Vector Space Semantic Parsing: A Framework for Compositional ...

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Organizers: Alexandre Allauzen, LIMSI-CNRS/Université Paris-Sud Hugo Larochelle, Université de de Sherbrooke Richard Socher, Stanford University Christopher Manning, Stanford University Program Committee: Yoshua Bengio, Université de Montréal Antoine Bordes, Université Technologique de Compiègne Léon Bottou, Microsoft Research Xavier Carreras, Universitat Politècnica de Catalunya Shay Cohen, Columbia University Michael Collins, Columbia University Ronan Collobert, IDIAP Research Institute Kevin Duh, Nara Institute of Science and Technology Dean Foster, University of Pennsylvania Percy Liang, Stanford University Andriy Mnih, Gatsby Computational Neuroscience Unit John Platt, Microsoft Research Holger Schwenk, Université du Maine Jason Weston, Google Guillaume Wisniewski, LIMSI-CNRS/Université Invited Speaker: Xavier Carreras, Universitat Politècnica de Catalunya Mirella Lapata, University of Edinburgh v
Page 1 and 2: ACL 2013 51st Annual Meeting of the
Page 3: Introduction In recent years, there
Page 9 and 10: Conference Program 9:00 Opening (9:
Page 11 and 12: Vector Space Semantic Parsing: A Fr
Page 13 and 14: Part of speech Syntactic category E
Page 15 and 16: Semantic type Example syntactic cat
Page 17 and 18: very big elephant 1,0 very big mous
Page 19 and 20: Proceedings of the 2010 Conference
Page 21 and 22: Learning from errors: Using vector-
Page 23 and 24: Figure 1: An overview of our method
Page 25 and 26: 3.1 Learning The error on a parse t
Page 27 and 28: Rank 5 (Joachims, 2006), which runs
Page 29 and 30: Richard Socher, John Bauer, Christo
Page 31 and 32: word’s selectional preferences fo
Page 33 and 34: us to account for both relation r a
Page 35 and 36: Model Finklestein (Corr.) ESL (% Ac
Page 37 and 38: IC Corpus ECB Corpus Prec Rec F-1 A
Page 39 and 40: Diana McCarthy, Rob Koeling, Julie
Page 41 and 42: achieved a decrease in perplexity c
Page 43 and 44: mation about the words to make bett
Page 45 and 46: #Vectors mapping to Model Caps VocS
Page 47 and 48: System Test Baseline+ngram 23.81 +W
Page 49 and 50: Holger Schwenk, Daniel Dchelotte, a
Page 51 and 52: idea (Sec. 2). Second, we fully des
Page 53 and 54: S S VP NP ✚❩ ✟✟ ❍❍ ✟
Page 55 and 56:
withλvalues in the range 0-0.4. Sy
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Figure 4: Running time with respect
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Sameer Pradhan, Wayne Ward, Kadri H
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hind the state of the art or are sp
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||u|| = 〈u, u〉 1/2 . We use the
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[ ] W1 ∗ , W2 ∗ , A ∗ u 1 , .
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0.00 0.05 0.10 0.15 0.20 0.25 0.30
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Applicative structure in vector spa
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# feature HLBL original HLBL scaled
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Tom McArthur. 1998. Living Words: L
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WSMs and measures have never before
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compositionality score c en can be
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Figure 1: All the parameters of WSM
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WSM Measure wAvg(of ρ) ρAN-VO-SV
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WSM parameters VSM tags trans. VSM
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would best be represented in a cont
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in the same fashion. Modeling negat
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Functional Representation in Negati
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sentative power of the MV-RNN model
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Towards Dynamic Word Sense Discrimi
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Algorithm 1 MSRI training for all t
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Given a pair of sentences, systems
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tion to human word similarity judge
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A Generative Model of Vector Space
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To turn this into a top-down proces
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E N/N , and, for each group of adje
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Input Training vector (“basic pri
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References L Douglas Baker and Andr
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lows. In the next section, we revie
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Likelihood (ML) criterion using the
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Collection Model ARI NMI PLSA 41.0
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NMI 0.92 0.9 0.88 0.86 0.84 TDT 0.9
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P. Quelhas, F. Monay, J.-M. Odobez,
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The contributions of this paper are
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ate of 3.2% on pairs of parse tree
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Algorithm 2: Training the convoluti
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Question: What is the name of the B
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Recurrent Convolutional Neural Netw
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Hello HAL do you read me s i S I x
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s Open the pod bay doors HAL i I'm
Page 135 and 136:
Center A: Do you repair your own ca
Page 137:
Author Index Andreas, Jacob, 91 Bai
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