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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•

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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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