Abstract
In traditional lexical chain extraction tasks, researchers typically focus on identifying simple lexical items based on surface grammatical relations, often overlooking compound words with underlying semantic frameworks. To address this limitation, the task of Nominal Compound Chain Extraction (NCCE) has emerged. This task aims to identify and cluster nominal compounds sharing the same semantic theme, thereby providing richer semantic information and facilitating a deeper understanding of the latent themes within documents. In this study, we fine-tune the large language model Qwen2-0.5b, employ data augmentation techniques, and introduce Chain-of-Thought (CoT) information from large models as an auxiliary aid, significantly enhancing the model’s document comprehension capabilities.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Bai, J., et al.: Qwen technical report. arXiv preprint arXiv:2309.16609 (2023)
Dettmers, T., Pagnoni, A., Holtzman, A., Zettlemoyer, L.: Qlora: efficient finetuning of quantized llms. Adv. Neural Inf. Process. Syst. 36 (2024)
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)
Dong, Z., Dong, Q., Hao, C.: Hownet and its computation of meaning. In: Coling 2010: Demonstrations, pp. 53–56 (2010)
Ercan, G., Cicekli, I.: Using lexical chains for keyword extraction. Inf. Process. Manag. 43(6), 1705–1714 (2007)
Fei, H., Ren, Y., Zhang, Y., Ji, D., Liang, X.: Enriching contextualized language model from knowledge graph for biomedical information extraction. Brief. Bioinf. 22(3), bbaa110 (2021)
Huang, L., et al.: A survey on hallucination in large language models: principles, taxonomy, challenges, and open questions. arXiv preprint arXiv:2311.05232 (2023)
Li, B., Fei, H., Ren, Y., Ji, D.: Nominal compound chain extraction: a new task for semantic-enriched lexical chain. In: Zhu, X., Zhang, M., Hong, Yu., He, R. (eds.) NLPCC 2020. LNCS (LNAI), vol. 12430, pp. 119–131. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-60450-9_10
Li, C., Zhang, L., Zheng, Q., Zhao, Z., Chen, Z.: User preference prediction for online dialogue systems based on pre-trained large model. In: CCF International Conference on Natural Language Processing and Chinese Computing, pp. 349–357. Springer, Heidelberg (2023). https://doi.org/10.1007/978-3-031-44699-3_31
Li, Z., Ding, N., Liu, Z., Zheng, H., Shen, Y.: Chinese relation extraction with multi-grained information and external linguistic knowledge. In: Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pp. 4377–4386 (2019)
Liu, X., Luo, Z., Huang, H.: Jointly multiple events extraction via attention-based graph information aggregation. arXiv preprint arXiv:1809.09078 (2018)
Mallick, C., Dutta, M., Das, A.K., Sarkar, A., Das, A.K.: Extractive summarization of a document using lexical chains. In: Nayak, J., Abraham, A., Krishna, B.M., Chandra Sekhar, G.T., Das, A.K. (eds.) Soft Computing in Data Analytics. AISC, vol. 758, pp. 825–836. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-0514-6_78
Rallapalli, S.: Interpretation of nominal compounds using structured knowledgebases. Computer Science & Engineering (M. Sc.) thesis. Hyderabad: LTRC, IIIT (2017)
Wei, J., et al.: Chain-of-thought prompting elicits reasoning in large language models. Adv. Neural Inf. Process. Syst. 35, 24824–24837 (2022)
Yunpeng, Q., Wenling, W.: Using semantic model to build lexical chains. Data Anal. Knowl. Disc. 32(9), 34–41 (2016)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2025 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Li, C., Zhang, L., Guo, H., Zheng, Q. (2025). Nominal Compound Chain Extraction Enhanced by Chain-of-Thought Information. In: Wong, D.F., Wei, Z., Yang, M. (eds) Natural Language Processing and Chinese Computing. NLPCC 2024. Lecture Notes in Computer Science(), vol 15363. Springer, Singapore. https://doi.org/10.1007/978-981-97-9443-0_28
Download citation
DOI: https://doi.org/10.1007/978-981-97-9443-0_28
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-97-9442-3
Online ISBN: 978-981-97-9443-0
eBook Packages: Computer ScienceComputer Science (R0)