Preface -- 1.Introduction -- Part I Background -- 2.Linguistic and Cognitive Evidence About Anaphora -- 3. Early Approaches to Anaphora Resolution: Theoretically Inspired and Heuristic-Based -- Part II Resources -- 4.Annotated Corpora and Annotation Tools -- 5.Evaluation Metrics -- 6.Evaluation Campaigns -- 7.Preprocessing Technology -- 8.Off-the-shelf Tools -- Part III Algorithms -- 9.The Mention-Pair Model -- 10.Advanced Machine Learning Models for Coreference Resolution -- 11.Integer Linear Programming for Coreference Resolution -- 12.Extracting Anaphoric Agreement Properties from Corpora -- 13.Detecting Non-reference and Non-anaphoricity -- 14.Using Lexical and Encyclopedic Knowledge -- Part IV Applications -- 15.Coreference Applications to Summarization -- 16.Towards a Procedure Model for Developing Anaphora Processing Applications -- Part V Outlook -- 17.Challenges and Directions of Further Research -- Index.

This book lays out a path leading from the linguistic and cognitive basics, to classical rule-based and machine learning algorithms, to todays 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 books 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.