Unsupervised word Segmentation Based on Word Influence

Yan, Ruohao; Zhang, Huaping; Silamu, Wushour; Hamdulla, Askar

doi:10.1109/icassp49357.2023.10096718

Cited by 2 publications

(1 citation statement)

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“…A series of experimental studies have demonstrated the robustness and interpretability of this model in open-domain Chinese word segmentation tasks. To address the lack of labeled data resources, Yan et al [28] proposed a concept of word influence. They argued that the influence between words can be divided into strong and weak influences, assuming they follow a Gaussian distribution.…”

Section: Related Workmentioning

confidence: 99%

A Benchmark for Morphological Segmentation in Uyghur and Kazakh

Abudouwaili,

Ruzmamat,

Abiderexiti

et al. 2024

Applied Sciences

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Morphological segmentation and stemming are foundational tasks in natural language processing. They have become effective ways to alleviate data sparsity in agglutinative languages because of the nature of agglutinative language word formation. Uyghur and Kazakh, as typical agglutinative languages, have made significant progress in morphological segmentation and stemming in recent years. However, the evaluation metrics used in previous work are character-level based, which may not comprehensively reflect the performance of models in morphological segmentation or stemming. Moreover, existing methods avoid manual feature extraction, but the model’s ability to learn features is inadequate in complex scenarios, and the correlation between different features has not been considered. Consequently, these models lack representation in complex contexts, affecting their effective generalization in practical scenarios. To address these issues, this paper redefines the morphological-level evaluation metrics: F1-score and accuracy (ACC) for morphological segmentation and stemming tasks. In addition, two models are proposed for morpheme segmentation and stem extraction tasks: supervised model and unsupervised model. The supervised model learns character and contextual features simultaneously, then feature embeddings are input into a Transformer encoder to study the correlation between character and context embeddings. The last layer of the model uses a CRF or softmax layer to determine morphological boundaries. In unsupervised learning, an encoder–decoder structure introduces n-gram correlation assumptions and masked attention mechanisms, enhancing the correlation between characters within n-grams and reducing the impact of characters outside n-grams on boundaries. Finally, comprehensive comparative analyses of the performance of different models are conducted from various points of view. Experimental results demonstrate that: (1) The proposed evaluation method effectively reflects the differences in morphological segmentation and stemming for Uyghur and Kazakh; (2) Learning different features and their correlation can enhance the model’s generalization ability in complex contexts. The proposed models achieve state-of-the-art performance on Uyghur and Kazakh datasets.

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Section: Related Workmentioning

confidence: 99%