morphologymorpheme 1/2 §grammatical morpheme in a predicative word phrase: (word phrase = a...

(c) Key-Sun Choi for Pan Localization 2005

MorphologyCHOI, Key-Sun

Korea Advanced Institute of Science & Technologyhttp://www.kaist.edu/

Korea Terminology Research Center for Language & Knowledge Engineering

http://www.korterm.org/

2(c) Key-Sun Choi for Pan Localization 2005

Overview

Morphology and its Computational Aspects will be presented. A general view of morphological analysis is introduced with focusing on its components and handling methodologies. An implementation is drawn with unknown word processing. In the context of localization, terminology is crucial for the domain-specific language processing. Two aspects on translation and transliteration will be shown.


Table of ContentsIntroduction: Morphology and Characteristics

Complexity of MorphologyTypes of AmbiguitiesExamples in English, Korean, Chinese, Japanese,

General RepresentationComponents of Morphological AnalysisImplementation SchemeUnknown Words

Terminology and Computation IssuesTranslation (Localization) of New TermsTransliteration

Romanized Form of Local Language

E.g. "data" = "資料" -- translation"deitə" (데이터) -- transliteration


Writing Systems

Segmented Textsa space between two word unitse.g. English

Unsegmented Textsno space in texte.g., Chinese, Japanese

Segmented + Unsegmented Textsa space before content word (noun, verb, adjective) in order to signal the new content word (=new concept)no space before functional word

"a room+in" for an English meaning "in a room"


Unsegmented Text: English (1)

Thisworkaimstoextractpossiblecausalrelationsthatexistbetweennounphrases.Somecausalrelationsaremanifestedbylexicalpatternslikecausalverbsandtheirsub-categorization.Weuselexicalpatternsasafiltertofindcausalitycandidatesandwetransferthecausalityextractionproblemtothebinaryclassification.Tosolvetheproblem,weintroduceprobabilitiesforwordpair,conceptpairandcuephrasethatcouldbeacausalitypattern.Theseprobabilitiesarelearnedfromtherawcorpusinanunsupervisedmanner. (IPM, 2005, D.S. Chang & K.-S. Choi)


Segmented Text: English (2)

This work aims to extract possible causal relations that exist between noun phrases. Some causal relations are manifested by lexical patterns like causal verbs and their sub-categorization. We use lexical patterns as a filter to find causality candidates and we transfer the causality extraction problem to the binary classification. To solve the problem, we introduce probabilities for word pair, concept pair and cue phrase that could be a causality pattern. These probabilities are learned from the raw corpus in an unsupervised manner. (IPM, 2005, D.S. Chang & K.-S. Choi)


Segmented+Unsegmented Text: English (3)

This work aims to+extract possible causal relations that+exist between+noun-phrases. Some causal relations are manifested by+lexical-patterns like+causal-verbs and their sub-categorization. We use lexical patterns as+a-filter to+find causality candidates+ and we transfer the causality extraction problem to+the-binary classification. To+solve the problem, we introduce probabilities for+word-pair, concept-pair+and cue-phrase that+could be a causality pattern. These probabilities are learned from+the-raw-corpus in+an unsupervised-manner. (IPM, 2005, D.S. Chang & K.-S. Choi)

"in+room" (preposition) = "room+in" (postposition)


German-English-Korean-Japanese Words

Fremdsprachenkenntnisseknowledge of foreign languages외국어지식

外国語知識

Naturwissenschaftnatural sciences자연과학

自然科學

German Examples from Benjamin Tsou (ACL 2000)


Computational Morphology

MorphemeA minimal meaningful elements"computational" = "comput+ation+al"

Morphological AnalysisSegmentation

To divide into morphemes or words

To include lemmatization (= to find its stem)

CategorizationTo assign Part-of-speech category (POS tags)

To assign Semantic features


Morphological Analysis: e.g.,

These probabilities are learned from the raw corpus in an unsupervised manner.

Original textThese probability+Plural be+Plural+Present learn+PPfrom the raw corpus in a+Vowel un_supervise+PPmanner .

Lemmatized textThese/pron probability/noun+pluralare/be_verb+plural+present learn/verb_ed/ppfrom/prep the/article raw/adjective corpus/nounin/prep an/article un/prefix_supervis/verb_ ed/pp manner/noun ./period

part-of-speech categorization (POS tagging/Annotation)


Word Formation

Types of LanguagesInflectional

e.g., Latincanta-bo

Analytic - derivatione.g., English I will sing

Agglutinative - concatenatione.g., Korean, Japanese, Hindi, Turkish, na-neun norayha-keyssda


Morphology:how to describe your language

e.g., a Korean case

your native language: (Korean)na+neun norayha+keyssda

Grammar: I+subjective sing+will (future & intention)I+subjective postposition sing+ future&intention ending

meaning: (English)I will sing


Grammatical morphemes(functional words)

Postpositionto express a case feature (or a functional role)

subjective case, objective case, etc.e.g., I sings a song. (I = subjective, a song = objective)

to represent semantic roles of constituentstypically Noun + postposition

Endingto represent features like tenses, aspects, moods and voicesto derive relative clausestypically Verb or Adjective + endings for its Auxiliary Verbs orsentence connectivese.g.,: I song sing+will (I will sing a song.)


Example of Grammatical Morpheme 1/2

Grammatical morpheme in a predicative word phrase: (word phrase = a segmentation unit)

"(someone) went but"

ga syeoss seumnida man가 셨 습니다 만

manseumnidaeosssiga

“go” honorfic Pasttense

Declarativemood concessive

가 시 만었 습니다

Verb AUX Particle Ending Postposition

Predicate Grammatical Morphemes

Predicative word phrase


jib eseo buteo man

집 만

eun

에서 부터 은

집 에서 부터 만 은

jib eseo buteo man eun“house” place origin concessive topical marker

Adverbial postpos.

Example of Grammatical Morpheme 2/2

Grammatical morpheme in a substantive word phrase"(It should be prohibited) from home, even (others cannot do)"

Noun postposition postposition

Grammatical morphemesNoun

Substantive word phrase


Language units

GraphemeAlphabet, a minimal unit

consonant: (e.g.,) b, c, d, f, ...vowel: (e.g.,) a, e, i, o, u, ...

Syllable2 or 3 graphemes form a syllablee.g., ga, sun, wa, ...

Word phrase (or word)Segmentation unit (or spacing unit)it consists of one or more morphemes

word phraseword phrasesyllablesyllable

graphemegraphemeMorphemeMorpheme


Types of Ambiguities- Homonymy -

English examples:lead (metal) lead (past tense is "led")swallow (bird) swallow (through mouth)Bill (name) bill (for payment)

What is the word's part-of-speech?


Types of Ambiguities in Word Phrase 1/2

Types of ambiguity in word phrase analysisAmbiguity in Segmentation + POS tag

gamyeon

Ambiguity in POS taggameulgoto (English simulation)

– go (N or V)

Ambiguity in lemmatizationdowa

gamgam -eul-eul

gamgam -eul-eul

dobdob -a-a

dodo -wa-wa

Verb

Ending

Noun

postposition

N

N p

p

p

N

V

e

eV

V e

gaga -myeon-myeon

ga-myeonga-myeonN

eV


Types of Ambiguities in Word Phrase 2/2

Mixed typega-si-neun

gaga

gasigasi

-si--si- -neun-neuneV f

Verb

Ending

NounN

V

e

Prefinal ending

Postpositionp

f

galgal -si--si- -neun-neuneV f

-neun-neunpN

gasigasi -neun-neuneV


Metrics of Ambiguity

Considering ambiguities Average ambiguities per an word phrase : 3.5 In case of 1-syllable morphems : 2.8 ambiguities averageIn case of 2-syllable morphemes : 1.13 ambiguities average

*Randomly selected 15 morphemes from the most frequent 10000 morpheme

Considering phonetic transformation rulesAbout 20 rules are needed.

More than 50 sub-rules (considering contextual information)

Complexity of n-syllable word phrase segmentationsAt least 2n-1

e.g., n=4 (Korean example)동서남북[dong-seo-nam-buk]

– east-west-south-north동 서 남 북

동 서 남 북

동 남 북서

동 서 남 북

동 서 남 북

남 북동 서

동 서 남 북

동 서 남 북


Typical Ambiguities of Word Phrase Analysis

Categorial Disambiguatione.g., gam+eun (Korean example),

Noun+ParticleVerb stem + Ending

Segmentation Disambiguatione.g., gamgineun

Noun+Particle = gamgi+neunVerb stem + Ending+Particle = gam+gi+neun

Stem Identificatione.g., naneun

Noun+particle = na+neunVerb stem+ending = nal+neun (with verbal ending change)


Types of Verbal Stem Variations (1/2)

Consonant deletions: (Korean examples)l-deletions (semi-regular)

nol+gonol+nigga = no+nigga

s-deletions (semi-irregular)is+gois+nigga = i+eu+nigga

Consonant Alternationsd/l-alternation

geod+gogeod+nigga = geol+eu+nigga

Korean -English

nol - playgo - andnigga -because geod - walkdob - help


Types of Verbal Stem Variations (2/2)

Glidizationb/u-alternatione.g.,

dob+godob+nigga = do+u+nigga


Language Processing

Analysisinput: Cambodianeunoutput: Cambodia/Noun + neun/topical particle

Generationinput: Cambodia/Noun + neun/topical particleoutput: Cambodianeun


Typical problems of Generation (0/6)

Ordering

Phonological ConstraintsOpen vs Closed SyllableVowel HarmonyBridgingContraction (optional and obligatory)

Recursion



OrderingEnglish: ordering of auxiliary verbs in a sentence

well-ordered: The lion would have been being attackedill-ordered: The lion have would been attacked being

Many Asian Languages: ordering on agglutinization

well-ordered: ga+syeoss+seumnida+man (Korean)ill-ordered: ga+man+syeoss+seumnida

ga syeoss seumnida man가 셨 습니다 만

manseumnidaeosssiga“go” honorfic Past

tenseDeclarative

mood concessive

가 시 만었 습니다

Verb AUX Particle Ending Postposition



Phonological Constraints (1/4)Open vs. Closed Syllable

Noun with open syllable (last grapheme is a vowel) + Postposition starting with consonant

– e.g., bird + neunNoun with closed syllable (last grapheme is a consonant) + Postposition starting with vowel

– e.g., animal + eun"The choice of case-marking particles depends on the syllable structure of the last syllable of each nominal stem."



Phonological Constraints (2/4)Vowel Harmony: Clear and Dark Vowels

The first syllable of a verbal stem that ends in a clear (dark) vowel takes the clear (dark) vowel as one of its possible bridges

– clear vowel: e.g., a, o– dark vowel: e.g., eo [ə]

e.g.,– clear+clear: jab+a– dark+dark: meog+eo



Phonological Constraints (3/4)Bridging

Verbal stems ending in a non-liquid consonant normally require the bridge "-eu-" to combine with an ending like "-myeon-"

– jab+eu-myeon– nol+myeon ("l" of "nol" is liquid consonant)

Verbal stems with a derived liquid required the bridge.– geol+myeon– geod+myeon = geol+eu-myeon (by d/l-alternation)



Phonological Constraints (4/4)Contraction: optional and obligatory

Optional: bo+ass-da =boa+ss-daObligatory: *o+ass-da vs. oa+ss-daNo contraction: y+eoss-da vs. *yeo+ss-da



RecursionParticle recursion prohibition

b'ang (bread)+man-eun*b'ang (bread)+man-eun+man-eun

Tense recursion prohibitionjab+ass-da (past declarative)jab+ass-eoss-da (perfect past declarative)*jab+ass-eoss-eoss-da

Bridge recursion prohibitionjab+eu-si-myeon (honorific-condition)jab+eu-si-eoss-eu-myeon (honorific-past-condition)*jab+eu-si-eoss-eu-si-eoss-eu-myeon


Language and Morphology

EnglishLemmatizer + POS taggerA word has at most several word forms

ChineseSegmentation System + POS taggerWord has one word form.

Korean (Japanese)Morphological Analyzer is more essential than other language

Complex SegmentationFrequent AgglutinationWord Formation

– The Chinese characters in common use


Complexity Comparison with other languages

Complexity?

In English, we have only to consider each word form.In Chinese, segmentation rather than morphological analysis.In Korean, MA should process the segmentation and agglutination simultaneously. Much more Complexity in segmentation and analyzing functional words. (Japanese is similar with Korean )

Spacing

Order of verb formsPer one verb

Complexity of Segmentation

English

Word form

5

Easy

Korean

Word Phrase

More than 5000

Very Hard

Chinese

No

1

Very Hard


Table of Contents

Introduction: Morphology and Characteristics English, Korean, Chinese, Japanese,

Complexity of MorphologyTypes of Ambiguities


Terminology and Computation Issues


General Morphological Analyzer: 2 phases

Candidate generationGenerates possible sequences of morphemes

Segmentation– Lemmatization - Recovery of phonetic changes

Processing for unknown word

Candidate selection“POS Tagging”Methodologies

Statistical methods: e.g., if we see two or three consecutive words and their POS tags, we can predict what the current word's tag is. (Noun-Verb-Noun) Rule-based methodsHybrid methods


General Scheme of Implementation

Morphological AnalyzerMorphological Analyzer

ParserParser

POS POS TaggerTaggerStatsticsStatstics

Linguistic RulesLinguistic Rules

Additional Additional ComponentsComponents

Unknown wordsUnknown words

Symbols/NumbersSymbols/Numbers

Exceptional Exceptional word phraseword phrase

PostPost--processingprocessing

Foreign wordsForeign words

Basic ComponentsBasic Components

CodeCode SystemSystem

DictionaryDictionaryPhonologicalPhonologicalRulesRules

ConnectionConnectionRulesRules

Algorithms Algorithms and and

Data StructureData Structure


Implementation (2/3)

Morphological connection rulesidiomspatternsweighted rules

Dictionaries (Lexicons)general lexicon

level of segmentation depends on the lexicon

domain-specific dictionarye.g., economics, law, patent (science & technology), ...

user-defined dictionary


Implementation (3/3)Diagram of word segmentation system for Chinese

Disambiguation

POS-Tagging

Unknown WordsIdentification

GenerateSubstrings

Word Matching

MorphologicalRules,Idioms,Patterns

Unknown WordModels

ChooseLexicon orLexicons

AccessLexicon

Find wordcandidates

level ofsegmentation

DomainSpecific/

User Defined Lexicon

GeneralLexicon

HeuristicRules

from (Keh-Jiann Chen, 2000)


Morphological Analysis of English

Affix Analysis : un+happy, be+er(X)

Additional ProcessingAbbreviationEx) I’d I wouldSymbols, Numerals and UnitsProcessing Idiomatic ExpressionUnknown Word Processing


Part of speech Tagging (English)Stochastic Part-of-Speech Tagging

Hidden Markov Model and Viterbi AlgorithmUsing Markov assumption – efficient

A Categorization Problem: Machine LearningDecision Tree, Neural Network, Markov Random Field, ...

FliesFlies like alike a flower.flower.NounNoun Prep. Art.Art. VerbVerb VerbVerb Noun NounNoun

Noun

flies/N

flies/V

like/V

like/N

like/P

a/ART

a/N

flower/V

flower/N


Part of speech Tagging (English)

Rule based Part-of-Speech TaggingTransformation-Based [Brill95] [Brill92]

Using contextual informationif Noun X Noun, then X may be Verb

Hybrid ModelIntegrating a stochastic taggerand rule-based system stochastic: tri-gram

P(Noun Verb Noun) = 0.2, P(Noun Noun Noun) = 0.01

Integrated ModelMaximum Entropy Model“Classifier Combination for Improved Lexical Disambiguation” [Brill 99]

Various Models have complementary behaviors.

Initial StateInitial State

LearnerLearner

Unannotated Text

Annotated Text Truth

RulesRulesRules


Corpora A collection of linguistic data, either written texts or a transcription of recorded speech, which can be used as a starting-point of linguistic description or as a means of verifying hypotheses about a language

David Crystal, A Dictionary of Linguistics and PhoneticsA collection of naturally occurring language text, chosen to characterize a state or variety of a language.

John Sinclair, Corpus, Concordance, CollectionText Corpora

Written LanguageSpoken Language

CorporaEnglish: Brown, LOB, BNC and Penn TreeBankKorean: KAIST Corpus and Sejong CorpusJapan: Nihon Keizai Shimbun, EDR CorpusChina: Beijing University, LIVAC (HK City U.), Sinica corpus

Statistics can be obtained from a large Corpus.


Code System for Implementation (Korean)

Standard (KSC5601, KSC5700, KSX001)Syllable-based – 2-byte-codeincluding common Chinese Characters

Combinative Code (one Korean character = CV(C))Grapheme-based – 2-byte-code

Other internal code systems for MAN-byte code

3 byte code

2-3 variable byte Symbol code (Romanization style)

5bitsInitial ConsonantInitial Consonant

5bitsVowelVowel

5bitsFinal consonantFinal consonant

1bitMSB

1byte 1byte

word phraseword phrasesyllablesyllable

graphemegraphemeMorphemeMorpheme


Basic Components of Morphological Analyzer (1/2)

DictionaryLemma, Part-of-speech, Semantic Feature(optional)Structure

Connection RulesConnection Table (POS bigram)Word Formation Graph (n-gram)

Phonological RulesDeclarative Rules

2-level model

Procedural Rules


Basic Components of Morphological Analyzer (2/2)

Algorithms and Data StructuresTop-Down and Bottom-Up

Chart Parsing Recursive Parsing

Data StructureChart, Table, Tree, Lattice, Graphs

Disambiguation (Tagging phase)Dictionary Searching

All words in dictionary + rules for unknown words

Stem dictionary + rule = Derived dictionary

Stochastic ModelRule-based ModelHybrid Model, Integrated Model Simple Heuristics

preferring to the longest morpheme.


Word Segmentation in Chinese- A Heuristics -

Maximal matching ruleThe most plausible segmentation is the three word sequence with the maximal length.This heuristic rules achieves as high as 99.69% accuracy and a high applicability of 93.21%, i.e. the 93.21% of the ambiguities were resolved by this rule. [Chen & Liu 1992]完成鑑定

完成鑑定報

完成鑑定報告 (finish judgment report)


Additional Components of Analyzer

Unknown Word AnalysisSuffix, endings and preposition information

Symbol / Number expressionTemplatesEx) “2005年 6月 20日” (2005/6/20), “3千 4百圓” ($3,400), “+82-42-869-5565” (telephone number)

Foreign LanguagesTV, [kom-pyu-teo] (computer)

Exceptional Word PhraseWord Phrase Dictionary

Post-ProcessingSpacing ProblemProcessing Non-Standards language


Characteristics of different types of unknown words

Possibly infinite number of elements but with closed form representations, such as numeric type compounds:

2005年 = 2005-year

Open-ended types without closed form representations, such as

proper names: "Microsoft"derived words: "computer-ize"compounds: "computer desk"abbreviation (acronym): "LG" (Lucky-Goldstar)

from ACL [Chen, 2000]


Proper Nameswith Transliteration

PersonalBill Clinton (bil klintən, 克林頓)Jian Zemin (jiang z'əmin, 江澤民)

GeographicalKorea (koria, 韓國)Cambodia (kambodia, カンボジア）

Brands/OrganizationPanasonic (panasonik, 파나소닉,樂聲)Samsung (samsəŋ, 삼성, 三星, サムソン)


Numeric Expression Representation- Regular Expression -

To represent the type of unknown words with possibly infinite number of elements but with a closed form representation,

such as numbers, dates, times, determinant-measure compounds, etc.

e.g.,Number → Digit Number | DigitDigit → 0 | 1 | 2 | ... | 9


Methodologies

Tabular ParsingSegmentation by looking-up dictionary

Syllable information-based modelSegmentation by syllable information

Multi-phase filteringBrute-force

Etc.Head-tail : simple model for segmentation


POS Tagging: Statistical approaches

Statistical ModelsHMM-based

HMM on Morpheme sequences (bi-gram, tri-gram)

HMM Using Word Phrase Structure(HMM using Intra-/inter- word phrases information)

Weighted NetworkMaximum Entropy Model

More information can be integrated into model

Pros Easy for training, guarantees not bad performance

ConsDifficult to tune or modify Requires more space


POS Tagging: other approaches

Rule-based approachTransformation Rules (Eric Brill’s Style)Pros and cons

Difficult to get rules and maintain consistency of rulesEasy to lexicalize

Hybrid ApproachStatistical approach + Rule Based ApproachPros and cons

Guarantees a good performanceDifficult to integrate


Using word phrase Structure in Tagging

“An HMM POS tagger for Korean based on Wordphrase” (J, Shin 1994) : simple model

“Two-ply HMM” (J. Kim 1997)

HMM variation using POS tags of head and tail of word phrase

H

T$

H

T

H

T

$

Each word phrase has a conventional tagging HMM.Morpheme / POS tags

$

NounN

Noun

Objective.

N

Noun ModifierN p

AdverbAA

Subjective.

p

pNSNS

NMNM

a

NONO

Verb V connectingePCPC

Verb V final ePFPF


HMM

Hidden Markov ModelWhat is the sequence of nodes?

transient probability usually 1- or 2- or 3-gram

What is the tag (or label) of the node? ("hidden")


Applications

Machine TranslationSpell checking and correction

Spell correctionAuto-spacing and spacing correction

Information RetrievalExtracting index terms – noun and noun phrasesQuestion Answering

Natural Language InterfaceText-To-Speech

Concordance


Application: TTS and IR- A Japanese Example -

Text to speech synthesisWord segmentation of orthographic text

試験|の|最中|に|映画|へ|行った

(I went to movie during examination)

Homograph disambiguation (grapheme to phoneme)最中 → saichu (during), monaka (Japanese sweets)行った → itta (went), okonatta (did)

Information retrieval (indexing)Word segmentation of orthographic text

試験|の|最中|に|映画|へ|行った

(I went to movie during examination)Part of speech tagging, keyword extraction, stemming

試験 (examination), 映画 (movie)

from the slide (Nagata, ACL2000)


Evaluation of Morphological Analysis

CorrectnessRecallPrecision

In unit of morpheme and word phrases

Processing SpeedRobustness

Processing erroneous inputSpacingSpell errors

Effectiveness of ResultEvaluation of Tag set


Recall and Precision

Recall = how much the correct one was hit from the whole setPrecision = how much is correct among the system generated set


Resources and Tools

Corpora and Tagsets for morphological analysis

CorpusContest for standardization of POS tagset.

Visualization ToolTo provide a visible process understandable to user and easy to debug for developer


Table of Contents

Introduction: Morphology and Characteristics English, Korean, Chinese, Japanese,

Complexity of MorphologyTypes of Ambiguities


Terminology and Computation IssuesTranslation (Localization) of New TermsTransliteration


C.K. Ogden/I.A. Richards, The Meaning of MeaningA Study in the Influence of Language upon Thought and The Science of SymbolismLondon 1923, 10th edition 1969

CONCEPT

Referent

Refers To Symbolizes

Stands For

“Orange”,“ClipArt”

What is Terminology?

from the slide of [Bargmeyer, Bruce, Open Metadata Forum, Berlin, 2005]


ReferentReferent

ConceptConcept

TermTermtroutSalmo truttabrown trouttruite

Definition: Any of several game fishes of the genus Salmo, related to the salmon...

Registering TerminologyRegistering Terminology

Refers To Symbolizes

Stands For

from the slide of [Bargmeyer, Bruce, Open Metadata Forum, Berlin, 2005]


Terminology Localization:Recommending Translations of Term

using Term constituents

Sense

Usage

ConstituentsConstituents

Transliteration

IS-A/Causalrelations

Definition

TerminologyTerminology


Goals

To Give Information about Word Formation (Term constituent)

Usage of each term constituentsFrequencies of each term constituentsTranslation Patterns of each term constituents

To Generate a Translation list for new Foreign (English) term

To recommend translation of new terms


Scope

In order to translate new English terms into Korean terms

1) Producing translation candidates2) Selecting the relevant one among them (or Ranking)

1)自動振動制御2) 自動振動調節3) 自動周波制御4) 自動周波調節

•“automatic frequency control”•Automatic “jadoŋ (自動)”,

•Frequency “jindoŋ (振動)”, “jupa (周波)”

•Control “jeə (制御)”, “jojəl (調節)”

1) Producing translation candidates1) Producing translation candidates 2) Ranking2) Ranking


Problems occur in resolving the meaning of the conceptual unit

Homonyms

Synonyms or variations


Homonyms

Mainly caused by Sino-Korean words, pronunciation of which derive directly from Chinese e.g.) ‘-gi’ in biology

Need to know their proper meaning and context

Olfactory organ‘hu-gak+gi’Organ (器)

Growth phase‘saeng-jang+gi’Stage, phase (期)

Contraction period‘su-chuk+gi’Period (紀)

Amino group‘a-mi-no+gi’Group (基)

English termKorean termMeaning of ‘-gi’


Synonyms or variations

Mainly caused by various ways of translationsBecause many Korean terms are from foreign origin

e.g.) abdominal in biologyTranslated into two Sino-Korean wordsTranslated into a pure Korean word

Need to know whether they indicate the same meaning

abdominal fin‘bae+ji-neu-leo-mi’‘bae’

abdominal cavity‘bok+gang’‘bok’ (腹)

abdominal appendage‘bok-bu+bu-sok-ji’‘bok-bu’ (腹部)

English termKorean termKorean translations


Synonyms

‘a-se-teu-san’아세트산

‘gok-ryul’곡률

‘bae’배

‘bok’복

‘gub-eun+yul’굽은률

curvaturechemistry

‘cho-san’ 초산

aceticphysics

‘bok-bu’복부

abdominalBiology

KoreanEnglishDomain


Homonyms

element

crystal

region

‘su-jeong’수정

correction

Chemistry

‘yo-so’요소

urea

Physics

‘ji-bang’지방

fat

Biology

Korean English Domain


Domain Dependency

Different translations depending on domains

Cell

Cell

English

‘jeon-ji’A single unit that converts radiant energy into electric energy

Chemistry, Physics

‘se-po’The smallest structural unit of an organism

Biology

KoreanDifferent Meaning Domain


Terminology Localization:Automatic English-to-Korean

transliteration

Sense

Usage

Constituents

TransliterationTransliteration

IS-A/Causalrelations

Definition

TerminologyTerminology


Goal

For the given English word or term,Accuracy

to generate correct transliterations

Diversityto generate various transliteration variations as many as possible.

data

deitə

deita

de-ta

TransliterationSystem

Diversity

1) 데이터2) 데이타3) 데타

Ranking

Accuracy


Step 1: Input an English word to be transliterated

datadata

Transliteration

push the transliteration button


Step 2: Transliteration

Ranked transliteration results for ‘data’


Step 3: Result validation (Optional)

Click !!


Language varies over

TIME and SPACE

different sense/usage in different domaindifferent meaning in different time


Contact Point:CHOI, Key-Sun

[email protected] Science Division, KAIST

373-1 Guseong-dong Yuseong-gu Daejeon 305-701 S.Korea

Korea Advanced Institute of Science & Technologyhttp://www.kaist.edu/

Korea Terminology Research Center for Language & Knowledge Engineeringhttp://www.korterm.org/

Bank of Language Resources, KNRRChttp://bola.kaist.ac.kr/

ISO/TC37/SC4 for Language Resource Management Standardshttp://www.tc37sc4.org/

International Joint Conference on Natural Language Processinghttp://www.afnlp.org/IJCNLP05/


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morphologymorpheme 1/2 §grammatical morpheme in a predicative word phrase: (word phrase = a...

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