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Research on Named Entity Recognition Based on Prompt Learning 3

Research on Named Entity Recognition Based on Prompt Learnin...

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3rd international conference on Digital Society and Intelligent Systems, DSInS 2023

作者： Zhou, Qing Yong, Tso Lamao, Dongzhi Cairang, Jiacuo College of Information Science and Technology Tibet University China Key Laboratory of Tibetan Information Technology and Artificial Intelligence of Tibet China Engineering Research Center for Tibetan Language Information Technology under the Ministry of Education Lhasa Tibet850000 China

ISBN: (纸本)9798350331387

Named Entity Recognition is the identification of named entities from a sequence of text. The pre-training language model is widely used in natural language processing tasks, but the named entity recognition task is different from the pre-training model task, so the pre-training model cannot be fully used. This article proposes a named entity recognition method based on prompt learning. Different templates are inserted into the input, and the pre-trained language model is used to transform the named entity recognition problem into a mask prediction problem. The mask prediction ability of the model is utilized to predict different mask positions, compensating for the different effects of the named entity recognition task and the pre-trained model task. External knowledge is integrated into the category mapping. Expanding the range of mapping words to improve recognition rate. Named entity recognition experiments were conducted on two datasets, WNUT16 and WNUT17. The appropriate template and category mapping words led to an F1 score of 68.96% in the WNUT16 dataset, which increased by 9.46 percentage points. The precision and recall rates were 64.42% and 67.72%, respectively. In the WNUT17 dataset, the F1 score was 69.62%, increasing by 9.17 percentage points. The precision and recall rates were 73.39% and 71.51%, respectively. © 2023 IEEE.

关键词： Mapping

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PeQA: A Massive Persian Question-Answering and Chatbot Dataset 12

PeQA: A Massive Persian Question-Answering and Chatbot Datas...

引用

12th international conference on Computer and knowledge engineering, ICCKE 2022

作者： Arshia, Fatemeh Zahra Keyvanrad, Mohammad Ali Sadidpour, Saeedeh Sadat Mohammadi, Sayed Mohammad Reza Malek Ashtar University of Technology Faculty of Electrical Computer Engineering Tehran Iran

ISBN: (数字)9781665476133

ISBN: (纸本)9781665476133

TA question-answering (QA) system is an application able to communicate with humans using natural language processing. Modelling a dialogue between humans and machines is considered one of the most important tasks of Artificial Intelligence (AI). Creating a Chatbot with a good performance in modelling human-machine conversations is still one of the unsolved challenges in this field. Although Chatbots have many applications, in general, they should understand users' meaning through their words and provide them with relevant answers. In the past, Chatbot architectures mainly relied on rules or statistical methods. With the advent of deep learning methods, trainable neural networks soon replaced the traditional models. These sorts of deep models are highly affected by the dataset that would be fed into them, and there is no big enough one available in the Persian language We present a huge dataset of 14 million Persian tweets from tweeter that is meticulously processed to create a rich collection of 420,000 pairs of question-answer data. We also present modelling results on Transformers, including Sensibleness and Specificity Average (SSA) and the BLEU metric. We will release our dataset, modelling code, and models publicly 1.1https://***/mut-deep/PEQA © 2022 IEEE.

关键词： Deep learning

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Incomplete Observations Bias Suppression for Abductive natural language Inference

Incomplete Observations Bias Suppression for Abductive Natur...

引用

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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A Review on Deep Learning Based Question Answering with natural language processing in Healthcare

A Review on Deep Learning Based Question Answering with Natu...

引用

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

作者： Abdul Saleem Javeed C Gopala Krishnan Dept. of Computer Science & Engineering GITAM School of Technology Bengaluru INDIA

ISBN: (数字)9798350359688

ISBN: (纸本)9798350359695

Over the Internet, an efficient approach and promising solution to retrieve significant information envisages the beginning of Question Answering Systems (QAS). Because of data sources availability, the deep learning model based on open domain QAS focuses a substantial quantity of research work. Because of the lack of medical datasets, less attention is received in the medical domain. The deep learning (DL) utility increases the development of huge data in the healthcare domain. In the past years, popularity has grown in hardware parallelism and quick data storage availability. The challenge, pipelines, and tasks involved in medical NLP and medical imaging deploy the existing review of research in this work. The medical NLP and imaging fields deploy DL structures with its extensive survey is presented here. Over all 39 research papers are collected for this survey analysis from the years 2017 to 2023. The diagnosis is enhanced with medical imaging, natural language processing, and DL combination to detect the appropriate combination. Each of the outcomes is shown graphically. The information provided in this survey is quite useful for newcomers carrying out research in medical informatics.

关键词： Deep learning Surveys Reviews Soft sensors Pipelines Medical services Question answering (information retrieval)

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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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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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Few-Shot Table-to-Text Generation with Structural Bias Attention 20th

Few-Shot Table-to-Text Generation with Structural Bias Atten...

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20th Pacific Rim international conference on Artificial Intelligence, PRICAI 2023

作者： Liu, Di Wang, Weihua Bao, Feilong Gaov, Guanglai College of Computer Science Inner Mongolia University Hohhot China National and Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China Inner Mongolia Key Laboratory of Mongolian Information Processing Technology Hohhot China

ISBN: (纸本)9789819970216

Table-to-text generation task refers to converting tabular data into language text to facilitate easier understanding and analysis of the table. Recently, pre-trained models have made significant advancements in this kind of tasks. However, the inherent structural differences between tabular data and text, and the lack of domain-specific knowledge in few-shot datasets, make it challenging for pre-trained models to generate faithful text. To solve these problems, we proposed a framework that encodes tables by obtaining structural bias attention through pruning full self-attention, distinguishing the importance of cells from a structural perspective. We use the pre-trained model with the structural bias framework to the generation component of Prototype-to-Generation. To encourage prototype memory to adhere to the table content and generate more accurate and aligned sentences, we employ Reinforcement Learning. We conducted extensive experiments on three few-shot table datasets. Compared to previous advanced methods, our model achieved superior performance across multiple evaluation metrics. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Reinforcement learning

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Automated Disease Diagnosis Using natural language processing and Deep Learning

Automated Disease Diagnosis Using Natural Language Processin...

引用

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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Enhancing Telugu Abusive language Detection Using Word Embeddings and BERT Models

Enhancing Telugu Abusive Language Detection Using Word Embed...

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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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