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Compound Embedding Features for Semi-supervised Learning
Mo Yu
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Tiejun Zhao
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Daxiang Dong
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Hao Tian
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Dianhai Yu
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Paper Details:
Month: June
Year: 2013
Location: Atlanta, Georgia
Venue:
NAACL |
Citations
URL
Learning Sense-specific Word Embeddings By Exploiting Bilingual Resources
Jiang Guo
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Wanxiang Che
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Haifeng Wang
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Ting Liu
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Revisiting Embedding Features for Simple Semi-supervised Learning
Jiang Guo
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Wanxiang Che
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Haifeng Wang
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Ting Liu
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Neural Networks for Open Domain Targeted Sentiment
Meishan Zhang
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Yue Zhang
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Duy-Tin Vo
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Combining Discrete and Continuous Features for Deterministic Transition-based Dependency Parsing
Meishan Zhang
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Yue Zhang
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Drop-out Conditional Random Fields for Twitter with Huge Mined Gazetteer
Eunsuk Yang
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Young-Bum Kim
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Ruhi Sarikaya
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Yu-Seop Kim
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K-Embeddings: Learning Conceptual Embeddings for Words using Context-Embeddings: Learning Conceptual Embeddings for Words using Context
Thuy Vu
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D. Stott Parker
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Word Embeddings as Features for Supervised Coreference Resolution
Iliana Simova
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Hans Uszkoreit
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Assessing the Corpus Size vs. Similarity Trade-off for Word Embeddings in Clinical NLP
Kirk Roberts
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http://www.chokkan.org/software/crfsuite
Field Of Study
Linguistic Trends
Syntax
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Machine Translation
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English
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