Pattern matching for case analysis: a computational definition of closeness

Delisle, Sylvain; Copeck, Terry; Śzpakowicz, Stan; Barker, Ken

doi:10.1109/icci.1993.315357

Cited by 3 publications

(3 citation statements)

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“…To process a pair P not encountered previously, the system builds a graph centered on the main element (often the head) of P . This idea was inspired by Delisle et al (1993), who used a list of arguments surrounding the main verb together with the verb's subcategorization information and previously processed examples to analyse semantic roles (case relations). In recent approaches, syntactic information is translated into features which, together with information from FrameNet, WordNet or VerbNet, will be used with ML tools to make predictions for each example in the test set (Carreras and Marquez, 2004;Carreras and Marquez, 2005).…”

Section: Syntactic-semantic Graph-matchingmentioning

confidence: 99%

Matching syntactic-semantic graphs for semantic relation assignment

Năstase

Śzpakowicz

2006

Proceedings of TextGraphs: The First Workshop on Graph Based Methods for Natural Language Processing on the First Workshop on G

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We present a graph-matching algorithm for semantic relation assignment. The algorithm is part of an interactive text analysis system. The system automatically extracts pairs of syntactic units from a text and assigns a semantic relation to each pair. This is an incremental learning algorithm, in which previously processed pairs and user feedback guide the process. After each assignment, the system adds to its database a syntactic-semantic graph centered on the main element of each pair of units. A graph consists of the main unit and all syntactic units with which it is syntactically connected. An edge contains information both about syntax and about semantic relations for use in further processing. Syntactic-semantic graph matching is used to produce a list of candidate assignments for 63.75% of the pairs analysed, and in 57% of situations the correct relations is one of the system's suggestions; in 19.6% of situations it suggests only the correct relation.

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Section: Syntactic-semantic Graph-matchingmentioning

confidence: 99%

Matching syntactic-semantic graphs for semantic relation assignment

Năstase

Śzpakowicz

2006

Proceedings of TextGraphs: The First Workshop on Graph Based Methods for Natural Language Processing on the First Workshop on G

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“…If CMP C has never been encountered, a partial matching algorithm finds the nearest neighbour marker pattern CMP N among previous clauses. The measure of CMP distance for the nearest neighbour calculation is defined in Delisle et al (1993). Case patterns corresponding to CMP N contain candidate cases for the current CP.…”

Section: A Closer Look At Case Analysis In Haikumentioning

confidence: 99%

Systematic construction of a versatile case system

et al. 1997

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Case systems abound in natural language processing. Almost any attempt to recognize and uniformly represent relationships within a clause – a unit at the centre of any linguistic system that goes beyond word level statistics – must be based on semantic roles drawn from a small, closed set. The set of roles describing relationships between a verb and its arguments within a clause is a case system. What is required of such a case system? How does a natural language practitioner build a system that is complete and detailed yet practical and natural? This paper chronicles the construction of a case system from its origin in English marker words to its successful application in the analysis of English text.

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“…If a perfect match is not found, a procedure attempts to find the best partial match of constituents. The technique presented in Delisle et al (1993) is briefly summarized at the end of this subsection.…”

Section: Case Analysis Of a Clause Case Analysis Can Now Be Applied mentioning

confidence: 99%

Interactive Semantic Analysis of Technical Texts

Delisle

Barker

Copek

et al. 1996

Computational Intelligence

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Sentence syntax is the basis for organizing semantic relations in TANKA, a project that aims to acquire knowledge from technical text. Other hallmarks include an absence of precoded domain-specific knowledge; significant use of public-domain generic linguistic information sources; involvement of the user as a judge and source of expertise; and learning from the meaning representations produced during processing. These elements shape the realization of the TANKA project: implementing a trainable text processing system to propose correct semantic interpretations to the user. A three-level model of sentence semantics, including a comprehensive Case system, provides the framework for TANKA'S representations. Text is first processed by the DIPETT parser, which can handle a wide variety of unedited sentences. The semantic analysis module HAIKU then semi-automatically exhacts semantic patterns fromthe parse trees and composes them into domain knowledge representations. HAIKU'S dictionaries and main algorithm are descried with the aid of examples and traces of user interaction. Encouraging experimental results described and evaluated.

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Pattern matching for case analysis: a computational definition of closeness

Cited by 3 publications

References 5 publications

Matching syntactic-semantic graphs for semantic relation assignment

Matching syntactic-semantic graphs for semantic relation assignment

Systematic construction of a versatile case system

Interactive Semantic Analysis of Technical Texts

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