Abstract
Grading subjective questions of specialty text is a kind of text inference task. Since there are many specialty terms and concepts, it is difficult to judge the knowledge contained in a text as the usual way on inferring a general text. In this paper, we propose a specialty text inference model by extracting the structural knowledge from text. We first propose a knowledge graph construction method for the extraction of knowledge from specialty texts. By combining the constructed knowledge features with the text semantic features, we design the specialty text inference model. Finally, we use real datasets from a national professional exam to validate the soundness of the knowledge graph construction method and the performance of the inference model. The experiments under different training set sizes and network structures are also conducted to detailly analyze the design of our method. The experimental results show the effectiveness and practicality of our approach.
This work was supported by the National Nature Science Foundation of China, NSFC (62376138) and the Innovative Development Joint Fund Key Projects of Shandong NSF (ZR2022LZH007).
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Xia, T., Wang, J., Liu, T., Jiang, H., Sun, Y. (2024). Extracting Structural Knowledge for Professional Text Inference. In: Sun, Y., Lu, T., Wang, T., Fan, H., Liu, D., Du, B. (eds) Computer Supported Cooperative Work and Social Computing. ChineseCSCW 2023. Communications in Computer and Information Science, vol 2013. Springer, Singapore. https://doi.org/10.1007/978-981-99-9640-7_25
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