CS224d Project Final Report

CS224D Project Final Report Summarizing Reviews and Predicting Rating for Yelp Dataset

The report explores the use of Recurrent Neural Networks (RNN) and Convolutional Neural Networks (CNN) in summarising text reviews and predicting review rating for the Yelp dataset. I use the fact that the reviews are labelled (by rating) to extract important sentences/words, which are then used as the summary for the review. I use an interesting evaluation technique to measure the relevance of...

Author Attribution with CNN’s

In this report, the results from my CS224D final project are given and explained. The project was based on the application of relatively new neural network architectures, namely convolutional neural networks over word embeddings, to the task of authorship identification. The problem was posed as a classification task and models were evaluated over two datasets, a baseline of my own collection a...

CS224D Final Report: Deep Recurrent Attention Networks for LATEX to Source

For our project, we wanted to explore the problem of recognizing LTEX expressions and translating them to source using a deep neural network. Previous work involving attention models have improved sequence to sequence mappings and greatly helped in digit recognition. Inspired by these previous work, we implemented an attention model to recognize simple LTEX expressions and also tested it on a s...

Extensions to Tree-Recursive Neural Networks for Natural Language Inference

Understanding textual entailment and contradiction is considered fundamental to natural language understanding. Tree-recursive neural networks, which exploit valuable syntactic parse information, achieve state-of-the-art accuracy among pure sentence encoding models for this task. In this course project for CS224D, we explore two extensions to tree-recursive neural networks deep TreeLSTMs and at...

Document Title : GIAVAP – final report final PROJECT FINAL REPORT