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Deep Neural language-agnostic Multi-task Text Classifier 21

Deep Neural Language-agnostic Multi-task Text Classifier

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21st ieee international conference on Data Mining (ieee ICDM)

作者： Gawron, Karol Pogoda, Michal Ropiak, Norbert Swedrowski, Michal Kocon, Jan Wroclaw Univ Sci & Technol Dept Artificial Intelligence Wroclaw Poland

ISBN: (纸本)9781665424271

Many publications prove that the creation of a multiobjective machine learning model is possible and reasonable. Moreover, we can see significant gains in expanding the knowledge domain, increasing prediction quality, and reducing the inference time. New developments in cross-lingual knowledge transfer open up a range of possibilities, particularly in working with low-resource languages. With a motivation to explore the latest subfields of natural language processing and their interactions, we decided to create a multi-task multilingual model for the following text classification tasks: functional style, domain, readability, and sentiment. The paper discusses the effectiveness of particular language-agnostic approaches to Polish and English and the effectiveness and validity of the multi-task model.

关键词： deep learning language-agnostic multi-task text classification

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Negation Handling and Domain Generalization in Sentiment Analysis using Machine Learning Models

Negation Handling and Domain Generalization in Sentiment Ana...

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knowledge engineering and Communication Systems (ICKECS), international conference on

作者： Shrnivas Biradar G T Raju K M Divakar Dept. of Computer Science and Engineering SJC Institute of Technology Chickaballapu Dept. of Information Science and Engineering SJC Institute of Technology Chickaballapur

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

Conventional lexicon-based approaches to sentiment analysis typically lack the necessary methods to properly identify the negation window, making it impossible to model negation. An enormous increase in sentiment-rich electronic and social media has been observed daily. Negation modifiers cause problems for Sentiment Classification techniques and have the power to entirely change the discourse’s meaning. Therefore, it becomes essential to manage them well. Opinion mining or sentiment analysis is the study of people’s attitudes, feelings, and views as they are expressed in written language. It is one of the busiest text mining and natural language processing research projects. Even though sentiment analysis research has gained popularity in the field of natural language processing, for this problem, the state-of-the-art machine learning approach is based on Bag of Words. But the BOW model pays little attention to polarity shift, which could have a distinct overall effect. One of the main issues with doing sentimental analysis on any given text or sentence is handling polarity shift, which is what this study attempts to address. Sentiment analysis use natural language processing principles to identify negation in the text. Our goal is to identify the negation effect on customer reviews that, although appearing good, are actually negative. The suggested modified negation methodology helps to increase classification accuracy by providing a method for computing negation identification. In terms of review classification by accuracy, precision, and recall, this approach yielded a noteworthy outcome. When test and training data are from distinct domains, machine learning faces the challenge of domain generalization. Despite the large body of research on cross-domain text classification, the majority of current methods concentrate on one-to-one or many-to-one domain adaptation. Our domain generalization method regularly outperforms state-of-the-art domain adaption methods, a

关键词： Text mining Sentiment analysis Analytical models Adaptation models Accuracy Reviews Social networking (online)

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Harnessing Gated Recurrent Units for Enhanced Emotion Recognition in Deep Learning

Harnessing Gated Recurrent Units for Enhanced Emotion Recogn...

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2024 international conference on Artificial Intelligence and Emerging Technology, Global AI Summit 2024

作者： Sachdeva, Bhumi Gill, Kanwarpartap Singh Kumar, Mukesh Rawat, Ramesh Singh Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Graphic Era Hill University Uttarakhand Dehradun 248002 India Graphic Era Deemed to be University Uttarakhand Dehradun 248002 India

ISBN: (纸本)9798350379716

This study explores emotion analysis in natural language processing (NLP) utilizing bidirectional gated recurrent units (GRUs). Unlike traditional models that process data in a single direction, bidirectional GRUs are more effective as they capture dependencies in sequential data from both past and future contexts. This bidirectional capability is crucial for tasks like emotion analysis, where context plays a significant role. The proposed model is built using a sequential framework. The initial step involves an embedding layer that converts word indices into dense vectors, essential for managing textual data. To prevent overfitting, a dropout layer with a rate of 0.5 is included. Two bidirectional GRU layers follow, each returning sequences to capture detailed temporal dependencies. A batch normalization layer is applied to stabilize and accelerate the training process. An additional bidirectional GRU layer further enhances the model's ability to capture complex dependencies from both directions. This layer has six units and uses a softmax activation function to classify emotions into six categories. The final layer is dense. The Adam optimizer is employed, using sparse categorical cross-entropy as the loss function, and accuracy is the metric used to evaluate performance. This design showcases the effectiveness of bidirectional GRUs for emotion analysis, achieving a 94% accuracy rate. The model excels in predicting emotions due to its ability to identify intricate relationships in sequential data, as evidenced by the results. The model summary includes the components and parameter choices contributing to these impressive results. © 2024 ieee.

关键词： Bidirectional Dependencies Bidirectional GRU Emotion Analysis Model Accuracy natural language processing (NLP) Sequential Data Textual Data

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Part-of-Speech Tagging of Parallel Corpus based on Convolutional Neural Network

Part-of-Speech Tagging of Parallel Corpus based on Convoluti...

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Distributed Computing and Electrical Circuits and Electronics (ICDCECE), ieee international conference on

作者： Nancao Ma Liaoning Institute of Science and Engineering Jinzhou China

ISBN: (数字)9798331533663

ISBN: (纸本)9798331533670

Parallel corpora part-of-speech tagging is an indispensable part in the application field of natural language information processing. The core work of this paper is to build a mathematical model of part-of-speech tagging based on CNNbased parallel corpus, which includes three aspects. Firstly, the basic CNN model is studied. Based on the artificial neuron model, the convolutional layer, the pooled layer and the fully connected layer are studied. Then, we study the maximum entropy model, integrate various information into one model, and carry out parts-of-speech tagging and syntactic analysis to improve the accuracy of natural language processing. Finally, the learning process of CNN is studied, the output is obtained by using forward propagation, and the error is fed back to the trainable parameters of the network through back propagation to realize parameter update. On this basis, NIST corpus is selected to compare the model proposed in this paper with the model commonly used in CNN, which proves that the model proposed in this paper has advantages in the accuracy of part-of-speech tagging.

关键词： Accuracy Computational modeling Semantics Information processing Tagging Feature extraction Mathematical models Entropy Convolutional neural networks Machine translation

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Question generation based on knowledge base

Question generation based on knowledge base

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Artificial Intelligence, Robotics, and Communication (ICAIRC), ieee international conference on

作者： Xin Huang Ligang Dong Xian Jiang Bin Zhuge Luping Yu Yiyang Yu School of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou China Zhejiang Shiqiang Group Co. Ltd. Hangzhou China

ISBN: (数字)9798331531225

ISBN: (纸本)9798331531232

Question generation refers to automatically generating reasonable and relevant questions based on given text and answers. The application prospects of question generation technology are very broad and it has become one of the research hotspots in the field of natural language processing. In knowledge-based question generation, most models only focus on the answer entity in the knowledge base and ignore other information related to the entity, while many models are not adaptable to out-of-vocabulary predicates. To address this shortcoming, this paper proposes a controlled generation framework based on a knowledge base, which controls the generator through constraints to ensure that the given answers and key entities in the subgraph appear in the generated questions. At the same time, an optimization strategy for the pre-trained generation model is proposed, which first adjusts the parameters in the decoder and then freezes the parameters in the encoder, so that the model can retain the characteristics of the hidden predicates in the pre-trained model, thereby improving the model's adaptability to out-of-vocabulary predicates and improving the quality of generated questions.

关键词： Training Adaptation models knowledge based systems Training data Question generation Data models Encoding Generators Optimization Robots

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LLM and RAG-Based Question Answering Assistant for Enterprise knowledge Management

LLM and RAG-Based Question Answering Assistant for Enterpris...

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international conference on Computer Science and engineering (UBMK)

作者： Gürkan Şahin Karya Varol Burcu Kuleli Pak R&D Centre Adesso Türkiye İstanbul Türkiye

ISBN: (数字)9798350365887

ISBN: (纸本)9798350365894

Large language models (LLM) have become integral to many natural language processing applications, particularly in the area of automatic question answering. In this study, a question answering system was developed to enable Adesso Turkiye employees to access internal company information quickly and accurately. A Retrieval Augmented Generation (RAG)-based question answering framework was constructed by utilizing multiple large language models and embedding techniques, along with content curated by experts in human resources and information security. The performance of the system was evaluated using ROUGE, BLEU, and accuracy metrics, and the results indicated high levels of success. Future work will focus on enhancing performance through the use of different language models, enriching the system with datasets from various domains, and integrating the developed system into MS Teams to ensure accessibility for employees.

关键词： Measurement Computer science Accuracy Large language models Information security Companies Question answering (information retrieval) Vectors knowledge management Usability

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At Scaled Research Opportunity Identification: The Marriage of Explicit and Parametric knowledge

At Scaled Research Opportunity Identification: The Marriage ...

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ieee international conference on e-Business engineering (ICEBE)

作者： Naif N. Alotaibi Morteza Saberi Madhushi Bandara Elizabeth Chang School of Computer Science University of Technology Sydney Australia Department of Information Technology College of Computers and Information Technology Taif University Saudi Arabia School of Information and Communication Technology Griffith University Australia

ISBN: (数字)9798350365856

ISBN: (纸本)9798350365863

In an era of rapid digital transformation, orga-nizations often grapple with inadequate and reactive strategic planning, leading to potential market capitalization losses and missed opportunities. Traditional methods such as SWOT analysis, though invaluable, largely rely on localized and subjective information, overlooking the vast sea of external events that can herald new opportunities or threats. This position paper elucidates a novel approach that combines explicit and parametric knowledge, aiming to bridge this critical gap. We introduce an innovative framework that augments the capabilities of pretrained language models (PLMs) for enhanced opportunity identification and pertinent information retrieval. Central to this framework is the integration of a Bayesian Network (BN)-based analytical opportunity model, which is complemented by a knowledge-aware prompt tuning model. Together, these modules facilitate efficient identification of emerging opportunities from an expansive array of web resources. Beyond the technical novelty, our model marks a paradigm shift in strategic decision-making, propelling organizations to factor in comprehensive external insights. As a practical application, our objective is to create an artificial intelligence (AI) model that serves as a central entity for research and educational institutions. This model will offer researchers many options such as team building, access to relevant grant opportunities, identification of upcoming research ideas, and more. This effort not only facilitates the development of proactive organizational strategies but also demonstrates the possibilities of integrating traditional and AI-driven approaches in decision-making processes.

关键词： Analytical models Navigation Decision making Organizations Strategic planning Propulsion natural language processing Bayes methods Artificial intelligence Tuning

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VTN-EG: CLIP-Based Visual and Textual Fusion Network for Entity Grounding

VTN-EG: CLIP-Based Visual and Textual Fusion Network for Ent...

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Machine Learning and natural language processing (MLNLP), international conference on

作者： Shiyi Hao Linjie Wang Yifeng Zheng Chen Wang Yunfei Liu Shengyang Li Technology and Engineering Center for Space Utilization Chinese Academy of Sciences Beijing China Key Laboratory of Space Utilization Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350354973

ISBN: (纸本)9798350354980

In the field of space science and utilization, we have constructed a domain knowledge graph based on text data. However, a significant problem with this knowledge graph is the absence of a large number of domain-relevant multimodal data. Entity grounding is the principal approach to addressing this issue, which refers to aligning external images with corresponding text entities. Currently, entity grounding has issues of entity name ambiguity and entity image sparseness. In response to these issues, we propose a method called VTN-EG, within which we design three new modules: prompt network module, attention module and decision module. The initial two modules use prompt network and cross-attention mechanism respectively to augment the expressiveness of image and text encoding. In the decision module, we obtain the final results based on the strategy of using the prompt network in visual discrimination process and not using it in text discrimination process. To support algorithmic research, we construct a specialized entity grounding dataset called the Chinese Space Science and Utilization Entity Grounding (SSUEG) and select four deep learning methods for comparison. Extensive experiments on SSUEG and two public datasets have proved the advancement and generalization of VTN-EG. Finally, we demonstrate the effectiveness and necessity of algorithm improvements through ablation experiments.

关键词： Deep learning Visualization Image coding Grounding Design methodology knowledge graphs natural language processing

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Video Question Answering Using Clip-Guided Visual-Text Attention

Video Question Answering Using Clip-Guided Visual-Text Atten...

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ieee international conference on Image processing

作者： Shuhong Ye Weikai Kong Chenglin Yao Jianfeng Ren Xudong Jiang School of Computer Science University of Nottingham Ningbo China Nottingham Ningbo China Beacons of Excellence Research and Innovation Institute University of Nottingham Ningbo China China School of Electrical & Electronic Engineering Nanyang Technological University

Cross-modal learning of video and text plays a key role in Video Question Answering (VideoQA). In this paper, we propose a visual-text attention mechanism to utilize the Contrastive language-Image Pre-training (CLIP) trained on lots of general domain language-image pairs to guide the cross-modal learning for VideoQA. Specifically, we first extract video features using a TimeSformer and text features using a BERT from the target application domain, and utilize CLIP to extract a pair of visual-text features from the general-knowledge domain through the domain-specific learning. We then propose a Cross-domain Learning to extract the attention information between visual and linguistic features across the target domain and general domain. The set of CLIP-guided visual-text features are integrated to predict the answer. The proposed method is evaluated on MSVD-QA and MSRVTTQA datasets and outperforms state-of-the-art methods.

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Development of Part-of-Speech tagger for a low-resource endangered language 4

Development of Part-of-Speech tagger for a low-resource enda...

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4th international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Gore, Toshal Khatavkar, Vaibhav College of Engineering Pune Department of Computer Engineering Pune India

ISBN: (纸本)9781665474368

India is one of the multilingual countries where large number of languages are spoken, major languages being Hindi, Bengali and Marathi. Indian languages have limited research done in the natural language processing (NLP) domain. This is because Indian languages use Brahmic script alphabets, instead of Latin alphabets, which is very difficult for NLP to understand and process. Most of the Indian languages have many dialects and also have many distinct linguistic characteristics as compared to English. Also, there are many Indian languages which are on the verge of extinction and there is very little progress done on NLP for such languages in order to preserve them. The size of dataset available for such low resource languages is very small. One such language is Katkari, which is an endangered Indian tribal language, and a dialect of Marathi language. The purpose of this work is to develop a Part-of-Speech (POS) tagger for Katkari language. POS tagging is a technique in which each word in the text is assigned a POS label based on its context. The POS taggers for several Indian languages are developed, but for Katkari language, work is yet to be done. Hence, this paper presents a POS tagger for Katkari language which is built with the help of Hidden Markov Model (HMM) and Viterbi algorithm. The Katkari POS tagger was compared with POS taggers of other Indian languages and the accuracy of the Katkari POS tagger was obtained as 86.84%. © 2022 ieee.

关键词： Hidden Markov models

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