Papers.

Approximating Learned Search Control Knowledge

Principles of Metareasoning

Efficient text searching of regular expressions

A trivial algorithm whose analysis is not: A continuation

Expected behaviour of B+-trees under random insertions

Modeling splits in file structures

String searching algorithms revisited

Efficient text searching of regular expressions

The subsequence graph of a text

Improved string searching

Updating the UMLS Metathesaurus: A Model

Speaker Independent Phonetic Transcription oP Fluent Speech Por Large Vocabulary Speech Recognition

Representing Medical Knowledge in the Form of Structured Text: The Development of Current Disease Descriptions*

Lexical Mapping in the UMLS Metathesaurus

Kaiwa Ridge Field-Check, Lanikai, Kailua, Oahu Tmk 4-2-02:17

Implementing Meta-1: The First Version of the UMLS Metathesaurus*

Efficient Text Searching of Regular Expressions (Extended Abstract)

Constraint satisfaction with delayed evaluation

Conservation of design knowledge. [of large complex spaceborne systems]

A New Approach to Text Searching (correction).

A HyperCard Implementation of Meta-1: The First Version of the UMLS Metathesaurus*

Searching with uncertainty extended abstract

Toward a Bio-Medical Thesaurus: Building the Foundation of the UMLS

Searching with uncertainty

Multiple Simultaneous Interpretations of Ambiguous Sentences

Intervocabulary Mapping Within the UMLS: The Role of Lexical Matching*

A survey of planning and scheduling research at the NASA Ames Research Center

Taking: A Study in Lexical Network Theory

Proceedings of the Thirteenth Annual Meeting of the Berkeley Linguistics Society (1987), pp. 195-206

Toward treating English nominals correctly

Nickel-hydrogen Battery Recharge Management for In-orbit Operations

The shape and alignment of rising intonation

Sources of information in the social sciences

Some average measures in m-ary search trees

A behavioral model of ethical and unethical decision making

Group Commit Timers and High Volume Transaction Systems

Inference in Text Understanding

The problem of deciding what was implied by a written text, of ‘‘reading between the lines’ ’ isthe problem of inference. Toextract proper inferences from a text requires a great deal of general knowledge on the part of the reader. Past approaches have often postulated an algorithm tuned to process a particular kind of knowledge structure (such as ascript, or a plan). An alternative, unified approach is proposed. The algorithm recognizes six very general classes of inference, classes that are not dependent on individual knowledge structures, but instead rely on patterns of connectivity between concepts. The complexity has been effectively shifted from the algorithm to the knowledge base; new kinds of knowledge structures can be added without modifying the algorithm. 1. The Problem of Inferencing The reader of a text is faced with a formidable task: recognizing the individual words of the text, deciding how they are structured into sentences, determining the explicit meaning of each sentence, and also making inferences about the likely implicit meaning of each sentence, and the implicit connections between sentences. An inference is defined to be any assertion which the reader

Reactions to "Funding Online Services from the Materials Budget"

Questions about connectionist models of natural language

Stressing English noun compounds correctly

Synthesis of falling nuclear pitch accents

A Unified Theory of Inference for Text Understanding

Natural languages, such as English, are difficult to understand not only because of the variety of forms that can be expressed, but also because of what is not explicitly expressed. The problem of deciding what was implied by a text, or "reading between the lines" is the problem of inference. For a reader to extract the proper set of inferences from a text (the set that was intended by the text's author) requires a great deal of general knowledge on the part of the reader, as well as a capability to reason with this knowledge. When the "reader" is a computer program, it becomes very difficult to represent this knowledge so that it will be accessible when needed. <p> Past approaches to the problem of inference have often concentrated on a particular type of knowledge structure (such as a script) and postlulated an algorithm tuned to process just that type of structure. The problem with this approach is that it is difficult to modify the algorithm when it comes time to add a new type of knowledge structure. <p> An alternative, unified approach is proposed. This approach is formalized in a computer program named FAUSTUS. The algorithm recognizes six very general classes of inference, classes that are not dependent on individual knowledge structures. Rather, the classes describe general kinds of connections between concepts. New kinds of knowledge can be added without modifying the algorithm. Thus, the complexity has been shifted from the algorithm to the knowledge ba

Getting Started in Dairy Farming: Case Studies

Text to speech—An overview

Evaluating RECONSIDER

Transaction Monitoring Facility

High Transaction Rates in a Distributed System

Synthesis by rule of english intonation patterns

Words and Sounds

"Expertness" from structured text?

Getting Started in Dairy Farming: A study of Farm Entry Processes and Experiences in New York State