564 research outputs found
Structural Attention Neural Networks for improved sentiment analysis
We introduce a tree-structured attention neural network for sentences and
small phrases and apply it to the problem of sentiment classification. Our
model expands the current recursive models by incorporating structural
information around a node of a syntactic tree using both bottom-up and top-down
information propagation. Also, the model utilizes structural attention to
identify the most salient representations during the construction of the
syntactic tree. To our knowledge, the proposed models achieve state of the art
performance on the Stanford Sentiment Treebank dataset.Comment: Submitted to EACL2017 for revie
Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks
Because of their superior ability to preserve sequence information over time,
Long Short-Term Memory (LSTM) networks, a type of recurrent neural network with
a more complex computational unit, have obtained strong results on a variety of
sequence modeling tasks. The only underlying LSTM structure that has been
explored so far is a linear chain. However, natural language exhibits syntactic
properties that would naturally combine words to phrases. We introduce the
Tree-LSTM, a generalization of LSTMs to tree-structured network topologies.
Tree-LSTMs outperform all existing systems and strong LSTM baselines on two
tasks: predicting the semantic relatedness of two sentences (SemEval 2014, Task
1) and sentiment classification (Stanford Sentiment Treebank).Comment: Accepted for publication at ACL 201
Sentiment classification using treeābased gated recurrent units
Dissertation presented as the partial requirement for obtaining a Master's degree in Information Management, specialization in Knowledge Management and Business IntelligenceNatural Language Processing is one of the most challenging fields of Artificial Intelligence. The past 10 years, this field has witnessed a fascinating progress due to Deep Learning. Despite that, we havenāt achieved to build an architecture of models that can understand natural language as humans do. Many architectures have been proposed, each of them having its own strengths and weaknesses. In this report, we will cover the tree based architectures and in particular we will propose a different tree based architecture that is very similar to the Tree-Based LSTM, proposed by Tai(2015).
In this work, we aim to make a critical comparison between the proposed architecture -Tree-Based GRU- with Tree-based LSTM for sentiment classification tasks, both binary and fine-grained
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