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Non-autoregressive Korean Morphological Analysis with Word Segment Information
http://doi.org/10.5626/JOK.2023.50.8.653
This paper introduces a non-autoregressive Korean morphological analyzer. The proposed morphological analyzer utilizes a transformer encoder to encode a given sentence and employs two non-autoregressive decoders for morphological analysis. Each decoder generates a morpheme sequence and a corresponding POS tag sequence, which are then combined to produce the final morphological analysis. Additionally, this paper leverages word segment information within the sentence to predict the target sequence length, mitigating performance degradation resulting from incorrect target sequence length predictions. Experimental results show that the proposed non-autoregressive Korean morphological analyzer outperforms all non-autoregressive baselines. It achieves comparable accuracy to an autoregressive Korean morphological analyzer while it performs nearly 14.76 times faster than the autoregressive Korean morphological analyzer.
Detecting Design Infringement Using Multi-Modal Visual Data and Auto Encoder based on Convolutional Neural Network
Jeonggeol Kim, Jiyou Seo, Chanjae Lee, Seongmin Jo, Seungmin Kim, Seokmin Yoon, Young Yoon
http://doi.org/10.5626/JOK.2022.49.2.137
Recently, it has become very difficult to distinguish between counterfeit products and authentic goods, and the volume of these forgeries is increasing at an alarming rate. Prompt detection of these counterfeit products is challenging since only humans can identify these forgeries through trained expertise. In this paper, given the photograph and design drawing, we use convolutional neural networks and auto-encoders to detect the possible infringement of design rights without dissembling or damaging the suspected items. We have developed an easy-to-expand system that supports the constant addition of new goods to be examined. We present the result of our system tested with a set of authentic and forged goods.
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