This paper describes two approaches to assigning prosodic phrase structure and pauses to text and investigates the impact of errors in the assignments for different granularities of prosodic phrase structure. One approach uses a cascaded combination of models trained separately for prediction of prosodic phrase structure and pauses and the other uses a model trained for the joint prediction task directly. Objective measurements show similar performance for both approaches while perceptual evaluations show a slight preference for an optimised cascaded combination of prosodic phrase structure and pause models using a single-level encoding of prosodic phrase structure.

This collection of invited papers covers a lot of ground in its nearly 800 pages, so any review of reasonable length will necessarily be selective.However, there are a number of features that make the book as a whole a comparatively easy and thoroughly rewarding read.Multiauthor compendia of this kind are often disjointed, with very little uniformity from chapter to chapter in terms of breadth, depth, and format.Such is not the case here.Breadth and depth of treatment are surprisingly consistent, with coherent formats that often include both a little history of the field and some thoughts about the future.The volume has a very logical structure in which the chapters flow and follow on from each other in an orderly fashion.There are also many crossreferences between chapters, which allow the authors to build upon the foundation of one another's work and eliminate redundancies.Specifically, the contents consist of 38 survey papers grouped into three parts: Fundamentals; Processes, Methods, and Resources; and Applications.Taken together, they provide both a comprehensive introduction to the field and a useful reference volume.In addition to the usual author and subject matter indices, there is a substantial glossary that students will find invaluable.Each chapter ends with a bibliography, together with tips for further reading and mention of other resources, such as conferences, workshops, and URLs.Part I covers the full spectrum of linguistic levels of analysis from a largely theoretical point of view, including phonology, morphology, lexicography, syntax, semantics, discourse, and dialogue.The result is a layered approach to the subject matter that allows each new level to take the previous level for granted.However, the authors do not typically restrict themselves to linguistic theory.For example, Hanks's chapter on lexicography characterizes the deficiencies of both hand-built and corpus-based dictionaries, as well as discussing other practical problems, such as how to link meaning and use.The phonology and morphology chapters provide fine introductions to these topics, which tend to receive short shrift in many NLP and AI texts.Part I ends with two chapters, one on formal grammars and one on complexity, which round out the computational aspect.This is an excellent pairing, with Martín-Vide's thorough treatment of regular and context-free languages leading into Carpenter's masterly survey of problem complexity and practical efficiency.Part II is more task based, with a focus on such activities as text segmentation, partof-speech tagging, parsing, word sense disambiguation, anaphora resolution, speech recognition, and text generation.(One might wonder why speech recognition is in

This paper describes how an online directory of expert witnesses was created from jury verdict and settlement documents using text mining techniques. We have created an expert witness directory that contains over 100,000 expert profiles, based on approximately 300,000 jury verdict and settlement documents, publicly available professional license information, an expertise taxonomy, and automatic text mining techniques. This directory can be browsed by area of expertise as well as by location and name. In addition, expert profiles are automatically linked to medline articles and jury verdict and settlement documents. The supporting technologies that made this application possible include information extraction from text via regular expression parsing, record linkage through Bayesian based matching, and automatic rule-based classification. To the best of our knowledge, this is the largest expert witness directory of its kind and the first to be built using automatic text mining techniques.

A major problem facing online information services is how to index and supplement large document collections with respect to a rich set of categories. We focus upon the routing of case law summaries to various secondary law volumes in which they should be cited. Given the large number (> 13,000) of closely related categories, this is a challenging task that is unlikely to succumb to a single algorithmic solution. Our fully implemented and recently deployed system shows that a superior classification engine for this task can be constructed from a combination of classifiers. The multi-classifier approach helps us leverage all the relevant textual features and meta data, and appears to generalize to related classification tasks.

We describe a system that processes court opinions and retrieves related cases from a citator database, so that new cases can be linked to earlier ones that they impact. The design of the system combines information extraction, information retrieval and machine learning techniques in a novel way. The fully implemented program is capable of performing prior case retrieval at human levels of recall and acceptable levels of precision.

This paper describes a robust parsing algorithm for spoken language understanding. Comparing with the other work in robust parsing, we focus on building a parser that is robust to not only ill-formed spontaneous spoken language inputs but also under-specified grammars. Preliminary experiment results show that the parsing performance deteriorates more gracefully than another parser we have used when the grammar is more underspecified.

Article Free Access Share on Information extraction from case law and retrieval of prior cases by partial parsing and query generation Authors: Peter Jackson Computer Science Research Department, West Group, Aqueduct Building, Rochester, NY Computer Science Research Department, West Group, Aqueduct Building, Rochester, NYView Profile , Khalid Al-Kofahi Computer Science Research Department, West Group, Aqueduct Building, Rochester, NY Computer Science Research Department, West Group, Aqueduct Building, Rochester, NYView Profile , Chris Kreilick Computer Science Research Department, West Group, Aqueduct Building, Rochester, NY Computer Science Research Department, West Group, Aqueduct Building, Rochester, NYView Profile , Brian Grom Computer Science Research Department, West Group, Aqueduct Building, Rochester, NY Computer Science Research Department, West Group, Aqueduct Building, Rochester, NYView Profile Authors Info & Claims CIKM '98: Proceedings of the seventh international conference on Information and knowledge managementNovember 1998 Pages 60–67https://doi.org/10.1145/288627.288642Online:01 November 1998Publication History 9citation464DownloadsMetricsTotal Citations9Total Downloads464Last 12 Months23Last 6 weeks2 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF