This document appears to be notes from a technical presentation covering various natural language processing and machine learning topics including part-of-speech tagging, language modeling using n-grams and neural networks, and evaluating models based on metrics like true/false positives and negatives. It includes details on the training data and tools used, comparisons of n-gram and neural network approaches, and references to additional resources.

2. 1. 2. 3. 4.
:
▸
▸ [ ] PFI 3
▸ [ ] JR 5 1
▸ …
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2

3. 1. 2. 3. 4.
:
▸
▸ [ ] PFI 3
▸ [ ] JR 5 1
▸ …
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3

4. 1. 2. 3. 4.
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▸ 2 2
▸ 1 1
▸ 8 9 2
▸ 5/10
→ → →
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4
…

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:
▸ ←
▸ Contessa
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7. 1. 2. 3. 4.
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▸ 10:00 - 18:00
▸ 11:30
▸ [ ]
[ ]
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:
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▸
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Camel Up
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↑ ↑

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▸ → X’IAN
▸ → ALOHA TABLE
▸ →
▸ →
▸ →
▸ →
▸ →
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10.
※ …

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:
▸
[ ]
[ ] ↑
[ ]
[ ]
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:
i-1
12
w0 w1 … wi−1 wi
wi
P(wi |w0, …, wi−1)

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▸ : 0.1
13
w0 w1 … wi−1 wi
P(wi |w0, …, wi−1) < 0.1

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❌
⭕
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15. 1. 2. 3. 4.
▸ : : 233,873 sents
▸ :
12
▸ : MeCab UniDic, IPADIC Neural
▸ :
- N-gram → KenLM 5-gram
- Neural → LSTM 1 <unk>
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16. 1. 2. 3. 4.
N-gram :
▸ : UniDic, IPADIC
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N-gram

17. 1. 2. 3. 4.
Neural :
▸ : UniDic, IPADIC
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18. 1. 2. 3. 4.
Neural :
▸ :
18
Neural …

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▸ N-gram TP FP
word
▸ Neural N-gram
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1.
2.
2
▸ +α
-
- [ ] :
- :
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1.
2.
2
▸ +α
-
- [ ] :
- :
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23. ▸
- http://www.creativ.xyz/retrieva-intern-840
- https://nomoto-eriko.hatenablog.com/entry/2018/10/04/125940
▸
- https://speakerdeck.com/nomotoeriko/retoribaintancheng-guo-bao-gao
- https://speakerdeck.com/kajyuuen/zhuan-men-yong-yu-chou-chu-shou-fa-
falseyan-jiu-to-chou-chu-apurikesiyonfalsekai-fa
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