Publications

Displaying 301 - 320 of 320
  • Verkerk, A. (2014). Where Alice fell into: Motion events from a parallel corpus. In B. Szmrecsanyi, & B. Wälchli (Eds.), Aggregating dialectology, typology, and register analysis: Linguistic variation in text and speech (pp. 324-354). Berlin: De Gruyter.
  • Vernes, S. C., & Fisher, S. E. (2013). Genetic pathways implicated in speech and language. In S. Helekar (Ed.), Animal models of speech and language disorders (pp. 13-40). New York: Springer. doi:10.1007/978-1-4614-8400-4_2.

    Abstract

    Disorders of speech and language are highly heritable, providing strong
    support for a genetic basis. However, the underlying genetic architecture is complex,
    involving multiple risk factors. This chapter begins by discussing genetic loci associated
    with common multifactorial language-related impairments and goes on to
    detail the only gene (known as FOXP2) to be directly implicated in a rare monogenic
    speech and language disorder. Although FOXP2 was initially uncovered in
    humans, model systems have been invaluable in progressing our understanding of
    the function of this gene and its associated pathways in language-related areas of the
    brain. Research in species from mouse to songbird has revealed effects of this gene
    on relevant behaviours including acquisition of motor skills and learned vocalisations
    and demonstrated a role for Foxp2 in neuronal connectivity and signalling,
    particularly in the striatum. Animal models have also facilitated the identification of
    wider neurogenetic networks thought to be involved in language development and
    disorder and allowed the investigation of new candidate genes for disorders involving
    language, such as CNTNAP2 and FOXP1. Ongoing work in animal models promises
    to yield new insights into the genetic and neural mechanisms underlying human
    speech and language
  • Vernes, S. C. (2019). Neuromolecular approaches to the study of language. In P. Hagoort (Ed.), Human language: From genes and brain to behavior (pp. 577-593). Cambridge, MA: MIT Press.
  • De Vos, C., & Zeshan, U. (2012). Introduction: Demographic, sociocultural, and linguistic variation across rural signing communities. In U. Zeshan, & C. de Vos (Eds.), Sign languages in village communities: Anthropological and linguistic insights (pp. 2-23). Berlin: Mouton De Gruyter.
  • De Vos, C. (2012). Kata Kolok: An updated sociolinguistic profile. In U. Zeshan (Ed.), Sign languages in village communities: Anthropological and linguistic insights (pp. 381-386). Berlin: Mouton de Gruyter.
  • De Vos, C. (2012). The Kata Kolok perfective in child signing: Coordination of manual and non-manual components. In U. Zeshan, & C. De Vos (Eds.), Sign languages in village communities: Anthropological and linguistic insights (pp. 127-152). Berlin: Mouton de Gruyter.
  • Weber, A., & Broersma, M. (2012). Spoken word recognition in second language acquisition. In C. A. Chapelle (Ed.), The encyclopedia of applied linguistics. Bognor Regis: Wiley-Blackwell. doi:10.1002/9781405198431.wbeal1104.

    Abstract

    In order to decode the message of a speaker, listeners have to recognize individual words in the speaker's utterance.
  • Windhouwer, M., Petro, J., Newskaya, I., Drude, S., Aristar-Dry, H., & Gippert, J. (2013). Creating a serialization of LMF: The experience of the RELISH project. In G. Francopoulo (Ed.), LMF - Lexical Markup Framework (pp. 215-226). London: Wiley.
  • Windhouwer, M., & Wright, S. E. (2012). Linking to linguistic data categories in ISOcat. In C. Chiarcos, S. Nordhoff, & S. Hellmann (Eds.), Linked data in linguistics: Representing and connecting language data and language metadata (pp. 99-107). Berlin: Springer.

    Abstract

    ISO Technical Committee 37, Terminology and other language and content resources, established an ISO 12620:2009 based Data Category Registry (DCR), called ISOcat (see http://www.isocat.org), to foster semantic interoperability of linguistic resources. However, this goal can only be met if the data categories are reused by a wide variety of linguistic resource types. A resource indicates its usage of data categories by linking to them. The small DC Reference XML vocabulary is used to embed links to data categories in XML documents. The link is established by an URI, which servers as the Persistent IDentifier (PID) of a data category. This paper discusses the efforts to mimic the same approach for RDF-based resources. It also introduces the RDF quad store based Relation Registry RELcat, which enables ontological relationships between data categories not supported by ISOcat and thus adds an extra level of linguistic knowledge.
  • Windhouwer, M., & Wright, S. E. (2013). LMF and the Data Category Registry: Principles and application. In G. Francopoulo (Ed.), LMF: Lexical Markup Framework (pp. 41-50). London: Wiley.
  • Wittenburg, P., Trilsbeek, P., & Wittenburg, F. (2014). Corpus archiving and dissemination. In J. Durand, U. Gut, & G. Kristoffersen (Eds.), The Oxford Handbook of Corpus Phonology (pp. 133-149). Oxford: Oxford University Press.
  • Wittenburg, P., & Ringersma, J. (2013). Metadata description for lexicons. In R. H. Gouws, U. Heid, W. Schweickard, & H. E. Wiegand (Eds.), Dictionaries: An international encyclopedia of lexicography: Supplementary volume: Recent developments with focus on electronic and computational lexicography (pp. 1329-1335). Berlin: Mouton de Gruyter.
  • Wittenburg, P., Drude, S., & Broeder, D. (2012). Psycholinguistik. In H. Neuroth, S. Strathmann, A. Oßwald, R. Scheffel, J. Klump, & J. Ludwig (Eds.), Langzeitarchivierung von Forschungsdaten. Eine Bestandsaufnahme (pp. 83-108). Boizenburg: Verlag Werner Hülsbusch.

    Abstract

    5.1 Einführung in den Forschungsbereich Die Psycholinguistik ist der Bereich der Linguistik, der sich mit dem Zusammenhang zwischen menschlicher Sprache und dem Denken und anderen mentalen Prozessen beschäftigt, d.h. sie stellt sich einer Reihe von essentiellen Fragen wie etwa (1) Wie schafft es unser Gehirn, im Wesentlichen akustische und visuelle kommunikative Informationen zu verstehen und in mentale Repräsentationen umzusetzen? (2) Wie kann unser Gehirn einen komplexen Sachverhalt, den wir anderen übermitteln wollen, in eine von anderen verarbeitbare Sequenz von verbalen und nonverbalen Aktionen umsetzen? (3) Wie gelingt es uns, in den verschiedenen Phasen des Lebens Sprachen zu erlernen? (4) Sind die kognitiven Prozesse der Sprachverarbeitung universell, obwohl die Sprachsysteme derart unterschiedlich sind, dass sich in den Strukturen kaum Universalien finden lassen?
  • Wright, S. E., Windhouwer, M., Schuurman, I., & Kemps-Snijders, M. (2013). Community efforts around the ISOcat Data Category Registry. In I. Gurevych, & J. Kim (Eds.), The People's Web meets NLP: Collaboratively constructed language resources (pp. 349-374). New York: Springer.

    Abstract

    The ISOcat Data Category Registry provides a community computing environment for creating, storing, retrieving, harmonizing and standardizing data category specifications (DCs), used to register linguistic terms used in various fields. This chapter recounts the history of DC documentation in TC 37, beginning from paper-based lists created for lexicographers and terminologists and progressing to the development of a web-based resource for a much broader range of users. While describing the considerable strides that have been made to collect a very large comprehensive collection of DCs, it also outlines difficulties that have arisen in developing a fully operative web-based computing environment for achieving consensus on data category names, definitions, and selections and describes efforts to overcome some of the present shortcomings and to establish positive working procedures designed to engage a wide range of people involved in the creation of language resources.
  • Zavala, R. (2000). Multiple classifier systems in Akatek (Mayan). In G. Senft (Ed.), Systems of nominal classification (pp. 114-146). Cambridge University Press.
  • Zhang, Y., Chen, C.-h., & Yu, C. (2019). Mechanisms of cross-situational learning: Behavioral and computational evidence. In Advances in Child Development and Behavior; vol. 56 (pp. 37-63).

    Abstract

    Word learning happens in everyday contexts with many words and many potential referents for those words in view at the same time. It is challenging for young learners to find the correct referent upon hearing an unknown word at the moment. This problem of referential uncertainty has been deemed as the crux of early word learning (Quine, 1960). Recent empirical and computational studies have found support for a statistical solution to the problem termed cross-situational learning. Cross-situational learning allows learners to acquire word meanings across multiple exposures, despite each individual exposure is referentially uncertain. Recent empirical research shows that infants, children and adults rely on cross-situational learning to learn new words (Smith & Yu, 2008; Suanda, Mugwanya, & Namy, 2014; Yu & Smith, 2007). However, researchers have found evidence supporting two very different theoretical accounts of learning mechanisms: Hypothesis Testing (Gleitman, Cassidy, Nappa, Papafragou, & Trueswell, 2005; Markman, 1992) and Associative Learning (Frank, Goodman, & Tenenbaum, 2009; Yu & Smith, 2007). Hypothesis Testing is generally characterized as a form of learning in which a coherent hypothesis regarding a specific word-object mapping is formed often in conceptually constrained ways. The hypothesis will then be either accepted or rejected with additional evidence. However, proponents of the Associative Learning framework often characterize learning as aggregating information over time through implicit associative mechanisms. A learner acquires the meaning of a word when the association between the word and the referent becomes relatively strong. In this chapter, we consider these two psychological theories in the context of cross-situational word-referent learning. By reviewing recent empirical and cognitive modeling studies, our goal is to deepen our understanding of the underlying word learning mechanisms by examining and comparing the two theoretical learning accounts.
  • Zuidema, W., & Fitz, H. (2019). Key issues and future directions: Models of human language and speech processing. In P. Hagoort (Ed.), Human language: From genes and brain to behavior (pp. 353-358). Cambridge, MA: MIT Press.
  • Zwitserlood, I. (2012). Classifiers. In R. Pfau, M. Steinbach, & B. Woll (Eds.), Sign Language: an International Handbook (pp. 158-186). Berlin: Mouton de Gruyter.

    Abstract

    Classifiers (currently also called 'depicting handshapes'), are observed in almost all signed languages studied to date and form a well-researched topic in sign language linguistics. Yet, these elements are still subject to much debate with respect to a variety of matters. Several different categories of classifiers have been posited on the basis of their semantics and the linguistic context in which they occur. The function(s) of classifiers are not fully clear yet. Similarly, there are differing opinions regarding their structure and the structure of the signs in which they appear. Partly as a result of comparison to classifiers in spoken languages, the term 'classifier' itself is under debate. In contrast to these disagreements, most studies on the acquisition of classifier constructions seem to consent that these are difficult to master for Deaf children. This article presents and discusses all these issues from the viewpoint that classifiers are linguistic elements.
  • Zwitserlood, I. (2014). Meaning at the feature level in sign languages. The case of name signs in Sign Language of the Netherlands (NGT). In R. Kager (Ed.), Where the Principles Fail. A Festschrift for Wim Zonneveld on the occasion of his 64th birthday (pp. 241-251). Utrecht: Utrecht Institute of Linguistics OTS.
  • Zwitserlood, I., Perniss, P. M., & Ozyurek, A. (2013). Expression of multiple entities in Turkish Sign Language (TİD). In E. Arik (Ed.), Current Directions in Turkish Sign Language Research (pp. 272-302). Newcastle upon Tyne: Cambridge Scholars Publishing.

    Abstract

    This paper reports on an exploration of the ways in which multiple entities are expressed in Turkish Sign Language (TİD). The (descriptive and quantitative) analyses provided are based on a corpus of both spontaneous data and specifically elicited data, in order to provide as comprehensive an account as possible. We have found several devices in TİD for expression of multiple entities, in particular localization, spatial plural predicate inflection, and a specific form used to express multiple entities that are side by side in the same configuration (not reported for any other sign language to date), as well as numerals and quantifiers. In contrast to some other signed languages, TİD does not appear to have a productive system of plural reduplication. We argue that none of the devices encountered in the TİD data is a genuine plural marking device and that the plural interpretation of multiple entity localizations and plural predicate inflections is a by-product of the use of space to indicate the existence or the involvement in an event of multiple entities.

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