Pedestrian trajectory prediction using BiRNN encoder–decoder framework

Wu, Jiaxu; Woo, Hanwool; Tamura, Yusuke; Moro, Alessandro; Massaroli, Stefano; Yamashita, Atsushi; Asama, Hajime

doi:10.1080/01691864.2019.1635910

Cited by 13 publications

(1 citation statement)

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“…where p m represents the mean of accuracy and r m represents the mean of recall. e proposed automated model was compared with Bi-LSTM [42], CNN [43], and BiRNN [44] on the above three publicly available datasets, and the experimental results are shown in Figure 6. It can be seen that the performance of the method in this paper on two datasets (MSR and CTB7) is significantly better than the other methods.…”

Section: Micro F1mentioning

confidence: 99%

A Data-Driven Model for Automated Chinese Word Segmentation and POS Tagging

Wang

2022

Computational Intelligence and Neuroscience

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Chinese natural language processing tasks often require the solution of Chinese word segmentation and POS tagging problems. Traditional Chinese word segmentation and POS tagging methods mainly use simple matching algorithms based on lexicons and rules. e simple matching or statistical analysis requires manual word segmentation followed by POS tagging, which leads to the inability to meet the practical requirements for label prediction accuracy. With the continuous development of deep learning technology, data-driven machine learning models provide new opportunities for automated Chinese word segmentation and POS tagging. erefore, a data-driven automated Chinese word segmentation and POS tagging model is proposed in order to address the above problems. Firstly, the main idea and overall framework of the proposed automated model are outlined, and the tagging strategy and neural network language model used are described. Secondly, two main optimisations are made on the input side of the model: (1) the use of word2Vec for the representation of text features, thus representing the text as a distributed word vector; and (2) the use of an improved AlexNet for e cient encoding of long-range word, and the addition of an attention mechanism to the model. Finally, on the output side, an additional auxiliary loss function was designed to optimise the Chinese text based on its frequency. e experimental results show that the proposed model can signi cantly improve the accuracy and operational e ciency of Chinese word segmentation and POS tagging compared with other existing models, thus verifying its e ectiveness and advancement.

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Section: Micro F1mentioning

confidence: 99%