Publications

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.

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?

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.

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.

Abstract

Psycholinguistic research has typically portrayed speech production as a relatively automatic process. This is because when errors are made, they occur as seldom as one in every thousand words we utter. However, it has long been recognised that we need some form of control over what we are currently saying and what we plan to say. This capacity to both monitor our inner speech and self-correct our speech output has often been assumed to be a property of the language comprehension system. More recently, it has been demonstrated that speech production benefits from interfacing with more general cognitive processes such as selective attention, short-term memory (STM) and online response monitoring to resolve potential conflict and successfully produce the output of a verbal plan. The conditions and levels of representation according to which these more general planning, monitoring and control processes are engaged during speech production remain poorly understood. Moreover, there remains a paucity of information about their neural substrates, despite some of the first evidence of more general monitoring having come from electrophysiological studies of error related negativities (ERNs). While aphasic speech errors continue to be a rich source of information, there has been comparatively little research focus on instances of speech repair. The purpose of this Frontiers Research Topic is to provide a forum for researchers to contribute investigations employing behavioural, neuropsychological, electrophysiological, neuroimaging and virtual lesioning techniques. In addition, while the focus of the research topic is on novel findings, we welcome submission of computational simulations, review articles and methods papers.

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.

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.

Abstract

Although in many respects sign languages have a similar structure to that of spoken languages, the different modalities in which both types of languages are expressed cause differences in structure as well. One of the most striking differences between spoken and sign languages is the influence of the interface between grammar and PF on the surface form of utterances. Spoken language words and phrases are in general characterized by sequential strings of sounds, morphemes and words, while in sign languages we find that many phonemes, morphemes, and even words are expressed simultaneously. A linguistic model should be able to account for the structures that occur in both spoken and sign languages. In this paper, I will discuss the morphological/ morphosyntactic structure of signs in Nederlandse Gebarentaal (Sign Language of the Netherlands, henceforth NGT), with special focus on the components ‘place of articulation’ and ‘handshape’. I will focus on their multiple functions in the grammar of NGT and argue that the framework of Distributed Morphology (DM), which accounts for word formation in spoken languages, is also suited to account for the formation of structures in sign languages. First I will introduce the phonological and morphological structure of NGT signs. Then, I will briefly outline the major characteristics of the DM framework. Finally, I will account for signs that have the same surface form but have a different morphological structure by means of that framework.