Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 24 for “"Pre-trained Language Models"”.
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Pre-trained Language Models for Clinical Systematic Literature Reviews
… two datasets to benchmark the performance of Pretrained Language Model (PLM) based entity and relation extraction models as well as the effect of domain specific pre-training prior to their fine-tuning. Our results show evidence to the effectiveness of pre-training using masked language …
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Knowledge representation and behavior understanding with pre-trained language models
Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2024-09-16 without embargo terms
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A Case for Pre-trained Language Models in Systems Engineering
… are immensely complex. Extensive sets of natural language requirements guide the development of such systems. As such, tools to assist system engineers in managing and extracting information from these requirements must also scale to match the complexity of these systems. However, the systems …
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Pairwise embedding for event coreference resolution
… in information extraction research and natural language understanding areas. Recently, the pre-trained language models emerging in modern Natural Language Processing (NLP) community provide a new perspective of solving classical NLP tasks. This thesis presents a novel, extensible, and …
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Information Retrieval with Dense and Sparse Representations
… systems, relies on effective textual representation and semantic matching. However, current approaches can lose nuanced lexical detail information due to an information bottleneck in dense retrieval, or rely on exact lexical matching and thus overlook the broader contextual relevance …
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Neural Sequence Modeling for Domain-Specific Language Processing: A Systematic Approach
… enormous open access online data, large-scale pre-trained language models have shown great modeling and generalization capacity for sequential data. However, not all domains benefit equally from the rapid development of neural sequence modeling. Domains like healthcare and software engineering …
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Automated structuring of text space with minimal supervision
… Recent advances in deep learning and large pre-trained language models have made great progress in natural language understanding. However, we still face some major challenges: (1) text classification still relies on substantial amount of labeled training data; (2) most current text …
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Post-processing Techniques for Word Embedding
… has been a significant breakthrough in natural language processing (NLP). Although word representation has improved remarkably and resulted in better performance in downstream NLP applications, interpretability of word embeddings remains a challenge. Post-processing techniques have been …
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Efficient Knowledge Transfer and Adaptation for Speech and Beyond
… and multimodal modeling. First, we provide a comprehensive framework for class-incremental spoken language understanding, allowing models to incrementally learn new intents and entities while retaining previously acquired knowledge. Using knowledge distillation and rehearsal-based strategies, we …
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Intelligent dialog agent modeling in human-centered artificial intelligence applications
… have been integral in which ways humans and AI models can interact across multiple domains such as healthcare, customer service, and education. With the recent advancements in deep learning and pre-trained language models (PLMs), dialog systems have shown impressive performance in various …
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A Study on the Application of Natural Language Processing Methods to Scientific Text
… papers published in journals or deposited in preprint servers each day makes it difficult for scientists to stay on top of their respective areas of study, leading to a state of "information overload''. This trend is accelerating, with the total number of published papers climbing by ~9% …
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Beyond pre-training: continual learning and hallucinations in transformer-based language models.
Pre-trained transformer models have become the norm for various language modelling tasks from document similarity analysis and text classification to natural language generation. Despite the impressive performance on benchmark datasets, adopting pretrained models for real-world applications often …
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Self-supervision strategies for post-ASR error correction in clinical domain.
… a promising solution by transcribing spoken language into written text, enabling clinicians to dictate notes in real-time. However, using ASR systems in medical environments remains challenging due to the need for high accuracy in a critical domain filled with specialised terminology. Despite …
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Deep learning applied to the assessment of online student programming exercises
… provision of qualitative feedback. Four tasks: language modeling, detecting idiomatic code, semantic code search, and predicting variable names are considered in detail. First, deep learning models are applied to the task of language modeling source code. A comparison is made between the …
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Exploring Loss Functions in Machine Learning
… evaluating, and optimizing machine learning models, directly impacting their effectiveness and efficiency in solving specific tasks. We explore three new loss functions and their applications. Softmax Cross-Entropy Loss, stands as a prevalent choice in neural network classification tasks. It …
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Response Generation in Longitudinal Dialogues
… or slot-filling tasks with stereotypical user models "\textit{averaged}" among users. In contrast, the level of personalization in LDs is beyond a set of personal preferences and can not be learned from a limited set of persona statements. Advancement in human evaluation is another required …
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TOWARDS AUTOMATED CONTRACT ANALYSIS: APPLYING LANGUAGE MODELS TO RISK IDENTIFICATION IN THE CONTEXT OF PUBLIC-PRIVATE PARTNERSHIPS
… and legal disputes for contracting parties. Previous research has extensively examined the identification and allocation of project risks between contracting parties, predominantly employing questionnaire surveys, interviews, or content analysis methods. These studies depict common practices …
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Using Principles from Cognitive Science to Analyze and Guide Language-Related Neural Networks
Natural language, while central to human experience, is not uniquely the domain of humans. AI systems, typically neural networks, exhibit startling language processing capabilities from generating plausible text to modeling simplified language evolution. To what extent are such AI models learning …
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Diagnosis for Patient and GP: Dialogue-based Self-Diagnosis with Disease-Symptoms Graph and Referral Letter Classification
… chal- lenges, more so after the exigencies precipitated by the COVID-19 pandemic, central to which is the pervasive shortage of medi- cal resources at each health system level. This study explores two strategies aimed at alleviating these pressures by harness- ing the potential of artificial …
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Interpretable Multi-hop Question Answering
… of information retrieval in the field of Natural Language Processing, aims to build a system to answer natural language questions posed by humans. Since QA tasks can be used to quantify the understanding and reasoning ability of intelligent systems, a large number of QA datasets have been …
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