Urban gas pipeline NER: leveraging semantic similarity for knowledge extraction

Yanzhu Hu; Kefan Wang; Guokai Zhang; Xiaoyu Liu; Song Wang

doi:10.1117/12.3032808

5 July 2024 Urban gas pipeline NER: leveraging semantic similarity for knowledge extraction

Yanzhu Hu, Kefan Wang, Guokai Zhang, Xiaoyu Liu, Song Wang

Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 131841N (2024) https://doi.org/10.1117/12.3032808
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia

Abstract

To address the challenge of recognizing specialized terminology in gas safety, we introduce a novel Named Entity Recognition (NER) model that integrates semantic similarity. Unlike traditional NER algorithms such as BERT+BiLSTM+CRF, this approach incorporates a word similarity weighting layer. This layer improves the recognition of entities related to Chinese urban gas pipeline standards by utilizing a proposed algorithm that maps inter-character similarity to vector weight values within the word embedding layer. Experimental results validate the model's superiority, exhibiting a notable improvement of 0.051 in the F1 metric.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yanzhu Hu, Kefan Wang, Guokai Zhang, Xiaoyu Liu, and Song Wang "Urban gas pipeline NER: leveraging semantic similarity for knowledge extraction", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 131841N (5 July 2024); https://doi.org/10.1117/12.3032808

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