Development Of Linguistic Linked Open Data Resources For Collaborative Data Intensive Research In The Language Sciences
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Development of Linguistic Linked Open Data Resources for Collaborative Data-Intensive Research in the Language Sciences
Making diverse data in linguistics and the language sciences open, distributed, and accessible: perspectives from language/language acquistiion researchers and technical LOD (linked open data) researchers. This volume examines the challenges inherent in making diverse data in linguistics and the language sciences open, distributed, integrated, and accessible, thus fostering wide data sharing and collaboration. It is unique in integrating the perspectives of language researchers and technical LOD (linked open data) researchers. Reporting on both active research needs in the field of language acquisition and technical advances in the development of data interoperability, the book demonstrates the advantages of an international infrastructure for scholarship in the field of language sciences. With contributions by researchers who produce complex data content and scholars involved in both the technology and the conceptual foundations of LLOD (linguistics linked open data), the book focuses on the area of language acquisition because it involves complex and diverse data sets, cross-linguistic analyses, and urgent collaborative research. The contributors discuss a variety of research methods, resources, and infrastructures. Contributors Isabelle Barrière, Nan Bernstein Ratner, Steven Bird, Maria Blume, Ted Caldwell, Christian Chiarcos, Cristina Dye, Suzanne Flynn, Claire Foley, Nancy Ide, Carissa Kang, D. Terence Langendoen, Barbara Lust, Brian MacWhinney, Jonathan Masci, Steven Moran, Antonio Pareja-Lora, Jim Reidy, Oya Y. Rieger, Gary F. Simons, Thorsten Trippel, Kara Warburton, Sue Ellen Wright, Claus Zinn
Research Methods in Language Acquisition
Author: Barbara Lust
language: en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date: 2016-11-07
Language acquisition research is challenging—the intricate behavioral and cognitive foundations of speech are difficult to measure objectively. The audible components of speech, however, are quantifiable and thus provide crucial data. This practical guide synthesizes the authors’ decades of experience into a comprehensive set of tools that will allow students and early career researchers in the field to design and conduct rigorous studies that produce reliable and valid speech data and interpretations. The authors thoroughly review specific techniques for obtaining qualitative and quantitative speech data, including how to tailor the testing environments for optimal results. They explore observational tasks for collecting natural speech and experimental tasks for eliciting specific types of speech. Language comprehension tasks are also reviewed so researchers can study participants’ interpretations of speech and conceptualizations of grammar. Most tasks are oriented towards children, but special considerations for infants are also reviewed, as well as multilingual children. Chapters also provide strategies for transcribing and coding raw speech data into reliable data sets that can be scientifically analyzed. Furthermore, they investigate the intricacies of interpretation so that researchers can make empirically sound inferences from their data and avoid common pitfalls that can lead to unscientific conclusions.
Linguistic Linked Data
This is the first monograph on the emerging area of linguistic linked data. Presenting a combination of background information on linguistic linked data and concrete implementation advice, it introduces and discusses the main benefits of applying linked data (LD) principles to the representation and publication of linguistic resources, arguing that LD does not look at a single resource in isolation but seeks to create a large network of resources that can be used together and uniformly, and so making more of the single resource. The book describes how the LD principles can be applied to modelling language resources. The first part provides the foundation for understanding the remainder of the book, introducing the data models, ontology and query languages used as the basis of the Semantic Web and LD and offering a more detailed overview of the Linguistic Linked Data Cloud. The second part of the book focuses on modelling language resources using LD principles, describing how to model lexical resources using Ontolex-lemon, the lexicon model for ontologies, and how to annotate and address elements of text represented in RDF. It also demonstrates how to model annotations, and how to capture the metadata of language resources. Further, it includes a chapter on representing linguistic categories. In the third part of the book, the authors describe how language resources can be transformed into LD and how links can be inferred and added to the data to increase connectivity and linking between different datasets. They also discuss using LD resources for natural language processing. The last part describes concrete applications of the technologies: representing and linking multilingual wordnets, applications in digital humanities and the discovery of language resources. Given its scope, the book is relevant for researchers and graduate students interested in topics at the crossroads of natural language processing / computational linguistics and the Semantic Web / linked data. It appeals to Semantic Web experts who are not proficient in applying the Semantic Web and LD principles to linguistic data, as well as to computational linguists who are used to working with lexical and linguistic resources wanting to learn about a new paradigm for modelling, publishing and exploiting linguistic resources.