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1. CPBNet:Concentrate,Parallel and Bimodal Network for Logistics Scene Text Detection and Recognition | |||
MA Yu-Chen | |||
Computer Science and Technology 28 February 2024 | |||
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Abstract:In the logistics industry, express sorting has always been an important link in ensuring smooth transportation of logistics. The recognition of logistics forms directly determines the sorting efficiency, but how to effectively improve the accuracy of text recognition of logistics forms in complex sorting environments is still a research challenge. In this article, we believe that the limitations of existing text models in logistics sorting scenarios mainly come from: 1) the interference caused by complex environments; 2) the incompatibility of recognition accuracy and speed; 3) single-modal limitation. Accordingly, this paper proposes CPBnet based on the principles of concentrate,parallel and bimodal. First, we corrected the form angularly, geometrically, and photometrically for complex scenes. Then, using a parallel method, the Attention mechanism is added to the visual model to guide the CTC training strategy, and the more accurate characteristics of the Attention model are used to train the backbone network to obtain better convolution features, and then the CTC branch is used for prediction, thereby ensuring Speed at inference. Finally, a language model is added after the visual model for semantic correction. The language model fully learns the input contextual information to make up for the visual semantic deficiency. There are basically very few pictures of sorting scenes in existing general text data sets. The lack of data in the field of sorting scenes has created a bottleneck for the application of deep learning in sorting scenes. Therefore, this article simulates real form data, prepares a sorting scene data set by itself, and proves through a large number of experiments that CPBNet has advantages on this data set and achieves the most advanced results. | |||
TO cite this article:MA Yu-Chen. CPBNet:Concentrate,Parallel and Bimodal Network for Logistics Scene Text Detection and Recognition[OL].[28 February 2024] http://en.paper.edu.cn/en_releasepaper/content/4762301 |
2. Relation Extraction Method for Chinese Public Opinion Based on Transferring Pre-trained Models and Merging Multiple Features | |||
ZHANG Yunkai,CHENG Bo | |||
Computer Science and Technology 27 February 2024 | |||
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Abstract:The public opinion has profoundly and extensively impacted the society, making the research on relation extraction(RE) in the field of public opinion crucial. Many existing relation extraction models either focus solely on basic information of Chinese characters or fail to fully leverage pre-trained models for extraction. Therefore, this paper proposes a relation extraction model, CwTransRE, which incorporates basic Chinese character information, glyph information, pinyin information and Chinese word information through transferring pre-trained models. CwTransRE enhances the effectiveness of relation extraction in two key aspects: firstly, besides basic Chinese character information, the integrated glyph, pinyin and Chinese word information enriches the semantic features of embeddings; secondly, the introduction of pre-trained models aids in obtaining more accurate embeddings, especially when dealing with relatively small training datasets. Experimental results on an open-source public opinion dataset demonstrate that our model achieves an F1 score of 0.703, outperforming NovelTagging, GraphRel(1p), GraphRel(2p) , TAG-JE and CasRel. | |||
TO cite this article:ZHANG Yunkai,CHENG Bo. Relation Extraction Method for Chinese Public Opinion Based on Transferring Pre-trained Models and Merging Multiple Features[OL].[27 February 2024] http://en.paper.edu.cn/en_releasepaper/content/4762184 |
3. Data Augmentation Based Biaffine Attention Coreference Resolution Algorithm | |||
LI Yuanxin | |||
Computer Science and Technology 11 May 2023 | |||
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Abstract:Event text generally has a variety of descriptions. Event coreference resolution aims to find the similarity matching between two kinds of event text descriptions, which can help professionals find the same event from multi-source information. We propose a data augmentation based biaffine attention Coreference resolution algorithm, DEBACR. Roberta was selected as the pre-training model for feature extraction. Various data enhancement methods such as back translation and FGM were used to enhance the generalization ability of the model. We propose a fusion model of global and local information, which combines the coreference relation judgment of trigger word consistency, event elements consistency and event overall consistency. By using the biaffine attention mechanism, the important features are screened out for resolution and classification. Experimental results show that the accuracy of the algorithm is effectively improved in multiple Chinese event coreference resolution datasets than semantic similarity model based on Siamese network. | |||
TO cite this article:LI Yuanxin. Data Augmentation Based Biaffine Attention Coreference Resolution Algorithm[OL].[11 May 2023] http://en.paper.edu.cn/en_releasepaper/content/4760702 |
4. A Graph Learning based Approach for Similar Text Recommendation in Enforcement Document of Urban Management | |||
Liu Huiyong,Xiong Songlin | |||
Computer Science and Technology 31 March 2023 | |||
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Abstract:The research on text classification is the most basic part of the natural language processing sector in the field of machine learning. Since the topic was proposed, quite a few methods have been produced to solve this problem. However, most of the current text classification methods are for text classification of short texts or long texts containing a certain specific content. There are relatively few studies on text classification methods that contain multiple texts and specific structural structures. In the professional field of urban management and law enforcement documents, the composition of documents has the characteristics of multiple texts and specific structural structures. In order to complete the text classification work of this type of task, it is necessary to adopt some targeted methods. In the past decade, deep learning has achieved great success in various fields. In order to improve the accuracy and efficiency of such text classification, methods of introducing graph structure and deep learning are considered. Combining the characteristics of such texts, this paper mainly studies the graph convolutional neural network algorithm for such text classification. In order to extract more information from the text, construct a graph on the entire corpus, use the vocabulary in the text and the text itself as nodes in the graph, use the vocabulary co-occurrence matrix information to construct the edge between texts, and use the vocabulary The edge between words and words is constructed by using the position context relationship between them, and the edge between words and text is constructed by using the frequency of words appearing in the text, and then the multi-text classification problem is regarded as a graph node classification problem. After iteration and optimization of the algorithm, the final conclusion is that the method of using the graph convolutional neural network can greatly improve the accuracy of this type of multi-text classification within the acceptable efficiency loss range. And further improve the accuracy of text recommendation and tailoring suggestions in the follow-up work. | |||
TO cite this article:Liu Huiyong,Xiong Songlin. A Graph Learning based Approach for Similar Text Recommendation in Enforcement Document of Urban Management[OL].[31 March 2023] http://en.paper.edu.cn/en_releasepaper/content/4759931 |
5. Using Pre-Trained Language Models for Aspect Category Sentiment Analysis in a Simple Causal Inference way | |||
XIAO Zhiyi | |||
Computer Science and Technology 22 March 2023 | |||
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Abstract:Aspect category sentiment analysis (ACSA) is a fine-grained sentiment analysis task that aims to identify the sentiment polarity to wards an explicit or implicit aspect (term) in a sentence. While pre trained language models like BERT have shown promising results in this area, they can also have inaccuracies caused by spurious correlations that do not necessarily reflect the sentiment. The bias in pre-trained language models and the unbalanced distribution of the finetuning data always leads to high accuracy in test data, but it could perform poorly on the prediction in reality. Inspired by the stability of causality, we propose counterfactual ACSA (CF-ACSA), a model-agnostic framework that reduces language bias by separating the direct language effect from the total effect on sentiment. We first identify the spurious correlations be tween term and sentiment polarity through a semi-supervised approach. Next, we use the spurious term we find to construct counterfactual sam ples for the original data. Finally, we make causal inferences and get an unbiased result between counterfactual samples and the original data. Extensive experiments demonstrate the effctiveness of our model, and results show that it outperforms the state-of-the-art methods on ASAP datasets. | |||
TO cite this article:XIAO Zhiyi. Using Pre-Trained Language Models for Aspect Category Sentiment Analysis in a Simple Causal Inference way[OL].[22 March 2023] http://en.paper.edu.cn/en_releasepaper/content/4759903 |
6. Stroke-encoding and Pinyin-learning Enhanced Chinese Pre-trained Language Representation Model | |||
ZHOU Tong | |||
Computer Science and Technology 22 March 2023 | |||
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Abstract:Language models pre-trained on large unlabeled corpora have proven to be very effective in improving many downstream NLP tasks. However, existing language models are primarily designed for English, and less consideration has been given to the more abundant semantic information that Chinese characters imply. The unique semantically related stroke sequence pattern and polyphony enable the enhancement of a Chinese language representation model. Masked language models, such as BERT, are also plagued by inefficient training data utilization, requiring more iterations to complete training. In light of these shortcomings, we propose an improved, customized Chinese pre-trained language model based on the transformer, called SPCLM (Stroke-encoding and Pinyin-learning enhanced Chinese pre-trained Language representation Model). SPCLM contains stroke encoders and an additional pronunciation prediction task. Moreover, the autoregressive objection and mask prediction jointly assist in model formulation. Experimental results demonstrate that SPCLM outperforms other baseline methods, achieving comparable results on five Chinese NLP tasks, with insufficient pre-training, including natural language inference, semantic similarity, named entity recognition, sentiment analysis, and question answering. | |||
TO cite this article:ZHOU Tong. Stroke-encoding and Pinyin-learning Enhanced Chinese Pre-trained Language Representation Model[OL].[22 March 2023] http://en.paper.edu.cn/en_releasepaper/content/4759900 |
7. Online Chinese Polyphone Disambiguation with Progressive Neural Networks | |||
ZHANG Yi-Fei | |||
Computer Science and Technology 16 March 2023 | |||
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Abstract:There has been plenty of research on Chinese polyphonic disambiguation (CPD) problems. However, badcases are always found in real-life products. To fix such bad cases without affecting system performance on known cases is a rigid demand. In this paper, continual learning is introduced to CPD problems, and Progressive Neural Networks (PNN) is used to learn new knowledge from bad cases without sacrificing system performance on old datasets. The experimental results show that the proposed method can repair the badcase without forgetting the feature of the original dataset. Compared with the traditional finetune method, the accuracy of the model on the old dataset decreases by nearly 20\%. Our method can ensure that the accuracy of the original dataset just decreases by about 0.3\% after learning the new feature data, and the time consumption is acceptable. Potential improvements like weight pruning are also discussed. | |||
TO cite this article:ZHANG Yi-Fei. Online Chinese Polyphone Disambiguation with Progressive Neural Networks[OL].[16 March 2023] http://en.paper.edu.cn/en_releasepaper/content/4759451 |
8. Using perceptual loss to improve video caption generation | |||
Ye Zhou,Hu Yanzhu | |||
Computer Science and Technology 10 March 2022 | |||
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Abstract:In the task of video caption generation by deep learning methods, Binary Cross Entropy is often used as the loss function for end-to-end model training, and evaluation metrics such as BLEU, which do not consider semantics, are used as the main judge of model performance, which is quite negative to the actual semantic effect of the generated captions. In contrast, in the image generation task, there is perceptual loss that can be used to learn hidden semantic knowledge for inferencing. This paper is inspired by the perceptual loss in image generation task and proposes to use the perceptual loss function to help optimize statements for video caption generation. On the MSR-VTT dataset, compared with the traditional method using only Binary Cross Entropy, the method proposed in our paper makes it possible to improve the SPICE metrics while ensuring the effect of metrics such as BLEU. | |||
TO cite this article:Ye Zhou,Hu Yanzhu. Using perceptual loss to improve video caption generation[OL].[10 March 2022] http://en.paper.edu.cn/en_releasepaper/content/4756787 |
9. Deep Parallel Neural Network for Event Temporal Relation Classification | |||
Sun Chang,Gao Sheng | |||
Computer Science and Technology 13 April 2021 | |||
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Abstract:Temporal relation classification is an active natural language understanding research field. Partly because of the lack of high-quality datasets, most of the methods proposed in other papers do not use neural network or they only use simple neural network with external knowledge. In this task, we use Convolution Neural Network (CNN) to discover the complex interaction between events and extract time-related keyword information, and we use Long Short-Term Memory (LSTM) to capture temporal context information in sentences, and then connect the two kinds of information for temporal relation classification. We don\'t use any external knowledge, including dependency trees. In the latest MATRES dataset, the performance of our model is better than the state-of-the-art result. | |||
TO cite this article:Sun Chang,Gao Sheng. Deep Parallel Neural Network for Event Temporal Relation Classification[OL].[13 April 2021] http://en.paper.edu.cn/en_releasepaper/content/4754461 |
10. A Dual-Attentive and Hybrid Word-Character Model for Chinese Short Text Summarization | |||
Li Yufeng,Xu Weiran | |||
Computer Science and Technology 24 December 2020 | |||
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Abstract:Automatic text summarization is an important field for NLP, which includes the extractive and the abstractive method. Among many languages, Chinese has many special properties, such as rich character semantic expressions, flexible abbreviation. Moreover, insufficient training samples are also a problem. In this paper, we propose a dual-attentive and word-character Chinese text summarization model. The hybrid word-character approach (HWC) will preserve the advantages of both word based and character-based representations, which are very suitable for Chinese language. And the extractive and abstractive methods are combined to accurately capture the key information and gain the essence of articles with less supervised samples. We evaluate our model using the ROUGE evaluation on a widely used Chinese Dataset LCSTS2.0. The experimental results show that the model is very effective. | |||
TO cite this article:Li Yufeng,Xu Weiran. A Dual-Attentive and Hybrid Word-Character Model for Chinese Short Text Summarization[OL].[24 December 2020] http://en.paper.edu.cn/en_releasepaper/content/4753279 |
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