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

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Page 1 and 2:
ACL 2013 51st Annual Meeting of the
Page 3:
Introduction In recent years, there
Page 7: Table of Contents Vector Space Sema
Page 10 and 11: Poster session (continued) 12:30 Lu
Page 12 and 13: Input: Log. Form: “red ball”
Page 14 and 15: the NP/N λx.x red N/N λx.A red x
Page 16 and 17: dicted and target distributions, an
Page 18 and 19: typed objects lie in the same vecto
Page 20 and 21: Peter D. Turney. 2006. Similarity o
Page 22 and 23: (1999), who suggested that the real
Page 24 and 25: mation. In addition, using a new se
Page 26 and 27: Figure 6: Positive rate, negative r
Page 28 and 29: References Rens Bod, Remko Scha, an
Page 30 and 31: A Structured Distributional Semanti
Page 32 and 33: Figure 1: Sample sentences & triple
Page 34 and 35: el as its three axes. We explore th
Page 36 and 37: as well as our framing of word comp
Page 38 and 39: References Marco Baroni and Roberto
Page 40 and 41: Letter N-Gram-based Input Encoding
Page 42 and 43: Hidden my house my house Visibl
Page 44 and 45: … m my y h se us Hidden Visible m
Page 46 and 47: line of the systems presented in Ta
Page 48 and 49: References Yoshua Bengio, Réjean D
Page 50 and 51: Transducing Sentences to Syntactic
Page 52 and 53: t s “We booked the flight” →
Page 54 and 55: D 3(s) = ❀ PRP + ❀ V + DT ❀ +
Page 56 and 57: Output Model λ = 0 λ = 0.2 λ = 0
Page 60 and 61: General estimation and evaluation o
Page 62 and 63: Guevara (2010) and Zanzotto et al.
Page 64 and 65: ⃗p = A u ⃗v where A u is a matr
Page 66 and 67: stituents. For this reason, we must
Page 68 and 69: References Hervé Abdi and Lynne Wi
Page 70 and 71: GOOD VERTICAL safe out raise level
Page 72 and 73: HLBL HLBL SENNA 4lang original scal
Page 74 and 75: Determining Compositionality of Wor
Page 76 and 77: ability of a word in the investigat
Page 78 and 79: terminer) wheel” and “reinvent
Page 80 and 81: ing LSA are depicted. Discussion As
Page 82 and 83: WSM Measure wAvg(of ρ) ρAN-VO-SV
Page 84 and 85: “Not not bad” is not “bad”:
Page 86 and 87: activation functions in recursive n
Page 88 and 89: logic experiment in Socher et al. (
Page 90 and 91: tinction without the need to move t
Page 92 and 93: T. K. Landauer and S. T. Dumais. 19
Page 94 and 95: This differs from the classical Ran
Page 96 and 97: Hungarian Algorithm First cosine si
Page 98 and 99: Features: MSRpar MSRvid SMTeuroparl
Page 100 and 101: Joseph Reisinger and Raymond J. Moo
Page 102 and 103: phrase meaning in vector space. •
Page 104 and 105: By Bayes’ rule, p(x|a, n; Θ) ∝
Page 106 and 107: 4.2 Learning from distributional re
Page 108 and 109:
W = (w 1 , w 2 , · · · , w n ) a
Page 110 and 111:
Aggregating Continuous Word Embeddi
Page 112 and 113:
trieval, Sivic and Zisserman propos
Page 114 and 115:
Another advantage is that the propo
Page 116 and 117:
e 50 100 200 300 400 500 CLEF 4.0 6
Page 118 and 119:
Y. Bengio, J. Louradour, R. Collobe
Page 120 and 121:
Answer Extraction by Recursive Pars
Page 122 and 123:
Algorithm 1: Auto-encoders co-train
Page 124 and 125:
NP NP ) , ADJP , NP ( NP - NP Engli
Page 126 and 127:
System Short Answer Short Answer Sh
Page 128 and 129:
References Y. Bengio and R. Ducharm
Page 130 and 131:
problem of paragraphs, of tence, mo
Page 132 and 133:
m1 k 4 only on the length of s and
Page 134 and 135:
Dialogue Act Label Example Train (%
Page 136 and 137:
[Calhoun et al.2010] Sasha Calhoun,
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