The last decade has been one of dramatic progress in the field of Natural Language Processing (NLP). This hitherto largely academic discipline has found itself at the center of an information revolution ushered in by the Internet age, as demand for human-computer communication and informa­ tion access has exploded. Emerging applications in computer-assisted infor­ mation production and dissemination, automated understanding of news, understanding of spoken language, and processing of foreign languages have given impetus to research that resulted in a new generation of robust tools, systems, and commercial products. Well-positioned government research funding, particularly in the U. S. , has helped to advance the state-of-the­ art at an unprecedented pace, in no small measure thanks to the rigorous 1 evaluations. This volume focuses on the use of Natural Language Processing in In­ formation Retrieval (IR), an area of science and technology that deals with cataloging, categorization, classification, and search of large amounts of information, particularly in textual form. An outcome of an information retrieval process is usually a set of documents containing information on a given topic, and may consist of newspaper-like articles, memos, reports of any kind, entire books, as well as annotated image and sound files. Since we assume that the information is primarily encoded as text, IR is also a natural language processing problem: in order to decide if a document is relevant to a given information need, one needs to be able to understand its content.

Reversible grammar allows computational models to be built that are equally well suited for the analysis and generation of natural language utterances. This task can be viewed from very different perspectives by theoretical and computational linguists, and computer scientists. The papers in this volume present a broad range of approaches to reversible, bi-directional, and non-directional grammar systems that have emerged in recent years. This is also the first collection entirely devoted to the problems of reversibility in natural language processing. Most papers collected in this volume are derived from presentations at a workshop held at the University of California at Berkeley in the summer of 1991 organised under the auspices of the Association for Computational Linguistics. This book will be a valuable reference to researchers in linguistics and computer science with interests in computational linguistics, natural language processing, and machine translation, as well as in practical aspects of computability.

Reversible grammar allows computational models to be built that are equally well suited for the analysis and generation of natural language utterances. This task can be viewed from very different perspectives by theoretical and computational linguists, and computer scientists. The papers in this volume present a broad range of approaches to reversible, bi-directional, and non-directional grammar systems that have emerged in recent years. This is also the first collection entirely devoted to the problems of reversibility in natural language processing. Most papers collected in this volume are derived from presentations at a workshop held at the University of California at Berkeley in the summer of 1991 organised under the auspices of the Association for Computational Linguistics. This book will be a valuable reference to researchers in linguistics and computer science with interests in computational linguistics, natural language processing, and machine translation, as well as in practical aspects of computability.

Reversible grammar allows computational models to be built that are equally well suited for the analysis and generation of natural language utterances. This task can be viewed from very different perspectives by theoretical and computational linguists, and computer scientists. The papers in this volume present a broad range of approaches to reversible, bi-directional, and non-directional grammar systems that have emerged in recent years. This is also the first collection entirely devoted to the problems of reversibility in natural language processing. Most papers collected in this volume are derived from presentations at a workshop held at the University of California at Berkeley in the summer of 1991 organised under the auspices of the Association for Computational Linguistics. This book will be a valuable reference to researchers in linguistics and computer science with interests in computational linguistics, natural language processing, and machine translation, as well as in practical aspects of computability.

The last decade has been one of dramatic progress in the field of Natural Language Processing (NLP). This hitherto largely academic discipline has found itself at the center of an information revolution ushered in by the Internet age, as demand for human-computer communication and informa­ tion access has exploded. Emerging applications in computer-assisted infor­ mation production and dissemination, automated understanding of news, understanding of spoken language, and processing of foreign languages have given impetus to research that resulted in a new generation of robust tools, systems, and commercial products. Well-positioned government research funding, particularly in the U. S. , has helped to advance the state-of-the­ art at an unprecedented pace, in no small measure thanks to the rigorous 1 evaluations. This volume focuses on the use of Natural Language Processing in In­ formation Retrieval (IR), an area of science and technology that deals with cataloging, categorization, classification, and search of large amounts of information, particularly in textual form. An outcome of an information retrieval process is usually a set of documents containing information on a given topic, and may consist of newspaper-like articles, memos, reports of any kind, entire books, as well as annotated image and sound files. Since we assume that the information is primarily encoded as text, IR is also a natural language processing problem: in order to decide if a document is relevant to a given information need, one needs to be able to understand its content.

The last decade has been one of dramatic progress in the field of Natural Language Processing (NLP). This hitherto largely academic discipline has found itself at the center of an information revolution ushered in by the Internet age, as demand for human-computer communication and informa­ tion access has exploded. Emerging applications in computer-assisted infor­ mation production and dissemination, automated understanding of news, understanding of spoken language, and processing of foreign languages have given impetus to research that resulted in a new generation of robust tools, systems, and commercial products. Well-positioned government research funding, particularly in the U. S. , has helped to advance the state-of-the­ art at an unprecedented pace, in no small measure thanks to the rigorous 1 evaluations. This volume focuses on the use of Natural Language Processing in In­ formation Retrieval (IR), an area of science and technology that deals with cataloging, categorization, classification, and search of large amounts of information, particularly in textual form. An outcome of an information retrieval process is usually a set of documents containing information on a given topic, and may consist of newspaper-like articles, memos, reports of any kind, entire books, as well as annotated image and sound files. Since we assume that the information is primarily encoded as text, IR is also a natural language processing problem: in order to decide if a document is relevant to a given information need, one needs to be able to understand its content.

Reversible grammar allows computational models to be built that are equally well suited for the analysis and generation of natural language utterances. This task can be viewed from very different perspectives by theoretical and computational linguists, and computer scientists. The papers in this volume present a broad range of approaches to reversible, bi-directional, and non-directional grammar systems that have emerged in recent years. This is also the first collection entirely devoted to the problems of reversibility in natural language processing. Most papers collected in this volume are derived from presentations at a workshop held at the University of California at Berkeley in the summer of 1991 organised under the auspices of the Association for Computational Linguistics. This book will be a valuable reference to researchers in linguistics and computer science with interests in computational linguistics, natural language processing, and machine translation, as well as in practical aspects of computability.

Reversible grammar allows computational models to be built that are equally well suited for the analysis and generation of natural language utterances. This task can be viewed from very different perspectives by theoretical and computational linguists, and computer scientists. The papers in this volume present a broad range of approaches to reversible, bi-directional, and non-directional grammar systems that have emerged in recent years. This is also the first collection entirely devoted to the problems of reversibility in natural language processing. Most papers collected in this volume are derived from presentations at a workshop held at the University of California at Berkeley in the summer of 1991 organised under the auspices of the Association for Computational Linguistics. This book will be a valuable reference to researchers in linguistics and computer science with interests in computational linguistics, natural language processing, and machine translation, as well as in practical aspects of computability.

Reversible grammar allows computational models to be built that are equally well suited for the analysis and generation of natural language utterances. This task can be viewed from very different perspectives by theoretical and computational linguists, and computer scientists. The papers in this volume present a broad range of approaches to reversible, bi-directional, and non-directional grammar systems that have emerged in recent years. This is also the first collection entirely devoted to the problems of reversibility in natural language processing. Most papers collected in this volume are derived from presentations at a workshop held at the University of California at Berkeley in the summer of 1991 organised under the auspices of the Association for Computational Linguistics. This book will be a valuable reference to researchers in linguistics and computer science with interests in computational linguistics, natural language processing, and machine translation, as well as in practical aspects of computability.

The last decade has been one of dramatic progress in the field of Natural Language Processing (NLP). This hitherto largely academic discipline has found itself at the center of an information revolution ushered in by the Internet age, as demand for human-computer communication and informa­ tion access has exploded. Emerging applications in computer-assisted infor­ mation production and dissemination, automated understanding of news, understanding of spoken language, and processing of foreign languages have given impetus to research that resulted in a new generation of robust tools, systems, and commercial products. Well-positioned government research funding, particularly in the U. S. , has helped to advance the state-of-the­ art at an unprecedented pace, in no small measure thanks to the rigorous 1 evaluations. This volume focuses on the use of Natural Language Processing in In­ formation Retrieval (IR), an area of science and technology that deals with cataloging, categorization, classification, and search of large amounts of information, particularly in textual form. An outcome of an information retrieval process is usually a set of documents containing information on a given topic, and may consist of newspaper-like articles, memos, reports of any kind, entire books, as well as annotated image and sound files. Since we assume that the information is primarily encoded as text, IR is also a natural language processing problem: in order to decide if a document is relevant to a given information need, one needs to be able to understand its content.