The Balancing Act , in presenting a collection of excellent, in-depth, current studies of many areas in NLP, ranging from grammar acquisition to lexical semantics, definitively establishes the clear advantages to be gained from combining statistical techniques with linguistic analysis. As such it marks a major turning point in the field, and is essential reading for anyone interested in new and exciting research techniques.
The subject of The Balancing Act , the integration of statistical and symbolic models of natural language, is perhaps the leading research area among the community of speech and language researchers today. This book does an excellent job of conveying the wide variety of methods currently being pursued that bring together these two types of models, and the central issues that arise in attempting a rapprochement.
Marti's Lectures & Workshops
The first book to address the increasingly important problem of combining symbolic and statistical information in language processing. A valuable resource both for linguists interested in the emerging role of statistics in the understanding of the nature of language, and for computational linguists looking for methodological suggestions and empirical results. This balancing act succeeds, judiciously weighing against each other the quantitative and qualitative methods in computing over natural languages. Both are right and both are inadequate, taken separately, and this book is a serious contribution to resolving this technological paradox.
Empiricism has come of age.
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I have to admit that I enjoyed the good old days when no when took us seriously and we could afford to lob cheap shots at the establishment, but I find it far more rewarding these days to see the issues being discussed in such a responsible way. The Balancing Act celebrates the end of the stand-off between statistical and symbolic approaches to language, for which we can all be grateful.
Though the focus is on computer systems, the issues affect us all: how do text frequencies affect the way we learn language, store it, modify it, and use it? The eight chapters all argue clearly and persuasively that we must learn a new balancing trick: how to combine notions like 'rule,' 'principle,' and 'leixcal item' with statistical ideas about weighting and probability. A very positive message which I enjoyed reading.
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Both symbolic and statistical approaches surely have much to contribute to our understanding of language, yet they have typically been presented as irreconcilable opposites, in effect forcing researchers to choose which of the two 'camps' they wish to belong to. The papers collected into this book make the refreshing argument that statistical and symbolic approaches are not at odds with one another, but that one can fruitfully combine insights from both.
The Balancing Act is thus a 'must read' for anyone who wants an unbiased understanding of the computational modeling of natural language. The statistical and symbolic approaches to language have emerged from different starting points and methodologies and have tended to focus on different goals. The resulting tension and confusion has obscured the fact that both approaches can make crucial and often complementary contributions to a deeper understanding of how language works. The papers in this volume show that this is indeed the case: they carefully articulate the theoretical advantages of combining techniques and describea number of concrete experiments that illustrate and support a synthesis of both approaches.
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