Roy J. Byrd

Knowledge portals and the emerging digital knowledge workplace

A fundamental aspect of knowledge management is capturing knowledge and expertise created by knowledge workers as they go about their work and making it available to a larger community of colleagues. Technology can support these goals, and knowledge portals have emerged as a key tool for supporting knowledge work. Knowledge portals are single-point-access software systems intended to provide easy and timely access to information and to support communities of knowledge workers who share common goals. In this paper we discuss knowledge portal applications we have developed in collaboration with IBM Global Services, mainly for internal use by Global Services practitioners. We describe the role knowledge portals play in supporting knowledge work tasks and the component technologies embedded in portals, such as the gathering of distributed document information, indexing and text search, and categorization; and we discuss new functionality for future inclusion in knowledge portals. We share our experience deploying and maintaining portals. Finally, we describe how we view the future of knowledge portals in an expanding knowledge workplace that supports mobility, collaboration, and increasingly automated project workflow.

Automatic glossary extraction: beyond terminology identification

This paper describes a method for automatically extracting domain-specific glossaries from large document collections. We show that, compared with current text analysis methods for extracting technical terminology from text, our extracted glossaries more successfully support applications requiring knowledge of domain concepts. After presenting our methods, we illustrate the output of GlossEx, our glossary extraction tool, and present an informal evaluation of its performance.

Lexical navigation: visually prompted query expansion and refinement

We have designed a document search and retrieval system, termed Lesical Navigation, which provides an interface allowing a user to expand or refine a query based on the actual content of the collection. In this work we have designed a client-server system written in Java to allow users to issue queries, have additional terms suggested to them, explore lexical relationships, and view documents based on keywords they contain. Lexical nehvorks containing domain-specific vocabularies and relationships are automatically extracted from the collection and play an important role in this navigation process. The Lexical Navigation methodology constitutes a powerful set of tools for searching large text collections.

Automatic identification of heart failure diagnostic criteria, using text analysis of clinical notes from electronic health records

OBJECTIVE Early detection of Heart Failure (HF) could mitigate the enormous individual and societal burden from this disease. Clinical detection is based, in part, on recognition of the multiple signs and symptoms comprising the Framingham HF diagnostic criteria that are typically documented, but not necessarily synthesized, by primary care physicians well before more specific diagnostic studies are done. We developed a natural language processing (NLP) procedure to identify Framingham HF signs and symptoms among primary care patients, using electronic health record (EHR) clinical notes, as a prelude to pattern analysis and clinical decision support for early detection of HF. DESIGN We developed a hybrid NLP pipeline that performs two levels of analysis: (1) At the criteria mention level, a rule-based NLP system is constructed to annotate all affirmative and negative mentions of Framingham criteria. (2) At the encounter level, we construct a system to label encounters according to whether any Framingham criterion is asserted, denied, or unknown. MEASUREMENTS Precision, recall, and F-score are used as performance metrics for criteria mention extraction and for encounter labeling. RESULTS Our criteria mention extractions achieve a precision of 0.925, a recall of 0.896, and an F-score of 0.910. Encounter labeling achieves an F-score of 0.932. CONCLUSION Our system accurately identifies and labels affirmations and denials of Framingham diagnostic criteria in primary care clinical notes and may help in the attempt to improve the early detection of HF. With adaptation and tooling, our development methodology can be repeated in new problem settings.

Multi-document summarization by visualizing topical content

This paper describes a framework for multi-document summarization which combines three premises: coherent themes can be identified reliably; highly representative themes, running across subsets of the document collection, can function as multi-document summary surrogates; and effective end-use of such themes should be facilitated by a visualization environment which clarifies the relationship between themes and documents. We present algorithms that formalize our framework, describe an implementation, and demonstrate a prototype system and interface.

The talent system: TEXTRACT architecture and data model

We present the architecture and data model for TEXTRACT, a document analysis framework for text analysis components. The framework and components have been deployed in research and industrial environments for text analysis and text mining tasks.

Visualization-enabled multi-document summarization by Iterative Residual Rescaling

This paper describes a novel approach to multi-document summarization, which explicitly addresses the problem of detecting, and retaining for the summary, multiple themes in document collections. We place equal emphasis on the processes of theme identification and theme presentation. For the former, we apply Iterative Residual Rescaling (IRR); for the latter, we argue for graphical display elements. IRR is an algorithm designed to account for correlations between words and to construct multi-dimensional topical space indicative of relationships among linguistic objects (documents, phrases, and sentences). Summaries are composed of objects with certain properties, derived by exploiting the many-to-many relationships in such a space. Given their inherent complexity, our multi-faceted summaries benefit from a visualization environment. We discuss some essential features of such an environment.

Discovering Relationships among Word Senses

The direction that future development of natural language processing systems will take is toward increased semantic capability. Systems will need to behave as though they understand the texts that they process. An important prerequisite for this type of behavior is the creation of lexical knowledge bases in which word senses are clearly identified, endowed with appropriate lexical information, and correctly related to one another. This paper discusses issues that arise in creating such a knowledge base, paying particular attention to the process of discovering relationships among the word senses it contains.

An actor-based programming system

A programming system is described with which applications are built by defining collections of communicating objects, called actors. The actor programming system provides a uniform environment in which distributed applications can be automated in a highly modular and efficient manner. The systems design is based on the formal theory of actors, with certain modifications made for the sake of efficiency. We describe our view of the actor system, and an implementation of that view. We also discuss applications built on, and contemplated for, the actor system.

CREATING AND QUERYING LEXICAL DATA BASES

Users of computerized dictionaries require powerful and flexible tools for analyzing and manipulating the information in them. This paper discusses a system for grammatically describing and parsing entries from machine-readable dictionary tapes and a lexical data base representation for storing the dictionary information. It also describes a language for querying, formatting, and maintaining dictionaries and other lexical data stored with that representation.