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Intelligent Database Systems Lab

國立雲林科技大學National Yunlin University of Science and Technology

Learning Formulation and Transformation Rules for Multilingual Named Entities

Advisor ： Dr. Hsu

Reporter ： Chun Kai Chen

Author ： Hsin-Hsi Chen, Changhua Yang and Ying Lin

Proceedings of the ACL 2003


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I. M.Outline

Motivation Objective Introduction Multilingual Named Entity Corpora Rule Mining Experimental Results Conclusions Personal Opinion


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I. M.Motivation

The past works on multilingual named entities emphasizes on the transliteration issues

However, the transformation between named entities in different languages is not transliteration only─ Victoria Fall- 維多利亞瀑布─ Little Rocky Mountains- 小落磯山脈─ Kenmare- 康美爾─ East Chicago- 東芝加哥


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I. M.Objective

Propose a method extract─ formulation rules of named entities for individual

languages─ transformation rules for mapping among languages

Application of the results on cross language information retrieval (CLIR)


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I. M.Introduction(1/3)

In the past, named entity extraction ─ mainly focuses on general domains─ employed to various applications such as information r

etrieval, question-answering


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I. M.Introduction(2/3) Most of the previous approaches

─ dealt with monolingual named entity extraction─ Chen et al.(1998) extended it to cross-language information retrieval (C

LIR) A grapheme-based model was ( 字母 )

─ proposed to compute the similarity between Chinese transliteration name and English name.

Lin and Chen (2000) further classified the works into two directions─ forward transliteration (Wan and Verspoor, 1998)─ backward transliteration (Chen et al., 1998; Knight and Graehl, 199

8),─ proposed a phoneme-based model


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I. M.Introduction(3/3)

This paper will study ─ the issues of languages and named entity types on the

choices of translation and transliteration. ─ We focus on three more challenging named entities onl

y, i.e., named people named locations named organizations


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I. M.Multilingual Named Entity Corpora

NICT location name corpus─ Developed by Ministry of Education of Taiwan in 1995─ consists of three parts

Foreign location name, Chinese transliteration/translation name, country name (Victoria Fall, “ 維多利亞瀑布” (wei duo li ya pu bu), South Africa)

CNA personal name and organization corpora─ are used by news reporters to unify the name translitera

tion/translation in news stories


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I. M.Rule Mining

Frequency-Based Approach with a Bilingual Dictionary

Keyword Extraction without a Bilingual Dictionary

Extraction of Transformation Rules Extraction of Keywords at a Distance


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I. M.Learning Formulation and Transformation Rules

Frequency-Based with a Bilingual Dictionary

Keyword Extraction without a Bilingual Dictionary

Generate candidatesCount the frequency (TFIDF)

Victoria FallVictoria, “ 維多利亞” Fall, “ 瀑布”

World Taiwanese Association “ 世台會”

Decompose E

(s6) {Catalan Mountain , 卡太蘭山 }(s7) {Aletschhorn Mountain , 阿利奇赫恩山 }

{Catalan Mountain , 卡太蘭山 }{Catalan , 卡太蘭山 }{e1, 卡太太蘭蘭山 }{e1, …}{e1, 卡太蘭山 }

{Mountain , 卡太蘭山 }{e2, 卡太太蘭蘭山 }{e2, …}{e2, 卡太蘭山 }

{Aletschhorn Mountain , 阿利奇赫恩山 }{Aletschhorn , 阿利奇赫恩山 }{e1, 阿利利奇奇赫赫恩恩山 }{e1, …}{e1, 阿利奇赫恩山 }

{Mountain , 阿利奇赫恩山 }{e2, 阿利利奇奇赫赫恩恩山 }{e2, …}

{Mountain, “ 山” (shan)}

Extraction of Transformation Rules

(s6’) γ mountain ⇔ δ 山(s7’) γ mountain ⇔ δ 山(s8’) γ Strait ⇔ δ 海峽(s9’) γ, Strait of ⇔ δ 海峽

Extraction of Keywords at a Distance

“American Civil Liberties Union”.“American ∆ Liberties Union”“American Civil ∆ Union”“American ∆ Union”

Dictionary

“Mountain” ⇔ “ 山”


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I. M.Frequency-Based Approach with a Bilingual Dictionary We postulate

─ transliterated term is usually an unknown word and not listed in a lexicon

─ translated term often appears in a lexicon

Under this postulation ─ translated term( 翻譯詞 ) occurs more often in a corpus

Fall, “ 瀑布”─ transliterated term( 音譯詞 ) only appears very few

Victoria, “ 維多利亞”


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I. M.Frequency-based method(1/2) Simple frequency-based method will compute the frequencies

of terms and use them to tell out the transliteration and translation parts in a named entity─ Compute word frequencies of each word in the foreign name list─ Keep those words

appear more than a threshold appear in a common foreign dictionary these words form candidates of simple keywords

Mountain─ Examine the foreign word list again─ Cluster the Chinese name list

based on foreign keywords here a bilingual dictionary may be consulted “Mountain” ⇔ “ 山”


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I. M.Frequency-based method(2/2) NICT location name corpus

─ River ( 河 , he), Island ( 島 , dao), Lake ( 湖 ,hu), Mountain ( 山 , shan), Bay ( 灣 , wan), Mountain ( 峰 , feng), Peak ( 峰 , feng)

─ “Mountain” ⇔ “ 山” (shan) and “ 峰” (feng)─ “峰” (feng) ⇔ “Mountain” and “Peak”

CNA organization name corpus─ Suffix

Association ( 協會 , xie hui), University ( 大學 , da xue)─ Prefix

International ( 國際 , guo ji), World ( 世界 ,shi jie), American ( 美國 , mei guo)


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I. M.Keyword Extraction without a Bilingual Dictionary (problem) Abbreviation is common adopted in translation,

dictionary-based approach is hard to capture this phenomenon─ (World Taiwanese Association,“ 世台會” )

Here another approach without dictionary is proposed


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I. M.Keyword Extraction without a Bilingual Dictionary (process)(s6) Aletschhorn Mountain ⇔ 阿利奇赫恩山

─ {e1, s1 s2 … st} {Aletschhorn , 阿利奇赫恩

山 } {e1, 阿利利奇奇赫赫恩恩山 } {e1, 阿利奇利奇赫奇赫恩赫恩

山 } {e1, 阿利奇赫利奇赫恩奇赫恩山 } {e1, 阿利奇赫恩利奇赫恩山 } {e1, 阿利奇赫恩山 }

─ {e2, s1 s2 … st} {Mountain , 阿利奇赫恩

山 } {e2, 阿利利奇奇赫赫恩恩山 } {e2, 阿利奇利奇赫奇赫恩赫恩

山 } {e2, 阿利奇赫利奇赫恩奇赫恩山 } {e2, 阿利奇赫恩利奇赫恩山 } {e2, 阿利奇赫恩山 }

(s7) Catalan Mountain ⇔ 卡太蘭山─ {e1, s1 s2 … st}

{Catalan , 卡太蘭山 } {e1, 卡太太蘭蘭山 } {e1, 卡太蘭太蘭山 } {e1, 卡太蘭山 }

─ {e2, s1 s2 … st} {Mountain , 卡太蘭山 } {e2, 卡太太蘭蘭山 } {e2, 卡太蘭太蘭山 } {e2, 卡太蘭山 }

•{e, c} whose frequency > 2 are kept•{Mountain, “ 山” (shan)}


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I. M.Keyword Extraction without a Bilingual Dictionary (algorithm) {Ej, Cj}

─ Ej is a foreign named entity─ Cj is a Chinese named entity

decompose the named entities─ Ej

comprises m words w1·w2…wm a candidate segment ep, q is defined as wp … wq

─ Cj has n syllables s1·s2…sn a candidate segment cx, y is defined as sx … sy

─ we can get pairs of {ep, q, cx, y} from {Ej, Cj}. group and count

─ the pairs collected from the multilingual named entity list─ count the frequency for each occurrence─ pairs with higher frequency denote significant segment pairs


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I. M.Keyword Extraction without a Bilingual Dictionary (example) Example

─ All the pairs {e, c} whose frequency > 2 are kept─ {Mountain, “ 山” (shan)} and {Strait, “ 海峽” (ha

i xia)} appear twice

(s6) Aletschhorn Mountain ⇔ 阿利奇赫恩山(s7) Catalan Mountain ⇔ 卡太蘭山(s8) Cook Strait ⇔ 科克海峽(s9) Dover, Strait of ⇔ 多佛海峽


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I. M.Keyword Extraction without a Bilingual Dictionary (problem) Two issues have to be addressed

─ redundancy which may exist in the pairs of segments should be eliminated carefully

─ e may be translated to more than one synonym “Association” ⇔“ 協會” (xie hui) and “ 聯誼會” (lian yi hui)

A metric to deal with the above issues is proposed)1 (log 2 iiii c)idf(c}) f({e,c})score({e,c

) (max

) (

}tf{e,c

}{e,ctf })f({e,c

jj

i

i

)(log 2

)df(c

N )idf(c

i

i

}) ,({max arg icescore c


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I. M.Extraction of Transformation Rules

Chinese location name keyword ─ tends to be located in the rightmost─ the remaining part is a transliterated name

Foreign location name keyword ─ tends to be either located in the rightmost, or permuted by some preposi

tions, comma, and the transliterating part

(s6) Aletschhorn Mountain ⇔ 阿利奇赫恩山(s7) Catalan Mountain ⇔ 卡太蘭山(s8) Cook Strait ⇔ 科克海峽(s9) Dover, Strait of ⇔ 多佛海峽

(s6’) γ mountain ⇔ δ 山(s7’) γ mountain ⇔ δ 山(s8’) γ Strait ⇔ δ 海峽(s9’) γ, Strait of ⇔ δ 海峽


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I. M.Extraction of Keywords at a Distance

(s12) and (s13)─ English compound keyword is separated and so is its corresponding Chi

nese counterpart

(s14) and (s15)─ English compound keyword is connected in ─ but the corresponding Chinese translation is separated

(s12) American Podiatric medical Association ⇔ 美國足病醫療學會(s13) American Public Health Association ⇔ 美國公共衛生學會(s14) American Society for Industrial Security ⇔ 美國工業安全協會(s15) American Society of Newspaper Editors ⇔ 美國報紙編輯人協會


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I. M.Extraction of Keywords at a Distance

Introduce a symbol ∆ to cope with the distance issue─ “American Civil Liberties Union”.─ “American ∆ Liberties Union”─ “American Civil ∆ Union”─ “American ∆ Union”


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I. M.Experimental Analysis (corpus) NICT location corpus

─ Total 122 keyword pairs are identified─ Total 230 transformation rules─ On the average, a keyword pair corresponds to 1.89 transformation rules

CNA personal names─ are composed of more than one Word

(100 / 50,586)─ the number of keywords extracted is only a few

De ⇔ 戴 (dai), La ⇔ 拉 (la), De La ⇔ 戴拉 (dai la), Du ⇔ 杜 (du), David ⇔ 大衛 (da wei)

CNA organization─ are composed of more than one Word

(12,885 / 14,658)─ 5,229 keyword pairs are extracted─ most of the keyword pairs are meaning translated


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I. M.Experimental Analysis (classify) We classify these keyword pairs into the following types

─ Meaning translation common location keywords

Bir ⇔ 井 (jing), Ain ⇔ 泉 (quan),Bahr ⇔ 河 (he), Cerro ⇔ 山 (shan) Direction

Central ⇔ 中 (zhong), East ⇔ 東 (dong), etc.) size (e.g., Big ⇔ 大 (da)), length (e.g, Long ⇔ 長 (zhang)), color (e.g., Black ⇔ 黑 (hei), Blue ⇔ 藍 (lan), etc.)

the specificity of place or area Crystal ⇔ 結晶 , Diamond⇔ 鑽石 (zuan shi)

─ Phoneme transliteration keywords Dera ⇔ 德拉 (de la), Monte⇔ 蒙特 (meng te), Los ⇔ 洛斯 (luo si) 伊利莎白 (yi li sha bai), Edward ⇔ 愛德華 (ai de hua) Total 39 terms belong to this type. It occupies 31.97%.

─ Some keywords in type (1) are transliterated Bay ⇔ 貝 (Bay), Beach ⇔ 比奇 (bi qi) Total 14 keywords (11.48%) are extracted.


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I. M.Experimental Results

NICT location corpus─ Total 122 keyword pairs are identified─ Total 230 transformation rules─ On the average, a keyword pair corresponds to 1.89 tra

nsformation rules keyword pair mountain ⇔ 山 (shan)

─ Four transformation rules (1) γα ⇔ δβ (234) (2) γ, α ⇔ δβ (45) (3) γ, αγ ⇔ δβ (1) (4) γαγ ⇔ δβ (1)


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I. M.Application on CLIR


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I. M.Conclusion and Remarks

This paper proposes corpus-based approaches ─ extract the formulation rules and the translation/transliteration

rules among multilingual named entities

Two types of evaluation─ partition the corpora into two parts, one for training and the other

one for testing─ integrating our method in a cross language information retrieval

system

Further applications ─ will be explored in the future and the methodology will be

extended to other types of named entities


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I. M.Personal Opinion

Drawback─ Lack analysis about time complexity

Application─ Construct Chinese-English rules apply to IR

Future Work─ Adopt transliterated / translated term issue