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Readability-guided Idiom-aware Sentence Simplification (RISS) for Chinese 23

23rd Chinese National Conference on Computational Linguistics, CCL 2024

作者： Zhang, Jingshen Chen, Xinglu Qiu, Xinying Wang, Zhimin Feng, Wenhe School of Information Science and Technology Guangzhou China Faculty of Chinese Language and Culture Guangzhou China Laboratory of Language Engineering and Computing Guangdong University of Foreign Studies Guangzhou China

ISBN: (纸本)9780000000002

Chinese sentence simplification faces challenges due to the lack of large-scale labeled parallel corpora and the prevalence of idioms. To address these challenges, we propose Readability-guided Idiom-aware Sentence Simplification (RISS), a novel framework that combines data augmentation techniques. RISS introduces two key components: (1) Readability-guided Paraphrase Selection (RPS), a method for mining high-quality sentence pairs, and (2) Idiom-aware Simplification (IAS), a model that enhances the comprehension and simplification of idiomatic expressions. By integrating RPS and IAS using multi-stage and multi-task learning strategies, RISS outperforms previous state-of-the-art methods on two Chinese sentence simplification datasets. Furthermore, RISS achieves additional improvements when fine-tuned on a small labeled dataset. Our approach demonstrates the potential for more effective and accessible Chinese text simplification. © 2024 China National Conference on Computational Linguistics.

关键词： Computational linguistics

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On book thickness parameterized by the vertex cover number

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Science China(Information Sciences) 2022年第4期65卷 112-113页

作者： Yunlong LIU Jie CHEN Jingui HUANG School of Information Science and Engineering Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal University

Dear editor,Book thickness for graphs forms a major theme in graph theory and has a broad application in the fields of sorting permutations, fault tolerant VLSI design, parallel computing, and others. The book Thickness problem, even if the vertex order is taken as part of the input, was shown to be NP-complete in general [1, 2]. Just because of this, parameterized algorithms have been proposed to deal with it [3].

关键词： Book Thickness Vertex Cover Number Linear Order 1-page embedding graph kernel

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Intelligent Clinical Diagnosis and Treatment Model for Hemorrhagic Stroke using Ensemble Learning Algorithms 5

Intelligent Clinical Diagnosis and Treatment Model for Hemor...

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5th International Conference on Artificial Intelligence and Computer engineering, ICAICE 2024

作者： Liang, Xingcheng Zhang, Zhenhua Mo, RuJian Zhou, ZhiBiao Guangdong University of Foreign Studies Laboratory of Language Engineering and Computing Guangzhou China School of Mathematics and Statistics Guangdong University of Foreign Studies Guangzhou China

ISBN: (纸本)9798331528911

Hemorrhagic stroke accounts for 10-15% of stroke cases, posing a significant burden on patients and society. This study aims to develop an intelligent clinical diagnosis and treatment model for hemorrhagic stroke by integrating statistical models and algorithms-Bayesian parameter-optimized logistic regression, random forest, XGBoost, LightGBM, and CatBoost-with patient imaging data to improve treatment outcomes and prognosis accuracy. We utilized follow-up imaging data within 48 hours of hemorrhagic stroke onset to explore factors related to hematoma expansion risk. Feature engineering was performed using a random forest classifier for feature selection, followed by constructing logistic regression and random forest models optimized through Bayesian parameter search to predict hematoma expansion probabilities, evaluated with the Brier score. Additionally, we developed XGBoost, LightGBM, and CatBoost models to predict patients' 90-day modified Rankin Scale (mRS) scores, analyzing the relationship between prognosis and factors such as personal history, disease history, treatment methods, and imaging features, with model performance compared using mean squared error (MSE). The results indicated that the random forest model was superior in predicting hematoma expansion risk, while the CatBoost model outperformed both XGBoost and LightGBM for 90-day mRS score prediction. Prognostic analysis revealed that hematoma volume, edema volume, and age positively influenced mRS scores, whereas a history of stroke did not significantly impact mRS scores. Continuous monitoring of hematoma and edema volumes, along with age considerations, is crucial for effective patient management. © 2024 IEEE.

关键词： Logistic regression

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Deep Broad Learning for Emotion Classification in Textual Conversations

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Tsinghua Science and Technology 2024年第2期29卷 481-491页

作者： Sancheng Peng Rong Zeng Hongzhan Liu Lihong Cao Guojun Wang Jianguo Xie Center for Linguistics and Applied Linguistics and Laboratory of Language Engineering and ComputingGuangdong University of Foreign StudiesGuangzhou 510006China Guangdong Provincial Key Laboratory of Nanophotonic Functional Materials and Devices South China Normal UniversityGuangzhou 510006China School of Computer Science and Cyber Engineering Guangzhou UniversityGuangzhou 510006China Modern Education Technology Center Guangdong University of Foreign StudiesGuangzhou 510006China

Emotion classification in textual conversations focuses on classifying the emotion of each utterance from textual *** is becoming one of the most important tasks for natural language processing in recent ***,it is a challenging task for machines to conduct emotion classification in textual conversations because emotions rely heavily on textual *** address the challenge,we propose a method to classify emotion in textual conversations,by integrating the advantages of deep learning and broad learning,namely *** aims to provide a more effective solution to capture local contextual information(i.e.,utterance-level)in an utterance,as well as global contextual information(i.e.,speaker-level)in a conversation,based on Convolutional Neural Network(CNN),Bidirectional Long Short-Term Memory(Bi-LSTM),and broad *** experiments have been conducted on three public textual conversation datasets,which show that the context in both utterance-level and speaker-level is consistently beneficial to the performance of emotion *** addition,the results show that our proposed method outperforms the baseline methods on most of the testing datasets in weighted-average F1.

关键词： emotion classification textual conversation Convolutional Neural Network(CNN) Bidirectional Long Short-Term Memory(Bi-LSTM) broad learning

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Multimodal language Data and Platform Construction for LCTLs Teaching 23

Multimodal Language Data and Platform Construction for LCTLs...

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15th International Conference on Machine Learning and computing, ICMLC 2023

作者： Wang, Yuan Jin, Kai Liu, Wuying Shandong Key Laboratory of Language Resources Development and Application Ludong University China School of Foreign Languages Qilu University of Technology China Laboratory of Language Engineering and Computing Guangdong University of Foreign Studies China

ISBN: (纸本)9781450398411

The progress of artificial intelligence technology has brought great opportunities for the development of Less Commonly Taught languages (LCTLs) teaching. However, the scarcity of LCTLs resources still plagues the current teaching platform. This paper is committed to building multimodal language data including foreign language vocabulary and translation, bilingual example sentences, phonetic symbols and audio, morphological similarity recommendation, semantic similarity recommendation, etc., and using full-text index and Web Service technology to build a new LCTLs teaching platform. The detailed steps are: (1) acquiring and processing multimodal language data through web pages such as TED and Opensubtitles;(2) constructing the inverted indexes used the HashMap and storing the index files in the cache of the platform, which avoids the heavy database intervention;(3) using the classical Levenshtein distance in the morphological similar words and semantic similar words recommendation algorithm. The platform implemented based on the above data and technology finally supports Japanese, Italian, Indonesian, Malaysian, Vietnamese, Filipino, and Myanmar seven languages. © 2023 ACM.

关键词： Websites

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Readability-Guided Idiom-Aware Sentence Simplification (RISS) for Chinese 23rd

Readability-Guided Idiom-Aware Sentence Simplification (RISS...

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23rd China National Conference on Computational Linguistics, CCL 2024

作者： Zhang, Jingshen Chen, Xinglu Qiu, Xinying Wang, Zhimin Feng, Wenhe School of Information Science and Technology Guangdong University of Foreign Studies Guangzhou China Faculty of Chinese Language and Culture Guangdong University of Foreign Studies Guangzhou China Laboratory of Language Engineering and Computing Guangdong University of Foreign Studies Guangzhou China

ISBN: (纸本)9789819783663

Chinese sentence simplification faces challenges due to the lack of large-scale labeled parallel corpora and the prevalence of idioms. To address these challenges, we propose Readability-guided Idiom-aware Sentence Simplification (RISS), a novel framework that combines data augmentation techniques. RISS introduces two key components: (1) Readability-guided Paraphrase Selection (RPS), a method for mining high-quality sentence pairs, and (2) Idiom-aware Simplification (IAS), a model that enhances the comprehension and simplification of idiomatic expressions. By integrating RPS and IAS using multi-stage and multi-task learning strategies, RISS outperforms previous state-of-the-art methods on two Chinese sentence simplification datasets. Furthermore, RISS achieves additional improvements when fine-tuned on a small labeled dataset. Our approach demonstrates the potential for more effective and accessible Chinese text simplification. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Multi-task learning

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Scheduling optimization for upstream dataflows in edge computing

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Digital Communications and Networks 2023年第6期9卷 1448-1457页

作者： Haohao Wang Mengmeng Sun Lianming Zhang Pingping Dong Yehua Wei Jing Mei College of Information Science and Engineering Hunan Normal UniversityChangsha410081China Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing Hunan Normal UniversityChangsha410081China Hunan Xiangjiang Artificial Intelligence Academy Hunan Normal UniversityChangsha410081China

Edge computing can alleviate the problem of insufficient computational resources for the user equipment,improve the network processing environment,and promote the user *** computing is well known as a prospective method for the development of the Internet of Things(IoT).However,with the development of smart terminals,much more time is required for scheduling the terminal high-intensity upstream dataflow in the edge server than for scheduling that in the downstream *** this paper,we study the scheduling strategy for upstream dataflows in edge computing networks and introduce a three-tier edge computing network *** propose a Time-Slicing Self-Adaptive Scheduling(TSAS)algorithm based on the hierarchical queue,which can reduce the queuing delay of the dataflow,improve the timeliness of dataflow processing and achieve an efficient and reasonable performance of dataflow *** experimental results show that the TSAS algorithm can reduce latency,minimize energy consumption,and increase system throughput.

关键词： Edge computing Time-slicing Dataflow scheduling Dynamic analysis

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Comparative Analysis of Para-social Interaction among Multiple Luxury Brands 4

Comparative Analysis of Para-social Interaction among Multip...

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4th International Conference on Natural language Processing, ICNLP 2022

作者： Yu, Dandan Liu, Wuying Ludong University Shandong Key Laboratory of Language Resources Development and Application Yantai264025 China Guangdong University of Foreign Studies Laboratory of Language Engineering and Computing Guangzhou510420 China

ISBN: (数字)9781665495448

ISBN: (纸本)9781665495448

The luxury industry is good at using the most advanced science and technology to promote product sales. With the recent rise of virtual spokespersons, more and more virtual spokespersons are widely used in luxury marketing. We studied the new advertising mode based on virtual spokespersons, and analyzed how luxury brands use virtual spokespersons to establish para-social interactions with their audiences. We also analyzed the data of likes, reposts and comments on the official Weibo of luxury brands such as Valentino, Dior, Ferragamo, Louis Vuitton, and March Jacobs that have published advertising Weibos which contain virtual spokespersons and do not include celebrity spokespersons and various marketing campaign. Finally, according to the conclusion of our analysis, we propose development suggestions for the promotion strategy of luxury brands' virtual spokespersons in Weibo or other media, and the way to establish para-social interaction between luxury brands and their audiences. © 2022 IEEE.

关键词： Marketing

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Byzantine-Robust Federated Learning on Non-IID Data via Inversing Artificial Gradients

Byzantine-Robust Federated Learning on Non-IID Data via Inve...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Li, Minyang Tang, Xiangyun Zhang, Tao Liu, Guangyuan Weng, Yu Minzu University of China Key Laboratory of Ethnic Language Intelligent Analysis and Security Governance China Beijing Jiaotong University School of Software Engineering China Nanyang Technological University College of Computing and Data Science Singapore

ISBN: (纸本)9798350351255

Federated Learning (FL) is a distributed machine learning framework that enhances privacy by enabling multiple participants to train a global model without sharing their raw data. However, FL still faces the threat posed by data and Byzantine attacks (e.g., data poisoning and model poisoning attacks) from malicious clients aimed to decrease the FL global training accuracy. Previous studies have proposed solutions to improve the robustness of FL systems. However, these robust approaches have not effectively defended against Byzantine attacks from malicious clients in non-Independent and Identically Distributed (non-IID) environments, decreasing overall training accuracy. In this work to address this issues, we propose a new defence framework that aims to enhance model accuracy against Byzantine attacks from malicious clients in non-IID environments. In our approach, the central server performs the Inverse Deep Learning Gradient attack using the gradient data submitted by users in each round, obtaining an inversed artificial gradient. We then assess the squared L2 norm difference between this inversed gradient and the actual gradients to detect malicious clients. Simultaneously, we assign weights to each client involved in the aggregation based on differences in the squared L2 norm, aiming to minimize the impact of malicious actors on the overall model. We have experimentally evaluated our method and demonstrated its ability to maintain training accuracy under attack in non-IID environments using standard datasets. © 2024 IEEE.

关键词： Federated learning

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Natural-Annotation-Based Malay Multiword Expressions Extraction and Clustering 19th

Natural-Annotation-Based Malay Multiword Expressions Extract...

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19th International Conference on Computational Linguistics and Intelligent Text Processing, CICLing 2018

作者： Liu, Wuying Wang, Lin Laboratory of Language Engineering and Computing Guangdong University of Foreign Studies Guangdong Guangzhou510420 China Xianda College of Economics and Humanities Shanghai International Studies University Shanghai200083 China

ISBN: (纸本)9783031237928

Multiword expression (MWE) is an optimal granularity of language reuse. However, no explicit boundaries between MWEs and other words causes a serious problem on automatic identification of MWEs for some less commonly taught languages. This paper addresses the issue of Malay MWEs extraction and clustering, and proposes a novel unsupervised extraction and clustering algorithm based on natural annotations. In our algorithm, we firstly use a binary classification for each space character to solve length-varying Malay MWEs extraction, secondly transfer natural document-level category annotations to MWE-level ones for Malay MWEs clustering, and finally distill out a general MWEs resource and several domain resources. The experimental results in the Malay dataset of 272,783 text documents show that our algorithm can extract MWEs precisely and dispatch them into domain clusters efficiently. © 2023, Springer Nature Switzerland AG.

关键词： Extraction

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