logo
https://doi.org/10.1075/ijcl.16098.dun
References (26)
References
Biber, B., Reppen, R., Schnur, E., & Ghanem, R. (2016). On the (non)utility of Juilland’s D to measure lexical dispersion in large corpora. International Journal of Corpus Linguistics, 21(4): 439–464. DOI logoGoogle Scholar
Church, K., & Hanks, P. (1990). Word association norms, Mutual Information, and lexicography. Computational Linguistics, 16(1): 22–29.Google Scholar
Daudaravičius, V., & Marcinkevičienė, R. (2004). Gravity counts for the boundaries of collocations. International Journal of Corpus Linguistics, 9(2): 321–348. DOI logoGoogle Scholar
Davies, M. (2008-). The Corpus of Contemporary American English (COCA): 520 million words, 1990-present. Available online at [URL] (last accessed June 2018).
Dunn, J. (2017). Computational learning of construction grammars. Language and Cognition, 9(2): 254–292. DOI logoGoogle Scholar
(2018). Finding variants for construction-based dialectometry: A corpus-based approach to regional CxGs. Cognitive Linguistics, 29(2): 275–311. DOI logoGoogle Scholar
Ellis, N. (2007). Language acquisition as rational contingency learning. Applied Linguistics, 27(1): 1–24. DOI logoGoogle Scholar
Erhan, D., Bengio, Y., Courville, A., Manzagol, P., Vincent, P., & Bengio, S. (2010). Why does unsupervised pre-training help deep learning? Journal of Machine Learning Research, 111: 625–660.Google Scholar
Evert, S. (2005). The Statistics of Word Co-Occurrences: Word Pairs and Collocations (Unpublished doctoral dissertation). Stuttgart, University of Stuttgart.Google Scholar
Gries, St. Th. (2008). Dispersions and adjusted frequencies in corpora. International Journal of Corpus Linguistics, 13(4): 403–437. DOI logoGoogle Scholar
(2010). Bigrams in registers, domains, and varieties: A bigram gravity approach to the homogeneity of corpora. In Mahlberg, M., Diaz, V. & Smith, C. (Eds.) Proceedings of the 2009 Corpus Linguistics Conference. Liverpool: University of Liverpool.Google Scholar
(2012). Frequencies, probabilities, and association measures in usage- / exemplar-based linguistics. Studies in Language, 11(3): 477–510.Google Scholar
(2013). 50-something years of work on collocations: What is or should be next. International Journal of Corpus Linguistics, 18(1): 137–165. DOI logoGoogle Scholar
Gries, St. 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, St. Th., & Stefanowitsch, A. (2004). Extending collostructional analysis: A corpus-based perspective on ‘alternations’. International Journal of Corpus Linguistics, 9(1): 97–129. DOI logoGoogle Scholar
Jelinek, F. (1990). Self-organizing language modeling for speech recognition. In A. Waibel & K. Lee (eds.), Readings in Speech Recognition (pp. 450–506). San Mateo, CA: Morgan Kaufmann. DOI logoGoogle Scholar
Koehn, P. (2005). Europarl: A parallel corpus for statistical machine translation. In Proceedings of the 10th Machine Translation Summit 2005 (pp. 79–86). Tokyo: Asia-Pacific Association for Machine Translation.Google Scholar
Michelbacher, L., Evert, S., & Schutze, H. (2007). Asymmetric association measures. In N. Nicolov, G. Angelova & R. Mitkov (Eds.), Proceedings of the 6th International Conference on Recent Advances in Natural Language Processing (RANLP) (pp. 367–372). Amsterdam/Philadelphia: John Benjamins. 367–372.Google Scholar
Nguyen, D. Q., Nguyen, D. Q., Pham, D. D., & Pham, S. B. (2016). A robust transformation-based learning approach using ripple down rules for part-of-speech tagging. AI Communications, 29(3): 409–422. DOI logoGoogle Scholar
Pecina, P. (2009). Lexical association measures and collocation extraction. Language Resources and Evaluation, 44(1/2): 137–158.Google Scholar
Pedersen, T. (1998). Dependent bigram identification. In J. Mostow & C. Rich (Eds.), Proceedings of the 15th National Conference on Artificial Intelligence (AAAI-98) (p. 1197). Menlo Park, CA: The AAAI Press.Google Scholar
Pennington, J., Socher, R., & Manning, C. (2014). GloVe: Global vectors for word representation. In B. Pang & W. Daelemans (Eds.), Proceedings of Empirical Methods in Natural Language Processing (EMNLP) (pp. 1532–1543) Stroudsburg, PA: Association for Computational Linguistics.Google Scholar
Shimohata, S., Sugio, T., & Nagata, J. (1997). Retrieving collocations by co-occurrences and word order constraints. In P. Cohen & W. Wahlster (Eds.), Proceedings of the Association for Computational Linguistics Annual Meeting (pp. 476–481). Stroudsburg, PA: Association for Computational Linguistics.Google Scholar
Wible, D., & Tsao, N. (2010). StringNet as a computational resource for discovering and investigating linguistic constructions. In M. Sahlgren & O. Knutsson (Eds.), Proceedings of the Workshop on Extracting and Using Constructions in Computational Linguistics (NAACL-HTL) (pp. 25–31). Stroudsburg, PA: Association for Computational Linguistics.Google Scholar
Wiechmann, D. (2008). On the computation of collostructional strength: Testing measures of association as expressions of lexical bias. Corpus Linguistics and Linguistic Theory, 4(2): 253–290. DOI logoGoogle Scholar
Zhai, C. (1997). Exploiting context to identify lexical atoms: A statistical view of linguistic context. In P. Brezillon (Ed.), Proceedings of the First International and Interdisciplinary Conference on Modeling and Using Contex (pp.119–129). Rio de Janeiro, Brazil.Google Scholar
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