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Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Paths
TLDR
This paper presents SDP-LSTM, a novel neural network to classify the relation of two entities in a sentence, which leverages the shortest dependency path (SDP) between two entities; multichannel recurrent neural networks, with long short term memory (L STM) units, pick up heterogeneous information along the SDP. Expand
Convolutional Neural Networks over Tree Structures for Programming Language Processing
TLDR
A novel tree-based convolutional neural network (TBCNN) is proposed for programming language processing, in which a convolution kernel is designed over programs' abstract syntax trees to capture structural information. Expand
Deep Code Comment Generation
TLDR
DeepCom applies Natural Language Processing (NLP) techniques to learn from a large code corpus and generates comments from learned features for better comments generation of Java methods. Expand
Natural Language Inference by Tree-Based Convolution and Heuristic Matching
TLDR
This model, a tree-based convolutional neural network (TBCNN) captures sentence-level semantics; then heuristic matching layers like concatenation, element-wise product/difference combine the information in individual sentences. Expand
How Transferable are Neural Networks in NLP Applications?
TLDR
In this paper, systematic case studies are conducted and an illuminating picture is provided on the transferability of neural networks in NLP. Expand
Sequence to Backward and Forward Sequences: A Content-Introducing Approach to Generative Short-Text Conversation
TLDR
This paper proposes seq2BF, a “sequence to backward and forward sequences” model, which generates a reply containing the given keyword, and significantly outperforms traditional sequence-to-sequence models in terms of human evaluation and the entropy measure. Expand
Summarizing Source Code with Transferred API Knowledge
TLDR
Experiments on large-scale real-world industry Java projects indicate that the proposed novel approach, named TL-CodeSum, is effective and outperforms the state-of-the-art in code summarization. Expand
Discriminative Neural Sentence Modeling by Tree-Based Convolution
TLDR
This paper proposes a tree-based convolutional neural network (TBCNN) for discriminative sentence modeling that outperforms previous state-of-the-art results, including existing neural networks and dedicated feature/rule engineering. Expand
Deep code comment generation with hybrid lexical and syntactical information
TLDR
Experimental results demonstrate that the method Hybrid-DeepCom outperforms the state-of-the-art by a substantial margin and the results show that reducing the out- of-vocabulary tokens improves the accuracy effectively. Expand
A Syntax-based approach to measuring the degree of inconsistency for belief bases
TLDR
A normalized framework for measuring the degree of inconsistency of a belief base which unifies the impact of both consistent subsets and minimal inconsistent subsets is proposed and satisfies all the properties deemed necessary by common consent to characterize an intuitively satisfactory measure. Expand
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