@inproceedings{eb24d174f0f648b2bfd8919d53f73b08,
title = "Towards re-defining relation understanding in financial domain",
abstract = "We describe our experiences in participating in the scored task for the 2017 FEIII Data Challenge. Our approach is to model the problem as a binary classification problem and train an ensemble model leveraging domain features that capture financial terminology. We share challenge results for our submission, which performed well achieving the highest score in four out of six evaluation criteria. We describe semantic complexities encountered with regards to the task definition and ambiguities in the labeled dataset. We present an alternative task formulation Relationship Validation that addresses some of these semantic complexities and demonstrate how our approach naturally extends to this simplified task definition.",
keywords = "FEIII, Financial Domain, Information Extraction, Relation Understanding, Text Classification",
author = "Chenguang Wang and Doug Burdick and Laura Chiticariu and Rajasekar Krishnamurthy and Yunyao Li and Huaiyu Zhu",
note = "Publisher Copyright: {\textcopyright} 2017 ACM.; 3rd International Workshop on Data Science for Macro-Modeling with Financial and Economic Datasets, DSMM 2017 ; Conference date: 14-05-2017",
year = "2017",
month = may,
day = "14",
doi = "10.1145/3077240.3077254",
language = "English",
series = "Proceedings of the 3rd International Workshop on Data Science for Macro-Modeling with Financial and Economic Datasets, DSMM 2017 - In conjunction with the ACM SIGMOD/PODS Conference",
publisher = "Association for Computing Machinery, Inc",
booktitle = "Proceedings of the 3rd International Workshop on Data Science for Macro-Modeling with Financial and Economic Datasets, DSMM 2017 - In conjunction with the ACM SIGMOD/PODS Conference",
}