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Kenneth J. McKeever

dblp:16/1490 · DBLP profile ↗
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2ranked-venue papers
0as first author
0since 2021 · last 1998
—ORCID · none

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

Artificial intelligence and machine learning · 2

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
Information extraction and text analysis · 50% Knowledge representation and reasoning · 50%

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
temporal reasoning
0.011997
An Empirical Approach to Temporal Reference Resolution · EMNLP 1997
Natural language and speech › Information extraction and text analysis › temporal information extraction
temporal reference resolution
0.011997
An Empirical Approach to Temporal Reference Resolution · EMNLP 1997

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

empirical approach · 0.0
YearPublicationVenuePosition
1998 An Empirical Approach to Temporal Reference Resolution
abstract
Scheduling dialogs, during which people negotiate the times of appointments, are common in everyday life. This paper reports the results of an in-depth empirical investigation of resolving explicit temporal references in scheduling dialogs. There are four phases of this work: data annotation and evaluation, model development, system implementation and evaluation, and model evaluation and analysis. The system and model were developed primarily on one set of data, and then applied later to a much more complex data set, to assess the generalizability of the model for the task being performed. Many different types of empirical methods are applied to pinpoint the strengths and weaknesses of the approach. Detailed annotation instructions were developed and an intercoder reliability study was performed, showing that naive annotators can reliably perform the targeted annotations. A fully automatic system has been developed and evaluated on unseen test data, with good results on both data sets. We adopt a pure realization of a recency-based focus model to identify precisely when it is and is not adequate for the task being addressed. In addition to system results, an in-depth evaluation of the model itself is presented, based on detailed manual annotations. The results are that few errors occur specifically due to the model of focus being used, and the set of anaphoric relations defined in the model are low in ambiguity for both data sets.
Janyce Wiebe, Thomas P. O'Hara, Thorsten Öhrström-Sandgren, Kenneth J. McKeever
J. Artif. Intell. Res.4
1997 An Empirical Approach to Temporal Reference Resolution
Janyce Wiebe, Thomas P. O'Hara, Kenneth J. McKeever, Thorsten Öhrström-Sandgren
EMNLP3