George R. Krupka

Lexico-semantic pattern matching as a companion to parsing in text understanding

Ordinarily, one thinks of the problem of natural language understanding as one of making a single, left-to-right pass through an input, producing a progressively refined and detailed interpretation. In text interpretation, however, the constraints of strict left-to-right processing are an encumbrance. Multi-pass methods, especially by interpreting words using corpus data and associating units of text with possible interpretations, can be more accurate and faster than single-pass methods of data extraction. Quality improves because corpus-based data and global context help to control false interpretations; speed improves because processing focuses on relevant sections.The most useful forms of pre-processing for text interpretation use fairly superficial analysis that complements the style of ordinary parsing but uses much of the same knowledge base. Lexico-semantic pattern matching, with rules that combine lexical analysis with ordering and semantic categories, is a good method for this form of analysis. This type of pre-processing is efficient, takes advantage of corpus data, prevents many garden paths and fruitless parses, and helps the parser cope with the complexity and flexibility of real text.

Computational aspects of discourse in the context of MUC-3

Discourse comprises those phenomena that usually do not arise when processing a single sentence. It appears to be the most difficult and probably the least understood aspect of automated message understanding. Five out of fifteen sites on a MUC-3 survey listed discourse as their main weakness and an area in which to concentrate future research. Virtually all systems presented here take a sentence-by-sentence approach to text understanding. Parsing and domain-dependent interpretation of sentences or sentence fragments (usually the latter) are followed by modules that attempt to connect these interpretations into a coherent whole. This paper gives an overview of the modules that make the transition from the interpretation of sentences to the interpretation of the text that contains these sentences. Systems presented in this paper exhibit various degrees of the following discourse understanding capabilities:• identifying portions of text that describe different domain events; this includes the capability of recognizing a single event and the capability of distinguishing multiple events;• resolving references:- pronoun references, e.g., finding the referent of It in the sentence It took place this morning,- proper name references, e.g., understanding that Luis Galan may be referred to as Senator Galan;- definite references, e.g., deciding what is the referent for The attack in the sentence The attack look us by surprise.• discourse representation : representation at the message level.

GE: description of the NLTooLSET system as used for MUC-3

The GE NLTooLSET is a set of text interpretation tools designed to be easily adapted to new domains. This report summarizes the system and its performance on the MUC-3 task.

Generic text processing: a progress report

A generic natural language system, without modification, can effectively analyze an arbitrary input at least to the level of word sense tagging. Considerable research has addressed the transportability of natural language systems, but not generic text processing capabilities. For example, previous DARPA-sponsored work [1, 2] produced transportable interfaces to database systems. Each new application of these interfaces generally required modifications to lexicons, new semantic knowledge bases, and other specialized features. The most that natural language text processing systems have accomplished has been the parsing of arbitrary text, without any real semantic analysis.

A cognitive model of the auditor's going-concern judgment

This article presents a cognitive model of the auditors going‐concern judgment, called the GCX model, that is based on the analysis of transcripts of interviews with expert auditors. It proposes a specific set of knowledge and reasoning skills to model the auditors performance of the going‐concern judgment. the GCX model has been implemented and tested in a computer program, called “GCX,” using data drawn from a realworld company, and its performance qualitatively matches that of expert auditors.

GE NLTooLSET: MUC-3 test results and analysis

This paper reports on the GE NLTooLSET customization effort for MUC-3, and analyzes the results of the TST2 run. Although our own tests had shown steady improvement between TST1 and TST2, our official scores on TST2 were lower than on TST1. The analysis of this unexpected result explains some of the details of the MUC-3 test, and we propose ways of looking at the scores to distinguish different aspects of system performance.