By the same authors

A Framework for Constructing Temporal Models from Texts

Research output: Contribution to conferencePaper

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A Framework for Constructing Temporal Models from Texts. / Alfonseca, Enrique; Manandhar, Suresh.

2002. 456-460.

Research output: Contribution to conferencePaper

Harvard

Alfonseca, E & Manandhar, S 2002, 'A Framework for Constructing Temporal Models from Texts' pp. 456-460.

APA

Alfonseca, E., & Manandhar, S. (2002). A Framework for Constructing Temporal Models from Texts. 456-460.

Vancouver

Alfonseca E, Manandhar S. A Framework for Constructing Temporal Models from Texts. 2002.

Author

Alfonseca, Enrique ; Manandhar, Suresh. / A Framework for Constructing Temporal Models from Texts.

Bibtex - Download

@conference{3a069e0b68b043ffba547c4469732d4a,
title = "A Framework for Constructing Temporal Models from Texts",
abstract = "We describe here the algorithm and criteria we have used for the identification of events in a text, and for finding the temporal relations between them. Some of our ad hoc heuristics provide very good results, with precision and recall values around 90{\%}, that are able to provide us a fairly good ordering of events from different kinds of texts. There are still some difficult cases not covered by the heuristics, and we are working in annotating a bigger training set so we can learn rules for them.",
author = "Enrique Alfonseca and Suresh Manandhar",
year = "2002",
language = "Undefined/Unknown",
pages = "456--460",

}

RIS (suitable for import to EndNote) - Download

TY - CONF

T1 - A Framework for Constructing Temporal Models from Texts

AU - Alfonseca, Enrique

AU - Manandhar, Suresh

PY - 2002

Y1 - 2002

N2 - We describe here the algorithm and criteria we have used for the identification of events in a text, and for finding the temporal relations between them. Some of our ad hoc heuristics provide very good results, with precision and recall values around 90%, that are able to provide us a fairly good ordering of events from different kinds of texts. There are still some difficult cases not covered by the heuristics, and we are working in annotating a bigger training set so we can learn rules for them.

AB - We describe here the algorithm and criteria we have used for the identification of events in a text, and for finding the temporal relations between them. Some of our ad hoc heuristics provide very good results, with precision and recall values around 90%, that are able to provide us a fairly good ordering of events from different kinds of texts. There are still some difficult cases not covered by the heuristics, and we are working in annotating a bigger training set so we can learn rules for them.

M3 - Paper

SP - 456

EP - 460

ER -