JMTF: A Joint Model for Chinese Measurable Quantitative Information Extraction based on Table Filling

Recently, measurable quantitative information extraction from unstructured texts has attracted increasing attention in various fields of industry. However, due to the issues of error propagation and insufficient deep interactions between entities and relations, the accurate extraction of measurable quantitative information remains as a challenging task. To address these issues, this paper proposes a joint model based on table filling (JMTF) for measurable quantitative information extraction task. The core of this model introduces a co-attention mechanism to achieve bidirectional interactions between recognition and association subtasks, thereby avoiding problems such as feature confusion or insufficient interaction. Additionally, the model employs an optimized BERT-based encoder (TEncoder) for text encoding. TEncoder improves ability of the model to capture long-range contextual information of text by incorporating direction-awareness, distance-awareness, and unscaled attention. To further improve performance of the model in Chinese text, TEncoder also integrates the inherent features of Chinese characters, such as pinyin and glyphs, which help handle the ambiguity of polysemous words and homophones. The experiments evaluate the JMTF model on a standardized quantitative information dataset of 3106 Chinese text sentences. The results show that our JMTF achieves F1 values of 87.01% and 85.95% for MQI recognition and association, respectively, outperforming the best baseline at 86.42% and 84.65%, demonstrating its advantages in measurable quantitative information extraction.