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An Effective Neural Network Model for Graph-based Dependency Parsing
Wenzhe Pei
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Tao Ge
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Baobao Chang
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Paper Details:
Month: July
Year: 2015
Location: Beijing, China
Venue:
ACL |
IJCNLP |
Citations
URL
Semi-supervised Dependency Parsing using Bilexical Contextual Features from Auto-Parsed Data
Eliyahu Kiperwasser
|
Yoav Goldberg
|
Improved Dependency Parsing using Implicit Word Connections Learned from Unlabeled Data
Wenhui Wang
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Baobao Chang
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Mairgup Mansur
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A Sensitivity Analysis of (and Practitioners’ Guide to) Convolutional Neural Networks for Sentence Classification
Ye Zhang
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Byron Wallace
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A Novel Neural Network Model for Joint POS Tagging and Graph-based Dependency Parsing
Dat Quoc Nguyen
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Mark Dras
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Mark Johnson
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A Transition-based System for Universal Dependency Parsing
Hao Wang
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Hai Zhao
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Zhisong Zhang
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Probabilistic Graph-based Dependency Parsing with Convolutional Neural Network
Zhisong Zhang
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Hai Zhao
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Lianhui Qin
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A Search-Based Dynamic Reranking Model for Dependency Parsing
Hao Zhou
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Yue Zhang
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Shujian Huang
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Junsheng Zhou
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Xin-Yu Dai
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Jiajun Chen
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Graph-based Dependency Parsing with Bidirectional LSTM
Wenhui Wang
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Baobao Chang
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Improving Dependency Parsing Using Sentence Clause Charts
Vincent Kríž
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Barbora Hladká
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Deep Multitask Learning for Semantic Dependency Parsing
Hao Peng
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Sam Thomson
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Noah A. Smith
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Simple and Accurate Dependency Parsing Using Bidirectional LSTM Feature Representations
Eliyahu Kiperwasser
|
Yoav Goldberg
|
Easy-First Dependency Parsing with Hierarchical Tree LSTMs
Eliyahu Kiperwasser
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Yoav Goldberg
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An empirical study for Vietnamese dependency parsing
Dat Quoc Nguyen
|
Mark Dras
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Mark Johnson
|
http://sourceforge.net/projects/
https://code.google.com/p/word2vec/
Field Of Study
Linguistic Trends
Embeddings
Language
English
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