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

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WSM Measure wAvg(of ρ) ρAN-VO-SV ρAN ρVO ρSV ρNN VSM 1 SU 1 0.28 0.03 0.01 0.51 0.04 0.62 VSM 2 EN 1 0.26 0.19 0.08 0.29 0.04 0.69 VSM 3 CO 1 0.32 0.26 0.24 0.23 0.25 0.65 VSM 1 NE 1 0.32 0.19 0.36 0.25 -0.13 0.73 LSA 1 SU 2 0.31 0.06 0.05 0.50 0.20 0.59 LSA 2 EN 2 0.50 0.40 0.39 0.55 0.32 0.78 LSA 3 CO 1 0.48 0.36 0.29 0.60 0.42 0.69 LSA 2 NE 2 0.44 0.33 0.34 0.40 0.44 0.67 HAL 1 SU 3 0.29 0.16 0.09 0.32 0.34 0.56 HAL 2 EN 3 0.36 0.28 0.33 0.35 0.26 0.53 HAL 3 CO 1 0.24 0.22 0.25 0.16 0.15 0.42 HAL 4 NE 3 0.21 0.14 0.02 0.33 0.06 0.47 COALS 1 SU 4 0.42 0.28 0.28 0.54 0.30 0.59 COALS 2 EN 2 0.49 0.44 0.52 0.51 0.07 0.72 COALS 2 CO 1 0.47 0.40 0.47 0.51 0.07 0.74 COALS 2 NE 4 0.52 0.48 0.55 0.50 0.21 0.74 RI 1 SU 5 0.30 0.14 0.14 0.29 0.12 0.72 RI 2 EN 3 0.44 0.34 0.37 0.54 0.20 0.63 RI 3 CO 1 0.23 0.23 0.29 0.17 0.17 0.26 RI 2 NE 5 0.31 0.26 0.26 0.42 0.04 0.44 Reddy-WSM - 0.35 - - - - StatMix - 0.33 - - - - Krcmar-COALS - 0.42 0.42 0.69 0.24 - Krcmar-LSA - 0.50 0.50 0.56 0.41 - Table 4: The Spearman correlations ρ of the best performing (wAvg) combinations of particular WSMs and Measures trained in TranValD applied to TestD. The highest correlation values in the particular columns and the correlation values which are not statistically different from them (p < 0.05) are in bold (yet we do not know how to calculate the stat. significance for the wAvg(of ρ) column). Reddy-WSM and StatMix stand for the best performing system based on WSMs and association measures, respectively, applied to the DISCO task (Biemann and Giesbrecht, 2011). Krcmar-COALS and Krcmar-LSA stand for the best published results achieved upon the dataset presented in Krčmář et al. (2012) and Krčmář et al. (2013), respectively. The parameters of WSMs and Measures corresponding to the indexes are depicted in Tables 5 and 6, respectively. Jeff Mitchell and Mirella Lapata. 2008. Vector-based models of semantic composition. In Proceedings of ACL-08: HLT, pages 236–244, Columbus, Ohio. Preslav Nakov, Antonia Popova, and Plamen Mateev. 2001. Weight functions impact on lsa performance. In Proceedings of the EuroConference Recent Advances in Natural Language Processing (RANLP’01), pages 187–193. Athanasios Papoulis. 1990. Probability & statistics. Prentice Hall. Darren Pearce. 2002. A Comparative Evaluation of Collocation Extraction Techniques. In Proceedings of the Third International Conference on Language Resources and Evaluation, LREC. Pavel Pecina. 2008. Reference data for Czech collocation extraction. In Proceedings of the LREC 2008 Workshop Towards a Shared Task for Multiword Expressions, pages 11–14, Marrakech, Morocco. European Language Resources Association. Siva Reddy, Diana McCarthy, and Suresh Manandhar. 2011a. An empirical study on compositionality in compound nouns. In Proceedings of 5th International Joint Conference on Natural Language Processing, pages 210–218, Chiang Mai, Thailand, November. Asian Federation of Natural Language Processing. Siva Reddy, Diana McCarthy, Suresh Manandhar, and Spandana Gella. 2011b. Exemplar-based wordspace model for compositionality detection: Shared task system description. In Proceedings of the Workshop on Distributional Semantics and Compositionality, pages 54–60, Portland, Oregon, USA. 72
WSM parameters VSM tags trans. VSM 1 noT noNo VSM 2 yesT noNo VSM 3 yesT noIdf LSA tags trans. dim. LSA 1 noT logEnt 900 LSA 2 yesT noNo 300 LSA 3 noT noIdf 300 HAL tags win s. ret. c. HAL 1 noT 5 20000 HAL 2 yesT 5 20000 HAL 3 noT 2 10000 HAL 4 yesT 5 all COALS tags ret. c. COALS 1 noT 7000 COALS 2 yesT 7000 RI tags win. s. vec. s. perm. RI 1 noT 2 4000 no RI 2 noT 4 4000 no RI 3 noT 2 4000 yes Table 5: Parameters of WSMs (Section 2) which, combined with particular Measures, achieved the highest average correlation in TrValD. Douglas L. Rohde, Laura M. Gonnerman, and David C. Plaut. 2005. An improved model of semantic similarity based on lexical co-occurrence. Unpublished manuscript. Magnus Sahlgren, Anders Holst, and Pentti Kanerva. 2008. Permutations as a means to encode order in word space. In V. Sloutsky, B. Love, and K. Mcrae, editors, Proceedings of the 30th Annual Conference of the Cognitive Science Society, pages 1300–1305. Cognitive Science Society, Austin, TX. Measure parameters SU sim. ∗ W H M SU 1 cos plus log 30 3 SU 2 cos plus log 100 5 SU 3 cos mult log 12 2 SU 4 cos mult log 80 4 SU 5 cos mult log 4 3 EN sim. func. x EN 1 cos min sim EN 2 cos avg sim EN 3 cos min –dist CO sim. ∗ CO 1 cos ⊕ NE sim. O NE 1 cos 1000 NE 2 cos 500 NE 3 cos 50 NE 4 cor 500 NE 5 cos 20 Table 6: Parameters of Measures (Section 3) which, combined with particular WSMs, achieved the highest average correlation in TrValD. highdimensional semantic space. In ACM SIGIR 2004 Workshop on Mathematical/Formal Methods in Information Retrieval. Peter D. Turney and Patrick Pantel. 2010. From frequency to meaning: vector space models of semantics. J. Artif. Int. Res., 37(1):141–188. Magnus Sahlgren. 2005. An introduction to random indexing. In Methods and Applications of Semantic Indexing Workshop at the 7th International Conference on Terminology and Knowledge Engineering, Leipzig, Germany. Magnus Sahlgren. 2006. The Word-Space Model: Using distributional analysis to represent syntagmatic and paradigmatic relations between words in highdimensional vector spaces. Ph.D. thesis, Stockholm University. Gerard Salton. 1971. The SMART Retrieval System; Experiments in Automatic Document Processing. Prentice-Hall, Inc., Upper Saddle River, NJ, USA. Dawei Song, Peter Bruza, and Richard Cole. 2004. Concept learning and information inferencing on a 73
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ACL 2013 51st Annual Meeting of the
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Introduction In recent years, there
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Table of Contents Vector Space Sema
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Poster session (continued) 12:30 Lu
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Input: Log. Form: “red ball”
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the NP/N λx.x red N/N λx.A red x
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dicted and target distributions, an
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typed objects lie in the same vecto
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Peter D. Turney. 2006. Similarity o
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(1999), who suggested that the real
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mation. In addition, using a new se
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Figure 6: Positive rate, negative r
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References Rens Bod, Remko Scha, an
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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 58 and 59: References James A. Anderson. 1973.
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 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
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m1 k 4 only on the length of s and
Page 134 and 135:
Dialogue Act Label Example Train (%
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[Calhoun et al.2010] Sasha Calhoun,
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