af011/nlp_projects

Text Encoding and Classification using different types of Encoders

Text Encoding and Classification Project

This is my Text Encoding and Classification Project...! The project demonstrates various encoding techniques, with a built classification model for each technique. These encodings and models can be used for various natural language processing (NLP) tasks. Below, I have provided details about each encoder and its corresponding classification model.

One-Hot Encoding:

• One-Hot Encoding is a simple yet effective way to represent words in a binary format. In this model, each word is assigned a unique index in the vocabulary, and its one-hot encoded vector has a single element set to 1, while all others are 0.

Classification Model:

Bag of Words (BoW):

• BoW represents a document as a vector of word frequencies. It doesn't consider word order but is useful for text classification and information retrieval.

Classification Model: In my BoW project, i used the IMDB Movie Reviews dataset to build a powerful text classification model. It effectively distinguishes between positive and negative sentiment in movie reviews, providing valuable insights for film enthusiasts and critics.

N-grams:

• N-grams represent sequences of adjacent words of a specified length (e.g., unigrams, bigrams, trigrams). They capture local word patterns.

Classification Model: In my N-grams project, I employed a comprehensive dataset of fake reviews to build a robust text classification model, enabling accurate detection of deceptive content and safeguarding the integrity of online platforms.

TF-IDF (Term Frequency-Inverse Document Frequency):

• TF-IDF represents the importance of words in a document relative to a collection of documents. It's often used for information retrieval and document ranking.

Classification Model: Utilizing TF-IDF, my project facilitates the generation of concise summaries based on user-provided text. This feature leverages TF-IDF scores to distill essential information, offering users succinct summaries that encapsulate the main points of their input.

Word Embeddings:

• Word embeddings, such as Word2Vec, GloVe, and FastText, represent words as dense, continuous-valued vectors in a lower-dimensional space. These embeddings capture semantic relationships between words and are used for various NLP tasks.

Classification Model:

The choice of encoding depends on the specific NLP task, dataset, and the properties of the text you are working with. Different encodings may be more suitable for tasks such as text classification, sentiment analysis, named entity recognition, machine translation, and more.