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Ethel Schuster

dblp:54/147 · DBLP profile ↗
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4ranked-venue papers
3as first author
0since 2021 · last 2005
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 3 · 3 first-authorGraphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-authorHuman-computer interaction and ubiquitous computing · 1Applied, interdisciplinary, general and emerging computing · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence
1 paper
Knowledge representation and reasoning · 100%
Theoretical computer science
1 paper
Automata and formal languages · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Knowledge, reasoning and agents › Knowledge representation and reasoning
user modeling		 0.011985
Grammars as User Models · IJCAI 1985
Automata and formal languages
formal grammars		 0.011985
Grammars as User Models · IJCAI 1985

Methods — techniques the papers use, named apart from their topics

grammar induction · 0.0
YearPublicationVenuePosition
2005 Evaluating Scientific Abstracts with a Genre-specific Rubric
Sandra M. Aluísio, Ethel Schuster, Valéria Delisandra Feltrim, Adalberto Pessoa, Osvaldo N. Oliveira
 AIED2
1988 Anaphoric reference to events and actions: a representation and its advantages
Ethel Schuster
 COLING1
1986 The role of native grammars in correcting errors in second language learning
abstract
This paper describes VP2, a system that has been implemented to tutor nonnative speakers in English. This system differs from many tutoring systems by employing an explicit grammar of its user's native language. This grammar enables VP2 to customize its responses by addressing problems due to interference of the native language. The system focuses on the acquisition of English verb‐particle and verb‐prepositional phrase constructions. Its correction strategy is based upon comparison of the native language grammar with an English grammar. VP2 is a modular system: its grammar of a user's native language can easily be replaced by a grammar of another language. The problems and solutions presented in this paper are related to the more general question of how modelling previous knowledge facilitates instruction in a new skill.
Ethel Schuster
 Comput. Intell.1
1985 Grammars as User Models
Ethel Schuster
 IJCAI1