Abstract
This paper develops a construction-based dialectometry capable of identifying previously unknown constructions and measuring the degree to which a given construction is subject to regional variation. The central idea is to learn a grammar of constructions (a CxG) using construction grammar induction and then to use these constructions as features for dialectometry. This offers a method for measuring the aggregate similarity between regional CxGs without limiting in advance the set of constructions subject to variation. The learned CxG is evaluated on how well it describes held-out test corpora while dialectometry is evaluated on how well it can model regional varieties of English. The method is tested using two distinct datasets: First, the International Corpus of English representing eight outer circle varieties; Second, a web-crawled corpus representing five inner circle varieties. Results show that the method (1) produces a grammar with stable quality across sub-sets of a single corpus that is (2) capable of distinguishing between regional varieties of English with a high degree of accuracy, thus (3) supporting dialectometric methods for measuring the similarity between varieties of English and (4) measuring the degree to which each construction is subject to regional variation. This is important for cognitive sociolinguistics because it operationalizes the idea that competition between constructions is organized at the functional level so that dialectometry needs to represent as much of the available functional space as possible.
Acknowledgements
This research was supported in part by an appointment to the Visiting Scientist Fellowship at the National Geospatial-Intelligence Agency administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the U.S. Department of Energy and NGA. The views expressed in this presentation are the author’s and do not imply endorsement by the DoD or the NGA.
Appendix
A Spatially-conditioned constructions
This appendix contains five of the top constructions for each region. The models ultimately depend on a large number of constructions, each of which has a relatively small degree of conditioning. A small number of highly predictive features for a region indicates a shallow model that is exploiting some irregularity in a small number of samples from that region (cf. Koppel et al. 2007). Thus, these top features only include those with a feature weight less than 0.02, a threshold that removes a very small number of unusually predictive features that occur infrequently. In order to aid interpretation of these representations, examples of the semantic domains contained here are given in Appendix B.
| East Africa | Singapore |
| [<25>– adv – ‘that’] | [verb – ‘down’] |
| [‘one’ –<25>– pron] | [‘my’ – adj] |
| [‘out’ – ‘of’] | [det – verb – adv] |
| [‘one’ – pron] | [det –<25>– ‘as’] |
| [<25>– ‘from’ – noun] | [‘when’ – ‘the’] |
| Hong Kong | Australia |
| [pron – verb – pron – noun] | [‘people’ – adp] |
| [‘government’ – noun] | [<25>– ‘young’ – noun] |
| [noun – noun – ‘is’] | [<47>– conj] |
| [det – ‘world’] | [‘use’ – ‘of’] |
| [‘do’ –<25>– verb] | [aux – ‘only’] |
| India | Canada |
| [verb – pron – ‘is’] | [‘please’ – verb] |
| [adp – pron – pron – verb] | [‘all’ – adp] |
| [<25>– verb – ‘there’] | [<49>– noun –<25>] |
| [adp –<25>–<25>– ‘this’] | [‘for’ – adj – noun – adp] |
| [aux – ‘given’ –<25>] | [‘it’ – verb – det] |
| Ireland | New Zealand |
| [‘‘s – verb] | [‘high’ –<25>] |
| [<25>– ‘and’ – pron – aux] | [<25>– ‘required’ –<25>] |
| [‘‘s’ –<25>– adp] | [<49>– aux] |
| [‘say’ –<25>] | [‘you’ – ‘to’] |
| [‘said’ – pron] | [‘or’ – adp – det] |
| Jamaica | United Kingdom |
| [<25>– sconj –<25>– adv] | [‘are’ – verb –<25>–<25>–<25>] |
| [‘end’ – ‘of’] | [‘taken’ – adp] |
| [<25>– ‘in’ – noun – adp] | [‘down’ –<25>] |
| [‘would’ – verb –<25>–<25>–<25>] | [<25>– ‘this’ – verb] |
| [adp – ‘a’ –<25>–<25>– det] | [‘range’ – adp] |
| Nigeria | South Africa |
| [noun –<96>] | [‘you’ – ‘to’] |
| [sconj – ‘are’] | [det – ‘world’] |
| [noun – ‘from’ –<25>] | [<25>–<39>–<25>] |
| [‘of’ – ‘and’] | [‘where’ – pron –<25>] |
| [adp – ‘people’] | [‘your’ – adj] |
| Philippines | |
| [‘and’ – noun – conj] | |
| [<25>– ‘let’] | |
| [sconj –<25>– verb – pron] | |
| [‘that’ –<25>–<25>– adv –<25>] | |
| [adp – ‘other’ – noun] |
B Examples of semantic domains
This appendix shows 10 lexical items that belong to each of a select number of semantic domains, selected to aid interpretation of the example representations in Appendix A. A complete inventory of each semantic domain is contained in the external resources accompanying this paper.
| <25> | <39> | <47> |
|---|---|---|
| auditorium | wheelchairs | law |
| industry | contraband | concurrence |
| fundraisers | yard | severally |
| members | spare | exempts |
| press | depots | sentence |
| delighted | handpicked | federal |
| appeared | storage | purporting |
| wondered | assortment | administering |
| expecting | wheelie | certifying |
| discovering | torches | commissioners |
| <49> | <96> | |
| srt | occupations | |
| cetls | government-sponsored | |
| aba | homebuy | |
| rcr | anti-poverty | |
| cmg | burglary | |
| gnn | self-build | |
| lcs | householder | |
| gdl | landfill | |
| pss | dwellers | |
| ecc | municipal |
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