Towards Re-defining Relation Understanding in Financial Domain

Chenguang Wang, D. Burdick, Laura Chiticariu, R. Krishnamurthy, Yunyao Li, Huaiyu Zhu
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引用次数: 5

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.
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重新定义金融领域的关系理解
我们描述了我们参与2017年FEIII数据挑战的得分任务的经验。我们的方法是将问题建模为一个二元分类问题,并利用捕获金融术语的领域特征训练一个集成模型。我们分享了我们提交的挑战结果,该结果表现良好,在六项评估标准中的四项中获得了最高分。我们描述了在标记数据集中遇到的关于任务定义和歧义的语义复杂性。我们提出了另一种任务公式Relationship Validation,它解决了其中一些语义复杂性,并演示了我们的方法如何自然地扩展到这个简化的任务定义。
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Tensor Factors to Monitor the Co-Movement of Equity Prices Extracting Knowledge Graphs from Financial Filings: Extended Abstract Financial Entity Identification and Information Integration (FEIII) 2017 Challenge: The Report of the Organizing Committee Towards Re-defining Relation Understanding in Financial Domain Entity relationship ranking using differential keyword-role affinity
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