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Attention-based LSTM for Aspect-level Sentiment Classification
TLDR
This paper reveals that the sentiment polarity of a sentence is not only determined by the content but is also highly related to the concerned aspect, and proposes an Attention-based Long Short-Term Memory Network for aspect-level sentiment classification. Expand
Emotional Chatting Machine: Emotional Conversation Generation with Internal and External Memory
TLDR
This paper proposes Emotional Chatting Machine (ECM), the first work that addresses the emotion factor in large-scale conversation generation using three new mechanisms that respectively models the high-level abstraction of emotion expressions by embedding emotion categories. Expand
Movie review mining and summarization
TLDR
A multi-knowledge based approach is proposed, which integrates WordNet, statistical analysis and movie knowledge, and the experimental results show the effectiveness of the proposed approach in movie review mining and summarization. Expand
Commonsense Knowledge Aware Conversation Generation with Graph Attention
TLDR
This is the first attempt that uses large-scale commonsense knowledge in conversation generation, and unlike existing models that use knowledge triples (entities) separately and independently, this model treats each knowledge graph as a whole, which encodes more structured, connected semantic information in the graphs. Expand
Reinforcement Learning for Relation Classification From Noisy Data
TLDR
Experimental results show that the proposed novel model can deal with the noise of data effectively and obtains better performance for relation classification at the sentence level from noisy data. Expand
Learning to Identify Review Spam
TLDR
This paper exploits machine learning methods to identify review spam and provides a twoview semi-supervised method, co-training, to exploit the large amount of unlabeled data and shows that the proposed method is effective. Expand
Structure-Aware Review Mining and Summarization
TLDR
This paper proposes a new machine learning framework based on Conditional Random Fields that can employ rich features to jointly extract positive opinions, negative opinions and object features for review sentences and shows that structure-aware models outperform many state-of-the-art approaches to review mining. Expand
Contextual Combinatorial Bandit and its Application on Diversified Online Recommendation
TLDR
Experiments conducted on real-wold movie recommendation dataset demonstrate that the principled approach called contextual combinatorial bandit can effectively address the above challenges and hence improve the performance of recommendation task. Expand
TransG : A Generative Model for Knowledge Graph Embedding
TLDR
This paper proposes a novel generative model (TransG) to address the issue of multiple relation semantics that a relation may have multiple meanings revealed by the entity pairs associated with the corresponding triples. Expand
SSP: Semantic Space Projection for Knowledge Graph Embedding with Text Descriptions
TLDR
A semantic representation method for knowledge graph (KSR) is proposed, which imposes a two-level hierarchical generative process that globally extracts many aspects and then locally assigns a specific category in each aspect for every triple. Expand
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