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English single content morphemes in Persian structure: Applying the Matrix Language Frame Model (MLF) and 4M Model

International Journal of Bilingualism, 2017
Aim and objective: The aim of this study is to show how different English single content morphemes, in particular nouns and adjectives, occur in the Persian structure by applying the Matrix Language Frame and 4M models.
Marzieh Hadei, R. Ramakrishna
exaly   +3 more sources

Using the Matrix Language Frame model to measure the extent of word-order convergence in Welsh-English bilingual speech

Linguistik Aktuell, 2010
Word-order convergence is argued to be a processual mechanism in which bilinguals use morphemes from language A in an order which is more prominent in language B than in language A. In this chapter we examine whether there is word-order convergence in the speech of Welsh–English bilinguals using Myers-Scotton’s (2002) Matrix Language Frame model.
Margaret Deuchar
exaly   +3 more sources

Background for the Matrix Language-Frame Model: Production Models and Identifying the Matrix Language

1993
Abstract This chapter has two purposes. First, it develops independent motivation for the major arguments of the Matrix Language-Frame model to be presented in Chapters 4 and 5. Such motivation comes from psycholinguistic research on several fronts, but especially from research on speech errors as it relates to language production models.
exaly   +2 more sources

Regulating Two at Once, I: The Matrix Language-Frame Model

1993
Abstract This chapter and the following one present my current version of a frame-based model of CS. It is called the Matrix Language-Frame (MLF) model. This name highlights its two crucial aspects: (l) CS is envisioned as taking place within the constraints of a conceptual frame; (2) the frame is largely set by semantic and ...
exaly   +2 more sources

A Novel Sign Language Recognition Framework Using Hierarchical Grassmann Covariance Matrix

IEEE transactions on multimedia, 2019
Visual sign language recognition is an interesting and challenging problem. To create a discriminative representation, a hierarchical Grassmann covariance matrix (HGCM) model is proposed for sign description.
Hanjie Wang, Xiujuan Chai, Xilin Chen
semanticscholar   +1 more source

Implicit Geometry of Next-token Prediction: From Language Sparsity Patterns to Model Representations

arXiv.org
Next-token prediction (NTP) over large text corpora has become the go-to paradigm to train large language models. Yet, it remains unclear how NTP influences the mapping of linguistic patterns to geometric properties of the resulting model representations.
Yize Zhao   +3 more
semanticscholar   +1 more source

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