The document discusses concepts related to the generative lexicon theory and statistical semantics. It provides definitions and examples of key terms, such as the generative lexicon, word sense disambiguation, and the principle of compositionality. Examples are given to illustrate how statistical semantics can be used to analyze word usage patterns and determine word meanings.

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47. Statistical Semantics:#J
Statistical Semantics is the study of how the
statistical patterns of human word usage can be
used to figure out what people mean, at least to
a level sufficient for information access”(ACL
wiki)
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51. Principle of compositionality (Frege1892]
… the meaning of a complex expression is determined by
the meanings of its constituent expressions and the rules
used to combine them (wikipedia

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https://speakerdeck.com/mamoruk/a-tensor-based-factorization-model-of-semantic-compositionality
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59. Dynamic Convolutional Neural Network
[Kalchbrenner+14]
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73. ħăđƫ (1/3)
! [Pustejovsky95] James Pustejovsky.
The Generative Lexicon. MIT Press.
! [·¦05] ·¦ǍƆ.
(4+$. N%ˆư
! [Pustejovsky05] James Pustejovsky.
Introduction to Generative Lexicon. Foundations of
SemanticsƩĺ´ē.
http://www.cs.brandeis.edu/~jamesp/classes/LING130/
! [ě„06] ě„Ʋë.
Generative Lexicon2. Ɛ:ğ†NJ̓:ŏÌƈ
;8

74. ħăđƫ(2/3)
! [Cimiano+07] Philipp Cimiano, Johanna Wenderoth.
Automatic Acquisition of Ranked Qualia Structures from the
Web. ACL2007
! [ĻŖ+12] ĻŖ˜ƨ, ·Šť, ě„Ʋë.
(4$
(!')

75. .
0. NLP2012.
! [Mitchell+08] Jeff Mitchell, Mirella Lapata.
Vector-based Models of Semantic Composition. ACL2008.
! [Socher+12] Richard Socher, Brody Huval, Christopher D.
Manning, Andrew Y. Ng.
Semantic Compositionality through Recursive Matrix-Vector
Spaces. EMNLP2012.
;9

76. ħăđƫ(3/3)
! [Cruys+13] Tim Van de Cruys, Thierry Poibeau, Anna Korhonen.
A Tensor-based Factorization Model of Semantic
Compositionality. NAACL2013.
! [Kalchbrenner+14] Nal Kalchbrenner, Edward Grefenstette, Phil
Blunsom.
A Convolutional Neural Network for Modelling Sentences.
ACL2014.
! [Zelier+14] Matthew D. Zeiler, Rob Fergus.
Visualizing and Understanding Convolutional Networks.
ECCV2014.
! [Tsubaki+13] Masashi Tsubaki, Kevin Duh, Masashi Shimbo,
Yuji Matsumoto.
Modeling and Learning Semantic Co-Compositionality through
Prototype Projections and Neural Networks. EMNLP2013
;: