Next word prediction
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Next Word Prediction or what is also called Language Modeling is the task of predicting what word comes next. It is one of the fundamental tasks of NLP and has many applications. You might be using it daily when you write texts or emails without realizing it. You will have help for your education purpose
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Next word Prediction
This document discusses building a next word prediction model using deep learning methods like LSTMs. It will take text as input, preprocess and tokenize the data, then build a deep learning model to predict the next word based on the previous words. Simple word prediction models use n-grams to calculate conditional word probabilities based on occurrence counts from text corpora. Bigram and trigram models are discussed as ways to predict the next word based on the previous one or two words in a sequence.
Text prediction based on Recurrent Neural Network Language Model
The document describes a study on implementing text prediction using recurrent neural network (RNN) and long short-term memory (LSTM) models. It aims to compare standard RNN and LSTM models and see their performance on text prediction. The author implemented RNN and LSTM language models on text from Kafka's Metamorphosis and a dataset of chat posts. LSTM achieved 98.54% accuracy and was more effective than standard RNN for text prediction.
Natural Language Processing in AI
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Language Model (N-Gram).pptx
The document discusses N-gram language models. It explains that an N-gram model predicts the next likely word based on the previous N-1 words. It provides examples of unigrams, bigrams, and trigrams. The document also describes how N-gram models are used to calculate the probability of a sequence of words by breaking it down using the chain rule of probability and conditional probabilities of word pairs. N-gram probabilities are estimated from a large training corpus.
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Next word Prediction
This document discusses building a next word prediction model using deep learning methods like LSTMs. It will take text as input, preprocess and tokenize the data, then build a deep learning model to predict the next word based on the previous words. Simple word prediction models use n-grams to calculate conditional word probabilities based on occurrence counts from text corpora. Bigram and trigram models are discussed as ways to predict the next word based on the previous one or two words in a sequence.
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This document summarizes a lecture on automata theory, specifically discussing non-regular languages, the pumping lemma, and regular expressions. It introduces the language B={0n1n | n ≥ 0} as a non-regular language that cannot be recognized by a DFA. It then proves the pumping lemma theorem and uses it to show that B and the language of strings with equal numbers of 0s and 1s are non-regular. Finally, it defines regular expressions as a way to describe languages and provides examples of regular expressions and their meanings.
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The document discusses N-gram language models. It explains that an N-gram model predicts the next likely word based on the previous N-1 words. It provides examples of unigrams, bigrams, and trigrams. The document also describes how N-gram models are used to calculate the probability of a sequence of words by breaking it down using the chain rule of probability and conditional probabilities of word pairs. N-gram probabilities are estimated from a large training corpus.
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Next word prediction
- 2. What is next word prediction ? Next Word Prediction is the task of predicting what word comes next. Types of next word prediction ? * Manually Input and Distribution * Predict by machine learning
- 3. Manually Input and Distribution Physics like Chemistry love I Statistics s
- 4. Representing the above work Mathematically as conditional probabilities – P ( like | I) = 0.67 = 67% P ( love | I) = 0.33 = 33% P ( Physics | like) = P ( Chemistry | like) = 0.5 = 50% P ( Statistics | love) = 1 = 100%