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Stephen Pollock

dblp:37/5672 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 1988
—ORCID · none

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

Databases, 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.

Databases, data mining, and information retrieval
1 paper
Information retrieval · 77% Web and social media mining · 23%
Human-computer interaction and pervasive computing
1 paper
Human-AI interaction · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Information retrieval › information filtering
text filtering
0.011988
A Rule-Based Message Filtering System · ACM Trans. Inf. Syst. 1988
Web and social media mining › event detection
conflict detection
0.011988
A Rule-Based Message Filtering System · ACM Trans. Inf. Syst. 1988
Human-AI interaction › explainable AI
explanation interfaces
0.011988
A Rule-Based Message Filtering System · ACM Trans. Inf. Syst. 1988

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

text generation · 0.0rule-based screening · 0.0
YearPublicationVenuePosition
1988 A Rule-Based Message Filtering System
abstract
Much computerized support for knowledge workers has consisted of tools to handle low-level functions such as distribution, storage, and retrieval of information. However, the higher level processes of making decisions and taking actions with respect to this information have not been supported to the same degree. This paper describes the ISCREEN prototype system for screening text messages. ISCREEN includes a high-level interface for users to define rules, a component that screens text messages, and a conflict detection component that examines rules for inconsistencies. An explanation component uses text generation to answer user queries about past or potential system actions based on Grice's conversational maxims.
Stephen Pollock
ACM Trans. Inf. Syst.1