Kui Meng


2024

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MFE-NER: Multi-feature Fusion Embedding for Chinese Named Entity Recognition
Jiatong Li | Kui Meng
Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)

“In Chinese Named Entity Recognition, character substitution is a complicated linguistic phe-nomenon. Some Chinese characters are quite similar as they share the same components or havesimilar pronunciations. People replace characters in a named entity with similar characters togenerate a new collocation but refer to the same object. As a result, it always leads to unrecog-nizable or mislabeling errors in the NER task. In this paper, we propose a lightweight method,MFE-NER, which fuses glyph and phonetic features to help pre-trained language models handlethe character substitution problem in the NER task with limited extra cost. Basically, in the glyphdomain, we disassemble Chinese characters into Five-Stroke components to represent structurefeatures. In the phonetic domain, an improved phonetic system is proposed in our work, makingit reasonable to describe phonetic similarity among Chinese characters. Experiments demon-strate that our method performs especially well in detecting character substitutions while slightlyimproving the overall performance of Chinese NER.”

2022

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A Multi-Task Dual-Tree Network for Aspect Sentiment Triplet Extraction
Yichun Zhao | Kui Meng | Gongshen Liu | Jintao Du | Huijia Zhu
Proceedings of the 29th International Conference on Computational Linguistics

Aspect Sentiment Triplet Extraction (ASTE) aims at extracting triplets from a given sentence, where each triplet includes an aspect, its sentiment polarity, and a corresponding opinion explaining the polarity. Existing methods are poor at detecting complicated relations between aspects and opinions as well as classifying multiple sentiment polarities in a sentence. Detecting unclear boundaries of multi-word aspects and opinions is also a challenge. In this paper, we propose a Multi-Task Dual-Tree Network (MTDTN) to address these issues. We employ a constituency tree and a modified dependency tree in two sub-tasks of Aspect Opinion Co-Extraction (AOCE) and ASTE, respectively. To enhance the information interaction between the two sub-tasks, we further design a Transition-Based Inference Strategy (TBIS) that transfers the boundary information from tags of AOCE to ASTE through a transition matrix. Extensive experiments are conducted on four popular datasets, and the results show the effectiveness of our model.