Toward Automatic Compilationof Phrasal Thesaurus
Atsushi Fujita and Satoshi Sato (Nagoya Univ., JAPAN){fujita,ssato}@nuee.nagoya-u.ac.jp, http://paraphrasing.org/

Summary

Productive paraphrases

Phrasal thesaurus: beyond the word-based semantic computing
Generating productive paraphrases:
1. Generate candidate paraphrases
2. Filter out incorrect instances with a statistical measurement
Collecting non-productive paraphrases:
1. Determine the target vocabulary
2. Collect their paraphrases (e.g. literal phrases for idiom)Those traditionally represented with transformation patterns
Case/voice/verb alternation
Category-shifting (nominalization, light-verb construction)
Head-switching
General patterns lead to plenty of incorrect instances

Background & Goal
Words are not necessarily the appropriate unit of meaning
Phrasal thesaurus: beyond the word-based semantic computing
a natural extension of conventional word-based thesaurus
deals with predicate phrases accompanying complements

X show a A Y

X v(Y) adv(A)Employment shows a sharp decrease

Employment decreases sharply

Statistics show a gradual decline
The data show a specific distribution

X Statistics decline gradually
X The data distribute specifically

Our approach: over-generation + filtering/ranking
Over-generation step
Generate candidate paraphrases based on 3 sorts of knowledge
kakunin : o : isogu
NC V
checking: ACC: to hurry

Strategy
Productive

XVY

X’s V-ing of Y

Trans. Pat.
N:C:V ⇒ adv(V):vp(N)XVY

Y be V-PP by X

adv(isogu) : vp(kakunin)

X show a A Y
X wrote Y
X solves Y
burst into tears
comfortX v(Y) adv(A)X is the author of Y
X deals with Y
cried

Lex. Func.
adv(V)

Generate!!

Collect!!

consoleNon-productive paraphrases

Ongoing work

Lex. Func.
generates different
lexical items in certain
semantic relations.
kakunin-suru {φ, reru/rareru, seru/saseru} {φ, ta/da}
Its back-end is ENJI, a
Lexical Derivation DB.
isoide : {kakunin-suru : {φ, reru/rareru, seru/saseru} : {φ, ta/da}}Lex. Func.
v(N)Verb/VP paraphrase dictionary
For Sino-Japanese deverbal nouns + “suru (do)”opinion-ACC to explain-to go around(to go around explaining one’s opinion)

toite-mawaru

totemo nemui

to sleep-FE (get really)very
be sleepy(be very sleepy)

Gen. Func.
genTense()

Measure the quality of phrasal pair (s and t) as paraphrases

Grammaticality factor: structured N-gram language models
MDS
kitto

kare wakyou nokantoku kaigi ni wa kuru nai da u .

EOSkare wakyou nokantoku-kaigi ni-wa

EOS

CFDS
surelyhe TOP today GENcoach meeting(He will surely not come to today’s coach meeting.)

DAT-TOPkuru

nai-daro-u-.

comeNEG-must-.

Similarity factor: distributional similarity measures
BOW and MOD features from Web snippets
mokuteki de ,Interaction between predicate phrases and functional expressions
w/ TSUTSUJI: a dictionary of Japanese Functional Expressions
nemuku-te-shikata-ga-nai

Gen. Func.
genVoice()

Filtering/ranking stepkitto

to make a campaign tour

Gen. Func.
vp(N)in a hurry : { to check : { Active, Passive, Causative } : { Present, Past } }The lack of discussion on the goal of building a static resource
Essential from both viewpoints of engineering and lexicography
Our approach: determine the target + collect their paraphrases
Idiom/literal paraphrase dictionary
1. Compile a list of Japanese basic idioms
5 dictionaries for human → comparative list (3,629 idioms)
http://kotoba.nuee.nagoya-u.ac.jp/
2. Collect the counterpart for each basic idiom (ongoing)
From the gloss in those dictionaries
From corpus based on DS etc.

iken-oGen. Func.
generates a set of thesimplest phrases from
0-2 content words.
e.g. vp(N), np(N1,N2)

isoide {v(kakunin) : genVoice() : genTense()}Non-productiveyuuzei-suru

Trans. Pat.
gives a skeleton ofsyntactic variant atSSynt level.

[Matsuyoshi & Sato, 08]

MOD Features
Modifiee/D: mokuteki(aim)kakunin suru no ga
ime-jio

Modifier/D: ime-ji-o(image-ACC)

isoideGiven phrase
NSF Symposium @ New York, 2008.11.15