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We investigate how to use pretrained static word embeddings to deliver improved estimates of bilexical co-occurrence probabilities: conditional probabilities of one word given a single other word in a specific relationship. Such probabilities play important roles in psycholinguistics, corpus linguistics, and usage-based cognitive modeling of language more generally. We propose a log-bilinear model taking pretrained vector representations of the two words as input, enabling generalization based on the distributional information contained in both vectors. We show that this model outperforms baselines in estimating probabilities of adjectives given nouns that they attributively modify, and probabilities of nominal direct objects given their head verbs, given limited training data in Arabic, English, Korean, and Spanish.
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Comparing Single-Word Insertions and Multi-Word Alternations in Bilingual Speech: Insights from Pupillometry
Prominent sociolinguistic theories of language mixing have posited that single-word insertions of one language into the other are the result of a distinct process than multi-word alternations between two languages given that the former overwhelmingly surface morphosyntactically integrated into the surrounding language. To date, this distinction has not been tested in comprehension. The present study makes use of pupillometry to examine the online processing of single-word insertions and multi-word alternations by highly proficient Spanish-English bilinguals in Puerto Rico. Participants heard sentences containing target noun/adjective pairs (1) in unilingual Spanish, (2) where the Spanish noun was replaced with its English translation equivalent, followed by a Spanish post-nominal adjective, and (3) where both the noun and adjective appeared in English with the adjective occurring in the English pre-nominal position. Both types of language mixing elicit larger pupillary responses when compared to unilingual Spanish speech, though the magnitude of this difference depends on the grammatical gender of the target noun. Importantly, single-word insertions and multi-word alternations did not differ from one another. Taken together, these findings suggest that morphosyntactic integration is not the defining feature of single-word insertions, at least in comprehension, and that the comprehension system is tuned to the distributional properties of bilingual speech.
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- Award ID(s):
- 1823634
- PAR ID:
- 10412039
- Date Published:
- Journal Name:
- Languages
- Volume:
- 7
- Issue:
- 4
- ISSN:
- 2226-471X
- Page Range / eLocation ID:
- 267
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.3390/languages7040267
