Inverted index and Positional index for a set of collection to facilitate Boolean Model of IR. Inverted files and Positional files are the primary data structure to support the efficient determination of which documents contain specified terms and at which proximity.

An NLP project and exploration into semantic spaces.

It is a Turkish BERT-based model that will analyze people's bank complaints and classify them according to one of eight categories.

Ce fut mon prémier projet NLP où j'ai réalisé la détection de spam en utilisant les algorithmes d'embedding pour encorder mes textes. J'ai utilisé Random Forest et Milti-Layres Perceptrons pour la phase de classification. Ce qui a pemit l'obtension des précisions respective de 97% et 98%. J'ai aussi appris à documenter mes codes via sphinx

Toxic Comments Classifier using Doc2vec, Machine Learning and PyQT

Kaggle Competition: DataScience for good - CareerVillage.org

Semantic Classification of OERs with Word Embeddings

Machine learning / Deep learning / Data analysis notebooks, tools and scripts for learning purpose.

Baseline multiclass text classifiers

Short Machine Learning script using Python, Word2Vec and Doc2Vec to train a classifier on a dataset of job titles.

build model on TF-IDF, word2vec, doc2vec, Build a basic RNN

Efrei Paris - NLP Course M1

Addressing Political Bias in News Articles with Multinomial Regression

My submission for the Innoplexus Challenge 2018. It contains code and intermediate files.

word2vec_learn

word embedding with word2vec, doc2vec algorithms on friends tv show corpus/dataset

Data Science Advanced Seminar "Incomplete Network Imbedding"

My submission for Covid-19 challenges in Kaggle.