This book lays out a path leading from the linguistic and cognitive basics, to classical rule-based and machine learning algorithms, to today’s state-of-the-art approaches, which use advanced empirically grounded techniques, automatic knowledge acquisition, and refined linguistic modeling to make a real difference in real-world applications. Anaphora and coreference resolution both refer to the process of linking textual phrases (and, consequently, the information attached to them) within as well as across sentence boundaries, and to the same discourse referent. The book offers an overview of recent research advances, focusing on practical, operational approaches and their applications. In part I (Background), it provides a general introduction, which succinctly summarizes the linguistic, cognitive, and computational foundations of anaphora processing and the key classical rule- and machine-learning-based anaphora resolution algorithms. Acknowledging the central importance of shared resources, part II (Resources) covers annotated corpora, formal evaluation, preprocessing technology, and off-the-shelf anaphora resolution systems. Part III (Algorithms) provides a thorough description of state-of-the-art anaphora resolution algorithms, covering enhanced machine learning methods as well as techniques for accomplishing important subtasks such as mention detection and acquisition of relevant knowledge. Part IV (Applications) deals with a selection of important anaphora and coreference resolution applications, discussing particular scenarios in diverse domains and distilling a best-practice model for systematically approaching new application cases. In the concluding part V (Outlook), based on a survey conducted among the contributing authors, the prospects of the research field of anaphora processing are discussed, and promising new areas of interdisciplinary cooperation and emerging application scenarios are identified. Given the book’s design, it can be used both as an accompanying text for advanced lectures in computational linguistics, natural language engineering, and computer science, and as a reference work for research and independent study. It addresses an audience that includes academic researchers, university lecturers, postgraduate students, advanced undergraduate students, industrial researchers, and software engineers.

Anaphora processing is a central topic in the study of natural language and has long been the object of research in a wide range of disciplines. The correct interpretation of anaphora has also become increasingly important for real-world natural language processing applications, including machine translation, automatic abstracting, information extraction and question answering. This volume provides a unique overview of the processing of anaphora from a multi- and inter-disciplinary angle. It will be of interest and practical use to readers from fields as diverse as theoretical linguistics, corpus linguistics, computational linguistics, computer science, natural language processing, artificial intelligence, human language technology, psycholinguistics, cognitive science and translation studies. The readership includes but is not limited to university lecturers, researchers, postgraduate and senior undergraduate students.

Teaching computers to solve language problems is one of the major challenges of natural language processing. There is a large amount of interesting research devoted to this field. This book fills an existing gap in the literature with an up-to-date survey of the field, including the author’s own contributions. A number of different fields overlap in anaphora resolution – computational linguistics, natural language processing (NLP), grammar, semantics, pragmatics, discourse analysis and artificial intelligence. This book begins by introducing basic notions and terminology, moving onto early research methods and approaches, recent developments and applications, and future directions. It addresses various issues related to the practical implementation of anaphora systems, such as rules employed, algorithms implemented or evaluation techniques used. This is an ideal reference book for students and researchers in this particular area of computational linguistics. Since anaphora resolution is vital for the development of any practical NLP system, the book will be of interest to readers from both academia and industry.

The interpretation of anaphora - how we interpret expressions such as definite pronouns (he, she, it) and verbal elliptical phrases (such as "did so, too") in the course of ordinary conversation or reading - is an important aspect of language comprehension. In this book the author examines the research and evidence on anaphor interpretation within the context of the mental models theory of comprehension, arguing that the notion of a mental model is essential to the detailed description of the processes of anaphor resolution. The general philosophy of the mental models approach and the nature of mental models themselves and their role in language processing is discussed, followed by a review of methodological issues that bear on the interpretation of psychological research findings. Against this background, the author's own research on areas such as deep and surface anaphora, reference into anaphoric islands, the role of implicit causality in anaphor resolution and the use of pronouns to refer to characters introduced by stereotyped role names is presented. At all times the author's research is set within the context of the general literature on anaphor resolution derived from the disciplines of linguistics, psycholinguistics, philosophy and computational linguistics, ensuring that the book will be of interest to advanced students and researchers in these fields.

This volume brings together revised versions of a selection of papers presented at the 2003 International Conference on “Recent Advances in Natural Language Processing”. A wide range of topics is covered in the volume: semantics, dialogue, summarization, anaphora resolution, shallow parsing, morphology, part-of-speech tagging, named entity, question answering, word sense disambiguation, information extraction. Various ‘state-of-the-art’ techniques are explored: finite state processing, machine learning (support vector machines, maximum entropy, decision trees, memory-based learning, inductive logic programming, transformation-based learning, perceptions), latent semantic analysis, constraint programming. The papers address different languages (Arabic, English, German, Slavic languages) and use different linguistic frameworks (HPSG, LFG, constraint-based DCG). This book will be of interest to those who work in computational linguistics, corpus linguistics, human language technology, translation studies, cognitive science, psycholinguistics, artificial intelligence, and informatics.