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Sarcastic or Not:
Word Embeddings to Predict the
Literal or Sarcastic Meaning of Words
Debanjan Ghosh, Weiwei Guo, and Smaranda Muresan

EMNLP2015
1
Abstract
2
2 Collection of Target Words
3
2 Collection of Target Words
4
2 Collection of Target Words
4
2 Collection of Target Words


4
2 Collection of Target Words


4
2 Collection of Target Words


4
2 Collection of Target Words


4
2 Collection of Target Words
X
2 Collection of Target Words
5
t
φ ≥ 0.8
2 Collection of Target Words
X
2 Collection of Target Words
X
2 Collection of Target Words
X
3 Literal/Sarcastic Sense Disambiguation
t
t S L
t S L
6
3.1 Data Collection
S L 

L
Lsent
S Lsent
7
3.1 Data Collection


t
8
3.2 Learning Approaches
t
t S L
9
3.2.1 Distributional Approaches
t S L
vs vl
u u t
vu
vu vs vl
10
3.2.1 Distributional Approaches
11
3.2.1 Distributional Approaches
t S L
cos
12
13
3.2.1 Distributional Approaches
14
3.2.1 Distributional Approaches
•
15
3.2.1 Distributional Approaches
t vs vu Sim
vs vu ck wj
M Mjk ck wj 

cos
X
3.2.1 Distributional Approaches
M max Sim
max Mjk
max = 0 M
Sim vs vu vl
S L
X
suppose: u t
ck S
wju
Sim = 0
M
repeat until max = 0 or size(M) = (0, 0)
16
suppose: u t
ck S
wju
Sim = 0
M
repeat until max = 0 or size(M) = (0, 0)
16
suppose: u t
ck S
wju
M
Sim = 0.8
repeat until max = 0 or size(M) = (0, 0)
16
suppose: u t
ck S
wju
M
Sim = 0.8
repeat until max = 0 or size(M) = (0, 0)
16
suppose: u t
ck S
wju
M
Sim = 0.8
repeat until max = 0 or size(M) = (0, 0)
16
3.2.2 Classification Approaches
S L S Lsent


17
3.2.2 Classification Approaches
18
3.2.2 Classification Approaches
19
3.2.2 Classification Approaches
kernelwe
•
•
20
4 Results and Discussions
21
4 Results and Discussions
22
23
6 Conclusion and Future Work
X
6 Conclusion and Future Work
X
Presenter’s Comments
24

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第8回最先端NLP勉強会 EMNLP2015 Guosh et al Sarcastic or Not