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gurevych i people s web meets nlp constructed language resources 2013

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Textbook in PDF format In the last years, researchers from a variety of computer science fields including computer vision, language processing, and distributed computing have begun to investigate how collaborative approaches to the construction of information resources can improve the state-of-the-art. Collaboratively constructed language resources (CCLRs) have been recognized as a topic in the field of Natural Language Processing (NLP) and Computational Linguistics (CL). In this area, the application of collective intelligence has yielded CCLRs such as Wikipedia, Wiktionary, and other language resources constructed through crowdsourcing approaches, such as Games with a Purpose and Mechanical Turk. The emergence of CCLRs generated new challenges to the research field. Collaborative construction approaches yield new, previously unknown levels of coverage,whil e also bringing along new research issues related to the quality and the consistency of representations across domains and languages. Rather than a small group of experts, the data prepared by volunteers for knowledge construction comes from multiple sources, experts or non-experts with all gradations in-between in a crowdsourcing manager. The resulting data can be employed to address questions that were not previously feasible due to the lack of the respective large-scale resources for many languages, such as lexical-semantic knowledge bases or linguistically annotated corpora, including differences between languages and domains, or certain seldom occurring phenomena. The research on CCLRs has focused on studying the nature of resources, extracting valuable knowledge from them, and developing algorithms to apply the extracted knowledge in various NLP tasks. Because the CCLRs themselves present interesting characteristics that distinguish them from conventional language resources, it is important to study and understand their nature. The knowledge extracted from CCLRs can substitute for or supplement customarily utilized resources such as WordNet or linguistically annotated corpora in different NLP tasks. Other important research directions include interconnecting and managing CCLRs and utilizing NLP techniques to enhance the collaboration processes while constructing the resources. CCLRs contribute to NLP and CL research in many different ways, as demonstrated by the diversity and significance of the topics and resources addressed in the chapters of this volume. They promote the improvement of the respective methodologies, software, and resources to achieve a deeper understanding of the language, at a larger scale and more in-depth. As the topic of CCLRs matures. as a research area, it has been consolidated in a series of workshops in the major CL and artificial intelligence conferences, and a special issue of the Language Resources and Evaluation journal [1]. Besides, the community produced several widely used tools and resources. Examples of them include word sense alignments between WordNet, Wikipedia, and Wiktionary [2 – 4], folksonomy and named entity ontologies [5, 6], multiword terms [7], ontological resources [8, 9], annotated corpora [10], and Wikipedia andWiktionary APIs

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gurevych i people s web meets nlp constructed language resources 2013