Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Sign up for free
Menu
Search
Features
All features
Private URLs
Password Protection
Custom URLS
Scheduled publishing
Remove Branding
Restrict embedding
Deck Collections
Notes
Features
All features
Private URLs
Password Protection
Custom URLS
Scheduled publishing
Remove Branding
Restrict embedding
Deck Collections
Notes
Explore
Featured decks
Featured speakers
Programming
Technology
Storyboards
Explore
Featured decks
Featured speakers
Programming
Technology
Storyboards
Pricing
Search
Sign in
Sign up for free
[email protected]
Search
Daniel Tseng
January 14, 2013
Programming
100
0
Share
Embed
Copy iframe code
Copy JS code
Copy link
Start on current slide
[email protected]
Daniel Tseng
January 14, 2013
More Decks by Daniel Tseng
See All by Daniel Tseng
What-I-saw-the-EC-trend
kpman
0
180
Uitox-global-EC-trend
kpman
0
150
NTU PreConf @NTU.IM
kpman
0
180
Install wordpress on EC2
kpman
0
340
ITC_Lab#
[email protected]
kpman
1
75
招生說明會@ITSEED
kpman
0
53
[email protected]
kpman
0
60
Other Decks in Programming
See All in Programming
C#の現在地 進化の歴史と、AI時代の.NET Everywhere
neuecc
5
5.2k
全ての永続トークンを、生まれる前に消し去りたい!
sylph01
2
900
IBM Bob Dojo #1 仕様駆動開発入門
oniak3ibm
PRO
0
390
UPDATE をやめる — EF Core でマスタをバージョン管理する
panda728
PRO
0
1.2k
Atomic Design, Enforced: Scaling Mobile Design Systems (next.app devCon / droidCon Berlin 2026)
steliosf
PRO
0
170
Apple Intelligence を用いた個人情報誤送信防止、及びユーザーリクエスト体験の改善について
yukiny
0
350
自サークルのIdPからログアウトできるようになるまで
taiseiue
1
140
Herb in Rails 8.2: Your ERB views, now HTML-aware @ Rails World 2026, Austin, Texas
marcoroth
0
220
iOS 27でニュースアプリはどう変わる!? 〜日経電子版の新機能対応と、開発事例から〜
lynnswap
7
13k
Optunaを作ったときにやりたかったこと、その後 (2026/10/16, Optuna Meetup #3)
iwiwi
1
430
なぜCTOを降りてFDEを選んだのか?〜なぜプロダクト企業がFDEで顧客の現場に踏み込むのか〜
gonta
1
210
コーディング自動化の次へ。人の介入をゼロにするファクトリーとサポートAIによる実践
timakin
5
3.6k
Featured
See All Featured
Primal Persuasion: How to Engage the Brain for Learning That Lasts
tmiket
0
500
Ruling the World: When Life Gets Gamed
codingconduct
0
400
Avoiding the “Bad Training, Faster” Trap in the Age of AI
tmiket
0
270
A designer walks into a library…
pauljervisheath
211
25k
How to Think Like a Performance Engineer
csswizardry
28
2.9k
StorybookのUI Testing Handbookを読んだ
zakiyama
31
7k
GitHub's CSS Performance
jonrohan
1033
470k
Java REST API Framework Comparison - PWX 2021
mraible
34
9.8k
The Art of Delivering Value - GDevCon NA Keynote
reverentgeek
16
2.2k
A better future with KSS
kneath
240
18k
Leveraging LLMs for student feedback in introductory data science courses - posit::conf(2025)
minecr
1
440
HU Berlin: Industrial-Strength Natural Language Processing with spaCy and Prodigy
inesmontani
PRO
0
750
Transcript
IR final project -Food Type Parser 李維珣 曾建勳 江軍 2013.1
@NTU.IM
Agenda •Inspiration •Implementation •Demo •Future
Inspiration • Movie • Boy / Girl • Recipe
Inspiration • Movie • Boy / Girl • Recipe many
topic / title
Inspiration • Movie • Boy / Girl • Recipe many
topic / title bias / irony
Inspiration • Movie • Boy / Girl • Recipe many
topic / title bias / irony data / resource / interesting
Inspiration • Movie • Boy / Girl • Recipe many
topic / title bias / irony data / resource / interesting
Implementation
Implementation jsoup ->
Implementation jsoup icook.tw ->
Implementation jsoup icook.tw -> icook.tw/recipes/35822
Implementation jsoup icook.tw ->
Implementation jsoup icook.tw -> ->
Implementation jsoup icook.tw training data -> ->
Implementation jsoup icook.tw training data -> -> 500 docs ↑
Implementation -> labeling
Implementation -> labeling 義式
Implementation -> labeling 義式 法式
Implementation -> labeling 義式 法式 南洋
Implementation -> labeling 義式 法式 南洋 中式
Implementation -> labeling 義式 法式 南洋 中式 歐式
Implementation -> labeling 義式 法式 南洋 中式 歐式 美式
Implementation -> labeling 義式 法式 南洋 中式 歐式 美式 日式
Implementation -> labeling 義式 法式 南洋 中式 歐式 美式 日式
韓式
Implementation -> training data
Implementation -> training data mmseg4j
mmseg4j ?
mmseg4j 中式地瓜粥
mmseg4j 廣式 海鮮 粥
如果你有認真 聽我簡報的話
mmseg4j 中式地瓜粥
mmseg4j 中式 地瓜 粥
Implementation -> training data mmseg4j
Implementation -> training data mmseg4j ->
Implementation -> training data mmseg4j -> feature selection
Implementation -> training data mmseg4j -> feature selection LLR(log likelihood
ratio)
Implementation java servlet Apache Tomcat
Implementation ->
Implementation -> food name
Implementation -> food name url
Implementation -> food name url recipe
Implementation -> food name url recipe -> Naïve Bayes
Demo
Future precision increasing relative food suggestion
Q&A
Thank you