Abstract is missing.
- Vector Space Semantic Parsing: A Framework for Compositional Vector Space ModelsJayant Krishnamurthy, Tom M. Mitchell. 1-10 [doi]
- Learning from errors: Using vector-based compositional semantics for parse rerankingPhong Le, Willem H. Zuidema, Remko Scha. 11-19 [doi]
- A Structured Distributional Semantic Model : Integrating Structure with SemanticsKartik Goyal, Sujay Kumar Jauhar, Huiying Li, Mrinmaya Sachan, Shashank Srivastava, Eduard H. Hovy. 20-29 [doi]
- Letter N-Gram-based Input Encoding for Continuous Space Language ModelsHenning Sperr, Jan Niehues, Alex Waibel. 30-39 [doi]
- Transducing Sentences to Syntactic Feature Vectors: an Alternative Way to "Parse"?Fabio Massimo Zanzotto, Lorenzo Dell'Arciprete. 40-49 [doi]
- General estimation and evaluation of compositional distributional semantic modelsGeorgiana Dinu, Nghia The Pham, Marco Baroni. 50-58 [doi]
- Applicative structure in vector space modelsMárton Makrai, Dávid Márk Nemeskey, András Kornai. 59-63 [doi]
- Determining Compositionality of Expresssions Using Various Word Space Models and MethodsLubomír Krcmár, Karel Jeek, Pavel Pecina. 64-73 [doi]
- "Not not bad" is not "bad": A distributional account of negationKarl Moritz Hermann, Edward Grefenstette, Phil Blunsom. 74-82 [doi]
- Towards Dynamic Word Sense Discrimination with Random IndexingHans Moen, Erwin Marsi, Björn Gambäck. 83-90 [doi]
- A Generative Model of Vector Space SemanticsJacob Andreas, Zoubin Ghahramani. 91-99 [doi]
- Aggregating Continuous Word Embeddings for Information RetrievalStéphane Clinchant, Florent Perronnin. 100-109 [doi]
- Answer Extraction by Recursive Parse Tree DescentChristopher Malon, Bing Bai. 110-118 [doi]
- Recurrent Convolutional Neural Networks for Discourse CompositionalityNal Kalchbrenner, Phil Blunsom. 119-126 [doi]