Build unigram and bigram language models, implement Laplace smoothing and use the models to compute the perplexity of test corpora.

Typing Assistant provides the ability to autocomplete words and suggests predictions for the next word. This makes typing faster, more intelligent and reduces effort.

Next Word Prediction using n-gram Probabilistic Model with various Smoothing Techniques

This project is an auto-filling text program implemented in Python using N-gram models. The program suggests the next word based on the input given by the user. It utilizes N-gram models, specifically Trigrams and Bigrams, to generate predictions.

Opinion mining for provided data from various NLTK corpus to test/enhance the accuracy of the NaiveBayesClassifier model.

Data from a corpus of written Hawaiian

Jupyter Notebook for Natural Language Processing learning

A Bigram Language Model from scratch with no-smoothing and add-one smoothing. Outputs bigram counts, bigram probabilities and probability of test sentence.

NLP tutorials and guidelines to learn efficiently

(UNMAINTAINED)Fetch comments from the given video and determine sentiment towards the video is positive or negative

An Implementation of Bigram Anchor Words algorithm

The goal of this script is to implement three langauge models to perform sentence completion, i.e. given a sentence with a missing word to choose the correct one from a list of candidate words. The way to use a language model for this problem is to consider a possible candidate word for the sentence at a time and then ask the language model whic…

Using distibuctional semantics (word2vec family algorithms and the CADE framework) to learn word embeddings from the Italian literary corpuses we generated.

It's a python based n-gram langauage model which calculates bigrams, probability and smooth probability (laplace) of a sentence using bi-gram and perplexity of the model.

A Go n-gram indexer for natural language processing with modular tokenizers and data stores

Classe responsável por simplificar o processo de criação de um modelo Doc2Vec (gensim) com facilitadores para geração de um vocab personalizado e com a geração de arquivos de curadoria. Dicas usando elasticsearch e singlestore.

Performance evaluation of sentiment classification on movie reviews

Predicting next word with Natural Language Processing. Being able to predict what word comes next in a sentence is crucial when writing on portable devices that don't have a full size keyboard. However the same techniques used in texting application can be applied to a variety of other applications, for example: genomics by segmenting DNA, seque…

Sentiment Analysis / Opinion Mining for provided data in NLTK corpus using NaiveBayesClassifier Algorithm

A text mining analysis about requests to information access to São Paulo municipality in 2018