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RepL4NLP@ACL 2016: Berlin, Germany
- Phil Blunsom, Kyunghyun Cho, Shay B. Cohen, Edward Grefenstette, Karl Moritz Hermann, Laura Rimell, Jason Weston, Scott Wen-tau Yih:
Proceedings of the 1st Workshop on Representation Learning for NLP, Rep4NLP@ACL 2016, Berlin, Germany, August 11, 2016. Association for Computational Linguistics 2016, ISBN 978-1-945626-04-3 - Leila Arras
, Franziska Horn, Grégoire Montavon, Klaus-Robert Müller
Explaining Predictions of Non-Linear Classifiers in NLP. 1-7 - Petr Baudis, Silvestr Stanko, Jan Sedivý:
Joint Learning of Sentence Embeddings for Relevance and Entailment. 8-17 - Kris Cao, Marek Rei:
A Joint Model for Word Embedding and Word Morphology. 18-26 - Corina Dima
On the Compositionality and Semantic Interpretation of English Noun Compounds. 27-39 - Guy Emerson, Ann A. Copestake:
Functional Distributional Semantics. 40-52 - Jacob Hagstedt P. Suorra, Olof Mogren:
Assisting Discussion Forum Users using Deep Recurrent Neural Networks. 53-61 - Joo-Kyung Kim, Marie-Catherine de Marneffe, Eric Fosler-Lussier:
Adjusting Word Embeddings with Semantic Intensity Orders. 62-69 - Minsoo Kim, Dennis Singh Moirangthem, Minho Lee:
Towards Abstraction from Extraction: Multiple Timescale Gated Recurrent Unit for Summarization. 70-77 - Jey Han Lau, Timothy Baldwin:
An Empirical Evaluation of doc2vec with Practical Insights into Document Embedding Generation. 78-86 - Phong Le, Willem H. Zuidema
Quantifying the Vanishing Gradient and Long Distance Dependency Problem in Recursive Neural Networks and Recursive LSTMs. 87-93 - Phong Le, Marc Dymetman, Jean-Michel Renders:
LSTM-Based Mixture-of-Experts for Knowledge-Aware Dialogues. 94-99 - Pranava Swaroop Madhyastha
Mapping Unseen Words to Task-Trained Embedding Spaces. 100-110 - Ana Marasovic, Anette Frank:
Multilingual Modal Sense Classification using a Convolutional Neural Network. 111-120 - Antonio Valerio Miceli Barone
Towards cross-lingual distributed representations without parallel text trained with adversarial autoencoders. 121-126 - Jeff Mitchell:
Decomposing Bilexical Dependencies into Semantic and Syntactic Vectors. 127-136 - Henry Nassif, Mitra Mohtarami, James R. Glass:
Learning Semantic Relatedness in Community Question Answering Using Neural Models. 137-147 - Paul Neculoiu, Maarten Versteegh, Mihai Rotaru:
Learning Text Similarity with Siamese Recurrent Networks. 148-157 - Thien Huu Nguyen, Lisheng Fu, Kyunghyun Cho, Ralph Grishman:
A Two-stage Approach for Extending Event Detection to New Types via Neural Networks. 158-165 - Tobias Norlund, David Nilsson, Magnus Sahlgren:
Parameterized context windows in Random Indexing. 166-173 - Maria Pelevina, Nikolay Arefiev, Chris Biemann, Alexander Panchenko:
Making Sense of Word Embeddings. 174-183 - Yonatan Ramni, Oded Maimon, Eugene Khmelnitsky:
Pair Distance Distribution: A Model of Semantic Representation. 184-192 - Gábor Recski
Measuring Semantic Similarity of Words Using Concept Networks. 193-200 - Stefan Ruseti, Traian Rebedea, Stefan Trausan-Matu:
Using Embedding Masks for Word Categorization. 201-205 - Prasanna Sattigeri, Jayaraman J. Thiagarajan:
Sparsifying Word Representations for Deep Unordered Sentence Modeling. 206-214 - Richard Searle, Megan Bingham-Walker:
Why "Blow Out"? A Structural Analysis of the Movie Dialog Dataset. 215-221 - Imran A. Sheikh
Learning Word Importance with the Neural Bag-of-Words Model. 222-229 - Konstantin Sokolov
A Vector Model for Type-Theoretical Semantics. 230-238 - Eleni Triantafillou, Jamie Ryan Kiros, Raquel Urtasun, Richard S. Zemel:
Towards Generalizable Sentence Embeddings. 239-248 - Yusuke Watanabe, Kazuma Hashimoto, Yoshimasa Tsuruoka
Domain Adaptation for Neural Networks by Parameter Augmentation. 249-257 - Dirk Weissenborn:
Neural Associative Memory for Dual-Sequence Modeling. 258-266
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