This document discusses various topics in natural language processing (NLP) including neural networks (NN), word embeddings, recurrent neural networks (RNNs), and evaluation metrics for dialogue systems. It provides examples of recent papers on NLP tasks like text classification and machine translation. Various Japanese morphological analyzers are also listed.

1. NN
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2. Koki Yasuda(@himanandayonaxa)
: (B4)
: , ,
: NLP
: nltk
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3. NN
▼ github
github.com/yasudadesu/nlp_analayze
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4. 1. NLP NN
2. NLP
3.
4.
5.
NN
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5. NN
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1. NLP NN
2. NLP
3.
4.
5.

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NLP NN

7. NLP
ACL
2011-2018
www.aclweb.org/
EMNLP
2011-2017
emnlp2017.net/
NAACL HLT
2013, 15, 16, 18
naacl2018.org/
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▼

8. ACL (Association for Computational Linguistics)
year_w …
year_r … accepted paper
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year_w …
year_r … accepted paper
ACL (Association for Computational Linguistics)

10. EMNLP (Empirical Methods in Natural Language Processing)
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11. NAACL HLT (North American Chapter of the Association for Computational Linguistics:
Human Language Technologies)
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NLP NN
’neural’
’model’

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NLP NN
Recent Trends in Deep Learning Based Natural Language Processing
Tom et al. arxiv.org/pdf/1708.02709.pdf
NN
70%

14. 1. NLP NN
2. NLP
3.
4.
5.
NN
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Slideshare -

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Slideshare -

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N-gram
(Embedding)
RNN, CNNSVM
NN

19. NN
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1. NLP NN
2. NLP
3.
4.
5.

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NN
NN
▼
▼
▼ 100
▶︎▶︎

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▼ ( )
▼ one-hot ( )
▶︎▶︎ ( )
NN
NN

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N-gram
(Embedding)
NN
RNN, CNNSVM

23. -
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MeCab, Janome
JUMAN++
:
:
/ / / / / / / / / /
/ / / / / /
/ / / / / / / /
/ / /

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▼
Twitter UGC
▼
RNN
[ + DEIM2018]
- NN

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▼ 20
(MT)
▼ ,
n-gram
)
- NN

26. - NN
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▼
▼ 1/10

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N-gram
(Embedding)
NN
RNN, CNNSVM

28. - one-hot
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-
▼
▼
cf. Sense Embeddings

30. NN
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arxiv.org/pdf/1605.07725.pdf
ADVERSARIAL
TRAINING METHODS FOR
SEMI-SUPERVISED TEXT CLASSIFICATION
NLP
Embedding
IMDB Embedding

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arxiv.org/pdf/1804.08166.pdf
Embedding
Word Embedding Perturbation
for Sentence Classification
NLP
NN

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1. NLP NN
2. NLP
3.
4.
5.
NN

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NN
https://www.rinna.jp/

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Ledge.ai -
ledge.ai/chatbot_market_size/

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▶︎
▶︎▶︎ Human-like
BLEU, ROUGE, METEOR

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-
BLEU ROUGE
(MT)
precision
MT
recall
, N-gram

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www.anlp.jp/proceedings/annual_meeting/2012/pdf_dir/E2-8.pdf
N-gram
( )
-

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How NOT To Evaluate Your Dialogue System:
An Empirical Study of Unsupervised Evaluation Metrics
for Dialogue Response Generation
arxiv.org/abs/1603.08023
-
BLEU
Embedding Based

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Towards an Automatic Turing Test:
Learning to Evaluate Dialogue Responses
arxiv.org/abs/1708.07149
-
ADEM
RNN
hierarchicalRNN[El Hihi and Bengio, 1995;Sordoni+ 2015]
[shang+, 2016]
Human-like

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-
Human-like

41. 1. NLP NN
2. NLP
3.
4.
5.
NN
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42. NLP
NN
NN
NN
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datascience.stackexchange.com/questions/13138/what-is-the-
difference-between-word-based-and-char-based-text-generation-rnns
What is the difference between
word-based and char-based text generation RNNs?
Neural Machine Translation of Rare Words with Subword Units
arxiv.org/abs/1508.07909
NMT