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Workshop on Continuous Vector Space Models and their Compositionality@ACL 2013: Sofia, Bulgaria
- Alexandre Allauzen, Hugo Larochelle, Christopher D. Manning, Richard Socher:
Proceedings of the Workshop on Continuous Vector Space Models and their Compositionality, CVSM@ACL 2013, Sofia, Bulgaria, August 9, 2013. Association for Computational Linguistics 2013, ISBN 978-1-937284-67-1 - Jayant Krishnamurthy, Tom M. Mitchell:
Vector Space Semantic Parsing: A Framework for Compositional Vector Space Models. 1-10 - Phong Le, Willem H. Zuidema, Remko Scha:
Learning from errors: Using vector-based compositional semantics for parse reranking. 11-19 - Kartik Goyal, Sujay Kumar Jauhar, Huiying Li, Mrinmaya Sachan, Shashank Srivastava, Eduard H. Hovy:
A Structured Distributional Semantic Model : Integrating Structure with Semantics. 20-29 - Henning Sperr, Jan Niehues, Alex Waibel:
Letter N-Gram-based Input Encoding for Continuous Space Language Models. 30-39 - Fabio Massimo Zanzotto, Lorenzo Dell'Arciprete:
Transducing Sentences to Syntactic Feature Vectors: an Alternative Way to "Parse"? 40-49 - Georgiana Dinu, Nghia The Pham, Marco Baroni:
General estimation and evaluation of compositional distributional semantic models. 50-58 - Márton Makrai, Dávid Márk Nemeskey, András Kornai:
Applicative structure in vector space models. 59-63 - Lubomír Krcmár, Karel Jezek, Pavel Pecina:
Determining Compositionality of Expresssions Using Various Word Space Models and Methods. 64-73 - Karl Moritz Hermann, Edward Grefenstette, Phil Blunsom:
"Not not bad" is not "bad": A distributional account of negation. 74-82 - Hans Moen, Erwin Marsi, Björn Gambäck:
Towards Dynamic Word Sense Discrimination with Random Indexing. 83-90 - Jacob Andreas, Zoubin Ghahramani:
A Generative Model of Vector Space Semantics. 91-99 - Stéphane Clinchant, Florent Perronnin:
Aggregating Continuous Word Embeddings for Information Retrieval. 100-109 - Christopher Malon, Bing Bai:
Answer Extraction by Recursive Parse Tree Descent. 110-118 - Nal Kalchbrenner, Phil Blunsom:
Recurrent Convolutional Neural Networks for Discourse Compositionality. 119-126
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