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5 papers · 2 filters
Jointly Modeling Topics and Intents with Global Order Structure
Bei Chen, Jun Zhu, Nan Yang +3
Modeling document structure is of great importance for discourse analysis and related applications. The goal of this research is to capture the document intent structure by modelin…
A Unified Tagging Solution: Bidirectional LSTM Recurrent Neural Network with Word Embedding
Peilu Wang, Yao Qian, Frank K. Soong +2
Bidirectional Long Short-Term Memory Recurrent Neural Network (BLSTM-RNN) has been shown to be very effective for modeling and predicting sequential data, e.g. speech utterances or…
Part-of-Speech Tagging with Bidirectional Long Short-Term Memory Recurrent Neural Network
Peilu Wang, Yao Qian, Frank K. Soong +2
Bidirectional Long Short-Term Memory Recurrent Neural Network (BLSTM-RNN) has been shown to be very effective for tagging sequential data, e.g. speech utterances or handwritten doc…
PTE: Predictive Text Embedding through Large-scale Heterogeneous Text Networks
Jian Tang, Meng Qu, Qiaozhu Mei
Unsupervised text embedding methods, such as Skip-gram and Paragraph Vector, have been attracting increasing attention due to their simplicity, scalability, and effectiveness. Howe…
Learning Better Word Embedding by Asymmetric Low-Rank Projection of Knowledge Graph
Fei Tian, Bin Gao, Enhong Chen +1
Word embedding, which refers to low-dimensional dense vector representations of natural words, has demonstrated its power in many natural language processing tasks. However, it may…