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Text Summarization Techniques for the Bengali language: Survey 6th

Text Summarization Techniques for the Bengali Language: Surv...

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6th international conference on Recent Innovations in Computing, ICRIC 2023

作者： Parul Garg, Kamal Deep Gupta, Divya Rakhra, Manik Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science and Engineering Chandigarh University Mohali140413 India School of Computer Science and Engineering Lovely Professional University Phagwara India

ISBN: (纸本)9789819728381

In the last days, there is an explosion of large amount of data from different sources because of increase of invaluable data. Text summarization is a method of extracting usable, valuable, and summarized information from a document’s content. Nowadays, for summarizing text, different techniques are available for different-different languages. One of the most practical applications of natural language processing is text summarization. It is popular for valuable information condensation. In today’s world, it plays a very important character in day-to-day life. this paper gives appropriate knowledge of different-different text summarization techniques. the research goes through the two types of text summary, extractive and abstractive, and contrasts the performance of Bengali among other Indian languages. In this paper, techniques used for extracting the data are discussed in brief. Extractive text summarization and abstractive text summarization are the different sorts of text summarization. the majority of the effective strategies for summarizing Bengali material were discussed in this work. Preprocessing and word reordering are two highly prevalent strategies that are employed by all procedures, as we discovered from our examination of several techniques. In today’s world as data on internet increase, rapidly new techniques are also developed for extracting relevant data. As in upcoming years, communication technologies will be also there with text summarization. Due to these innovations, the research and research development sectors have grown rapidly. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Text processing

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A New Feature Fusion Method Based on Pre-Training Model for Sequence Labeling 6

A New Feature Fusion Method Based on Pre-Training Model for ...

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6th international conference on Data Storage and Data Engineering, DSDE 2023

作者： Yu, Shichuan Yang, Yan School of Computer Science and Technology Heilongjiang University Harbin China Heilongjiang University Heilongjiang Key Laboratory of Database and Parallel Computing Harbin China

ISBN: (纸本)9798350333145

To fuse vocabulary features into the pre-training model is the mainstream data feature processing method for sequence labelling tasks. In general, the feature fusion methods that have been proposed at present are direct fusion outside the pre-training model or fusion of lexical features using attention mechanism. However, the study found that this way of vocabulary enhancement does not conform to the word formation rules of modern Chinese. In the Chinese language, it is easy to fuse irrelevant or even incorrect lexical features into the sequence using the above feature processing methods, which is bad for the experimental results of the Chinese sequence labelling task. To solve these problems, we propose to use Cosine Similarity Adapter to process lexical features in Chinese sequence labelling tasks. CSBERT is a hybrid model using this structure based on BERT, which conforms to the word formation rules of modern Chinese to a certain extent. It can fuse the features of the word into the character or eliminate the features of the word in the character according to the cosine similarity between the character vector and a word vector. the experimental results show that CSBERT has better ability to label Chinese sequences than the benchmark model. CSBERT has achieved the best experimental results such as F1-Score on 7 open datasets and the best ability of multi-label classification, which proves that the model has good practical value. © 2023 IEEE.

关键词： Classification (of information)

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Natural language processing Algorithms for Divergent thinking Assessment 6

Natural Language Processing Algorithms for Divergent Thinkin...

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6th IEEE Eurasian conference on Educational Innovation, ECEI 2023

作者： Lee, Hanmi Zhou, Wenqing Bai, HongHong Meng, Weiran Zeng, Tianli Peng, Kaiping Tong, Song Kumada, Takatsune Department of Psychology Tsinghua University Beijing China Graduate School of Informatics Kyoto University Kyoto Japan Positive Psychology Research Center Tsinghua University Beijing China

ISBN: (纸本)9798350334289

the manual assessment of creativity by human raters is coupled with unavoidable subjectivity and often costs much time and human resources. To address these issues, this paper explores how to apply natural language processing (NLP) methods to the assessment of creativity. Using the Alternative Use Task (AUT), participants were encouraged to generate ideas as fast as possible for a fixed time. It was hypothesized that the similarity of ideas would decrease over time in the AUT, considering the design fixation and the limitation of working memory. In the first study, 12 university students completed the AUT in paper-pencil form and generated a total of 376 responses. We applied two NLP models, namely BERT (Bidirectional Encoder Representations from Transformers) and USE (Universal Sentence Encoder), to assess the similarity of responses between individuals. the results did not confirm our hypothesis. One prominent reason might be that the applied models represent millions of sentence structures that are over-ecological and too dissimilar to the sentence structures participants had used while finishing the AUT. Nevertheless, the results did show that BERT and USE could more accurately express the semantic information of responses pace with the Latent Semantic Analysis, a popular computer-aided model for AUT response assessment. In study 2, we proposed an algorithm to reanalyze the 376 responses in study 1 based on word embedding with crowdsourced responses. there were 1690 crowdsourced responses collected from 550 participants who completed an online version of the AUT. the results supported our hypothesis and showed that the similarity of responses increases as time passes. this indicates the proposed algorithm would alleviate the influence of sentence structure in AUT tasks. the differences between BERT, USE, and proposed algorithms are discussed in relation to the assessment of creativity, and the implications for future work are explored in-depth. © 2023 IEEE.

关键词： Crowdsourcing

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Graph ConvolutionWord Embedding and Attention for Text Classification 2022

Graph ConvolutionWord Embedding and Attention for Text Class...

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6th international conference on Machine Learning and Soft Computing (ICMLSC)

作者： Yang, Yi Cui, Qihui Ji, Lijun Cheng, Zhuoran State Grid Shandong Elect Power Co Beijing Peoples R China Beijing Univ Posts & Telecommun Beijing Peoples R China

ISBN: (纸本)9781450387477

Text classification is an important and classic task of natural language processing. Deep neural networks are becoming more and more popular in text classification due to their expressive power and low requirements for feature engineering. However, the more flexible graph convolutional neural network is rarely used for this task. this paper proposes a text classification model (GENET) based on graph convolution word embedding and attention mechanism. the model can better combine the semantic and lexical information of the text with the discontinuous global word co-occurrence information and long-distance semantic information in the corpus. It breaks the shortcomings of traditional neural networks that have limited structure and can only learn local information. Experimental results show that our proposed text classification method out performs other models on multiple datasets. At the same time, it is proved through experiments that the word global information obtained by GCN is an important supplement to the word embedding representation.

关键词： Text classification Graph convolutional information fusion

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Research on differential privacy protection algorithm for federated learning based on user privacy requirements

Research on differential privacy protection algorithm for fe...

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IEEE international conference on Civil Aviation Safety and information technology (ICCASIT)

作者： Meijiao Wu Tianjin University of Technology Tianjin China

ISBN: (数字)9798350389418

ISBN: (纸本)9798350389425

With the proliferation of AI-driven services, including recommendation systems and natural language processing, data privacy and security have captured the attention of the world. Federated learning is widely used as an advanced privacy-protecting machine learning technique, but many studies have shown that it still faces multiple attacks, resulting in the disclosure of private information. In addition, although the methods of combining federated learning and differential privacy have been widely adopted, most of the current methods only focus on the uniform privacy budget allocation, ignoring the different privacy budget requirements caused by the uneven distribution of user data. To solve this problem, a federated learning algorithm for personalized differential privacy is proposed. According to the privacy requirements of users, the user privacy protection scheme is designed, and Gaussian noise is added in the federated learning process to achieve personalized protection of user privacy.

关键词： Privacy Differential privacy Federated learning Gaussian noise Natural language processing Security Resource management Protection information technology Recommender systems

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R-TES: Regularized Template Style for Generative Joint Relational Triple Extraction 6

R-TES: Regularized Template Style for Generative Joint Relat...

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6th international conference on Machine Learning and Natural language processing, MLNLP 2023

作者： Zhang, Liqi Yang, Lianhe Bai, Yinhao Zhu, Hongfei Liu, Xingyu School of Computer Science and Technology Tiangong University Tianjin China College of Software Nankai University Tianjin China School of Software Tiangong University Tianjin China

ISBN: (纸本)9798400709241

Joint Relational Triple Extraction (RTE) is an important task in the field of information extraction. With the development of the pre-Trained language models, the sequence-To-sequence (seq2seq) approaches have become one of the promising methods for this task, utilizing a predefined template to convert the relational triples into a structure target sequence, which can be easily decoded as relational triples. However, most existing seq2seq studies focus on the improvement of methods but ignore that template styles also have impacts on performance. Inspired by this idea, we first explore the effects of different template styles on performance and find that some template styles can help generate models to achieve better performance. Based on the above findings, we argue that different template styles lead to various understandings of the relation triple. therefore, we propose Regularized template style (R-TES) to improve the performance of a main template by reducing the gap between it and other selected templates. Specifically, R-TES uses the pre-Trained language model to select the templates with kullback-leibler (KL) divergence. then, we further reduce the gap between the main template and these selected templates by minimizing KL divergence. Experimental results show that our method outperforms state-of-the-Art methods on the publicly available dataset. © 2023 ACM.

关键词： information retrieval

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Leveraging Transformer Summarizer to Extract Sentences for Arabic Text Summarization 6

Leveraging Transformer Summarizer to Extract Sentences for A...

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6th international conference on AI in Computational Linguistics, ACLing 2024

作者： Zaiton, Hoda Fashwan, Amany Alansary, Sameh Institute of Applied Linguistics and Translation Faculty of Arts Alexandria University Alexandria Egypt Arab Academy for Science Technology and Maritime Transport Alexandria Egypt Alexandria University Faculty of Arts Phonetics and Linguistics Department Alexandria Egypt

Automatic Text Summarization (ATS) is one of the fastest-growing areas of Artificial Intelligence (AI), Machine Learning (ML), and Natural language processing (NLP). Automatic text summarizing aims to create summaries by extracting relevant information from input texts. Being able to study text non-sequentially allowed for large model training, making the Transformer the most widely used NLP model. this study focuses on extractive Arabic text summarization using various transformer-based summarization models. Two distinct scenarios are explored: summarization extracted from scored sentences and summarization generated from ranked (reversed) sentences. Experimental results demonstrate the effectiveness and performance of the transformer-based summarization models in the context of Arabic text summarization. the EASC corpus is used to evaluate our proposed summarization approach and ROUGE evaluation method is applied to determine the accuracy of the proposed approach. the findings of the study demonstrate notable performance when compared to other relevant works. the comparison between the two scenarios provides insights into the strengths and weaknesses of each approach. the scored sentence extraction scenario offers a more direct and content-focused summary generation, while the ranked sentence scenario provides flexibility in summarizing diverse text sources. © 2024 the Authors. Published by Elsevier B.V.

关键词： Arabic Text Summarization Extractive Arabic Text Summarization Natural language processing Transformer Summarizer

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Named Entity Recognition of Zhuang language Based on the Feature of Initial Letter in Word 2022

Named Entity Recognition of Zhuang Language Based on the Fea...

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6th international conference on Innovation in Artificial Intelligence (ICIAI)

作者： Zhang, Weiquan Tang, Suqin He, Danni Li, Tinghui Pan, Changchun Guangxi Normal Univ Sch Elect Engn Guilin Peoples R China Guangxi Normal Univ Fac Educ Guilin Peoples R China Guangxi Sci & Technol Normal Univ Sch Math & Comp Sci Laibin Peoples R China

ISBN: (纸本)9781450395502

Named entity recognition is an important task and basis for the intelligent information processing and knowledge representation learning of the Zhuang language. A BilSTM-CNN-CRF network model combining the uppercase and lowercase characters of words is proposed to be applied to the named entity recognition task of the Zhuang language, which lacks corpus for named entity labeling. Firstly, word2vec is used to train in unmarked Zhuang text to get the word vector of the Zhuang language. then convolutional neural network is used to extract the character features of Zhuang words, and the character feature vector is obtained. the above two vectors were connected with the initial case feature vectors, which are randomly generated, and then the connected vectors were input into a BilSTM-CNN-CRF model for training;thus, the end-to-end named entity recognition model of Zhuang language was constructed. Experimental results show that, without relying on artificial features and external dictionaries, the proposed method in this study is superior to contrastive models by achieving an 80.37% F1 value in the named entity recognition task, which leads to the realization of automated named entity recognition of Zhuang language.

关键词： Zhuang language Named entity recognition Long-short term memory network Convolutional neural network Conditional random field

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Path planning for indoor robots using natural language processing a chunk-based approach with cascaded conditional random fields

Path planning for indoor robots using natural language proce...

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6th international conference on Artificial Intelligence and advanced Manufacturing (AIAM 2024)

作者： Xu Zhu Division of Continuing Education University of California Irvine Irvine California 92697 US

ISBN: (数字)9781837242412

this paper presents a novel method for indoor robot navigation through natural language path description. the method focuses on extracting path information directly from natural language inputs to guide robots, without relying on pre-built high-precision maps. A chunk-based analysis technique is employed to enhance the robot’s comprehension of natural language by constructing cascaded conditional random fields (CCRFs). Initially, a detailed analysis of a corpus of path descriptions is conducted to uncover the relationship between syntax and semantics. Using this, noun phrases and semantic chunks are extracted to build path units. To address issues related to noun phrase interpretation, a reasoning method for noun entity relations is introduced, which incrementally constructs complete route information. the method’s performance is evaluated through several experiments, demonstrating its potential to enhance human-robot interaction in navigation tasks. the significance of this research lies in advancing natural language-guided robot navigation, making robots more autonomous and capable of harmoniously interacting in human environments.

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Office Management Chatbot Using Artificial Intelligence 1

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6th international conference on Soft Computing and Signal processing, ICSCSP 2023

作者： Rupin, Eathakota Singh, Tarandeep Jyothi, P. N. Siva Shaik, Subhani Bhutada, Sunil Telangana Yamnampet Hyderabad India

ISBN: (数字)9789819986286

ISBN: (纸本)9789819986279

A chatbot’s main aim is to make a conversation between both machines and human. the machine has been embedded data to identify the sentences and make a decision itself as a response to answer a query asked by the user. Chatbots will be completely based on a text-based user interface, allowing the user to type commands and receive text as well as text to speech response. Chatbots are usually services, remembering previous commands in order to provide functionality. It can be utilized securely by an even larger audience when chatbots technology is integrated with popular web services. the office inquiry chatbots will be built using artificial intelligence algorithms that analyse user’s questions and understand user’s message. the response principle is matching the input sentence from a user. the user can ask the question any office-related activities through the chatbot without physically available to the office for inquiry. the system analyses the queries and then displays to the user. With the help of artificial intelligence, the system answers the query asked by the employees with the knowledge and predictive information as depicted. the proposed design on chatbot aims to create a solution that improvises the overall automated selection of the queries and its relative analysis based on artificial intelligence. On the office chatbot, we use Python-based artificial intelligence techniques while using Flask-developed UI frame. © the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： web services

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