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The FA team participated in the Table Data Extraction (TDE) and Text-to-TableRelationship Extraction (TTRE) tasks of the NTCIR-17 Understanding ofNon-Financial Objects in Financial Reports (UFO). This paper reports ourapproach to solving the problems and discusses the official results. Wesuccessfully utilized various enhancement techniques based on the ELECTRAlanguage model to extract valuable data from tables. Our efforts resulted in animpressive TDE accuracy rate of 93.43 %, positioning us in second place on theLeaderboard rankings. This outstanding achievement is a testament to ourproposed approach's effectiveness. In the TTRE task, we proposed the rule-basedmethod to extract meaningful relationships between the text and tables task andconfirmed the performance.

FA Team at the NTCIR-17 UFO Task