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Michael Lebowitz

dblp:46/6586 · DBLP profile ↗
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17ranked-venue papers
15as first author
0since 2021 · last 1988
—ORCID · none

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

Artificial intelligence and machine learning · 16 · 14 first-authorGraphics, computer vision, multimedia, augmented reality and games · 11 · 10 first-authorDatabases, data management, data science and information retrieval · 1 · 1 first-author

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
7 papers
Knowledge representation and reasoning · 49% Representation and self-supervised learning · 15% Learning theory · 14%
Databases, data mining, and information retrieval
2 papers
Information retrieval · 77% Machine learning and data management · 23%
Computer architecture, parallel and distributed computing, and storage systems
1 paper
Memory systems · 100%

Topics — the 11 heaviest of 15, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Memory systems
non-volatile memory
0.011988
Deferred Commitment in UNIMEM: Waiting to Learn · ML 1988
Machine learning › Representation and self-supervised learning › representation learning
metric learning
0.011986
Not the Path to Perdition: The Utility of Similarity-Based Learning · AAAI 1986
Information retrieval
search engines
0.011985
Researcher: An Experimental Intelligent Information System · IJCAI 1985
Natural language and speech › Language models and text generation › text generation
story generation
0.011983
Creating a Story-Telling Universe · IJCAI 1983
Information retrieval › document retrieval
concept-based retrieval
0.011983
Intelligent Information Systems · SIGIR 1983
Machine learning and data management
concept learning
0.011983
Intelligent Information Systems · SIGIR 1983
Information retrieval
text analysis
0.011983
Intelligent Information Systems · SIGIR 1983
Machine learning › Learning theory
generalization
0.021981
The Nature of Generalization in Understanding · IJCAI 1981
Language and Memory: Generalization as a Part of Understanding · AAAI 1980
Knowledge, reasoning and agents › Knowledge representation and reasoning
knowledge structures
0.011979
Parsing Directly into Knowledge Structures · IJCAI 1979
Natural language and speech › Information extraction and text analysis
syntactic parsing
0.011979
Parsing Directly into Knowledge Structures · IJCAI 1979
Memory systems
memory hierarchy
0.011988
Deferred Commitment in UNIMEM: Waiting to Learn · ML 1988

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

deferred commitment · 0.0similarity-based learning · 0.0memory-based understanding · 0.0generalization-based memory · 0.0script-based reasoning · 0.0conceptual dependency · 0.0
YearPublicationVenuePosition
1988 Deferred Commitment in UNIMEM: Waiting to Learn
Michael Lebowitz
ML1
1987 Experiments with Incremental Concept Formation: UNIMEM
Michael Lebowitz
Mach. Learn.1
1986 Not the Path to Perdition: The Utility of Similarity-Based Learning
Michael Lebowitz
AAAI1
1986 UNIMEM, a General Learning System: An Overview
Michael Lebowitz
ECAI1
1985 Researcher: An Experimental Intelligent Information System
Michael Lebowitz
IJCAI1
1984 Using Memory in Text Understanding
Michael Lebowitz
ECAI1
1983 RESEARCHER: An Overview
Michael Lebowitz
AAAI1
1983 Creating a Story-Telling Universe
Michael Lebowitz
IJCAI1
1983 Intelligent Information Systems
abstract
Natural language processing techniques developed for Artificial Intelligence programs can aid in constructing powerful information retrieval systems in at least two areas. Automatic construction of new concepts allows a large body of information to be organized compactly and in a manner that allows a wide range of queries to be answered. Also, using natural language processing techniques to conceptually analyze the documents being stored in a system greatly expands the effectiveness of queries about given pieces of text. However, only robust conceptual analysis methods are adequate for such systems. This paper will discuss approaches to both concept learning, in the form of Generalization-Based Memory, and powerful, robust text processing achieved by Memory-Based Understanding. These techniques have been implemented in the computer systems IPP, a program that reads, remembers and generalizes from news stories about terrorism, and RESEARCHER, currently in the prototype stage, that operates in a very different domain (technical texts, patent abstracts in particular).
Michael Lebowitz
SIGIR1
1983 Memory-Based Parsing
Michael Lebowitz
Artif. Intell.1
1981 Cancelled Due To Lack Of Interest
Michael Lebowitz
IJCAI1
1981 IPP Program Description
Michael Lebowitz
IJCAI1
1981 The Nature of Generalization in Understanding
Michael Lebowitz
IJCAI1
1980 Language and Memory: Generalization as a Part of Understanding
Michael Lebowitz
AAAI1
1980 An Integrated Understander
Roger C. Schank, Michael Lebowitz, Lawrence Birnbaum
Am. J. Comput. Linguistics2
1979 Heading with a Purpose
abstract
Article Free Access Share on Reading with a purpose Author: Michael Lebowitz Yale University Yale UniversityView Profile Authors Info & Claims ACL '79: Proceedings of the 17th annual meeting on Association for Computational LinguisticsJune 1979 Pages 59–63https://doi.org/10.3115/982163.982179Online:29 June 1979Publication History 3citation229DownloadsMetricsTotal Citations3Total Downloads229Last 12 Months55Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my Alerts New Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF
Michael Lebowitz
ACL1
1979 Parsing Directly into Knowledge Structures
Roger C. Schank, Michael Lebowitz, Lawrence Birnbaum
IJCAI2