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Document Modeling with Gated Recurrent Neural Network for Sentiment Classification
Document level sentiment classification remains a challenge: encoding the intrinsic relations between sentences in the semantic meaning of a document. To address this, we introduce a neural networkExpand
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Aspect Level Sentiment Classification with Deep Memory Network
We introduce a deep memory network for aspect level sentiment classification. Unlike feature-based SVM and sequential neural models such as LSTM, this approach explicitly captures the importance ofExpand
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Learning Sentiment-Specific Word Embedding for Twitter Sentiment Classification
We present a method that learns word embedding for Twitter sentiment classification in this paper. Most existing algorithms for learning continuous word representations typically only model theExpand
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Effective LSTMs for Target-Dependent Sentiment Classification
Target-dependent sentiment classification remains a challenge: modeling the semantic relatedness of a target with its context words in a sentence. Different context words have different influences onExpand
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Adaptive Recursive Neural Network for Target-dependent Twitter Sentiment Classification
We propose Adaptive Recursive Neural Network (AdaRNN) for target-dependent Twitter sentiment classification. AdaRNN adaptively propagates the sentiments of words to target depending on the contextExpand
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Learning Semantic Representations of Users and Products for Document Level Sentiment Classification
Neural network methods have achieved promising results for sentiment classification of text. However, these models only use semantics of texts, while ignoring users who express the sentiment andExpand
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Building Large-Scale Twitter-Specific Sentiment Lexicon : A Representation Learning Approach
In this paper, we propose to build large-scale sentiment lexicon from Twitter with a representation learning approach. We cast sentiment lexicon learning as a phrase-level sentiment classificationExpand
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Sentiment Embeddings with Applications to Sentiment Analysis
We propose learning sentiment-specific word embeddings dubbed sentiment embeddings in this paper. Existing word embedding learning algorithms typically only use the contexts of words but ignore theExpand
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Modeling Mention, Context and Entity with Neural Networks for Entity Disambiguation
Given a query consisting of a mention (name string) and a background document, entity disambiguation calls for linking the mention to an entity from reference knowledge base like Wikipedia. ExistingExpand
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Target-Dependent Sentiment Classification with Long Short Term Memory
Target-dependent sentiment classification remains a challenge: modeling the semantic relatedness of a target with its context words in a sentence. Different context words have different influences onExpand
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