@inproceedings{sauri-etal-2006-slinket,
title = "{S}link{ET}: A Partial Modal Parser for Events",
author = "Saur{\'\i}, Roser and
Verhagen, Marc and
Pustejovsky, James",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Gangemi, Aldo and
Maegaard, Bente and
Mariani, Joseph and
Odijk, Jan and
Tapias, Daniel",
booktitle = "Proceedings of the Fifth International Conference on Language Resources and Evaluation ({LREC}{'}06)",
month = may,
year = "2006",
address = "Genoa, Italy",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2006/pdf/716_pdf.pdf",
abstract = "We present SlinkET, a parser for identifying contexts of event modality in text developed within the TARSQI (Temporal Awareness and Reasoning Systems for Question Interpretation) research framework. SlinkET is grounded on TimeML, a specification language for capturing temporal and event related information in discourse, which provides an adequate foundation to handle event modality. SlinkET builds on top of a robust event recognizer, and provides each relevant event with a value that specifies the degree of certainty about its factuality; e.g., whether it has happened or holds (factive or counter-factive), whether it is being reported or witnessed by somebody else (evidential), or if it is introduced as a possibility (modal). It is based on well-established technology in the field (namely, finite-state techniques), and informed with corpus-induced knowledge that relies on basic information, such as morphological features, POS, and chunking. SlinkET is under continuing development and it currently achieves a performance ratio of 70{\%} F1-measure.",
}
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<abstract>We present SlinkET, a parser for identifying contexts of event modality in text developed within the TARSQI (Temporal Awareness and Reasoning Systems for Question Interpretation) research framework. SlinkET is grounded on TimeML, a specification language for capturing temporal and event related information in discourse, which provides an adequate foundation to handle event modality. SlinkET builds on top of a robust event recognizer, and provides each relevant event with a value that specifies the degree of certainty about its factuality; e.g., whether it has happened or holds (factive or counter-factive), whether it is being reported or witnessed by somebody else (evidential), or if it is introduced as a possibility (modal). It is based on well-established technology in the field (namely, finite-state techniques), and informed with corpus-induced knowledge that relies on basic information, such as morphological features, POS, and chunking. SlinkET is under continuing development and it currently achieves a performance ratio of 70% F1-measure.</abstract>
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%0 Conference Proceedings
%T SlinkET: A Partial Modal Parser for Events
%A Saurí, Roser
%A Verhagen, Marc
%A Pustejovsky, James
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Gangemi, Aldo
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Odijk, Jan
%Y Tapias, Daniel
%S Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06)
%D 2006
%8 May
%I European Language Resources Association (ELRA)
%C Genoa, Italy
%F sauri-etal-2006-slinket
%X We present SlinkET, a parser for identifying contexts of event modality in text developed within the TARSQI (Temporal Awareness and Reasoning Systems for Question Interpretation) research framework. SlinkET is grounded on TimeML, a specification language for capturing temporal and event related information in discourse, which provides an adequate foundation to handle event modality. SlinkET builds on top of a robust event recognizer, and provides each relevant event with a value that specifies the degree of certainty about its factuality; e.g., whether it has happened or holds (factive or counter-factive), whether it is being reported or witnessed by somebody else (evidential), or if it is introduced as a possibility (modal). It is based on well-established technology in the field (namely, finite-state techniques), and informed with corpus-induced knowledge that relies on basic information, such as morphological features, POS, and chunking. SlinkET is under continuing development and it currently achieves a performance ratio of 70% F1-measure.
%U http://www.lrec-conf.org/proceedings/lrec2006/pdf/716_pdf.pdf
Markdown (Informal)
[SlinkET: A Partial Modal Parser for Events](http://www.lrec-conf.org/proceedings/lrec2006/pdf/716_pdf.pdf) (Saurí et al., LREC 2006)
ACL
- Roser Saurí, Marc Verhagen, and James Pustejovsky. 2006. SlinkET: A Partial Modal Parser for Events. In Proceedings of the Fifth International Conference on Language Resources and Evaluation (LREC’06), Genoa, Italy. European Language Resources Association (ELRA).