The FA team participated in the Table Data Extraction (TDE) and Text-to-Table
Relationship Extraction (TTRE) tasks of the NTCIR-17 Understanding of
Non-Financial Objects in Financial Reports (UFO). This paper reports our
approach to solving the problems and discusses the official results. We
successfully utilized various enhancement techniques based on the ELECTRA
language model to extract valuable data from tables. Our efforts resulted in an
impressive TDE accuracy rate of 93.43 %, positioning us in second place on the
Leaderboard rankings. This outstanding achievement is a testament to our
proposed approach's effectiveness. In the TTRE task, we proposed the rule-based
method to extract meaningful relationships between the text and tables task and
confirmed the performance.

本研究报告了我们在 NTCIR-17《金融报告中非金融对象的理解》（UFO）的 Table Data Extraction（TDE）和 Text-to-Table Relationship Extraction（TTRE）任务中解决问题的方法，并讨论了官方结果。我们成功地利用基于 ELECTRA 语言模型的各种增强技术来从表格中提取有价值的数据，使我们在 TDE 任务中取得了 93.43% 的令人瞩目的准确率，在排行榜上排名第二。这一卓越成就证明了我们提出的方法的有效性。在 TTRE 任务中，我们提出了基于规则的方法来提取文本和表格之间的有意义的关系，并验证了其性能。