LREC 2006 - Proceedings sorted by papers

Title	SlinkET: A Partial Modal Parser for Events
Authors	R. Sauri, M. Verhagen, J. Pustejovsk
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.
Keywords
Full paper	SlinkET: A Partial Modal Parser for Events

Title

SlinkET: A Partial Modal Parser for Events

Authors

R. Sauri, M. Verhagen, J. Pustejovsk

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.

Keywords

Full paper

SlinkET: A Partial Modal Parser for Events