1 "Computer, would you search the Web for references to our company and determine the 3 most common complaints mentioned about us?" "Sure, Kevin." "Then summarize and format your findings and insert it into the second page of my slide presentation." "No problem." "And also, could you give me a reminder notice a half hour before my plane is supposed to leave?"

OBJECTIVES To provide an overview and tutorial of natural language processing (NLP) and modern NLP-system design. TARGET AUDIENCE This tutorial targets the medical informatics generalist who has limited acquaintance with the principles behind NLP and/or limited knowledge of the current state of the art. SCOPE We describe the historical evolution of NLP, and summarize common NLP sub-problems...

In this introduction to the special issues, we begin by outlining a concrete example that indicates some of the motivations leading to the widespread use of inheritance networks in computational linguistics. This example allows us to illustrate some of the formal choices that have to be made by those who seek network solutions to natural language processing (NLP) problems. We provide some point...

s of Recent Technical Reports INTERACTIVE CLASSIFICATION A Technique for the Aquisition and Maintenance of Knowledge Bases, Tim Flnln and David Silverman, MS-CIS-84-17. The practical application of flame-based knowledge-based systems, such as in expert systems, requires the maintenance of potentially very large amounts of declarative knowledge stored in their knowledge bases (KBs). As a KB grow...

Data useful for biosurveillance are often only available in a free-text format that can be easily read and understood by a human but not by a computer. Natural language processing (NLP) refers to automated methods for converting free-text data into computer-understandable format (Allen, 1995). This conversion is necessary so that information stored in free-text format can contribute to detectio...

Our central research focus is on the automatic acquisition of knowledge about language (both syntactic and semantic) from corpora. We wish to understand how the knowledge so acquired can enhance natural language applications, including document retrieval, information extraction, and machine translation. In addition to experimenting with acquisition procedures, we are continuing to develop the i...

Over the last few years, a number of areas of natural language processing have begun applying graph-based techniques. These include, among others, text summarization, syntactic parsing, word sense disambiguation, ontology construction, sentiment and subjectivity analysis, text clustering. In this paper, we present some of the most successful graph-based representations and algorithms used in la...

Previous approaches to robustness in natural language processing usually treat deviant input by relaxing grammatical constraints whenever a successful analysis cannot be provided by “normal” means. This schema implies, that error detection always comes prior to error handling, a behaviour which hardly can compete with its human model, where many erroneous situations are treated without even not...

A large part of the information required for biology research can only be found in free-text form, as in MEDLINE abstracts, or in comment elds of relevant reports, as in GenBank feature table annotations. Such information is important for many t ypes of analysis, such as classiication of proteins into functional groups, extraction of protein-protein interaction facts, discovery of new functiona...