Vanessa Murdock

Significant amounts of information available today contain references to places on earth. Traditionally such information has been held as structured data and was the concern of Geographic Information Systems (GIS). However, increasing amounts of data in the form of unstructured text are available for indexing and retrieval that also contain spatial references. This monograph describes the field of Geographic Information Retrieval (GIR) that seeks to develop spatially-aware search systems and support user's geographical information needs. Important concepts with respect to storing, querying and analysing geographical information in computers are introduced, before user needs and interaction in the context of GIR are explored. The task of associating documents with coordinates, prior to their indexing and ranking forms the core of any GIR system, and different approaches and their implications are discussed. Evaluating the resulting systems and their components, and different paradigms for doing so continue to be an important area of research in GIR and are illustrated through several examples. The monograph provides an overview of the research field, and in so doing identifies key remaining research challenges in GIR.

Revision with unchanged content. Sentence Retrieval is the task of retrieving a relevant sentence in response to a query, a question, or a reference sentence. In this work we begin by demonstrating that because sentences are much smaller than documents, the performance of typical document retrieval systems on the retrieval of sentences is significantly worse. We propose several solutions to the problem of sentence retrieval, based on statistical translation models, and investigate these solutions the application areas of sentence retrieval for question answering, novelty detection, and information provenance. Statistical trans-lation models are appropriate for tasks where the sentence to be retrieved benefits from the addition of related terms and synonyms. The context of a sentence affects its meaning, and smoothing from the local context of the sentence improves retrieval. A brief investigation of conditional models for sentence retrieval suggests conditional models outperform language modeling approaches, for some tasks. This book is addressed to students and professionals working on language technology systems that are dependent on sentence or passage retrieval.