This document presents a method to predict whether words are used literally or sarcastically based on word embeddings. It collects target words and their contexts to learn embeddings. It then uses the embeddings to disambiguate literal and sarcastic senses of words using distributional and classification approaches. The distributional approach calculates cosine similarity between word embeddings, while the classification approach trains models on sentence embeddings. Evaluation shows the approaches perform well in predicting literal and sarcastic meanings of words. The authors conclude and discuss directions for future work.

1. Sarcastic or Not:
Word Embeddings to Predict the
Literal or Sarcastic Meaning of Words
Debanjan Ghosh, Weiwei Guo, and Smaranda Muresan
EMNLP2015
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2. Abstract
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3. 2 Collection of Target Words
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4. 2 Collection of Target Words
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5. 2 Collection of Target Words
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6. 2 Collection of Target Words
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7. 2 Collection of Target Words
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8. 2 Collection of Target Words
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9. 2 Collection of Target Words
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10. 2 Collection of Target Words
X

11. 2 Collection of Target Words
5
t
φ ≥ 0.8

12. 2 Collection of Target Words
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13. 2 Collection of Target Words
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14. 2 Collection of Target Words
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15. 3 Literal/Sarcastic Sense Disambiguation
t
t S L
t S L
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16. 3.1 Data Collection
S L
L
Lsent
S Lsent
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17. 3.1 Data Collection
t
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18. 3.2 Learning Approaches
t
t S L
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19. 3.2.1 Distributional Approaches
t S L
vs vl
u u t
vu
vu vs vl
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20. 3.2.1 Distributional Approaches
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21. 3.2.1 Distributional Approaches
t S L
cos
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22. 13

23. 3.2.1 Distributional Approaches
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24. 3.2.1 Distributional Approaches
•
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25. 3.2.1 Distributional Approaches
t vs vu Sim
vs vu ck wj
M Mjk ck wj
cos
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26. 3.2.1 Distributional Approaches
M max Sim
max Mjk
max = 0 M
Sim vs vu vl
S L
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27. suppose: u t
ck S
wju
Sim = 0
M
repeat until max = 0 or size(M) = (0, 0)
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28. suppose: u t
ck S
wju
Sim = 0
M
repeat until max = 0 or size(M) = (0, 0)
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29. suppose: u t
ck S
wju
M
Sim = 0.8
repeat until max = 0 or size(M) = (0, 0)
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30. suppose: u t
ck S
wju
M
Sim = 0.8
repeat until max = 0 or size(M) = (0, 0)
16

31. suppose: u t
ck S
wju
M
Sim = 0.8
repeat until max = 0 or size(M) = (0, 0)
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32. 3.2.2 Classification Approaches
S L S Lsent
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33. 3.2.2 Classification Approaches
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34. 3.2.2 Classification Approaches
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35. 3.2.2 Classification Approaches
kernelwe
•
•
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36. 4 Results and Discussions
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37. 4 Results and Discussions
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38. 23

39. 6 Conclusion and Future Work
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40. 6 Conclusion and Future Work
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41. Presenter’s Comments
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