Human motion sequence recognition based on correlation feature selection and multilayer perceptron

Gao, Yongjun; Wang, Mei; Yu, Yunlei; Zhang, Chen

doi:10.1117/12.2581258

Cited by 1 publication

(1 citation statement)

References 5 publications

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“…(2) The performance is based on text classification models, such as the Convolutional Neural Network (CNN) [22] , LSTM [23] , Gated Recurrent Unit (GRU) [24] , Bidirectional GRU (Bi-GRU) [25] , and Multi-Layer Perception (MLP) [26] , is performed. Meanwhile, the performance based on sequence labeling models, such as the Hidden Markov Model (HMM), Conditional Random Fields (CRF), Bi-LSTM, and Bi-LSTM-CRF, is performed.…”

Section: Double Ornament (" ")mentioning

confidence: 99%

A Tibetan Sentence Boundary Disambiguation Model Considering the Components on Information on Both Sides of Shad

Gao

et al. 2023

Tsinghua Sci. Technol.

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Sentence Boundary Disambiguation (SBD) is a preprocessing step for natural language processing.Segmenting text into sentences is essential for Deep Learning (DL) and pretraining language models. Tibetan punctuation marks may involve ambiguity about the sentences' beginnings and endings. Hence, the ambiguous punctuation marks must be distinguished, and the sentence structure must be correctly encoded in language models. This study proposed a component-level Tibetan SBD approach based on the DL model. The models can reduce the error amplification caused by word segmentation and part-of-speech tagging. Although most SBD methods have only considered text on the left side of punctuation marks, this study considers the text on both sides. In this study, 465 669 Tibetan sentences are adopted, and a Bidirectional Long Short-Term Memory (Bi-LSTM) model is used to perform SBD. The experimental results show that the F1-score of the Bi-LSTM model reached 96%, the most efficient among the six models. Experiments are performed on low-resource languages such as Turkish and Romanian, and high-resource languages such as English and German, to verify the models' generalization.

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Section: Double Ornament (" ")mentioning

confidence: 99%

A Tibetan Sentence Boundary Disambiguation Model Considering the Components on Information on Both Sides of Shad

Gao

et al. 2023

Tsinghua Sci. Technol.

View full text Add to dashboard Cite

show abstract

Human motion sequence recognition based on correlation feature selection and multilayer perceptron

Cited by 1 publication

References 5 publications

A Tibetan Sentence Boundary Disambiguation Model Considering the Components on Information on Both Sides of Shad

A Tibetan Sentence Boundary Disambiguation Model Considering the Components on Information on Both Sides of Shad

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