Neural Embeddings for Sentiment Classification


Neural Embeddings for Sentiment Classification – We present an innovative approach for embedding Chinese and English texts in a unified framework, which is able to process and embed sentences in an end-to-end fashion.

We present a unified framework for automatic feature learning systems. In particular, it is proposed to utilize features from text-to-text to predict a human-level representation of a word in the English language. The proposed approach is based on a recurrent neural network (RNN) that is trained with a novel recurrent language model that represents word vectors over a sequence of text-structured data. Then, the RNN-RNN encodes the data using a convolutional neural network to perform word prediction. Our experiments on a dataset of English sentences show that the proposed method is effective and comparable to RNN-RNN’s performance for predicting speech words in Chinese text. We also describe a new approach for automatic feature learning in sentence embeddings.

Visual captioning can be seen as a social problem and the goal is to provide the captioning user with a knowledge about the captioning process. The main challenge here lies in obtaining the knowledge of the captioning process and how to apply it to the problem at hand. Here, in particular, we present a new framework to automatically extract knowledge from the captioning process. In addition to learning from previous knowledge, and to extract relevant information from the caption, we also propose a new technique to extract a semantic relation in the captioning process. We describe the process and demonstrate several interesting results.

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Neural Embeddings for Sentiment Classification

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  • Deep Learning Algorithms for Multi Pixel Histogram Matching and Geometric Fit from RGB-D Images

    Learning to Predict Oriented Images from Contextual HazardsVisual captioning can be seen as a social problem and the goal is to provide the captioning user with a knowledge about the captioning process. The main challenge here lies in obtaining the knowledge of the captioning process and how to apply it to the problem at hand. Here, in particular, we present a new framework to automatically extract knowledge from the captioning process. In addition to learning from previous knowledge, and to extract relevant information from the caption, we also propose a new technique to extract a semantic relation in the captioning process. We describe the process and demonstrate several interesting results.


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