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- Title
Reassembling Fragmented Entity Names: A Novel Model for Chinese Compound Noun Processing.
- Authors
Pan, Yuze; Fu, Xiaofeng
- Abstract
In the process of classifying intelligent assets, we encountered challenges with a limited dataset dominated by complex compound noun phrases. Training classifiers directly on this dataset posed risks of overfitting and potential misinterpretations due to inherent ambiguities in these phrases. Recognizing the gap in the current literature for tailored methods addressing this challenge, this paper introduces a refined approach for the accurate extraction of entity names from such structures. We leveraged the Chinese pre-trained BERT model combined with an attention mechanism, ensuring precise interpretation of each token's significance. This was followed by employing both a multi-layer perceptron (MLP) and an LSTM-based Sequence Parsing Model, tailored for sequence annotation and rule-based parsing. With the aid of a rule-driven decoder, we reconstructed comprehensive entity names. Our approach adeptly extracts structurally coherent entity names from fragmented compound noun phrases. Experiments on a manually annotated dataset of compound noun phrases demonstrate that our model consistently outperforms rival methodologies. These results compellingly validate our method's superiority in extracting entity names from compound noun phrases.
- Subjects
CHINESE language; LANGUAGE models; NOUN phrases (Grammar); NOUNS; COMPLEX compounds
- Publication
Electronics (2079-9292), 2023, Vol 12, Issue 20, p4251
- ISSN
2079-9292
- Publication type
Article
- DOI
10.3390/electronics12204251