Originally published in 1997, this book is concerned with human language technology. This technology provides computers with the capability to handle spoken and written language. One major goal is to improve communication between humans and machines. If people can use their own language to access information, working with software applications and controlling machinery, the greatest obstacle for the acceptance of new information technology is overcome. Another important goal is to facilitate communication among people. Machines can help to translate texts or spoken input from one human language to the other. Programs that assist people in writing by checking orthography, grammar and style are constantly improving. This book was sponsored by the Directorate General XIII of the European Union and the Information Science and Engineering Directorate of the National Science Foundation, USA.

This book is aimed at providing an overview of several aspects of semantic role labeling. Chapter 1 begins with linguistic background on the definition of semantic roles and the controversies surrounding them. Chapter 2 describes how the theories have led to structured lexicons such as FrameNet, VerbNet and the PropBank Frame Files that in turn provide the basis for large scale semantic annotation of corpora. This data has facilitated the development of automatic semantic role labeling systems based on supervised machine learning techniques. Chapter 3 presents the general principles of applying both supervised and unsupervised machine learning to this task, with a description of the standard stages and feature choices, as well as giving details of several specific systems. Recent advances include the use of joint inference to take advantage of context sensitivities, and attempts to improve performance by closer integration of the syntactic parsing task with semantic role labeling. Chapter 3 also discusses the impact the granularity of the semantic roles has on system performance. Having outlined the basic approach with respect to English, Chapter 4 goes on to discuss applying the same techniques to other languages, using Chinese as the primary example. Although substantial training data is available for Chinese, this is not the case for many other languages, and techniques for projecting English role labels onto parallel corpora are also presented. Table of Contents: Preface / Semantic Roles / Available Lexical Resources / Machine Learning for Semantic Role Labeling / A Cross-Lingual Perspective / Summary

Considerable progress has been made in recent years in the development of dialogue systems that support robust and efficient human-machine interaction using spoken language. Spoken dialogue technology allows various interactive applications to be built and used for practical purposes, and research focuses on issues that aim to increase the system's communicative competence by including aspects of error correction, cooperation, multimodality, and adaptation in context. This book gives a comprehensive view of state-of-the-art techniques that are used to build spoken dialogue systems. It provides an overview of the basic issues such as system architectures, various dialogue management methods, system evaluation, and also surveys advanced topics concerning extensions of the basic model to more conversational setups. The goal of the book is to provide an introduction to the methods, problems, and solutions that are used in dialogue system development and evaluation. It presents dialogue modelling and system development issues relevant in both academic and industrial environments and also discusses requirements and challenges for advanced interaction management and future research. Table of Contents: Preface / Introduction to Spoken Dialogue Systems / Dialogue Management / Error Handling / Case Studies: Advanced Approaches to Dialogue Management / Advanced Issues / Methodologies and Practices of Evaluation / Future Directions / References / Author Biographies

This book introduces Chinese language-processing issues and techniques to readers who already have a basic background in natural language processing (NLP). Since the major difference between Chinese and Western languages is at the word level, the book primarily focuses on Chinese morphological analysis and introduces the concept, structure, and interword semantics of Chinese words. The following topics are covered: a general introduction to Chinese NLP; Chinese characters, morphemes, and words and the characteristics of Chinese words that have to be considered in NLP applications; Chinese word segmentation; unknown word detection; word meaning and Chinese linguistic resources; interword semantics based on word collocation and NLP techniques for collocation extraction. Table of Contents: Introduction / Words in Chinese / Challenges in Chinese Morphological Processing / Chinese Word Segmentation / Unknown Word Identification / Word Meaning / Chinese Collocations / Automatic Chinese Collocation Extraction / Appendix / References / Author Biographies

This book provides a precise and thorough description of the meaning and use of spatial expressions, using both a linguistics and an artificial intelligence perspective, and also an enlightening discussion of computer models of comprehension and production in the spatial domain. The author proposes a theoretical framework that explains many previously overlooked or misunderstood irregularities. The use of prepositions reveals underlying schematisations and idealisations of the spatial world, which, for the most part, echo representational structures necessary for human action (movement and manipulation). Because spatial cognition seems to provide a key to understanding much of the cognitive system, including language, the book addresses one of the most basic questions confronting cognitive science and artificial intelligence, and brings fresh and original insights to it.

Cross-Word Modeling for Arabic Speech Recognition utilizes phonological rules in order to model the cross-word problem, a merging of adjacent words in speech caused by continuous speech, to enhance the performance of continuous speech recognition systems. The author aims to provide an understanding of the cross-word problem and how it can be avoided, specifically focusing on Arabic phonology using an HHM-based classifier.

Linguistic annotation and text analytics are active areas of research and development, with academic conferences and industry events such as the Linguistic Annotation Workshops and the annual Text Analytics Summits. This book provides a basic introduction to both fields, and aims to show that good linguistic annotations are the essential foundation for good text analytics. After briefly reviewing the basics of XML, with practical exercises illustrating in-line and stand-off annotations, a chapter is devoted to explaining the different levels of linguistic annotations. The reader is encouraged to create example annotations using the WordFreak linguistic annotation tool. The next chapter shows how annotations can be created automatically using statistical NLP tools, and compares two sets of tools, the OpenNLP and Stanford NLP tools. The second half of the book describes different annotation formats and gives practical examples of how to interchange annotations between different formats using XSLT transformations. The two main text analytics architectures, GATE and UIMA, are then described and compared, with practical exercises showing how to configure and customize them. The final chapter is an introduction to text analytics, describing the main applications and functions including named entity recognition, coreference resolution and information extraction, with practical examples using both open source and commercial tools. Copies of the example files, scripts, and stylesheets used in the book are available from the companion website, located at the book website. Table of Contents: Working with XML / Linguistic Annotation / Using Statistical NLP Tools / Annotation Interchange / Annotation Architectures / Text Analytics

This book presents computational mechanisms for solving common language interpretation problems including many cases of reference resolution, word sense disambiguation, and the interpretation of relationships implicit in modifiers. The proposed memory and context mechanisms provide the means for representing and applying information about the semantic relationships between entities imposed by the cultural context. The effects of different 'context factors', derived from multiple sources, are combined for disambiguation and for limiting memory search; the factors having been created and manipulated gradually during discourse processing.

In this book, Peter Culicover introduces the analysis of natural language within the broader question of how language works - of how people use languages to configure words and morphemes in order to express meanings. He focuses both on the syntactic and morphosyntactic devices that languages use, and on the conceptual structures that correspond to particular aspects of linguistic form. He seeks to explain linguistic forms and in the process to show how these correspond with meanings.
The book's clear, step-by-step exposition is presented within the Simpler Syntax framework whose development has been led by the author and Ray Jackendoff over the last fifteen years. This integrates syntactic theory with the representation of conceptual structure and casts fresh light on the interface between syntax and semantics. It also enables elegant and economical analyses of natural language phenomena without recourse to such abstract devices as functional heads and uniform binary branching.
Peter Culicover opens his account with an overview of the nature of language and the aims of its analysis. He then divides the book into parts devoted to syntactic categories, syntactic structure and argument structure, argument realization, unbounded dependencies, and clausal structure. He provides exercises, problems, and suggestions for further reading throughout the book.

This volume is a collection of original contributions from outstanding scholars in linguistics, philosophy and computational linguistics exploring the relation between word meaning and human linguistic creativity. The papers present different aspects surrounding the question of what is word meaning, a problem that has been the centre of heated debate in all those disciplines that directly or indirectly are concerned with the study of language and of human cognition. The discussions are centred around a view of the mental lexicon, as outlined in the Generative Lexicon theory (Pustejovsky, 1995), which proposes a unified model for defining word meaning. The individual contributors present their evidence for a generative approach as well as critical perspectives, which provides for a volume where word meaning is not viewed only from a particular angle or from a particular concern, but from a wide variety of topics, each introduced and explained by the editors.

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

As online information grows dramatically, search engines such as Google are playing a more and more important role in our lives. Critical to all search engines is the problem of designing an effective retrieval model that can rank documents accurately for a given query. This has been a central research problem in information retrieval for several decades. In the past ten years, a new generation of retrieval models, often referred to as statistical language models, has been successfully applied to solve many different information retrieval problems. Compared with the traditional models such as the vector space model, these new models have a more sound statistical foundation and can leverage statistical estimation to optimize retrieval parameters. They can also be more easily adapted to model non-traditional and complex retrieval problems. Empirically, they tend to achieve comparable or better performance than a traditional model with less effort on parameter tuning. This book systematically reviews the large body of literature on applying statistical language models to information retrieval with an emphasis on the underlying principles, empirically effective language models, and language models developed for non-traditional retrieval tasks. All the relevant literature has been synthesized to make it easy for a reader to digest the research progress achieved so far and see the frontier of research in this area. The book also offers practitioners an informative introduction to a set of practically useful language models that can effectively solve a variety of retrieval problems. No prior knowledge about information retrieval is required, but some basic knowledge about probability and statistics would be useful for fully digesting all the details. Table of Contents: Introduction / Overview of Information Retrieval Models / Simple Query Likelihood Retrieval Model / Complex Query Likelihood Model / Probabilistic Distance Retrieval Model / Language Models for Special Retrieval Tasks / Language Models for Latent Topic Analysis / Conclusions

This book collects and introduces some of the best and most useful work in practical lexicography. It has been designed as a resource for students and scholars of lexicography and lexicology and to be an essential reference for professional lexicographers. It focusses on central issues in the field and covers topics hotly debated in lexicography circles. After a full contextual introduction Thierry Fontenelle divides the book into twelve parts - theoretical perspectives, corpus design, lexicographical evidence, word senses and polysemy, collocations and idioms, definitions, examples, grammar and usage, bilingual lexicography, tools and methods, semantic networks, and how dictionaries are used. The book is fully referenced and indexed.
The reader may be used independently for reference or as reading material for a course of study. It will also be an essential companion for The Oxford Guide to Practical Lexicography by Sue Atkins and Michael Rundell, to be published by OUP in 2008.

The lexicon is now a major focus of research in computational linguistics and natural language processing (NLP), as more linguistic theories concentrate on the lexicon and as the acquisition of an adequate vocabulary has become the chief bottleneck in developing practical NLP systems. This collection describes techniques of lexical representation within a unification-based framework and their linguistic application, concentrating on the issue of structuring the lexicon using inheritance and defaults. Topics covered include typed feature structures, default unification, lexical rules, multiple inheritance and non-monotonic reasoning. The contributions describe both theoretical results and implemented languages and systems, including DATR, the Stuttgart TFS and ISSCO's ELU. This book arose out of a workshop on default inheritance in the lexicon organized as a part of the Esprit ACQUILEX project on computational lexicography. Besides the contributed papers mentioned above, it contains a detailed description of the ACQUILEX lexical knowledge base (LKB) system and its use in the representation of lexicons extracted semi-automatically from machine-readable dictionaries.

This book provides a comprehensive introduction to Conversational AI. While the idea of interacting with a computer using voice or text goes back a long way, it is only in recent years that this idea has become a reality with the emergence of digital personal assistants, smart speakers, and chatbots. Advances in AI, particularly in deep learning, along with the availability of massive computing power and vast amounts of data, have led to a new generation of dialogue systems and conversational interfaces. Current research in Conversational AI focuses mainly on the application of machine learning and statistical data-driven approaches to the development of dialogue systems. However, it is important to be aware of previous achievements in dialogue technology and to consider to what extent they might be relevant to current research and development. Three main approaches to the development of dialogue systems are reviewed: rule-based systems that are handcrafted using best practice guidelines; statistical data-driven systems based on machine learning; and neural dialogue systems based on end-to-end learning. Evaluating the performance and usability of dialogue systems has become an important topic in its own right, and a variety of evaluation metrics and frameworks are described. Finally, a number of challenges for future research are considered, including: multimodality in dialogue systems, visual dialogue; data efficient dialogue model learning; using knowledge graphs; discourse and dialogue phenomena; hybrid approaches to dialogue systems development; dialogue with social robots and in the Internet of Things; and social and ethical issues.

This book develops a formal computational theory of writing systems. It offers specific proposals about the linguistic objects that are represented by orthographic elements; what levels of linguistic representation are involved and how they may differ across writing systems; and what formal constraints hold of the mapping relation between linguistic and orthographic elements. Based on the insights gained, Sproat then proposes a taxonomy of writing systems. The treatment of theoretical linguistic issues and their computational implementation is complemented with discussion of empirical psycholinguistic work on reading and its relevance for the computational model developed here. Throughout, the model is illustrated with a number of detailed case studies of writing systems around the world. This book will be of interest to students and researchers in a variety of fields, including theoretical and computational linguistics, the psycholinguistics of reading and writing, and speech technology.

This book explains how to build Natural Language Generation (NLG) systems - computer software systems which use techniques from artificial intelligence and computational linguistics to automatically generate understandable texts in English or other human languages, either in isolation or as part of multimedia documents, Web pages, and speech output systems. Typically starting from some non-linguistic representation of information as input, NLG systems use knowledge about language and the application domain to automatically produce documents, reports, explanations, help messages, and other kinds of texts. The book covers the algorithms and representations needed to perform the core tasks of document planning, microplanning, and surface realization, using a case study to show how these components fit together. It also discusses engineering issues such as system architecture, requirements analysis, and the integration of text generation into multimedia and speech output systems.

A primary problem in the area of natural language processing has been that of semantic analysis. This book aims to look at the semantics of natural languages in context. It presents an approach to the computational processing of English text that combines current theories of knowledge representation and reasoning in Artificial Intelligence with the latest linguistic views of lexical semantics. This results in distinct advantages for relating the semantic analysis of a sentence to its context. A key feature is the clear separation of the lexical entries that represent the domain-specific linguistic information from the semantic interpreter that performs the analysis. The criteria for defining the lexical entries are firmly grounded in current linguistic theories, facilitating integration with existing parsers. This approach has been implemented and tested in Prolog on a domain for physics word problems and full details of the algorithms and code are presented. Semantic Processing for Finite Domains will appeal to postgraduates and researchers in computational linguistics, and to industrial groups specializing in natural language processing.

Placing contemporary spoken English at the centre of phonological research, this book tackles the issue of language variation and change through a range of methodological and theoretical approaches. In doing so the book bridges traditionally separate fields such as experimental phonetics, theoretical phonology, language acquisition and sociolinguistics. Made up of 12 chapters, it explores a substantial range of linguistic phenomena. It covers auditory, acoustic and articulatory phonetics, second language pronunciation and perception, sociophonetics, cross-linguistic comparison of vowel reduction and methodological issues in the construction of phonological corpora. The book presents new data and analyses which demonstrate what phonologists, phoneticians and sociolinguists do with their corpora and show how various theoretical and experimental questions can be explored in light of authentic spoken data.

This is a collection of new papers by leading researchers on natural language parsing. In the past, the problem of how people parse the sentences they hear - determine the identity of the words in these sentences and group these words into larger units - has been addressed in very different ways by experimental psychologists, by theoretical linguists, and by researchers in artificial intelligence, with little apparent relationship among the solutions proposed by each group. However, because of important advances in all these disciplines, research on parsing in each of these fields now seems to have something significant to contribute to the others, as this volume demonstrates. The volume includes some papers applying the results of experimental psychological studies of parsing to linguistic theory, others which present computational models of parsing, and a mathematical linguistics paper on tree-adjoining grammars and parsing.

This book investigates the nature of generalization in language and examines how language is known by adults and acquired by children. It looks at how and why constructions are learned, the relation between their forms and functions, and how cross-linguistic and language-internal generalizations about them can be explained.
Constructions at Work is divided into three parts: in the first Professor Goldberg provides an overview of constructionist approaches, including the constructionist approach to argument structure, and argues for a usage-based model of grammar. In Part II she addresses issues concerning how generalizations are constrained and constructional generalizations are learned. In Part III the author shows that a combination of function and processing accounts for a wide range of language-internal and cross-linguistic generalizations. She then considers the degree to which the function of constructions explains their distribution and examines cross-linguistic tendencies in argument realization. She demonstrates that pragmatic and cognitive processes account for the data without appeal to stipulations that are language-specific.
This book is an important contribution to the study of how language operates in the mind and in the world and how these operations relate. It is of central interest for scholars and graduate-level students in all branches of theoretical linguistics and psycholinguistics. It will also appeal to cognitive scientists and philosophers concerned with language and its acquisition.

People often mean more than they say. Grammar on its own is typically insufficient for determining the full meaning of an utterance; the assumption that the discourse is coherent or 'makes sense' has an important role to play in determining meaning as well. Logics of Conversation presents a dynamic semantic framework called Segmented Discourse Representation Theory, or SDRT, where this interaction between discourse coherence and discourse interpretation is explored in a logically precise manner. Combining ideas from dynamic semantics, commonsense reasoning and speech act theory, SDRT uses its analysis of rhetorical relations to capture intuitively compelling implicatures. It provides a computable method for constructing these logical forms and is one of the most formally precise and linguistically grounded accounts of discourse interpretation currently available. The book will be of interest to researchers and students in linguistics and in philosophy of language.

A landmark in linguistics and cognitive science. Ray Jackendoff proposes a new holistic theory of the relation between the sounds, structure, and meaning of language and their relation to mind and brain. Foundations of Language exhibits the most fundamental new thinking in linguistics since Noam Chomsky's Aspects of the Theory of Syntax in 1965 -- yet is readable, stylish, and accessible to a wide readership. Along the way it provides new insights on the evolution of language, thought, and communication.

This volume is a collection of original contributions that address the problem of words and their meaning. This represents a still difficult and controversial area within various disciplines: linguistics, philosophy, and artificial intelligence. Although all of these disciplines have to tackle the issue, so far there is no overarching methodology agreed upon by researchers. The aim of the volume is to provide answers based on empirical linguistics methods that are relevant across all the disciplines and provide a bridge among researchers looking at word meaning from different angles.

This book explains how to build Natural Language Generation (NLG) systems--computer software systems that automatically generate understandable texts in English or other human languages. NLG systems use knowledge about language and the application domain to automatically produce documents, reports, explanations, help messages, and other kinds of texts. The book covers the algorithms and representations needed to perform the core tasks of document planning, microplanning, and surface realization, using a case study to show how these components fit together. It is essential reading for researchers interested in NLP, AI, and HCI; and for developers interested in advanced document-creation technology.