The limits of Spanglish?

Bullock, Barbara E.; Guzmán, Wally; Toribio, Almeida Jacqueline

doi:10.18653/v1/w19-2515

Cited by 3 publications

(4 citation statements)

References 28 publications

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“…Note that although later work by Bullock et al (2019) propose a normalized I-Index due to its dependence on a corpus's M-Index, we have intentionally chosen transcripts that are comparable in their M-Index. Specifically, the M-Index is close to 1 for three transcripts (03, 10, 16) and .50 for two (05, 27).…”

Section: Within-iu I-indexmentioning

confidence: 99%

“…The mismatch between actual bilingual codeswitching behavior and renditions of codeswitching (CS) has been exposed in previous work. In particular, unnatural switching is characterized by the shortness of language spans and the arbitrariness of language boundaries (Bullock et al, 2019). Yet to date, metrics of CS complexity have been word-level based (e.g., a 4word utterance with 3 switches, w L1 w L2 w L1 w L2 , Gambäck andDas, 2016:1851).…”

Section: Introductionmentioning

confidence: 99%

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Code-Switching Metrics Using Intonation Units

Pattichis,

LaCasse,

Trawick

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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Code-switching (CS) metrics in NLP that are based on word-level units are misaligned with true bilingual CS behavior. Crucially, CS is not equally likely between any two words, but follows syntactic and prosodic rules. We adapt two metrics, multilinguality and CS probability, and apply them to transcribed bilingual speech, for the first time putting forward Intonation Units (IUs) -prosodic speech segments -as basic tokens for NLP tasks. In addition, we calculate these two metrics separately for distinct mixing types: alternating-language multiword strings and single-word incorporations from one language into another. Results indicate that individual differences according to the two CS metrics are independent. However, there is a shared tendency among bilinguals for multi-word CS to occur across, rather than within, IU boundaries. That is, bilinguals tend to prosodically separate their two languages. This constraint is blurred when metric calculations do not distinguish multi-word and singleword items. These results call for a reconsideration of units of analysis in future development of CS datasets for NLP tasks.

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Section: Within-iu I-indexmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Code-Switching Metrics Using Intonation Units

Pattichis,

LaCasse,

Trawick

et al. 2023

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

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“…Computational work on sociolinguistic issues uses social media posts or other large corpora to detect lexical innovations and dialect regions (Donoso and Sánchez, 2017;Eisenstein, 2018;Grieve et al, 2018) or to examine the impact of sociodemographic factors (Bamman et al, 2014;Blodgett et al, 2016;Nguyen et al, 2013). Language contact has been addressed in terms of codeswitching (Bullock et al, 2019;Guzman et al, 2016), language choice (Eleta and Golbeck, 2014;Nguyen et al, 2015), and semantic integration of loanwords (Deng et al, 2019;Serigos, 2017;Takamura et al, 2017). Related work on Canadian English is limited to corpus construction (Cook and Brinton, 2017;Miletic et al, 2020) and descriptive analyses of online communication (Tagliamonte and Denis, 2008).…”

Section: Related Workmentioning

confidence: 99%

Detecting Contact-Induced Semantic Shifts: What Can Embedding-Based Methods Do in Practice?

Miletic¹,

Przewozny-Desriaux²,

Tanguy³

2021

Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

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This study investigates the applicability of semantic change detection methods in descriptively oriented linguistic research. It specifically focuses on contact-induced semantic shifts in Quebec English. We contrast synchronic data from different regions in order to identify the meanings that are specific to Quebec and potentially related to language contact. Type-level embeddings are used to detect new semantic shifts, and token-level embeddings to isolate regionally specific occurrences. We introduce a new 80-item test set and conduct both quantitative and qualitative evaluations.We demonstrate that diachronic word embedding methods can be applied to contactinduced semantic shifts observed in synchrony, obtaining results comparable to the state of the art on similar tasks in diachrony. However, we show that encouraging evaluation results do not translate to practical value in detecting new semantic shifts. Finally, our application of token-level embeddings accelerates manual data exploration and provides an efficient way of scaling up sociolinguistic analyses.

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“…For example, the unnormalized I-index would not be close to 1 unless the M-index of the corpus is close to 1, indicating that each language in the corpus is equally distributed. In order to decouple this metric from the underlying language distribution of the corpus, a normalized version of the I-index is developed by Bullock et al (2019) and is computed as follows:…”

Section: Metrics For Characterizing Code-switched Corporamentioning

confidence: 99%

Detecting de minimis Code-Switching in Historical German Books

Liu¹,

Smith

2020

Proceedings of the 28th International Conference on Computational Linguistics

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Code-switching has long interested linguists, with computational work in particular focusing on speech and social media data (Sitaram et al., 2019). This paper contrasts these informal instances of code-switching to its appearance in more formal registers, by examining the mixture of languages in the Deutsches Textarchiv (DTA), a corpus of 1406 primarily German books from the 17th to 19th centuries. We automatically annotate and manually inspect spans of six embedded languages (Latin, French, English, Italian, Spanish, and Greek) in the corpus. We quantitatively analyze the differences between code-switching patterns in these books and those in more typically studied speech and social media corpora. Furthermore, we address the practical task of predicting code-switching from features of the matrix language alone in the DTA corpus. Such classifiers can help reduce errors when optical character recognition or speech transcription is applied to a large corpus with rare embedded languages.

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The limits of Spanglish?

Cited by 3 publications

References 28 publications

Code-Switching Metrics Using Intonation Units

Code-Switching Metrics Using Intonation Units

Detecting Contact-Induced Semantic Shifts: What Can Embedding-Based Methods Do in Practice?

Detecting de minimis Code-Switching in Historical German Books

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