The Experts below are selected from a list of 108198 Experts worldwide ranked by ideXlab platform
Boris Katz - One of the best experts on this subject based on the ideXlab platform.
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reconstructing native Language typology from foreign Language Usage
arXiv: Computation and Language, 2014Co-Authors: Yevgeni Berzak, Roi Reichart, Boris KatzAbstract:Linguists and psychologists have long been studying cross-linguistic transfer, the influence of native Language properties on linguistic performance in a foreign Language. In this work we provide empirical evidence for this process in the form of a strong correlation between Language similarities derived from structural features in English as Second Language (ESL) texts and equivalent similarities obtained from the typological features of the native Languages. We leverage this finding to recover native Language typological similarity structure directly from ESL text, and perform prediction of typological features in an unsupervised fashion with respect to the target Languages. Our method achieves 72.2% accuracy on the typology prediction task, a result that is highly competitive with equivalent methods that rely on typological resources.
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reconstructing native Language typology from foreign Language Usage
Conference on Computational Natural Language Learning, 2014Co-Authors: Yevgeni Berzak, Roi Reichart, Boris KatzAbstract:This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF - 1231216.
Yevgeni Berzak - One of the best experts on this subject based on the ideXlab platform.
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reconstructing native Language typology from foreign Language Usage
arXiv: Computation and Language, 2014Co-Authors: Yevgeni Berzak, Roi Reichart, Boris KatzAbstract:Linguists and psychologists have long been studying cross-linguistic transfer, the influence of native Language properties on linguistic performance in a foreign Language. In this work we provide empirical evidence for this process in the form of a strong correlation between Language similarities derived from structural features in English as Second Language (ESL) texts and equivalent similarities obtained from the typological features of the native Languages. We leverage this finding to recover native Language typological similarity structure directly from ESL text, and perform prediction of typological features in an unsupervised fashion with respect to the target Languages. Our method achieves 72.2% accuracy on the typology prediction task, a result that is highly competitive with equivalent methods that rely on typological resources.
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reconstructing native Language typology from foreign Language Usage
Conference on Computational Natural Language Learning, 2014Co-Authors: Yevgeni Berzak, Roi Reichart, Boris KatzAbstract:This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF - 1231216.
Roi Reichart - One of the best experts on this subject based on the ideXlab platform.
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reconstructing native Language typology from foreign Language Usage
arXiv: Computation and Language, 2014Co-Authors: Yevgeni Berzak, Roi Reichart, Boris KatzAbstract:Linguists and psychologists have long been studying cross-linguistic transfer, the influence of native Language properties on linguistic performance in a foreign Language. In this work we provide empirical evidence for this process in the form of a strong correlation between Language similarities derived from structural features in English as Second Language (ESL) texts and equivalent similarities obtained from the typological features of the native Languages. We leverage this finding to recover native Language typological similarity structure directly from ESL text, and perform prediction of typological features in an unsupervised fashion with respect to the target Languages. Our method achieves 72.2% accuracy on the typology prediction task, a result that is highly competitive with equivalent methods that rely on typological resources.
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reconstructing native Language typology from foreign Language Usage
Conference on Computational Natural Language Learning, 2014Co-Authors: Yevgeni Berzak, Roi Reichart, Boris KatzAbstract:This work was supported by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF - 1231216.
Lawrence Phillips - One of the best experts on this subject based on the ideXlab platform.
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predicting foreign Language Usage from english only social media posts
North American Chapter of the Association for Computational Linguistics, 2018Co-Authors: Svitlana Volkova, Stephen Ranshous, Lawrence PhillipsAbstract:Social media is known for its multi-cultural and multilingual interactions, a natural product of which is code-mixing. Multilingual speakers mix Languages they tweet to address a different audience, express certain feelings, or attract attention. This paper presents a large-scale analysis of 6 million tweets produced by 27 thousand multilingual users speaking 12 other Languages besides English. We rely on this corpus to build predictive models to infer non-English Languages that users speak exclusively from their English tweets. Unlike native Language identification task, we rely on large amounts of informal social media communications rather than ESL essays. We contrast the predictive power of the state-of-the-art machine learning models trained on lexical, syntactic, and stylistic signals with neural network models learned from word, character and byte representations extracted from English only tweets. We report that content, style and syntax are the most predictive of non-English Languages that users speak on Twitter. Neural network models learned from byte representations of user content combined with transfer learning yield the best performance. Finally, by analyzing cross-lingual transfer – the influence of non-English Languages on various levels of linguistic performance in English, we present novel findings on stylistic and syntactic variations across speakers of 12 Languages in social media.
Bender Michael - One of the best experts on this subject based on the ideXlab platform.
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The importance of Language vocabulary and Language Usage for sociocultural adjustment among Indonesian adolescents from three bilingual ethnic groups
'Informa UK Limited', 2020Co-Authors: Sari, Betty Tjipta, Chasiotis Athanasios, Van De Vijver, Fons J. R., Bender MichaelAbstract:We investigated how the knowledge and Usage of two Languages relate to sociocultural adjustment in bilingual adolescent samples from three ethnic groups in Indonesia (214 Javanese, 108 Toraja, and 195 Chinese adolescents; 272 females; M-age = 14.33 years). We tested a model specifying that the vocabulary knowledge of each Language mediates the relation between Language Usage and sociocultural adjustment (here combining strongly correlated measures of adjustment to the ethnic and national culture). The results revealed the same partial mediation model in all groups; bilingualism is important for sociocultural adjustment in all ethnic groups. There were substantial group differences in ethnic Language vocabulary scores, but the correlations between ethnic Language Usage with sociocultural adjustment were the same across groups. Results also showed that ethnic Language Usage matters more than ethnic Language knowledge, and national Language knowledge matters more than ethnic Language knowledge for sociocultural adjustment. Moreover, our findings confirm that there is a Language shift going on in Indonesia because Bahasa Indonesia as national Language, which was the second Language in the past, has become the dominant Language across ethnic groups in Indonesia
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The importance of Language vocabulary and Language Usage for sociocultural adjustment among Indonesian adolescents from three bilingual ethnic groups
'Informa UK Limited', 2019Co-Authors: Sari, Betty Tjipta, Chasiotis Athanasios, Van De Vijver, Fons J.\ua0r., Bender MichaelAbstract:We investigated how the knowledge and Usage of two Languages relate to sociocultural adjustment in bilingual adolescent samples from three ethnic groups in Indonesia (214 Javanese, 108 Toraja, and 195 Chinese adolescents; 272 females; M = 14.33 years). We tested a model specifying that the vocabulary knowledge of each Language mediates the relation between Language Usage and sociocultural adjustment (here combining strongly correlated measures of adjustment to the ethnic and national culture). The results revealed the same partial mediation model in all groups; bilingualism is important for sociocultural adjustment in all ethnic groups. There were substantial group differences in ethnic Language vocabulary scores, but the correlations between ethnic Language Usage with sociocultural adjustment were the same across groups. Results also showed that ethnic Language Usage matters more than ethnic Language knowledge, and national Language knowledge matters more than ethnic Language knowledge for sociocultural adjustment. Moreover, our findings confirm that there is a Language shift going on in Indonesia because Bahasa Indonesia as national Language, which was the second Language in the past, has become the dominant Language across ethnic groups in Indonesia
