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Unlocking Transfer Learning's Potential in natural language processing: An Extensive Investigation and Evaluation

Unlocking Transfer Learning's Potential in Natural Language ...

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2024 international conference on Advances in Computing Research on Science engineering and Technology, ACROSET 2024

作者： Banerjee, Pradipta Kashyap, Swati Chandigarh University Department of Computer Science Engineering Mohali India

ISBN: (纸本)9798350388800

With the advent of transfer learning approaches, natural language processing (NLP) problems have experienced tremendous progress, as demonstrated by models such as Generative Pre-trained Transformers (GPT) and Bidirectional Encoder Representations from Transformers (BERT). The usefulness of such transfer learning strategies across a range of NLP tasks and domains is investigated in this work. The study uses a methodical methodology to assess BERT and GPT's performance on a wide range of tasks. In addition, the study evaluates the generalizability and flexibility of these models across a broad variety of disciplines, including social media, finance, legal, and biological literature. The study's methodology entails rigorous assessment utilizing task-specific standard metrics after pre-trained BERT and GPT models have been fine-tuned using task-specific datasets. To determine the relative benefits and drawbacks of transfer learning strategies in various contexts, comparative studies are carried out against baseline models and other cutting-edge methodologies. Additionally, the study looks at how the performance of BERT and GPT is affected by variables including task difficulty, dataset size, and domain specificity. The results provide a comprehensive understanding of the benefits and drawbacks of transfer learning strategies in a variety of NLP tasks and domains. While BERT performs admirably on tests requiring semantic comprehension and contextual knowledge, GPT is superior at producing text that is both cohesive and appropriate to the situation. Both models, however, show sensitivity to dataset features and idiosyncrasies unique to the domain, indicating the necessity for customized fine-tuning techniques for best results. All things considered, this study advances our knowledge of the usefulness and efficiency of transfer learning strategies and provides insightful information for academics and practitioners who want to use BERT, GPT, and related models in a variety

关键词： Semantics

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A natural language processing Approach to the Translation of Speech into Indian Sign language 2

A Natural Language Processing Approach to the Translation of...

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2nd international conference on Distributed Computing and Optimization Techniques, ICDCOT 2024

作者： Vaishnavi, Katta Akbar, Mohammad Fazlul Balaji, Tedla Reddy, Kesavarapu Vivek Rani, Maddu Sudha Department of Information Technology Seshadri Rao Gudlavalleru Engineering College Andhra Pradesh India

ISBN: (纸本)9798350382952

Communication is a critical aspect of every individual's interaction, and individuals typically exchange information in a variety of languages. However, individuals with hearing and speech impairments may encounter difficulties in communicating on par with the general population. Additionally, hearing-impaired individuals comprehend our thoughts through the use of sign language. Research indicates that acquiring sign language skills facilitates the comprehension of lip-reading as well as one's mother tongue. This is accurate for both literate and illiterate individuals throughout India. The approach involves converting speech into written text, which is going to go through text preprocessing utilizing natural language processing (NLP) techniques to enhance analysis. Text and voice input are accepted by the system, which compares these with the clips in the authors' database. It displays matching sign motions based on Indian Sign language grammatical rules as a result if they match;if not, it proceeds through the lemmatization and tokenization stages. natural language processing, which powers part-of-speech tagging, parsing, lemmatization, and tokenization, is the system's central component. An accuracy of 92% is achieved in the proposed strategy. © 2024 IEEE.

关键词： Audition

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Exploring the Application of Large language Models in Spoken language Understanding Tasks 2

Exploring the Application of Large Language Models in Spoken...

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2nd IEEE international conference on Sensors, Electronics and Computer engineering, ICSECE 2024

作者： Li, Mingjie Taiyuan University of Science and Technology Taiyuan China

ISBN: (纸本)9798350373646

This paper explores the field of Spoken language Understanding (SLU), a subdomain of natural language processing (NLP). SLU focuses on the processing and comprehension of spoken or voice input, aiming to extract meaningful information from user utterances. A key component of SLU is Automatic Speech Recognition (ASR), which converts spoken language into textual form. The paper outlines the objectives and significance of SLU, emphasizing its ability to enable computers to comprehend and act upon spoken instructions. By integrating ASR and subsequent language understanding techniques, SLU systems facilitate the development of more intuitive and efficient human-computer interactions. The paper provides a brief overview of the key techniques and components involved in SLU, highlighting the importance of accurate speech recognition and effective language parsing in achieving the desired outcomes. © 2024 IEEE.

关键词： Speech recognition

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Construction of a Biomedical knowledge Graph using natural language processing

Construction of a Biomedical Knowledge Graph using Natural L...

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2024 IEEE international conference on Emerging Technologies in Computer Science for Interdisciplinary Applications, ICETCS 2024

作者： Balachandra, Anumaneni Venkat Deepamala, N. Shobha, G. R.V. College of Engineering Department of CSE Bengaluru India

ISBN: (纸本)9798350372502

Biomedical Named Entity Linking (NEL) plays a crucial role in extracting valuable information from text for downstream tasks in bioinformatics. However, challenges like entity ambiguity and complex relationships hinder accurate entity linking. We demonstrate the effectiveness of BERN2, a novel neural network architecture designed to address these challenges and improve NEL performance, by linking entities to a knowledge graph stored in a Neo4j graph database. This combination allows for efficient exploration of intricate relationships between entities, facilitating advanced biomedical research. Furthermore, we showcase the utility of leveraging node embedding techniques on the knowledge graph for various machine-learning applications in the biomedical domain. Our findings suggest that BERN2, coupled with knowledge graphs and node embeddings, can significantly enhance biomedical information extraction and empower researchers with powerful tools for collaborative healthcare research and accelerated discoveries. © 2024 IEEE.

关键词： knowledge graph

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Automatic Identification Algorithm for Semantic Relations of Chinese natural language Entities

Automatic Identification Algorithm for Semantic Relations of...

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2022 IEEE international conference on knowledge engineering and Communication Systems, ICKES 2022

作者： Xinle, Chen Wenzhou Business College Zhejiang province 325035 China

ISBN: (纸本)9781665456371

Semantic parsing, in a sense, is a formal language process that translates natural language into computer recognizable language. Therefore, semantic parsing can be treated as a special machine translation problem. Modern Chinese semantic research oriented to natural language processing, on the one hand, can deepen our understanding of the nature and characteristics of Chinese, on the other hand, can provide valuable semantic resources for computer processing of Chinese. This research strives to make breakthroughs in both theory and practice. Based on the principle of serving natural language processing, it constructs a Chinese sentence semantic knowledge system, and on this basis, it conducts theoretical research and practical exploration on the construction of Chinese sentence semantic knowledge base, which can not only provide a useful knowledge system for Chinese information processing, but also provide a reference for the construction of large-scale Chinese sentence semantic knowledge base in the next stage. © 2022 IEEE.

关键词： Semantics

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TransERR: Translation-based knowledge Graph Embedding via Efficient Relation Rotation 30

TransERR: Translation-based Knowledge Graph Embedding via Ef...

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Joint 30th international conference on Computational Linguistics and 14th international conference on language Resources and Evaluation, LREC-COLING 2024

作者： Li, Jiang Su, Xiangdong Zhang, Fujun Gao, Guanglai College of Computer Science Inner Mongolia University Hohhot China National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian China Inner Mongolia Key Laboratory of Mongolian Information Processing Technology China

ISBN: (纸本)9782493814104

This paper presents a translation-based knowledge geraph embedding method via efficient relation rotation (TransERR), a straightforward yet effective alternative to traditional translation-based knowledge graph embedding models. Different from the previous translation-based models, TransERR encodes knowledge graphs in the hypercomplex-valued space, thus enabling it to possess a higher degree of translation freedom in mining latent information between the head and tail entities. To further minimize the translation distance, TransERR adaptively rotates the head entity and the tail entity with their corresponding unit quaternions, which are learnable in model training. We also provide mathematical proofs to demonstrate the ability of TransERR in modeling various relation patterns, including symmetry, antisymmetry, inversion, composition, and subrelation patterns. The experiments on 10 benchmark datasets validate the effectiveness and the generalization of TransERR. The results also indicate that TransERR can better encode large-scale datasets with fewer parameters than the previous translation-based models. Our code and datasets are available at https://***/dellixx/TransERR. © 2024 ELRA language Resource Association: CC BY-NC 4.0.

关键词： knowledge graph

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Can Large language Models Understand DL-Lite Ontologies? An Empirical Study

Can Large Language Models Understand DL-Lite Ontologies? An ...

引用

2024 conference on Empirical Methods in natural language processing, EMNLP 2024

作者： Wang, Keyu Qi, Guilin Li, Jiaqi Zhai, Songlin School of Computer Science and Engineering Southeast University Nanjing China Ministry of Education China

ISBN: (纸本)9798891761681

Large language models (LLMs) have shown significant achievements in solving a wide range of tasks. Recently, LLMs' capability to store, retrieve and infer with symbolic knowledge has drawn a great deal of attention, showing their potential to understand structured information. However, it is not yet known whether LLMs can understand Description Logic (DL) ontologies. In this work, we empirically analyze the LLMs' capability of understanding DL-Lite ontologies covering 6 representative tasks from syntactic and semantic aspects. With extensive experiments, we demonstrate both the effectiveness and limitations of LLMs in understanding DL-Lite ontologies. We find that LLMs can understand formal syntax and model-theoretic semantics of concepts and roles quite well. However, LLMs struggle with understanding TBox NI (Negative Inclusion) transitivity and handling ontologies with large ABoxes. We hope that our experiments and analyses provide more insights into LLMs and inspire to build more faithful knowledge engineering solutions. © 2024 Association for Computational Linguistics.

关键词： Computational linguistics

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ENRICHING MUSIC DESCRIPTIONS WITH A FINETUNED-LLM AND METADATA FOR TEXT-TO-MUSIC RETRIEVAL 49

ENRICHING MUSIC DESCRIPTIONS WITH A FINETUNED-LLM AND METADA...

引用

49th IEEE international conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Doh, SeungHeon Lee, TMinhee Jeong, Dasaem Nam, Vuhan Korea Adv Inst Sci & Technol Grad Sch Culture Technol Daejeon South Korea Sogang Univ Dept Comp Sci & Engn Seoul South Korea Sogang Univ Dept Art & Technol Seoul South Korea

ISBN: (纸本)9798350344868;9798350344851

Text-to-Music Retrieval, finding music based on a given natural language query, plays a pivotal role in content discovery within extensive music databases. To address this challenge, prior research has predominantly focused on a joint embedding of music audio and text, utilizing it to retrieve music tracks that exactly match descriptive queries related to musical attributes (i.e. genre, instrument) and contextual elements (i.e. mood, theme). However, users also articulate a need to explore music that shares similarities with their favorite tracks or artists, such as I need a similar track to Superstition by Stevie Wonder. To address these concerns, this paper proposes an improved Text-to-Music Retrieval model, denoted as TTMR++, which utilizes rich text descriptions generated with a finetuned large language model and metadata. To accomplish this, we obtained various types of seed text from several existing music tag and caption datasets and a knowledge graph dataset of artists and tracks. The experimental results show the effectiveness of TTMR++ in comparison to state-of-the-art music-text joint embedding models through a comprehensive evaluation involving various musical text queries. (1)

关键词： Music Informational Retrieval Text-to-Music Retrieval Large language Model

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Augmenting Content Retrieval Using NLP in AIML 4

Augmenting Content Retrieval Using NLP in AIML

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4th international conference on Advancement in Electronics and Communication engineering, AECE 2024

作者： Shrivastava, Kapil Quezada, Angeles Sharma, Karuna Marquez Lobato, Bogart Yail Vashishth, Nitish Gla University Department of Computer Engineering & Applications UP Mathura India Tijuana Baja California México Computer Science & Engineering Sdgi Global University Tecnológico Nacional de México Campus UP Ghaziabad India Computer Science & Engineering Sdgi Global University UP Ghaziabad India Electronics & Communication Engineering Raj Kumar Goel Institute of Technology UP Ghaziabad India

ISBN: (纸本)9798350364729

The paper Augmenting Content Retrieval Using NLP in AIML describes using natural language processing (NLP) with Artificial Intelligence Markup language (AIML) to enhance content retrieval. Among Markup languages, AIML is most commonly used in chatbots or conversational UI. On the other hand, NLP is a subfield of artificial intelligence that handles reading and understanding human language. Their abstract underscores the importance of improved content extraction for AIML systems that rely on a keyword-based approach, which wouldn't work very well as it fails to capture all elements in human language. Here, wide-scale use of the NLP techniques provides a breakthrough in the analysis. It allows understanding the meaning inherent in natural language and interpreting semanticity through context-sensitive, syntactic rule-based arrangement. The paper proposes a mix of NLP and AIML for better retrieval. Approach: In this technique, we first preprocess the user input using NLP techniques, i.e., POS tagging & Named Entity Recognition, to extract context-specific Keywords/Concepts. Then, these keywords and concepts are mapped with answers in the AIML-based system. The abstract also presents the experimental evaluation of the proposed strategy, which significantly outperforms conventional keyword-based retrieval for content. This proves that applying NLP in AIML systems can even increase the capability to understand and recover user inputs. © 2024 IEEE.

关键词： knowledge representation

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Intelligent Agents with LLM-based Process Automation 24

Intelligent Agents with LLM-based Process Automation

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30th ACM SIGKDD conference on knowledge Discovery and Data Mining

作者： Guan, Yanchu Wang, Dong Chu, Zhixuan Wang, Shiyu Ni, Feiyue Song, Ruihua Zhuang, Chenyi Ant Grp Hangzhou Peoples R China Zhejiang Univ Hangzhou Peoples R China Renmin Univ China Beijing Peoples R China

ISBN: (纸本)9798400704901

While intelligent virtual assistants like Siri, Alexa, and Google Assistant have become ubiquitous in modern life, they still face limitations in their ability to follow multi-step instructions and accomplish complex goals articulated in natural language. However, recent breakthroughs in large language models (LLMs) show promise for overcoming existing barriers by enhancing natural language processing and reasoning capabilities. Though promising, applying LLMs to create more advanced virtual assistants still faces challenges like ensuring robust performance and handling variability in real-world user commands. This paper proposes a novel LLM-based virtual assistant that can automatically perform multi-step operations within mobile apps based on high-level user requests. The system represents an advance in assistants by providing an end-to-end solution for parsing instructions, reasoning about goals, and executing actions. LLM-based Process Automation (LLMPA) has modules for decomposing instructions, generating descriptions, detecting interface elements, predicting next actions, and error checking. Experiments demonstrate the system completing complex mobile operation tasks in Alipay based on natural language instructions. This showcases how large language models can enable automated assistants to accomplish real-world tasks. The main contributions are the novel LLMPA architecture optimized for app process automation, the methodology for applying LLMs to mobile apps, and demonstrations of multi-step task completion in a real-world environment. Notably, this work represents the first real-world deployment and extensive evaluation of a large language model-based virtual assistant in a widely used mobile application with an enormous user base numbering in the hundreds of millions.

关键词： Intelligent Virtual Assistants Agents Large language Models Process Automation Explainable

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