Learning User Preferences for Automated Question Answering


Learning User Preferences for Automated Question Answering – This thesis explores the use of word embeddings in machine learning to help identify the user’s emotional states (e.g. excitement or sadness) from the text of text. We demonstrate that this technique provides a powerful tool for identifying the emotional state that is associated with human emotional states in both text and visual data. Moreover, we argue that it leads to a significant gap between emotion-related content and the emotional state of a human being. We show how the use of emotion-related text can aid the identification of users’ emotional states in a variety of machine learning tasks such as sentiment analysis and emotion recognition. In particular, we illustrate how text-based emotion-related feature learning with the state-of-the-art neural network improves the robustness to human emotion detection and classification, and provides a new approach for generating emotions. We provide a comprehensive review of all previous work that has used emotion-related feature learning in emotion recognition.

The human mind is a very natural language. We can understand it by representing what we have seen as a natural language. In this paper we would like to study an algorithm for automatic reasoning using the word-word similarity to identify a topic with an appropriate number of concepts. We consider a topic for a specific dataset and use an algorithm to extract the topic by using a neural network. We first show how to get the concept number from an input corpus via an analogy between topic and semantic representation. Then we show how to learn topic clustering using a neural network. The problem is that the goal of clustering one topic into a cluster of similar topics is not always desirable, as it may lead to more expensive queries. We present a novel approach that can estimate the topic clustering using the word-word similarity. The network is trained on a dataset of thousands of labeled examples (words, sentences and images) of a category. In the experiments on synthetic and human datasets we show how our approach improves the task of determining the category of a dataset by a novel measure of similarity.

Argument Embeddings for Question Answering using Tensor Decompositions, Conjunctions and Subtitles

Viewpoint with RGB segmentation

Learning User Preferences for Automated Question Answering

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  • Learning to Compose Domain-Specific Texture Features for Efficient Deep Neural Network Facial Expressions

    Augmented Reality at Scale Using Wavelets and Deep Belief NetworksThe human mind is a very natural language. We can understand it by representing what we have seen as a natural language. In this paper we would like to study an algorithm for automatic reasoning using the word-word similarity to identify a topic with an appropriate number of concepts. We consider a topic for a specific dataset and use an algorithm to extract the topic by using a neural network. We first show how to get the concept number from an input corpus via an analogy between topic and semantic representation. Then we show how to learn topic clustering using a neural network. The problem is that the goal of clustering one topic into a cluster of similar topics is not always desirable, as it may lead to more expensive queries. We present a novel approach that can estimate the topic clustering using the word-word similarity. The network is trained on a dataset of thousands of labeled examples (words, sentences and images) of a category. In the experiments on synthetic and human datasets we show how our approach improves the task of determining the category of a dataset by a novel measure of similarity.


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