Word-Prediction-Ngram

SSH connection daemon for Debian/Raspbian/Ubuntu/etc

NLP tutorials and guidelines to learn efficiently

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 which version of the sentence is the most probable one.

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

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

Testing & learning different nlp and lex techniques

A project featuring the use of various NLP techniques and ML algorithms like the topic modelling and paragraph embeddings, for document clustering. ??

[Tokenization, Topic Modeling, Sentiment Analysis, Network of Bigrams] The purpose of this project is to see if text mining techniques can ease better analysis for categorizing movies with just the Descriptions while ignoring the Genre from the dataset, IMDB_movies.csv, which is stored under the data frame variable, movies_desc. Tokenization (TF-DF) was used to increase efficiency to analyze term frequencies in movie Descriptions so that the conceptual theme of a movie franchise would be determined even if a person has never watched any of the films. Create mixtures of terms that are correlated to every topic and the mixture of topics that distinguishes each document through Topic Modeling in the dataset, IMDB_movies.csv. Sentimental Analysis focused on Movies with Sentimental Clusters that were using bing and NRC lexicons to see how Sent