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.

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

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.

The book presents a wide range of recent research results about parsing schemata, introducing formal frameworks and theoretical results while keeping a constant focus on applicability to practical parsing problems. The first part includes a general introduction to the parsing schemata formalism that contains the basic notions needed to understand the rest of the parts. Thus, this compendium can be used as an introduction to natural language parsing, allowing postgraduate students not only to get a solid grasp of the fundamental concepts underlying parsing algorithms, but also an understanding of the latest developments and challenges in the field. Researchers in computational linguistics will find novel results where parsing schemata are applied to current problems that are being actively researched in the computational linguistics community (like dependency parsing, robust parsing, or the treatment of non-projective linguistics phenomena). This book not only explains these results in a more detailed, comprehensive and self-contained way, and highlights the relations between them, but also includes new contributions that have not been presented.

Dependency analysis is increasingly used in computational linguistics and cognitive science. Surprisingly, compared with studies based on phrase structures, quantitative methods and dependency structure are rarely integrated in research.This is the first book that collects original contributions which quantitatively analyze dependency structures across different languages and text genres.

This Handbook represents the development of research and the current level of knowledge in the fields of syntactic theory and syntax analysis. Syntax can look back to a long tradition. Especially in the last 50 years, however, the interaction between syntactic theory and syntactic analysis has led to a rapid increase in analyses and theoretical suggestions. This second edition of the Handbook on Syntax adopts a unifying perspective and therefore does not place the division of syntactic theory into several schools to the fore, but the increase in knowledge resulting from the fruitful argumentations between syntactic analysis and syntactic theory. It uses selected phenomena of individual languages and their cross-linguistic realizations to explain what syntactic analyses can do and at the same time to show in what respects syntactic theories differ from each other. It investigates how syntax is related to neighbouring disciplines and investigate the role of the interfaces especially the relationship between syntax and phonology, morphology, compositional semantics, pragmatics, and the lexicon. The phenomena chosen bring together renowned experts in syntax, and represent the consensus reached as to what has to be considered as an important as well as illustrative syntactic phenomenon. The phenomena discuss do not only serve to show syntactic analyses, but also to compare theoretical approaches with each other.

Current language technology is dominated by approaches that either enumerate a large set of rules, or are focused on a large amount of manually labelled data. The creation of both is time-consuming and expensive, which is commonly thought to be the reason why automated natural language understanding has still not made its way into “real-life” applications yet. This book sets an ambitious goal: to shift the development of language processing systems to a much more automated setting than previous works. A new approach is defined: what if computers analysed large samples of language data on their own, identifying structural regularities that perform the necessary abstractions and generalisations in order to better understand language in the process? After defining the framework of Structure Discovery and shedding light on the nature and the graphic structure of natural language data, several procedures are described that do exactly this: let the computer discover structures without supervision in order to boost the performance of language technology applications. Here, multilingual documents are sorted by language, word classes are identified, and semantic ambiguities are discovered and resolved without using a dictionary or other explicit human input. The book concludes with an outlook on the possibilities implied by this paradigm and sets the methods in perspective to human computer interaction. The target audience are academics on all levels (undergraduate and graduate students, lecturers and professors) working in the fields of natural language processing and computational linguistics, as well as natural language engineers who are seeking to improve their systems.

The mental representation of language cannot be directly observed but must be inferred and modelled from its effects at second hand. Linguists have traditionally responded to this in two ways, either going for a fairly data-light approach and valuing theoretical creativity, or pursuing just those goals for which data is available and trusting to data-driven descriptive work. More recently, advances in technology and experimental techniques have made data gathering easier and more accessible, so that a theoretically informed but empirically based approach is rapidly growing in popularity. This synthesis permits linguists to combine the intellectual hypothesis generation of the theoreticians with the ability to deliver hard answers of the empiricist. This volume is a collection of papers in this direction, using mostly experiment methods to yield insights into syntactic and semantic structures, language processing, and acquisition. Papers report corpus data, neurological investigations, child language studies, and fieldwork from minority languages.

Syntax-Based Collocation Extraction is the first book to offer a comprehensive, up-to-date review of the theoretical and applied work on word collocations. Backed by solid theoretical results, the computational experiments described based on data in four languages provide support for the book’s basic argument for using syntax-driven extraction as an alternative to the current cooccurrence-based extraction techniques to efficiently extract collocational data. The work described in Syntax-Based Collocation Extraction focuses on using linguistic tools for corpus-based identification of collocations. It takes advantage of recent advances in parsing to propose a novel deep syntactic analytic collocation extraction that has applicability to a range of important core tasks in Computational Linguistics. The book is useful for anyone interested in computational analysis of texts, collocation phenomena, and multi-word expressions in general.

Computer parsing technology, which breaks down complex linguistic structures into their constituent parts, is a key research area in the automatic processing of human language. This volume is a collection of contributions from leading researchers in the field of natural language processing technology, each of whom detail their recent work which includes new techniques as well as results. The book presents an overview of the state of the art in current research into parsing technologies, focusing on three important themes: dependency parsing, domain adaptation, and deep parsing. The technology, which has a variety of practical uses, is especially concerned with the methods, tools and software that can be used to parse automatically. Applications include extracting information from free text or speech, question answering, speech recognition and comprehension, recommender systems, machine translation, and automatic summarization. New developments in the area of parsing technology are thus widely applicable, and researchers and professionals from a number of fields will find the material here required reading. As well as the other four volumes on parsing technology in this series this book has a breadth of coverage that makes it suitable both as an overview of the field for graduate students, and as a reference for established researchers in computational linguistics, artificial intelligence, computer science, language engineering, information science, and cognitive science. It will also be of interest to designers, developers, and advanced users of natural language processing systems, including applications such as spoken dialogue, text mining, multimodal human-computer interaction, and semantic web technology.