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Sarcastic or Not: Word Embeddings to Predict the Literal or Sarcastic Meaning of WordsDebanjan Ghosh, Weiwei Guo, and Smaranda MuresanEMNLP2015
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Abstract
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2 Collection of Target Words
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2 Collection of Target Words
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2 Collection of Target Words
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2 Collection of Target Words
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2 Collection of Target Words
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2 Collection of Target Words
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2 Collection of Target Words
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2 Collection of Target Words
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2 Collection of Target Words
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t
φ ≥ 0.8
2 Collection of Target Words
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2 Collection of Target Words
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2 Collection of Target Words
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3 Literal/Sarcastic Sense Disambiguation
t
t S L
t S L
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3.1 Data Collection
S L
L
Lsent
S Lsent
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3.1 Data Collection
t
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3.2 Learning Approachest
t S L
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3.2.1 Distributional Approachest S L
vs vl
u u t
vu
vu vs vl
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3.2.1 Distributional Approaches
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3.2.1 Distributional Approaches
t S L
cos
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3.2.1 Distributional Approaches
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3.2.1 Distributional Approaches
•
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3.2.1 Distributional Approaches
t vs vu Sim
vs vu ck wj
M Mjk ck wj
cos
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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
wj
u
Sim = 0
M
repeat until max = 0 or size(M) = (0, 0)16
suppose: u t
ck S
wj
u
Sim = 0
M
repeat until max = 0 or size(M) = (0, 0)16
suppose: u t
ck S
wj
u
M
Sim = 0.8repeat until max = 0 or size(M) = (0, 0)16
suppose: u t
ck S
wj
u
M
Sim = 0.8repeat until max = 0 or size(M) = (0, 0)16
suppose: u t
ck S
wj
u
M
Sim = 0.8repeat until max = 0 or size(M) = (0, 0)16
3.2.2 Classification ApproachesS L S Lsent
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3.2.2 Classification Approaches
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3.2.2 Classification Approaches
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3.2.2 Classification Approaches
kernelwe
•
•
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4 Results and Discussions
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4 Results and Discussions
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6 Conclusion and Future Work
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6 Conclusion and Future Work
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Presenter’s Comments
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