Paper: JU_CSE: A CRF Based Approach to Annotation of Temporal Expression, Event and Temporal Relations

ACL ID S13-2011
Title JU_CSE: A CRF Based Approach to Annotation of Temporal Expression, Event and Temporal Relations
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2013
Authors

In this paper, we present the JUCSE system, designed for the TempEval-3 shared task. The system extracts events and temporal infor- mation from natural text in English. We have participated in all the tasks of TempEval-3, namely Task A, Task B & Task C. We have primarily utilized the Conditional Random Field (CRF) based machine learning tech- nique, for all the above tasks. Our system seems to perform quite competitively in Task A and Task B. In Task C, the system?s per- formance is comparatively modest at the ini- tial stages of system development. We have incorporated various features based on differ- ent lexical, syntactic and semantic infor- mation, using Stanford CoreNLP and Wordnet based tools.