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2020 International Conference on Computer Big Data and Artificial Intelligence, ICCBDAI 2020

作者： Zhou, Luwang Huang, Zhimin Nie, Ying Dept. of Geography Graphics and Image Research and Development North China Institute of Computing Technology No.211 North Fourth Ring Road Beijing100084 China

Slot Filling (SF) is a critical part of spoken language understanding (SLU) which targets to capture semantic constituents from a specific utterance. It is considered as a sequence labeling issue. Currently, recurrent neural networks have shown promising effectiveness in this issue. Considering the effects of interrelated information within adjacent words and labels, we present that a novel approach consists of bi-directional gate recurrent unit (Bi-GRU), attention mechanism, and conditional random field (CRF). Our model can utilize interrelated information from words in the neighborhood, highlight key information, and exploit the dependencies within labels corresponding to surrounding words. The empirical experiments illustrate that our model significantly boosts the F1 score with around 1% and 5.1% relative enhancement on two public benchmark dataset ATIS and SNIPS separately. © Published under licence by IOP Publishing Ltd.

关键词： Recurrent neural networks

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Few-shot sketch recognition for plotting system

Few-shot sketch recognition for plotting system

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2021 International Conference on Green Communication, Network, and Internet of Things, GCNIoT 2021

作者： Yuan, Zelin Dept. of Geography Graphics and Image Research and Development North China Institute of Computing Technology No.211 North Fourth Ring Road Beijing100084 China

ISBN: (数字)9781510650473

ISBN: (纸本)9781510650466

Plotting system refers to a software system that can draw a series of vector symbols, typesetting, saving, reading and so on. The traditional plotting system uses key and mouse operation to draw. With the development of electronic equipment touch screen in recent years, the traditional menu selection is not convenient enough, but the way of using hand-painted sketch and recognized by the system is more natural and fast. Traditional image recognition methods need huge amount of data for training, but the vector symbol set in the plotting system is not a common symbol set, so it is difficult to obtain a large amount of data. Therefore, this paper proposes a β-TCVAE-Attention hand-drawn plot recognition method which can obtain better recognition accuracy in the case of small samples. The model first uses the image dataset to pre-Train βTCVAE, and then uses the small sample data of the plot sketch to input the β-TCVAE encoder to obtain the sketch features. After the sketch features are enhanced with the attention module, they are input into the Softmax classifier based on cosine similarity. The neural network is fine-Tuned according to the classification results of the classifier. Finally, when using network prediction, input the support set and query graph into the network for classification. In this paper, a 20-way-5-shot plotting data set is collected for experiment, and the experiment proves that the model has certain accuracy. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Touch screens

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research of Attention-Based Bi-GRU-CRF for Slot Filling

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Journal of Physics: Conference Series 2021年第1期1757卷

作者： Luwang Zhou Zhimin Huang Ying Nie Dept. of Geography Graphics and Image Research and Development North China Institute of Computing Technology No.211 North Fourth Ring Road Beijing 100084 PR China

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