Financial sentiment analysis plays a pivotal role in the financial domain. However, the task remains challenging due to the nuanced nature of financial sentiment, the need for high interpretability, and the scarcity o...
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Outcome-based education is currently recommended in every field of education by the accreditation bodies. Outcome-based education is the process of developing the curriculum in a well-defined framework. the stated out...
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In this paper, we present a processing pipeline for transforming natural language annotations in RDF graphs into machine-readable and interoperable semantic annotations. the pipeline uses Named Entity Recognition (NER...
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In view of the problem that TCM diagnosis and treatment data are complex, diverse and lack unified standards, this paper introduced computer informationtechnology, combined with NLP and LSTM models, to improve the in...
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ISBN:
(纸本)9783031882869
In view of the problem that TCM diagnosis and treatment data are complex, diverse and lack unified standards, this paper introduced computer informationtechnology, combined with NLP and LSTM models, to improve the intelligent analysis ability of TCM diagnosis and treatment data, and provide scientific support for clinicians through an auxiliary decision-making system, thereby improving the efficiency and accuracy of diagnosis and treatment. First, this paper collected and integrated a large amount of medical records, treatment records, prescription information and other data from different TCM diagnosis and treatment platforms and medical institutions, standardizes data in different formats, and used natural languageprocessing (NLP) technology for semantic analysis and data cleaning. then, it built a classification and prediction model based on LSTM (Long Short Time Memory) to realize intelligent diagnosis and treatment recommendation generation for common diseases in view of the complex symptoms and individual differences unique to TCM diagnosis and treatment. Finally, based on the previous analysis and model results, an auxiliary decision system was developed to provide doctors with auxiliary decision suggestions in the treatment of complex symptoms and medication regimen recommendations during the diagnosis and treatment process. the results showed that after the use of the intelligent auxiliary decision system, the diagnosis and treatment time reduction rate was between 21.8% and 23.0%, and the accuracy rate reached 88.0%. Compared with traditional manual analysis methods, the efficiency of TCM diagnosis and treatment data processing has been significantly improved after the application of computer informationtechnology. the introduction of computer informationtechnology provides a new solution for the intelligent analysis and decision-making support of TCM diagnosis and treatment data, effectively solving the problems of complex, heterogeneous and insuffici
Large language models (LLMs) based on the Transformer architecture are designed to understand and generate human-like text by learning patterns and relationships from vast amounts of textual data. these models have be...
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Radiology reports contain complex medical terminology and specialized knowledge, making them difficult for both patients and medical professionals to interpret. this study aims to address this challenge by developing ...
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ISBN:
(纸本)9791188428137
Radiology reports contain complex medical terminology and specialized knowledge, making them difficult for both patients and medical professionals to interpret. this study aims to address this challenge by developing a large-scale language model specifically designed for interpreting chest radiology reports. We focus on four key natural languageprocessing (NLP) tasks—summarization, paraphrasing, abbreviation interpretation, and question answering—using a synthetic dataset derived from the MIMIC-CXR reports and GPT-3.5 Turbo. To enhance the model’s performance, we propose a two-stage supervised fine-tuning (SFT) process, incorporating real-world medical data from PubMedQA and MedQA, in addition to the synthetic dataset. the resulting models, Model-1 and Model-2, were evaluated based on accuracy, conciseness, and clarity, using test data not seen during training. Experimental results demonstrated that the proposed two-stage SFT method achieved strong performance across all four tasks, providing comparable performance to models such as GPT-3.5, Bard, Llama2, and MedAlpaca in key evaluation metrics, despite using a relatively smaller number of parameters. these findings suggest that synthetic data, when combined with domain-specific datasets, can significantly improve the interpretive capabilities of large-scale language models in the medical domain. Copyright 2025 Global IT Research Institute (GIRI). All rights reserved.
Large language models (LLMs) have become a promising tool for automating complex tasks such as process model generation from text. In order to evaluate the capabilities of LLMs in generating process models, it is cruc...
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Sarcasm recognition is another aspect of natural languageprocessing (NLP) used for sentiment analysis. It uses mathematical inference to show and categorize a word or phrase’s polarity for sardonic. In literature, s...
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this research designs a system that, withthe help of NLP, would sum up the YouTube video transcripts properly without losing any important information. Day in day out, time keeps on recording increasing numbers of vi...
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this paper investigates Urdu news articles using the natural languageprocessing (NLP) concept graph generation technique. We gather news data (web scraping), do text pre-processing, translate it into Urdu, and calcul...
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ISBN:
(数字)9798331533038
ISBN:
(纸本)9798331533045
this paper investigates Urdu news articles using the natural languageprocessing (NLP) concept graph generation technique. We gather news data (web scraping), do text pre-processing, translate it into Urdu, and calculate the similarity between articles by performing cosine similarities. At last, we present concept graphs (also referred to as conjunction graphs)-relationships between news articles—to visually tell the tale. Our method gives insights into the structure and connections in an empirical news corpus, pointing out significant trends and patterns.
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