汤森路透提交的FEIII 2017挑战非计分任务

Proceedings of the 3rd International Workshop on Data Science for Macro--Modeling with Financial and Economic Datasets Pub Date : 2017-05-14 DOI:10.1145/3077240.3077244

E. Roman, B. Ulicny, Yi Du, Srijith Poduval, A. Ko

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引用次数: 0

摘要

在本文中，我们描述了一种机器学习方法，用于预测2017年FEIII挑战赛的非评分任务中提取的SEC三元组的角色。此外，我们还描述了一个由SEC三元组导出的图和数据分析。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Thomson Reuters' Submission to the FEIII 2017 Challenge Non-scored Tasks

In this paper we describe a machine learning approach to predict roles of extracted SEC triples for the non-scored task of the 2017 FEIII Challenge. In addition, we describe a graph and data analysis derived from SEC triples.

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Proceedings of the 3rd International Workshop on Data Science for Macro--Modeling with Financial and Economic Datasets

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