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      Body-anchored verbs and argument omission in two sign languages

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          Abstract

          Using quantitative methods, we analyze naturalistic corpus data in two sign languages, German Sign Language and Russian Sign Language, to study subject-omission patterns. We find that, in both languages, the interpretation of null subjects depends on the type of the verb. With verbs signed on the signer’s body (body-anchored verbs), null subjects are interpreted only as first person. With verbs signed in neutral space in front of the signer (neutral verbs), this restriction does not apply. We argue that this is an effect of iconicity: for body-anchored verbs, the signer’s body is a part of the iconic representation of the verbal event, and by default the body is interpreted as referring to the signer, that is, as first person. We develop a formal analysis using a mechanism of mixed agreement, taking inspiration from Matushansky’s ( 2013) account of mixed gender agreement in Russian. Specifically, we argue that body-anchored verbs bear an inherent feature that gives a first-person interpretation to null subjects. When a body-anchored verb is combined with an overt third-person subject, a feature mismatch occurs which is resolved in favor of the third person. Neutral verbs do not come with inherent feature-value specifications, thus allowing all person interpretations. We also explain how our analysis predicts the interpretation of null subjects in the context of role shift. With our account, we demonstrate that iconicity plays an active role in the grammar of sign languages, and we pin down the locus of the iconicity effect. While no iconic or modality-specific syntactic mechanisms are needed to account for the data, iconicity is argued to determine feature specification on a subset of sign language verbs.

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          Most cited references 68

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          Fitting Linear Mixed-Effects Models Using lme4

          Maximum likelihood or restricted maximum likelihood (REML) estimates of the parameters in linear mixed-effects models can be determined using the lmer function in the lme4 package for R. As for most model-fitting functions in R, the model is described in an lmer call by a formula, in this case including both fixed- and random-effects terms. The formula and data together determine a numerical representation of the model from which the profiled deviance or the profiled REML criterion can be evaluated as a function of some of the model parameters. The appropriate criterion is optimized, using one of the constrained optimization functions in R, to provide the parameter estimates. We describe the structure of the model, the steps in evaluating the profiled deviance or REML criterion, and the structure of classes or types that represents such a model. Sufficient detail is included to allow specialization of these structures by users who wish to write functions to fit specialized linear mixed models, such as models incorporating pedigrees or smoothing splines, that are not easily expressible in the formula language used by lmer. Journal of Statistical Software, 67 (1) ISSN:1548-7660
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            Person and Number in Pronouns: A Feature-Geometric Analysis

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              Ken Hale: A life in language

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                Author and article information

                Contributors
                Journal
                2397-1835
                Glossa: a journal of general linguistics
                Ubiquity Press
                2397-1835
                26 March 2019
                2019
                : 4
                : 1
                Affiliations
                [1 ]University of Amsterdam, Spuistraat 134, 1012 VB Amsterdam, NL
                [2 ]University of Bergen, Postboks 7805, 5020 Bergen, NO
                Article
                10.5334/gjgl.741
                Copyright: © 2019 The Author(s)

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. See http://creativecommons.org/licenses/by/4.0/.

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                Research

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