Typing Assistant provides the ability to autocomplete words and suggests predictions for the next word. This makes typing faster, more intelligent and reduces effort.
Right now the tokenize() function is splitting whenever a ' . ' character is found. Most of the time it's a correct approach to split a fine into sentences but sometimes the abbreviation like Dr., Mr., Mrs, etc. appear in a middle of a sentence and hence splits the sentence right there. I want to enhance the regex to not to spit the sentences on abbreviations.
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.
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, sequences speech recognition, automatic language translation or even as one student in the course suggested music sequence prediction.
Este é um programa de inteligência artificial simples para prever a próxima palavra baseada em uma string informado usando bigramas e trigramas baseados em um arquivo .txt. Existem dois códigos, um usando console e outro usando o tkinter.
Right now the tokenize() function is splitting whenever a ' . ' character is found. Most of the time it's a correct approach to split a fine into sentences but sometimes the abbreviation like Dr., Mr., Mrs, etc. appear in a middle of a sentence and hence splits the sentence right there. I want to enhance the regex to not to spit the sentences on abbreviations.