Natural Language Processing (NLP) & Text Mining Tutorial Using NLTK | NLP Tra...Edureka!
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The document outlines a certification course on Natural Language Processing (NLP) with Python, covering topics such as text mining, NLP applications, and components like natural language understanding and generation. It discusses the importance of tokenization, stemming, lemmatization, parts of speech, and named entity recognition. The course aims to provide skills for deriving meaningful information from text and understanding human language through NLP techniques.
The document discusses how to collaborate using huggingface datasets. It introduces huggingface datasets and explains why data collaboration is needed for ML/DL projects. It then covers uploading data to the huggingface hub, including creating a repository, and the three methods of uploading - uploading the script only, uploading the dataset only, or uploading both. The document also provides guidance on writing dataset scripts, including defining configurations, metadata, and the required classes.
This document provides an overview of the Natural Language Toolkit (NLTK), a Python library for natural language processing. It discusses NLTK's modules for common NLP tasks like tokenization, part-of-speech tagging, parsing, and classification. It also describes how NLTK can be used to analyze text corpora, frequency distributions, collocations and concordances. Key functions of NLTK include tokenizing text, accessing annotated corpora, analyzing word frequencies, part-of-speech tagging, and shallow parsing.
The document provides an overview of WordNet, a lexical database for the English language that organizes words based on their meanings and relationships rather than forms. It discusses WordNet's international relevance, its structural design, and major lexical relations like synonymy, polysemy, and antonymy. Additionally, it highlights its applications in various fields such as information retrieval, machine translation, and automatic text classification.
Natural Language Processing (NLP) & Text Mining Tutorial Using NLTK | NLP Tra...Edureka!
?
The document outlines a certification course on Natural Language Processing (NLP) with Python, covering topics such as text mining, NLP applications, and components like natural language understanding and generation. It discusses the importance of tokenization, stemming, lemmatization, parts of speech, and named entity recognition. The course aims to provide skills for deriving meaningful information from text and understanding human language through NLP techniques.
The document discusses how to collaborate using huggingface datasets. It introduces huggingface datasets and explains why data collaboration is needed for ML/DL projects. It then covers uploading data to the huggingface hub, including creating a repository, and the three methods of uploading - uploading the script only, uploading the dataset only, or uploading both. The document also provides guidance on writing dataset scripts, including defining configurations, metadata, and the required classes.
This document provides an overview of the Natural Language Toolkit (NLTK), a Python library for natural language processing. It discusses NLTK's modules for common NLP tasks like tokenization, part-of-speech tagging, parsing, and classification. It also describes how NLTK can be used to analyze text corpora, frequency distributions, collocations and concordances. Key functions of NLTK include tokenizing text, accessing annotated corpora, analyzing word frequencies, part-of-speech tagging, and shallow parsing.
The document provides an overview of WordNet, a lexical database for the English language that organizes words based on their meanings and relationships rather than forms. It discusses WordNet's international relevance, its structural design, and major lexical relations like synonymy, polysemy, and antonymy. Additionally, it highlights its applications in various fields such as information retrieval, machine translation, and automatic text classification.
ChatGPT is a natural language processing technology developed by OpenAI. This model is based on the GPT-3 architecture and can be applied to various language tasks by training on large-scale datasets. When applied to a search engine, ChatGPT enables the implementation of an AI-based conversational system that understands user questions or queries and provides relevant information.
ChatGPT takes user questions as input and generates appropriate responses based on them. Since this model considers the context of previous conversations, it can provide more natural dialogue. Moreover, ChatGPT has been trained on diverse information from the internet, allowing it to provide practical and accurate answers to user questions.
When applying ChatGPT to a search engine, the system searches for relevant information based on the user's search query and uses ChatGPT to generate answers to present along with the search results. To do this, the search engine provides an interface that connects with ChatGPT, allowing the user's questions to be passed to the model and the answers generated by the model to be presented alongside the search results.