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Learning Syntactic Categories Using Paradigmatic Representations of Word Context
Mehmet Ali Yatbaz
|
Enis Sert
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Deniz Yuret
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Paper Details:
Month: July
Year: 2012
Location: Jeju Island, Korea
Venue:
CoNLL |
EMNLP |
Citations
URL
Word Sense Induction Using Lexical Chain based Hypergraph Model
Tao Qian
|
Donghong Ji
|
Mingyao Zhang
|
Chong Teng
|
Congling Xia
|
Unsupervised Instance-Based Part of Speech Induction Using Probable Substitutes
Deniz Yuret
|
Mehmet Ali Yatbaz
|
Enis Sert
|
Learning grammatical categories using paradigmatic representations: Substitute words for language acquisition
Mehmet Ali Yatbaz
|
Volkan Cirik
|
Aylin Küntay
|
Deniz Yuret
|
Unsupervised Learning of Syntactic Structure with Invertible Neural Projections
Junxian He
|
Graham Neubig
|
Taylor Berg-Kirkpatrick
|
Explaining and Generalizing Skip-Gram through Exponential Family Principal Component Analysis
Ryan Cotterell
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Adam Poliak
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Benjamin Van Durme
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Jason Eisner
|
Redefining part-of-speech classes with distributional semantic models
Andrey Kutuzov
|
Erik Velldal
|
Lilja Øvrelid
|
Parsing with Context Embeddings
Ömer Kırnap
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Berkay Furkan Önder
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Deniz Yuret
|
Modeling Word Meaning in Context with Substitute Vectors
Oren Melamud
|
Ido Dagan
|
Jacob Goldberger
|
Clustering Paraphrases by Word Sense
Anne Cocos
|
Chris Callison-Burch
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Addressing Ambiguity in Unsupervised Part-of-Speech Induction with Substitute Vectors
Volkan Cirik
|
POS induction with distributional and morphological information using a distance-dependent Chinese restaurant process
Kairit Sirts
|
Jacob Eisenstein
|
Micha Elsner
|
Sharon Goldwater
|
Orthogonality of Syntax and Semantics within Distributional Spaces
Jeff Mitchell
|
Mark Steedman
|
Is “Universal Syntax” Universally Useful for Learning Distributed Word Representations?
Ivan Vulić
|
Anna Korhonen
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AI-KU: Using Substitute Vectors and Co-Occurrence Modeling For Word Sense Induction and Disambiguation
Osman Başkaya
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Enis Sert
|
Volkan Cirik
|
Deniz Yuret
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AI-KU: Using Co-Occurrence Modeling for Semantic Similarity
Osman Başkaya
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AI-KU at SemEval-2016 Task 11: Word Embeddings and Substring Features for Complex Word Identification
Onur Kuru
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The AI-KU System at the SPMRL 2013 Shared Task : Unsupervised Features for Dependency Parsing
Volkan Cirik
|
Husnu Sensoy
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Probabilistic Modeling of Joint-context in Distributional Similarity
Oren Melamud
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Ido Dagan
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Jacob Goldberger
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Idan Szpektor
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Deniz Yuret
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Unsupervised Neural Hidden Markov Models
Ke M. Tran
|
Yonatan Bisk
|
Ashish Vaswani
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Daniel Marcu
|
Kevin Knight
|
http://goo.gl/RoqEh
http://goo.gl/jzKH0
http://arxiv.org/abs/1205.5407v1
http://goo.gl/RoqEh
Field Of Study
Linguistic Trends
Embeddings
Task
Tagging
Machine Translation
Language
English
Child Language
Dataset
Child Language
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