JU_CSE: A CRF Based Approach to Annotation of Temporal Expression, Event and Temporal Relations
Anup Kumar Kolya, Amitava Kundu, Rajdeep Gupta, Asif Ekbal, Sivaji Bandyopadhyay · 2013
In this paper, we present the JUCSE system, designed for the TempEval-3 shared task. The system extracts events and temporal information 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 technique, for all the above tasks. Our system seems to perform quite competitively in Task A and Task B. In Task C, the system’s performance is comparatively modest at the initial stages of system development. We have incorporated various features based on different lexical, syntactic and semantic information, using Stanford CoreNLP and Wordnet based tools. 1