Tag: #TextMining
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Unveiling the Power of Word Embeddings with Gensim
In the realm of Natural Language Processing (NLP), word embeddings have emerged as a game-changer. Unlike traditional approaches that use words as features, word embeddings leverage dense, low-dimensional vectors to capture the meaning and usage of a word. One pioneering model in this domain is Word2Vec, developed by Thomas Mikolov and team at Google. In…
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Enhancing Sentiment Analysis with ELMo Embeddings: A TensorFlow Experiment
Introduction Natural Language Processing (NLP) has witnessed a significant boost with the advent of transfer learning. In this blog post, we explore ELMo Embeddings, a cutting-edge approach to word embeddings, leveraging a large unlabelled text corpus for enhanced sentiment analysis. We’ll delve into the implementation using TensorFlow and TensorFlow Hub. Preparation Let’s start by setting…
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Visualizing NLP with Pretrained Models – spaCy and StanfordNLP
Natural Language Processing (NLP) is a crucial aspect of understanding and processing human language using computational methods. In this tutorial, we will explore two popular NLP libraries – spaCy and StanfordNLP – and demonstrate their capabilities using pretrained models. spaCy – English NLP Let’s start with spaCy and an English example. We’ll use a snippet…
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Exploring Named Entity Recognition with Conditional Random Fields
Named Entity Recognition (NER) is a fundamental task in natural language processing that involves identifying and classifying entities, such as names of people, organizations, and locations, within a text. NER plays a crucial role in various applications, including information retrieval, question answering, and text summarization. In this blog post, we’ll dive into the world of…
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A Deep Dive into Text Classification with TF-IDF
Introduction: Unlocking the potential within textual data is a rewarding journey, and text classification, a cornerstone of Natural Language Processing (NLP), stands as a beacon in this exploration. In this blog post, we delve into the intricacies of text classification using Python, Pandas, NLTK, and scikit-learn. Our practical example revolves around travel and food-related sentences,…