Use of word embeddings and document similarity to solve word analogy problems
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Updated
Jan 25, 2022 - Python
Use of word embeddings and document similarity to solve word analogy problems
A tool for learning morphological word embeddings
Sentiment analysis using random forest and word-vectorization
Evaluated the word vectors learned from both nce and cross entropy loss functions using word analogy tests
Experiments for my master's thesis about automatic dictionary generation.
Domain Adaptation of Google's pre-trained Word Vectors to mid-19th century English Literature text | COL772 (NLP) @ IIT Delhi
ML, DL & NLP
Pre-trained Word Vector Models of 30+ Languages
Trying the random acts of pizza Kaggle challenge
Utilizing advanced NLP techniques and SentimentIntensityAnalyzer from the NLTK library, this script analyzes Google Play Store app reviews to extract and visualize user sentiments based on pre-defined topics, such as app interface and load time, offering valuable insights into user experience.
Taking a stab at the Quora Question pairs Kaggle Challenge
Refer Readme.md
Word2Vec implementation in tensorflow
word embeddings. Created Date: 12 Feb 2019
Implemented some of the models and techniques learned in NLP to help build systems that help in daily life.
spaCy-compatible tokenizers and word vectors for medical text
Personality plays a vital role in todays world.This uses the Twitter REST API to mine tweets for personality identification. We will use n-grams and word vectors for the hashtags, emoticons and phrases using NLP techniques. We will train the machine to classify the personality types by using a Naive- Bayes Text Classifier and to accurately predi…
Python interface for building, loading, and using GloVe vectors.
A package for reading/writing files containing pre-trained word embeddings and building "embedding matrices".
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