1. Final-modal particles
語氣詞
Final-modal particles
褚 靚 A

2. Key dealing
corpus
annotate
analyze

3. Corpus/
Lexicon
Build personal
Corpus
Lexicon

4. Corpus 臺灣政治大學漢語口語語料庫 NCCU Corpus of Spoken Chinese 聯合語料庫-聯合報 ？
PTT ?
聯合語料庫-聯合報 ？
中國傳媒大學媒體語言語料庫

5. PASS Summit 2011
11/15/2018
© 2011 Microsoft Corporation. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.
The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

6. Lexicon
Lexicon台
Lexicon陸
各自框架-lexicon
共同高頻-corpus

11. WHO/HOW 台生 台語料 陸生 陸語料 Annotate PASS Summit 2011 11/15/2018
© 2011 Microsoft Corporation. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.
The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

12. HOW? Annotate Sentence Polarity Confirmative degree Chunk based End/
Beginning
Gender
tendency
S1:————□1 N
S2:————□1
S3:————□1
S4:————□1 Y
S5:————□1
S6:————□1
S7:————□1
HOW?

13. Average the different ideas
Counting Predictive
polarity Valence
Calculate the average emotion polarity valence of each keyword
pos=1, neg=-1,neu=0
X+Y+Z=N (A constant value=all people)
X = The number of people prefer this sentence positive polarity Y = The number of people prefer this sentence negative polarity Z = The number of people prefer this sentence neutral polarity X+Y+Z= Total number of the people participating in annotation work
Predictive_ Polarity S1 = X*1+y*(-1) N
Average the different ideas

14. Average whole sentences
Counting Predictive
polarity Valence
Calculate the average emotion polarity valence of each keyword
pos=1, neg=-1,neu=0
S1+S2+……+Sn-1+Sn=M (A constant value=all sentences)
Polarity_S1 (P1)= Polarity_S2 (P2)= -0.6
………………………………………… Polarity_Sn (Pn)= 0.2
S1+S2+……+Sn-1+Sn= Total number of the sentences being annotated
Predictive_ Polarity Valence = P1+S2+……+Pn M
Average whole sentences

15. An example
Analyze

16. An example in comparison between”嗎”and”嘛”
PASS Summit 2011
11/15/2018
An example in comparison between”嗎”and”嘛”
© 2011 Microsoft Corporation. All rights reserved. Microsoft, Windows, Windows Vista and other product names are or may be registered trademarks and/or trademarks in the U.S. and/or other countries.
The information herein is for informational purposes only and represents the current view of Microsoft Corporation as of the date of this presentation. Because Microsoft must respond to changing market conditions, it should not be interpreted to be a commitment on the part of Microsoft, and Microsoft cannot guarantee the accuracy of any information provided after the date of this presentation. MICROSOFT MAKES NO WARRANTIES, EXPRESS, IMPLIED OR STATUTORY, AS TO THE INFORMATION IN THIS PRESENTATION.

17. Ideas welcomed!

18. Thanks for
listening