Nicoletta Calzolari

The relation between ontologies and language is currently at the forefront of natural language processing (NLP). Ontologies, as widely used models in semantic technologies, have much in common with the lexicon. A lexicon organizes words as a conventional inventory of concepts, while an ontology formalizes concepts and their logical relations. A shared lexicon is the prerequisite for knowledge-sharing through language, and a shared ontology is the prerequisite for knowledge-sharing through information technology. In building models of language, computational linguists must be able to accurately map the relations between words and the concepts that they can be linked to. This book focuses on the technology involved in enabling integration between lexical resources and semantic technologies. It will be of interest to researchers and graduate students in NLP, computational linguistics, and knowledge engineering, as well as in semantics, psycholinguistics, lexicology and morphology/syntax.

Collaboratively Constructed Language Resources (CCLRs) such as Wikipedia, Wiktionary, Linked Open Data, and various resources developed using crowdsourcing techniques such as Games with a Purpose and Mechanical Turk have substantially contributed to the research in natural language processing (NLP). Various NLP tasks utilize such resources to substitute for or supplement conventional lexical semantic resources and linguistically annotated corpora. These resources also provide an extensive body of texts from which valuable knowledge is mined. There are an increasing number of community efforts to link and maintain multiple linguistic resources. This book aims offers comprehensive coverage of CCLR-related topics, including their construction, utilization in NLP tasks, and interlinkage and management. Various Bachelor/Master/Ph. D. programs in natural language processing, computational linguistics, and knowledge discovery can use this book both as the main text and as a supplementary reading. The book also provides a valuable reference guide for researchers and professionals for the above topics.

Collaboratively Constructed Language Resources (CCLRs) such as Wikipedia, Wiktionary, Linked Open Data, and various resources developed using crowdsourcing techniques such as Games with a Purpose and Mechanical Turk have substantially contributed to the research in natural language processing (NLP). Various NLP tasks utilize such resources to substitute for or supplement conventional lexical semantic resources and linguistically annotated corpora. These resources also provide an extensive body of texts from which valuable knowledge is mined. There are an increasing number of community efforts to link and maintain multiple linguistic resources. This book aims offers comprehensive coverage of CCLR-related topics, including their construction, utilization in NLP tasks, and interlinkage and management. Various Bachelor/Master/Ph. D. programs in natural language processing, computational linguistics, and knowledge discovery can use this book both as the main text and as a supplementary reading. The book also provides a valuable reference guide for researchers and professionals for the above topics.

The relation between ontologies and language is currently at the forefront of natural language processing (NLP). Ontologies, as widely used models in semantic technologies, have much in common with the lexicon. A lexicon organizes words as a conventional inventory of concepts, while an ontology formalizes concepts and their logical relations. A shared lexicon is the prerequisite for knowledge-sharing through language, and a shared ontology is the prerequisite for knowledge-sharing through information technology. In building models of language, computational linguists must be able to accurately map the relations between words and the concepts that they can be linked to. This book focuses on the technology involved in enabling integration between lexical resources and semantic technologies. It will be of interest to researchers and graduate students in NLP, computational linguistics, and knowledge engineering, as well as in semantics, psycholinguistics, lexicology and morphology/syntax.