Background
Dependency Analysis of Japanese
Spoken Language via SVM
Kiyonori Ohtake
ATR Spoken Language Translation Research Laboratories
August 6, 2003

Dependency analysis for Japanese isimportant.
Many works for text have been done.
Few analyzers for spoken Japanese.
difficult task?
hard to develop analyzed spokenlanguage corpora?

Dependency Analysis of Japanese Spoken Language via SVM – p. 1/24

Objective

Dependency Analysis of Japanese Spoken Language via SVM – p. 2/24

Japanese Dependency Structure

To build a dependency analyzer for
spoken language with corpus-based
method.
To prepare a consistent dependency
analyzed corpus of spoken languagewith currently available resources.

私はI

今
now

瀋陽にin Shenyang

1. From left to right.
2. Do not cross each other.
3. One segment depends on only one
segment.
Dependency Analysis of Japanese Spoken Language via SVM – p. 4/24

Dependency Analysis of Japanese Spoken Language via SVM – p. 3/24

Dependency Analysis Approach
Rule-based
Corpus-basedusing ME, SVM, etc.
Why SVM?
very high generalization ability⇓
it works well if the learning corpus issmall.
Dependency Analysis of Japanese Spoken Language via SVM – p. 5/24

CaboCha: analyzer via SVM
Characteristics:
Cascaded chunking model [Kudo andMatsumoto 2002]
SVM tells whether a segment
depends on the next one or not
Available analyzed corpus is written
text (KUTC)

居ます
be staying

Resources
Analyzers
KNP: rule-based analyzer
CaboCha: analyzer with SVM
Analyzed corpora
KUTC: newspaper articles, directly
applicable to CaboCha
JDEP: conversations, not applicable
to CaboCha
Dependency Analysis of Japanese Spoken Language via SVM – p. 6/24

KNP: rule-based analyzer
Characteristics:
Detecting conjunctive structures,and then analyze with manuallybuilt rules.
Accuracy = 91% (for newspapers)

Accuracy = 89% (by 2-fold crossvalidation for newspapers)
Dependency Analysis of Japanese Spoken Language via SVM – p. 7/24

Dependency Analysis of Japanese Spoken Language via SVM – p. 8/24

Corpus - KUTC
KUTC: Kyoto University Text Corpus
newspaper articles, 40,000 sentences
analyzed with KNP and manuallycorrected
dependency segment: bunsetu
bunsetu: a phrasal unit that consists ofone or more morphemes.

Corpus - JDEPJDEP= Japanese Dependency
structures of ATR SLDB
travel-type conversations, 21,761
utterances
automatically analyzed and manuallycorrected
dependency segment: morpheme
Converted into KUTC format

Dependency Analysis of Japanese Spoken Language via SVM – p. 9/24

Dependency Analysis of Japanese Spoken Language via SVM – p. 10/24

Comparing two analyzers and two corpora

Corpus Cleaning

KUTC
Dep. acc. Sent. acc.
KNP
91.23%
57.17%
CaboCha97.36%
83.40%JDEPDep. acc. Sent. acc.
KNP
84.27%
69.54%
CaboCha88.43%
76.01%

Objective: to remove inconsistencies
How: repeating closed test with
CaboCha and correct manually.
very SIMPLE
Standard guideline: based on KUTC
annotation standard, and arranged forspoken language.

Dependency Analysis of Japanese Spoken Language via SVM – p. 11/24

Gain by Cleaning
Evaluated by 2-fold cross validation
Accuracy
KUTC JDEP
Before 88.85% 92.78%
After 89.92% 93.03%

Dependency Analysis of Japanese Spoken Language via SVM – p. 12/24

Discussions
Why JDEP results were higher?
Whether bunsetu is good segment for
spoken language dependency
analyzing?

Dependency Analysis of Japanese Spoken Language via SVM – p. 14/24

Dependency Analysis of Japanese Spoken Language via SVM – p. 13/24

Discussions

Discussions

Why JDEP results were higher?
Sentences in JDEP are shorter.
Whether bunsetu is good segment for
spoken language dependency
analyzing?

Why JDEP results were higher?
Sentences in JDEP are shorter.
Whether bunsetu is good segment for
spoken language dependency
analyzing?
⇒ No. Need to investigate further.

Dependency Analysis of Japanese Spoken Language via SVM – p. 14/24

Dependency Analysis of Japanese Spoken Language via SVM – p. 14/24

Open problemsConclusion

Much learning time.
To prepare properly segmentedcorpus.
How to analyze real spoken language.

Introduced available resources, andbuilt an analyzer via SVM.
Carried out the corpus cleaning.
Cleaning raised the acc. (1.07% for
KUTC, 0.25% for JDEP).
The analyzer resulted 93% acc. forspoken language.

Dependency Analysis of Japanese Spoken Language via SVM – p. 15/24

Dependency Analysis of Japanese Spoken Language via SVM – p. 16/24

Appendix - Features for SVM
Thank you!

Dynamic features:
modify or not?

B

B

Modifier …

AModifieeA…

C

Static features: head words,
parts-of-speech, functional words andinflection forms, and so on.
Dependency Analysis of Japanese Spoken Language via SVM – p. 17/24

Appendix - cascaded chunking
私は今

中国の 瀋陽にOOOStep 1. InitializationO

Dependency Analysis of Japanese Spoken Language via SVM – p. 18/24

Appendix - cascaded chunking

居ますOO tag means the dependency relation isundecided.

私は今OO

中国の 瀋陽にDD

私は今中国の 瀋陽にOO

DdeleteDO

Step 2. Each O tag depends onimmediate right hand side?
⇒ replace with D

Dependency Analysis of Japanese Spoken Language via SVM – p. 19/24

Appendix - cascaded chunking

Dependency Analysis of Japanese Spoken Language via SVM – p. 20/24

Appendix - cascaded chunking

居ます

私は今中国の 瀋陽に

OOO

D
deleteStep 3. Delete segments with D tag that
immediately follow O tag.

Dependency Analysis of Japanese Spoken Language via SVM – p. 21/24

居ます

居ます
O

Repeating Step 2 and 3.

Dependency Analysis of Japanese Spoken Language via SVM – p. 22/24

Appendix - cascaded chunking

私は今O

Ddelete中国の 瀋陽にAppendix - cascaded chunking

居ます

私はO

D
deleteRepeating Step 2 and 3.

今中国の 瀋陽に居ますO

Repeating Step 2 and 3.
And finished.
Dependency Analysis of Japanese Spoken Language via SVM – p. 23/24

Dependency Analysis of Japanese Spoken Language via SVM – p. 24/24