第8回最先端NLP勉強会で発表担当になった論文の紹介用スライドです。

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)
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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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