Recognizing textual entailment (RTE) has been proposed as a task in computational linguistics under a successful series of annual evaluation campaigns started in 2005 with the Pascal RTE-1 shared task. RTE is defined as the capability of a system to recognize that the meaning of a portion of text (usually one or few sentences) entails the meaning of another portion of text. Subsequently, the task has also been extended to recognizing specific cases of non-entailment, as when the meaning of the first text contradicts the meaning of the second text. Although the study of entailment phenomena in natural language was addressed much earlier, the novelty of the RTE evaluation was to propose a simple text-to-text task to compare human and system judgments, making it possible to build data sets and to experiment with a variety of approaches. Two main reasons likely contributed to the success of the initiative: First, the possibility to address, for the first time, the complexity of entailment phenomena under a data-driven perspective; second, the text-to-text approach allows one to easily incorporate a textual entailment engine into applications (e.g., question answering, summarization, information extraction) as a core inferential component. Given the large interest in RTE, a book on the topic is very much welcome. The book satisfies at least two needs in the community. On the one hand, it was time to collect and organize a large amount of research and publications produced in the last decade or so. Under this perspective a particular merit of the book is to set a homogeneous and general descriptive framework under which different approaches can be reported and compared with a uniform conceptual schema. On the other hand, after a decade of research in the field, it is the right time to open the discussion both to the results that have been achieved and to future challenges in RTE. With this perspective the book targets not only people interested in textual entailment but people interested in semantic inference and semantic processing at large, intended as a core capability of natural language understanding. Researchers working in related areas such as textual similarity, causality, and temporal reasoning, just to mention a few, may benefit from this monograph. The book is organized into six chapters, covering RTE motivations, task definitions, approaches and data-driven methodologies, relevant case studies, knowledge acquisition for RTE and, finally, future research perspectives. Chapter 1 provides the necessary context on textual entailment and RTE. Even a reader not familiar with the field can find in this chapter motivations, definitions, many
Recognizing Textual Entailment (RTE) shared tasks REF conducted annually from 2006 up until 2011 have been the primary drivers of textual entailment research in recent years.