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Author: Admin | 2025-04-28
IntroductionAs a bibliophile who enjoys extracting knowledge from books and articles, I’ve observed how modern reading habits have shifted towards skimming for relevant information amidst information overload. Recently, while reading an article on India’s upcoming tour of Australia, I found myself quickly skimming through the text, focusing mainly on headlines and details about Virat Kohli. This experience prompted me to explore the possibilities in Natural Language Processing (NLP), particularly in relation extraction and summarization. I realized the potential of building an information extraction model using machine learning techniques such as BERT and Bayes for automating data extraction tasks from diverse sources, including medical records. This project not only enhanced my NLP skills but also deepened my understanding of data science and the power of machine learning models in automating complex tasks.Learning OutcomesEvaluate the performance of natural language processing (NLP) models using appropriate metrics such as precision, recall, F1-score, and accuracy.Design and develop advanced NLP pipelines using techniques such as named entity recognition (NER), sentiment analysis, and text summarization.Evaluate the performance of NLP models across various tasks and datasets using standard evaluation metrics.Develop custom NLP solutions using NLTK’s extensive collection of corpora, lexical resources, and algorithms.Apply regular expressions for pattern matching and text search in large datasets and documents.Design and implement information retrieval systems using techniques such as vector space models, term frequency-inverse document frequency (TF-IDF), and document similarity metrics.Develop knowledge base-driven applications for tasks such as question answering, entity recognition, and recommendation systems.Table of contentsIntroductionWhat is Information Extraction?How Does Information
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