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Dependency Parsing
Dependency-based methods for syntactic parsing have become increasingly popular in natural language processing in recent years. This book gives a thorough introduction to the methods that are most widely used today. After an introduction to dependency grammar and dependency parsing, followed by a formal characterization of the dependency parsing problem, the book surveys the three major classes of parsing models that are in current use: transition-based, graph-based, and grammar-based models. It continues with a chapter on evaluation and one on the comparison of different methods, and it closes with a few words on current trends and future prospects of dependency parsing. The book presupposes a knowledge of basic concepts in linguistics and computer science, as well as some knowledge of parsing methods for constituency-based representations. Table of Contents: Introduction / Dependency Parsing / Transition-Based Parsing / Graph-Based Parsing / Grammar-Based Parsing / Evaluation / Comparison / Final Thoughts
Inductive Dependency Parsing
This book describes the framework of inductive dependency parsing, a methodology for robust and efficient syntactic analysis of unrestricted natural language text. Coverage includes a theoretical analysis of central models and algorithms, and an empirical evaluation of memory-based dependency parsing using data from Swedish and English. A one-stop reference to dependency-based parsing of natural language, it will interest researchers and system developers in language technology, and is suitable for graduate or advanced undergraduate courses.
Current and New Directions in Discourse and Dialogue
This volume covers key topics in the field from a variety of leading researchers. In one volume, readers gain exposure to several perspectives in the areas of corpus annotation and analysis, dialogue system construction, theoretical perspectives on communicative intention, context-based generation, and modeling of discourse structure. Based on the 2nd SIGdial workshop on Discourse and Dialogue held in conjunction with Eurospeech 2001, it is of interest to researchers and practitioners in dialogue and discourse processing.
Advances in Natural Language Processing
This book constitutes the refereed proceedings of the 6th International Conference on Natural Language Processing, GoTAL 2008, Gothenburg, Sweden, August 2008. The 44 revised full papers presented together with 3 invited talks were carefully reviewed and selected from 107 submissions. The papers address all current issues in computational linguistics and monolingual and multilingual intelligent language processing - theory, methods and applications.
Annotation, exploitation and evaluation of parallel corpora: TC3 I
Exchange between the translation studies and the computational linguistics communities has traditionally not been very intense. Among other things, this is reflected by the different views on parallel corpora. While computational linguistics does not always strictly pay attention to the translation direction (e.g. when translation rules are extracted from (sub)corpora which actually only consist of translations), translation studies are amongst other things concerned with exactly comparing source and target texts (e.g. to draw conclusions on interference and standardization effects). However, there has recently been more exchange between the two fields – especially when it comes to the annotation of parallel corpora. This special issue brings together the different research perspectives. Its contributions show – from both perspectives – how the communities have come to interact in recent years.
Semi-Supervised Learning and Domain Adaptation in Natural Language Processing
This book introduces basic supervised learning algorithms applicable to natural language processing (NLP) and shows how the performance of these algorithms can often be improved by exploiting the marginal distribution of large amounts of unlabeled data. One reason for that is data sparsity, i.e., the limited amounts of data we have available in NLP. However, in most real-world NLP applications our labeled data is also heavily biased. This book introduces extensions of supervised learning algorithms to cope with data sparsity and different kinds of sampling bias. This book is intended to be both readable by first-year students and interesting to the expert audience. My intention was to introd...
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.
