Stempel port to go.

Program that preprocesses a collection of documents to calculate the frequency of the most common terms and identify the keywords of each document. The first time will do it without using the stemming technique and without removing the stopwords. The second time will use these techniques.

Implementation of some famous machine learning algorithm from scratch

Text mining in Python

Jupyter Notebook for Natural Language Processing with Python. Please refer to the README.md file for the topics covered in this Notebook.

A multi-class classification problem where the objective is to read a question posted on the popular reference website, StackOverflow and predict the primary topics it deals with, i.e. tags which the question will be associated with.

To understand the impact on stock price based on the various news headlines.

Extract text content from an HTML page, process it, and extract unique words from the processed text. This notebook utilizes various text processing techniques including cleaning, normalization, tokenization, lemmatization or stemming, and stop words removal.

Gentle introduction to basic elasticsearch constructs boosting search: ngrams, shingles, stemmers, suggesters and fuzzy queries.

University of Tehran-Artificial Intelligence Spring 2021

Long english text passages are given, a genuine topic is needed to be assigned to the particular text passage. After cleaning the dataset, features were learnt using thidf approach, Linear SVC is used to get the final prediction

Stem words like Lucene (port of Lucene' stemmers to JavaScript)

Natural Language Processing with Hotel Reviews on Booking.com

Natural Language Processing Lab Experiments

1. Explored and prepared the data (Tokenization, Stemming, Stopwords, visualization, etc.) 2. Build a BOW and trained a KNN, Decision Tree, and SVM model 3. Evaluated the above models (confusion matrix, accuracy, classification report, etc.) 4. Used word2vec and build a CNN model 5. Compared the results with all above.

Information Retrieval - Stemming Nazief

Basic text preprocessing operations shown in jupyter notebook. You can play with them and look what are they doing. For stemming and lemmatization there are different options, I showed only what I prefer to use. Repository contains the data to play with taken from kaggle (can also be found here on github), but for convenience I attach it here.

Resources and initiatives for NLP