Traditionally,exam preparation involves manually analyzing past question papers to identify and prioritize key topics.This research proposes a data-driven solution to automate this process using techniques like Docume...Traditionally,exam preparation involves manually analyzing past question papers to identify and prioritize key topics.This research proposes a data-driven solution to automate this process using techniques like Document Layout Segmentation,Optical Character Recognition(OCR),and Latent Dirichlet Allocation(LDA)for topic modelling.This study aims to develop a system that utilizes machine learning and topic modelling to identify and rank key topics from historical exam papers,aiding students in efficient exam preparation.The research addresses the difficulty in exam preparation due to the manual and labour-intensive process of analyzing past exam papers to identify and prioritize key topics.This approach is designed to streamline and optimize exam preparation,making it easier for students to focus on the most relevant topics,thereby using their efforts more effectively.The process involves three stages:(i)Document Layout Segmentation and Data Preparation,using deep learning techniques to separate text from non-textual content in past exam papers,(ii)Text Extraction and Processing using OCR to convert images into machine-readable text,and(iii)Topic Modeling with LDA to identify key topics covered in the exams.The research demonstrates the effectiveness of the proposed method in identifying and prioritizing key topics from exam papers.The LDA model successfully extracts relevant themes,aiding students in focusing their study efforts.The research presents a promising approach for optimizing exam preparation.By leveraging machine learning and topic modelling,the system offers a data-driven and efficient solution for students to prioritize their study efforts.Future work includes expanding the dataset size to further enhance model accuracy.Additionally,integration with educational platforms holds potential for personalized recommendations and adaptive learning experiences.展开更多
Sentiment analysis is now more and more important in modern natural language processing,and the sentiment classification is the one of the most popular applications.The crucial part of sentiment classification is feat...Sentiment analysis is now more and more important in modern natural language processing,and the sentiment classification is the one of the most popular applications.The crucial part of sentiment classification is feature extraction.In this paper,two methods for feature extraction,feature selection and feature embedding,are compared.Then Word2Vec is used as an embedding method.In this experiment,Chinese document is used as the corpus,and tree methods are used to get the features of a document:average word vectors,Doc2Vec and weighted average word vectors.After that,these samples are fed to three machine learning algorithms to do the classification,and support vector machine(SVM) has the best result.Finally,the parameters of random forest are analyzed.展开更多
文摘Traditionally,exam preparation involves manually analyzing past question papers to identify and prioritize key topics.This research proposes a data-driven solution to automate this process using techniques like Document Layout Segmentation,Optical Character Recognition(OCR),and Latent Dirichlet Allocation(LDA)for topic modelling.This study aims to develop a system that utilizes machine learning and topic modelling to identify and rank key topics from historical exam papers,aiding students in efficient exam preparation.The research addresses the difficulty in exam preparation due to the manual and labour-intensive process of analyzing past exam papers to identify and prioritize key topics.This approach is designed to streamline and optimize exam preparation,making it easier for students to focus on the most relevant topics,thereby using their efforts more effectively.The process involves three stages:(i)Document Layout Segmentation and Data Preparation,using deep learning techniques to separate text from non-textual content in past exam papers,(ii)Text Extraction and Processing using OCR to convert images into machine-readable text,and(iii)Topic Modeling with LDA to identify key topics covered in the exams.The research demonstrates the effectiveness of the proposed method in identifying and prioritizing key topics from exam papers.The LDA model successfully extracts relevant themes,aiding students in focusing their study efforts.The research presents a promising approach for optimizing exam preparation.By leveraging machine learning and topic modelling,the system offers a data-driven and efficient solution for students to prioritize their study efforts.Future work includes expanding the dataset size to further enhance model accuracy.Additionally,integration with educational platforms holds potential for personalized recommendations and adaptive learning experiences.
基金National Natural Science Foundation of China(No.71331008)
文摘Sentiment analysis is now more and more important in modern natural language processing,and the sentiment classification is the one of the most popular applications.The crucial part of sentiment classification is feature extraction.In this paper,two methods for feature extraction,feature selection and feature embedding,are compared.Then Word2Vec is used as an embedding method.In this experiment,Chinese document is used as the corpus,and tree methods are used to get the features of a document:average word vectors,Doc2Vec and weighted average word vectors.After that,these samples are fed to three machine learning algorithms to do the classification,and support vector machine(SVM) has the best result.Finally,the parameters of random forest are analyzed.
