Towards a general theory of action and time
Artificial Intelligence
Robustness beyond shallowness: incremental deep parsing
Natural Language Engineering
Normalization and paraphrasing using symbolic methods
PARAPHRASE '03 Proceedings of the second international workshop on Paraphrasing - Volume 16
Annotating and measuring temporal relations in texts
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Coupling a linguistic formalism and a script language
CSLP '06 Proceedings of the Third Workshop on Constraints and Language Processing
SemEval-2007 task 15: TempEval temporal relation identification
SemEval '07 Proceedings of the 4th International Workshop on Semantic Evaluations
Use of Co-occurrences for Temporal Expressions Annotation
SPIRE '09 Proceedings of the 16th International Symposium on String Processing and Information Retrieval
Linguistic and temporal processing for discovering hospital acquired infection from patient records
KR4HC'10 Proceedings of the ECAI 2010 conference on Knowledge representation for health-care
Finding salient dates for building thematic timelines
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
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We present in this paper the work that has been developed at Xerox Research Centre Europe to build a robust temporal text processor. The aim of this processor is to extract events described in texts and to link them, when possible, to a temporal anchor. Another goal is to be able to establish temporal ordering between the events expressed in texts. One of the originalities of this work is that the temporal processor is coupled with a syntactic-semantic analyzer. The temporal module takes then advantage of syntactic and semantic information extracted from text and at the same time, syntactic and semantic processing benefits from the temporal processing performed. As a result, analysis and management of temporal information is combined with other kinds of syntactic and semantic information, making possible a more refined text understanding processor that takes into account the temporal dimension.