论文标题
Fincat：财务数字索赔分析工具
FiNCAT: Financial Numeral Claim Analysis Tool
论文作者
论文摘要
通过阅读财务文件做出投资决策，投资者需要区分索赔和索赔数字。在本文中，我们提出了一种自动执行的工具。它使用基于变压器的预训练的语言模型称为BERT来提取数字的上下文嵌入。此后，它使用基于逻辑回归的模型来检测数字是索赔或删节的。我们使用Finnum-3（英语）数据集来训练我们的模型。进行严格的实验后，我们在验证集上达到了0.8223的宏F1得分。我们已经开源此工具，可以从https://github.com/sohomghosh/fincat_financial_numeral_claim_analsisy_tool访问它
While making investment decisions by reading financial documents, investors need to differentiate between in-claim and outof-claim numerals. In this paper, we present a tool which does it automatically. It extracts context embeddings of the numerals using one of the transformer based pre-trained language model called BERT. After this, it uses a Logistic Regression based model to detect whether the numerals is in-claim or out-of-claim. We use FinNum-3 (English) dataset to train our model. After conducting rigorous experiments we achieve a Macro F1 score of 0.8223 on the validation set. We have open-sourced this tool and it can be accessed from https://github.com/sohomghosh/FiNCAT_Financial_Numeral_Claim_Analysis_Tool
