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Incomplete Observations Bias Suppression for Abductive natural language Inference

Incomplete Observations Bias Suppression for Abductive Natur...

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international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Yu Gu Xianlong Luo Meng Yang School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

Abductive natural language commonsense reasoning is a task aiming at inferring the most plausible explanation in narrative text for observed events. Previous works mostly concentrate on utilizing powerful pre-trained language models and making better use of excess training data to learn abundant event commonsense knowledge. However, the utilization of causal effect is hidden in the language reasoning process and the explicit constraint of the causal effect between events has not been explored, resulting in biased inference. The model may focus on one observed event and make the wrong prediction while ignoring the other helpful events. To reveal the problem we modify the original task by appending unrelated text to the context which won’t change the causal relation. And typical methods get worse in the new task as they are not good at utilizing the complementary between the two observations. Motivated by eliminating the shortcut from incomplete observation and utilizing the complementarity of the two observations, we propose an incomplete observation bias suppression method to guide the training process. Results show our approach can ease the problem revealed in the new task. Based on the proposed method and the new task, our method also get competitive result on the original task.

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Uncovering User Concerns and Preferences in Static Analysis Tools: a Topic Modeling Approach

Uncovering User Concerns and Preferences in Static Analysis ...

引用

Artificial Intelligence, Blockchain, and Internet of Things (AIBThings), IEEE international conference on

作者： Jones Yeboah Saheed Popoola School of Information Technology University of Cincinnati Cincinnati OH USA

ISBN: (数字)9798331529529

ISBN: (纸本)9798331529536

In the field of software development, Static Analysis Tool (SAT) play a crucial role in maintaining code quality and identifying potential defects early in the development process. Despite their widespread use, there is limited research on understanding user feedback and sentiments towards these tools. This study aims to bridge this gap by applying topic modeling techniques to user reviews of four (4) popular Static Analysis Tools (SATs): SonarQube, PMD, Checkstyle, and FindBugs. Using a comprehensive dataset of user reviews collected from various online platforms, we identify prevalent themes and topics discussed by users. Our analysis highlights the key aspects that users find beneficial and areas where improvements are needed. The findings provide valuable insights into user concerns and preferences, informing the development of more user-friendly and effective SATs. Additionally, this study contributes to the growing body of knowledge on applying natural language processing (NLP) techniques to software engineering research, offering a framework for future studies on user feedback analysis.

关键词： knowledge engineering Bridges Analytical models Codes Reviews Static analysis natural language processing Internet of Things Software engineering Software development management

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Automated Disease Diagnosis Using natural language processing and Deep Learning

Automated Disease Diagnosis Using Natural Language Processin...

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Advancement in Electronics & Communication engineering (AECE), international conference on

作者： Devesh Garg Chandra Mani Sanjay Srivastava Khel Prakash Jayant Birendra Kumar Saraswat Computer Science & Engineering Raj Kumar Goel Institute of Technology Ghaziabad UP India

ISBN: (数字)9798350364729

ISBN: (纸本)9798350364736

Current advances in natural language processing (NLP) and deep studying have made it possible to increase automatic structures for ailment analysis. By leveraging NLP strategies, such systems can procedure unfastened-form text, spotting precious patterns to extract capabilities from scientific information that may be used to appropriately identify disease. Deep learning algorithms can take those features and learn to recognize the presence of a sickness and appropriately perceive the most possibly diagnosis. Such mechanically generated diagnoses can then be used to guide clinicians in making appropriate decisions. This paper gives a top level view of automatic disorder diagnosis systems that use NLP and deep learning, highlighting key additives of those structures, and discussing present challenges and destiny guidelines of such systems. Computerized sickness analysis the usage of natural language processing and Deep studying is an rising field that leverages advances in herbal language processing (NLP) and deep getting to know to discover diagnosable diseases from patient-provided information. Moreover, such models can discover ways to interpret clinical information from herbal language sources which includes scientific records and different scientific files. Via applying NLP and deep gaining knowledge of techniques to patient facts, an automated diagnosis set of rules may want to discover affected person statistics this is indicative of a diagnosable disorder. In addition, such algorithms have capacity to similarly enhance the accuracy and speed of analysis so one can make sure patient health outcomes. Moreover, these algorithms will be included with current healthcare systems to be able to mechanically recommend remedies and in addition save charges related to healthcare shipping.

关键词： Deep learning Accuracy Information resources natural language processing Reliability Medical diagnosis Public healthcare Prognostics and health management Diseases Sports

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Biomedical Relation Extraction Using LLMs and knowledge Graphs

Biomedical Relation Extraction Using LLMs and Knowledge Grap...

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IEEE international conference on Big Data Computing Service and Applications (BigDataService)

作者： Pranav Chellagurki Sai Prasanna Kumar Kumaru Rahul Raghava Peela Neeharika Yeluri Carlos Rojas Jorjeta Jetcheva Computer Engineering Department San José State University San José United States

ISBN: (数字)9798350366389

ISBN: (纸本)9798350366396

Due to the rapid growth of research papers on biomedical topics, it has become increasingly important to make advancements in biomedical natural language processing (NLP). Biomedical NLP enables us to extract important information from text, such as new insights into the role of different genes in disease susceptibility, or the potential for drug therapies that are effective against one disease to work effectively against another. In this paper, we present a comparative evaluation of the binary relation classification capabilities of the current state-of-the-art binary relation classifier, BioBERT, against recently released open-source large language models, Gemma-2b, Gemma-7b, and Llama2-7b, which we fine-tune with the benchmark GAD and EU-ADR datasets. In addition, we quantify the potential of discovering new relationships by utilizing knowledge graphs built out of known binary relations.

关键词： Measurement Biological system modeling Medical treatment knowledge graphs Machine learning Benchmark testing Market research natural language processing Data mining Diseases

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Escort - natural language processing Based University Students Guidance System 7

Escort - Natural Language Processing Based University Studen...

引用

7th international conference on Information Technology Research, ICITR 2022

作者： Srinivasan, Deepika Uthayakumar, Abarna Thiyagalingam, Parathan Ravindran, Shanghavi De Silva, Hansi Kasthururarthna, Dharshana Sri Lanka Institute of Information Technology Department of Software Engineering Colombo Sri Lanka

ISBN: (数字)9798350332032

ISBN: (纸本)9798350332032

Universities play a significant role in students' lives and aim to provide the best education, knowledge, and experience. However, university students face many difficulties during their university life including language, communication issues, and the inability to get proper guidance for education and career improvement. This paper proposes an approach to overcome these issues. With the help of natural language processing (NLP), in this paper, several solutions are recommended for the mentioned issues. First, a chatbot helps to communicate with the university administration, where students can ask relevant administrative-related questions. A recommendation system is developed to provide solutions for their psychological issues. Another recommendation system is built for career guidance which will help students to identify the future career of their interest, and the provided mentors will be able to guide them. Another recommendation system identifies the performance of students in each module according to their performance level. The system recommends learning materials to improve their level. With these components, ESCORT - A university students' guidance system, will make students' life easier and more efficient. © 2022 IEEE.

关键词： Students

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Enhancing Telugu Abusive language Detection Using Word Embeddings and BERT Models

Enhancing Telugu Abusive Language Detection Using Word Embed...

引用

Recent Trends in Microelectronics, Automation, Computing and Communications Systems (ICMACC), international conference on

作者： Venkataramana Battula Nikhil Teja Nune Vivek Guptha Shiva Sai Rampuri Pradyumna Chacham Koushik Pyarasani Anirudh Bojji Computer Science and Engineering MVSR Engineering College Hyderabad India

ISBN: (数字)9798350366570

ISBN: (纸本)9798350366587

In the digital era, the surge of abusive content online underscores the necessity for robust content moderation. This study targets the challenges posed by abusive content in Telugu, a language with limited digital resources. The goal is an advanced framework to classify Telugu sentences as abusive or not, using FastText and IndicFast for word embedding. Various machine learning techniques like Decision Trees, SVM, KNN, Random Forest, RNN, LSTM, BiLSTM, GRU, and transfer learning with pre-trained BERT models are explored. Each method is evaluated for its ability to capture abusive language nuances. The study employs diverse word vector dimensions for comprehensive coverage, systematically evaluating techniques to identify optimal classification combinations. It also investigates transfer learning via pre-trained BERT models, leveraging knowledge from other datasets to enhance Telugu abusive language understanding. This research enriches natural language processing and content moderation knowledge. It offers insights into effective word embedding techniques and ML algorithms for low-resource languages, aiding in the development of sensitive content moderation tools for Telugu speakers, fostering a safer online space.

关键词： Support vector machines Adaptation models Transfer learning Nearest neighbor methods natural language processing Vectors Classification algorithms Multilingual Surges Random forests

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Continual Joint Event Extraction via Adaptive Bias Mixing

Continual Joint Event Extraction via Adaptive Bias Mixing

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IEEE international conference on Big Data and Cloud Computing (BdCloud)

作者： Chengeng Lu Yatian Shen School of Computer and Information Engineering Henan University Kaifeng China

ISBN: (数字)9798331510268

ISBN: (纸本)9798331510275

Continual event extraction is a practical task in natural language processing that requires models to learn quickly from new event types and data sources without forgetting pre-existing knowledge. It is important since language models often encounter new information to which they must efficiently adapt. Current work on continual event extraction either reuses existing parameters to learn from new tasks or statically assigns parameters specific to each new incoming task, incurring significant computational cost while preventing the potential knowledge sharing between tasks. To achieve a balance between the two, in this work, we present a means to adapt the model to incoming tasks in a parameter-efficient manner. We also incorporate metric learning to construct a prototypical network for maximum parameter efficiency. Experimental results on the ACE2005 dataset show that our framework maintains baseline performance with significantly smaller parameter sizes.

关键词： Measurement Deep learning Continuing education Adaptation models Computational modeling Soft sensors natural language processing Data models Computational efficiency Data mining

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Unsupervised word sense disambiguation method based on word vector 2

Unsupervised word sense disambiguation method based on word ...

引用

2nd IEEE international conference on Electronic Technology, Communication and Information, ICETCI 2022

作者： Chen, Yibo Hu, Lan Huang, Xin Zhang, Zuping Information and Communication Branch of State Grid Hunan Electric Power Company Limited Hunan Changsha China School of Computer Science and Engineering Central South University Hunan Changsha China

ISBN: (数字)9781728181158

ISBN: (纸本)9781728181158

Word Sense Disambiguation (WSD) is an open problem in natural language processing which is particularly challenging and useful in the unsupervised setting where all the words in any given text need to be disambiguated without using any labeled data. A disambiguated method was proposed which based on word vector and typical training method such as CBOW and Skip-gram model under sequence training or Negative sampling. The sequence training process and probability of typical models in word2vec are derived. 7 key parameters for training process are discussed about default and optimization. In order to achieve higher accuracy, different corpora are used and the parameters of the training dictionary are adjusted to optimize the results of word disambiguation and improve accuracy and rationality. The results are analyzed and the training model of different parameters and the results of WSD of the corpus are discussed. © 2022 IEEE.

关键词： natural language processing systems

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Live Streaming Data Analysis using Distributed Stochastic Bi-LSTM Model

Live Streaming Data Analysis using Distributed Stochastic Bi...

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2022 international conference on Communication, Computing and Internet of Things, IC3IoT 2022

作者： Kavitha Nair, R. Mathew, Jimsha K Deepika, R. Sharanyaa, S. Therasa, M. Computer Science and Engineering Acharya Institute of Technology Bangalore India Acharya Institute of Technology Information Science and Engineering Bangalore India Computer Science and Engineering Sri Sairam Engineering College Chennai India Panimalar Engineering College Information Technology Chennai India Panimalar Institute of Technology Computer Science and Engineering Chennai India

ISBN: (纸本)9781665479950

In the real world, currently there are more than millions bytes of social media data being created. With the help of modern computational capacity in addition to advanced research in the field of artificial intelligence, a great improvement is shown in natural language processing using deep learning techniques. These AI techniques including machine learning algorithms and deep learning algorithms make it possible to analyze the given tons of data. In addition to it, it also helps us to have a deep dive knowledge in people's mindset towards the current trend in media application. There is another problem of cyber bullying wherein many people start sharing and posting anonymous and fake information to make a significant impact by making riots just as fake heroes. By continuously monitoring these types of online riots will provide a clean and peaceful environment which in turn supports the government in a better way. This article focuses on performing sentiment analysis over a given real time social media data by categorizing it. The effective analysis of live streaming from social media applications is done with word2vec embedding method in natural language processing and trained using bi-directional LSTM model. The performance is compared with the LSTM model, and traditional machine learning techniques like naïve bayes and linear regression model and Bi-LSTM model outperforms the other techniques with 80%-85% accuracy with live streaming unconventional social media data. © 2022 IEEE.

关键词： Learning algorithms

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Backdoor Attack Detecting and Removing Based on knowledge Distillation for natural language Translation Model

Backdoor Attack Detecting and Removing Based on Knowledge Di...

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international conference on Computer and Communications (ICCC)

作者： Mao Chen Lihui Pang Qingyi Tan Yilong Tang Yulang Liu Wenwei Zhang School of Electrical Engineering University of South China Hengyang China Sino-German College of Intelligent Manufacturing Shenzhen Technology University Shenzhen China

The lack of interpretability in Deep Neural Networks makes them susceptible to backdoor attacks. The attacker mixes the poisoned data with triggers into a clean dataset, and uses it to train a backdoor model. This model maintains high accuracy on clean data while outputting the attacker’s desired target for the poisoned data. Due to the serious threat of backdoor attacks on DNN, backdoor defense on DNN is particularly important. In our work, we apply the knowledge distillation method in the visual field to natural language processing. The method of knowledge distillation involves removing toxic data from the poisoned training dataset and restoring the accuracy of the distilled model. In a defensive scenario, one assumption is that the defender can collect clean data without labels. We evaluated the effectiveness of knowledge distillation on strategies through two application scenarios in natural language processing and multiple models. By distilling and fine-tuning to disable backdoors, we further improved the classification accuracy of the distilled models. The experimental results indicate that the method of knowledge distillation can also effectively defend against backdoor attacks in natural language processing.

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