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The Oxford Handbook of Computational Linguistics
Ruslan Mitkov's highly successful Oxford Handbook of Computational Linguistics has been substantially revised and expanded in this second edition. Alongside updated accounts of the topics covered in the first edition, it includes 17 new chapters on subjects such as semantic role-labelling, text-to-speech synthesis, translation technology, opinion mining and sentiment analysis, and the application of Natural Language Processing in educational and biomedical contexts, among many others. The volume is divided into four parts that examine, respectively: the linguistic fundamentals of computational linguistics; the methods and resources used, such as statistical modelling, machine learning, and corpus annotation; key language processing tasks including text segmentation, anaphora resolution, and speech recognition; and the major applications of Natural Language Processing, from machine translation to author profiling. The book will be an essential reference for researchers and students in computational linguistics and Natural Language Processing, as well as those working in related industries.
Semantic Relations Between Nominals
Opportunity and Curiosity find similar rocks on Mars. One can generally understand this statement if one knows that Opportunity and Curiosity are instances of the class of Mars rovers, and recognizes that, as signalled by the word on, ROCKS are located on Mars. Two mental operations contribute to understanding: recognize how entities/concepts mentioned in a text interact and recall already known facts (which often themselves consist of relations between entities/concepts). Concept interactions one identifies in the text can be added to the repository of known facts, and aid the processing of future texts. The amassed knowledge can assist many advanced language-processing tasks, including sum...
Chinese Lexical Semantics
This book constitutes the thoroughly refereed post-workshop proceedings of the 18th Chinese Lexical Semantics Workshop, CLSW 2017, held in Leshan, China, in May 2017. The 48 full papers and 5 short papers included in this volume were carefully reviewed and selected from 176 submissions. They are organized in the following topical sections: lexical semantics; applications of natural language processing; lexical resources; and corpus linguistics.
Learning to Rank for Information Retrieval and Natural Language Processing, Second Edition
Learning to rank refers to machine learning techniques for training a model in a ranking task. Learning to rank is useful for many applications in information retrieval, natural language processing, and data mining. Intensive studies have been conducted on its problems recently, and significant progress has been made. This lecture gives an introduction to the area including the fundamental problems, major approaches, theories, applications, and future work. The author begins by showing that various ranking problems in information retrieval and natural language processing can be formalized as two basic ranking tasks, namely ranking creation (or simply ranking) and ranking aggregation. In rank...
Creating a More Transparent Internet
This book investigates how science can help mitigate social media's negative effects on communication and create more transparency.
Bitext Alignment
This book provides an overview of various techniques for the alignment of bitexts. It describes general concepts and strategies that can be applied to map corresponding parts in parallel documents on various levels of granularity. Bitexts are valuable linguistic resources for many different research fields and practical applications. The most predominant application is machine translation, in particular, statistical machine translation. However, there are various other threads that can be followed which may be supported by the rich linguistic knowledge implicitly stored in parallel resources. Bitexts have been explored in lexicography, word sense disambiguation, terminology extraction, compu...
European Language Equality
This open access book presents a comprehensive collection of the European Language Equality (ELE) project’s results, its strategic agenda and roadmap with key recommendations to the European Union on how to achieve digital language equality in Europe by 2030. The fabric of the EU linguistic landscape comprises 24 official languages and over 60 regional and minority languages. However, language barriers still hamper communication and the free flow of information. Multilingualism is a key cultural cornerstone of Europe, signifying what it means to be and to feel European. Various studies and resolutions have found a striking imbalance in the support of Europe’s languages through technologi...
The Handbook of Computational Linguistics and Natural Language Processing
This comprehensive reference work provides an overview of the concepts, methodologies, and applications in computational linguistics and natural language processing (NLP). Features contributions by the top researchers in the field, reflecting the work that is driving the discipline forward Includes an introduction to the major theoretical issues in these fields, as well as the central engineering applications that the work has produced Presents the major developments in an accessible way, explaining the close connection between scientific understanding of the computational properties of natural language and the creation of effective language technologies Serves as an invaluable state-of-the-art reference source for computational linguists and software engineers developing NLP applications in industrial research and development labs of software companies
Linguistic Structure Prediction
A major part of natural language processing now depends on the use of text data to build linguistic analyzers. We consider statistical, computational approaches to modeling linguistic structure. We seek to unify across many approaches and many kinds of linguistic structures. Assuming a basic understanding of natural language processing and/or machine learning, we seek to bridge the gap between the two fields. Approaches to decoding (i.e., carrying out linguistic structure prediction) and supervised and unsupervised learning of models that predict discrete structures as outputs are the focus. We also survey natural language processing problems to which these methods are being applied, and we address related topics in probabilistic inference, optimization, and experimental methodology. Table of Contents: Representations and Linguistic Data / Decoding: Making Predictions / Learning Structure from Annotated Data / Learning Structure from Incomplete Data / Beyond Decoding: Inference
Algebraic Structures in Natural Language
Algebraic Structures in Natural Language addresses a central problem in cognitive science concerning the learning procedures through which humans acquire and represent natural language. Until recently algebraic systems have dominated the study of natural language in formal and computational linguistics, AI, and the psychology of language, with linguistic knowledge seen as encoded in formal grammars, model theories, proof theories and other rule-driven devices. Recent work on deep learning has produced an increasingly powerful set of general learning mechanisms which do not apply rule-based algebraic models of representation. The success of deep learning in NLP has led some researchers to que...
