Deriving a Japanese-English Technical Lexicon
from the NTCIR Scientific Collections,
with Implications for Language Engineering
Fredric Gey
Visiting Researcher, NII
University of California, Berkeley
(Final Presentation before returning to USA September 5)
August 30, 2007
(appreciation to David K Evans for all his help)
Lexicon Development and
Potential Use for Transliteration Research
• Goal of the development
• Description of NTCIR 1 and 2 collections
• Method(s) used to create the lexicon
• Details about the lexicon
• Validation of the lexicon
• Research uses of the lexicon
– Transliteration
– Romanization and approximate matching
• Discussion of next steps
NII - Bilingual Technical Lexicon from NTCIRSic-
Goals of the Lexicon Development
• The NTCIR 1 and 2 test collections are the only available text
research collections derived from the science and technology
domains*
– Technical vocabulary translation is an important part of the
translation industry
– A freely-available technical lexicon may be of use by professional
translators
– The lexicon may also stimulate further interest in the collections
• Technical lexicons may be useful in linguistic research and
language engineering
– Technical terms in Japanese are often borrowed from English
(katakana)
– A technical lexicon may be useful for transliteration research and
matching new vocabulary between Japanese and English
* The NTCIR Patent collection may also be one, depending upon viewpoint
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The NTCIR-1/2 collections
• The NTCIR 1 and 2 test collections are actually three
sub-collections
• NTCIR-1 J-E gakkai collection (339,483 documents)
– Author abstracts of articles from 65 Japanese
scientific society-hosted conferences for the period
1988-1992
• NTCIR-2 J and E gakkai collection
– Extension of NTCIR-1 collection for years 1997-1999
– 77,433 English abstracts, 116,177 Japanese abstracts
– Independent files, not pre-joined
• NTCIR-2 J and E kaken collection
– Abstracts of funded research final reports 1988-1997
– 57,545 English abstracts, 287,071 Japanese abstracts
– Independent files, not pre-joined
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The NTCIR-1 J-E collection
•
•
•
•
•
•
339,483 documents (334,515
(98.5%) with Japanese abstracts)
Only 188,907 (55.6%) have
English abstracts, however
313,673 (92.3%) have authorassigned keywords (terms) in
both Japanese and English
While 65 societies are
represented, the bulk of the
documents come from a few
societies. For example, there are
only 15 documents from the
Japan Society for Wind
Engineering
The top 10 societies account for
82.7% of all documents
The bottom 30 account for only
2.7% of documents
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88,207(26.05%)
55,629(16.43%)
The Institute of
Electronics, Information
and Communication
Architechtural Institute of
Japan
27,191( 8.03%)
Information Processing
Society of Japan
23,395(6.91%)
The Society of Polymer
Science, Japan
21,352(6.31%)
Japan Society of Civil
Engineers
20,033(5.92%)
Japan Society for
Bioscience,Biotechnology
and Agrochemistry
18,226(5.38%)
The Institute of Electrical
Engineers of Japan
12,112(3.58%)
The Society of Instrument
and Control Engeneers
7,100(2.10%)
The Ceramic Society of
Japan
6,682(1.97%)
The Pharmaceutical
Society of Japan
The NTCIR-1 J-E example document
<ACCN>gakkai-0000185279</ACCN>
<TITL TYPE="kanji">動画像圧縮イメージセンサの検討</TITL>
<TITE TYPE="alpha">On-sensor Video Compression</TITE>
<AUPK TYPE="kanji">大野 洋 / 浜本 隆之 / 相澤 清晴 / 羽鳥 光俊 / 山崎 順一 / 丸山 裕孝</AUPK>
<AUPE TYPE="alpha">Ohno,Hiroshi / Hamamoto,Takayuki / Aizawa,Kiyoharu / Hatori,Mitsutoshi /
Yamazaki,Jun-ichi / Maruyama,Hirotaka</AUPE>
<CONF TYPE="kanji">画像応用研究会</CONF>
<CNFE TYPE="alpha">Technical Group on Applied Image Processing and System</CNFE>
<CNFD>1994. 08. 26</CNFD>
Abstract in Japanese
<ABST TYPE="kanji"><ABST.P>画像を扱う既存のシステムにおいては,画像獲得と画像処理はほぼ完全に
分離している。ところが画像技術の応用分野が広がるにつれ,イメージセンサに対して,高レート化,高機能化が
要求されるようになってきた。これらの要求に従来の枠組で対応していくと,画像情報を1次元の時系列信号と
して転送する場合,転送遅延がボトルネックとなってしまう。この問題に対して,センサ上で一部(あるいは全て)
の処理を実行し,画像取得と画像処理をより密接に関連させて解決しようというアプローチが検討され始めてい
る。</ABST.P><ABST.P>我々はセンサ上で適切な画像圧縮を施すことで,取得画像の高レート化(高速化,高
精細化)に対応することを考えている。本稿では,センサ上での動画像圧縮のためのアルゴリズム,およびその
チップへの実装について論じる。</ABST.P></ABST>
<ABSE TYPE="alpha"><ABSE.P>In this paper, we propose new computational image sensors which compress
image signal in the process of image acquisition. Conditional replenishment is used to reduce the band-width
necessary for image read-out. We also describe about the design of the experimental chip. This chip has an
Keywords in Japanese
extensible, parallel, architecture.</ABSE.P></ABSE>
<KYWD TYPE="kanji">画像センサ // コンピュテーショナルセンサ // 画像圧縮 // 画像符号化</KYWD>
<KYWE TYPE="alpha">Image Sensors // Computational Sensors // Image Compression // Image
Coding</KYWE>
Keywords in English
<SOCN TYPE="kanji">テレビジョン学会</SOCN>
<SOCE TYPE="alpha">The Institute of Television Engineers of Japan</SOCE>
</REC>
<REC>
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Lexicon creation methodology
• Remember, for the NTCIR-1 collection:
• 313,673 (92.3%) have author-assigned keywords (terms) in both
Japanese and English, while only 188,907 (55.6%) have English
abstracts
• Thus, pairing keywords may be more useful than the more
complicated task of pairing sentences in documents (the usual
approach of statistical machine translation) to align term pairs
• Pairing keywords is also a lot easier to program
• Some regularity and ordering of the keywords helps to facilitate the
process
• Thus we extracted keyword pairs and counted their occurrence in
the collection
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Lexicon creation methodology
• To further simplify, we observe that pairs seem
to be ordered in the original documents
gakkai-0000000016|KYWD|ワイエルシュトラス楕円関数 | 母数変換 | 等角写像 | テー
ター関数|KYWE| Weierstrass Elliptic Function | Modulus Translation |
Conformal Mapping | Theta Function
gakkai-0000006972|KYWD|等角写像解析 | 楕円関数 | 楕円テータ関数 | 特性インピー
ダンス|KYWE|conformal mapping analysis | elliptic function | Elliptic Theta
function | charactaristic impeadance
gakkai-0000024631|KYWD|アンテナ共用器 | 導波管 | フィルタ | 楕円関数 | サーキュ
レータ | マイクロ波|KYWE|Duplexer | Waveguide | Filter | Elliptic Function |
Circulator | Microwave
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Lexicon creation methodology
• Of course, not all records have keywords in both languages, nor do
they have the same number of keywords in each language – calls for
more statistical investigation
num EngKW & not JapKW
18,140
num JapKW & not EngKW
4,583
# neither JapKW nor EngKW
3,087
# w/ both JapKW and EngKW 313,673
total records
339,483
records where KYWE!=KYWJ 16,289 (5.2% of docs with both)
• I choose only records with both Japanese and English keywords
present. Where # English Keywords != # Japanese keywords, only
process min(|KYWE|,|KYWJ|) keywords in sequence
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Lexicon creation results: NTCIR-1
• For the NTCIR-1 collection we
obtain 598,439 unique J-E
pairs, with the following
distribution, with a very long
tail, which may include many
erroneous pairs, including
misspellings:
495 情報検索 | information retrieval
8 情報検索 | information retrival
7 検索 | information retrieval
4 情報検索 | information retieval
3 文書検索 | information retrieval
3 情報検索 | information retreival
3 情報収集 | information retrieval
2 情報検索 | information retrieving
1 情報検索 | information retrilval
1 情報検索 | information retrievol
1 情報検索 | information retrierol
1 情報検索 | information retreaval
NII - Bilingual Technical Lexicon from NTCIRSic-
Number of
Occurrences
Pair count
5 or more
34,044
4
11,698
3
23,063
2
64,726
1
464,908
Lexicon creation methodology: NTCIR-2 gakkai
• The NTCIR-2 Gakkai collection is just an extension of the NTCIR-1
collection to additional years, with the complication that you have to
join independent English and Japanese sub-collections. NTCIR-2
joined subset has the following keyword characteristics
EngKW & not JapKW
JapKW & not EngKW
# neither JapKW nor EngKW
# w/ both JapKW and EngKW
total records
records where KYWE!=KYWD
68
277
1996
71839
74180
2643 (3.7% of docs with both)
• I choose only records with both Japanese and English keywords
present. Where # English Keywords != # Japanese keywords, only
process min(|KYWE|,|KYWJ|) keywords in sequence
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Lexicon creation results: NTCIR-2 gakkai
•
For the NTCIR-2 gakkai subcollection we obtain 172,400
unique J-E pairs (compared to
598,439 for NTCIR-1) with the
following distribution, also with
a long tail:
528 シミュレーション | simulation
493 有限要素法 | finite element method
470 液状化 | liquefaction
466 インターネット| internet
412 遺伝的アルゴリズム|genetic algorithm
383 ニューラルネットワーク|neural network
245 ラジカル重合 | radical polymerization
245 データベース|database
235 アルミナ| alumina
228 鉄筋コンクリート|reinforced concrete
202 地震|earthquake
198 数値解析|numerical analysis
NII - Bilingual Technical Lexicon from NTCIRSic-
Number of
Occurrences
Pair count
5 or more
8,032
4
3,210
3
6,644
2
19,380
1
135,134
Lexicon creation: NTCIR-2 Kaken
• The NTCIR-2 Kaken collection is totally different from the Gakkai
subcollections. It derives from final reports of funded research. As
such it has more diversity. There are no ‘societies’ to anchor the text
by ‘domain.’ Example 1 Example 2 Example 3
• The statistical characteristics are also different:
EngKW & not JapKW
JapKW & not EngKW
# neither JapKW nor EngKW
# w/ both JapKW and EngKW
total records
records where KYWE!=KYWD
56
98
4
57354
57512
15530 (27.1% of docs with both)
• Thus the idea of ordered keyword pairing which works so well with
the Gakkai collections may be inappropriate for Kaken. We may
need to take a broader statistical association net.
NII - Bilingual Technical Lexicon from NTCIRSic-
NTCIR-2 Kaken keyword diversity – no
correspondence of E to J required of authors
kaken-j-0975082400|KYWE| Healt impaired children | Chronically Ill children | Healt
psychology | Healt education | Denelopment of concepts | LOC | self-care | Coping
behavior |KYWD| 病弱児 | 慢性疾患児 | 健康心理学 | 健康教育 | 概念発達 | LOC | セルフケ
ア | 対処行動 (good example of misspellings in English keywords)
kaken-j-0965522600|KYWE| environmental issues | mass media | public opinion | social
research | content analysis | effects of mass communication | global warming | social
psychology |KYWD| 環境問題 | マスメディア | 世論 | 社会調査 | 内容分析 | マスコミ効果論 |
地球温暖化 | 社会心理学
kaken-j-0861763900|KYWE| Methylglyoxal | D-Lactate | HPLC | 2-Methylquinoxaline | oPhenylenediamine | 4,5-Dichloro-o-phenylenediamine|KYWD|メチルグリオキサール | D乳酸 | HPLC | 2-メチルキノキサリン | オルトフェニレンジアミン | 4.5-ジクロロオルトフェニレンジア
ミン | 6.7-ジクロロメチルキノキサリン (good J-E correspondence)
kaken-j-0861806300| KYWE | Joro spider toxin | Purification of JSTX | Glutamate receptor
| Glutamate binding | 2,4-Dihydroxyphenylacetic acid |KYWD|グルタミン酸レセプター | ク
モ毒 | ラット脳シナプス膜 | クモ毒の精製 | クモ毒の作用機序
(seems to have little J-E correspondence)
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Lexicon creation results: NTCIR-2 Kaken
• For NTCIR-2 Kaken, using the
paired ordered keyword
assumption, we obtain 238,820
unique J-E pairs (compared to
172,400 for NT2-gakkai) also with
an even longer tail.
282 ラット| rat
266 モノクローナル抗体|monoclonal antibody
251 アポトーシス| apoptosis
243 サイトカイン| cytokine
236 遺伝子発現| gene expression
233 免疫組織化学| immunohistochemistry
188 シミュレーション| simulation
181 データベース| database
163 カルシウム| calcium
161 マウス| mouse
159 画像処理| image processing
151 セラミックス| ceramics
NII - Bilingual Technical Lexicon from NTCIRSic-
Number of
Occurrences
Pair count
5 or more
5,685
4
2,353
3
4,549
2
14,001
1
212,232
Lexicon creation: NTCIR-2 Kaken
another approach (thanks David)
• Maximalist approach, pair all
English and Japanese keywords
independently
E1 E2 E3 E4 E5
J1 J2 J3 J4 J5 J6 J7

(E1,J1)(E1,J2) … (E1,J7)
(E2,J1)(E2,J2) …
Produces >2 million pairs (2,219,878)
NII - Bilingual Technical Lexicon from NTCIRSic-
Number of
Occurrences
Pair count
5 or more
6,941
4
4,135
3
12,065
2
66,298
1
2,130,259
Lexicon creation: NTCIR-2 Kaken
final approach (thanks David) - unfinished)
• Pair all English and Japanese
keywords independently
E1 E2 E3 E4 E5
J1 J2 J3 J4 J5 J6 J7

(E1,J1)(E1,J2) … (E1,J7)
(E2,J1)(E2,J2) …
Produces >2 million pairs (2,219,878)
For each pair, collect the
following contingency table,
then you can compute an
association measure (Yates
Chisq or Dunning’s log
likelihood ratio)
Produces ranked list of most
likely translation for either Ei
or Jk
NII - Bilingual Technical Lexicon from NTCIRSic-
Jk
~Jk
Ei
a
b
~Ei
c
d
Lexicon Validation
• There are many ways to validate the results of the lexicon
– Have human beings who understand both Japanese and English validate the
pairs
– Use an external translation engine (say Google language tools Japanese 
English translator) to translate the Japanese and compare to English
NII - Bilingual Technical Lexicon from NTCIRSic-
Lexicon Validation (continued)
• Of course, not all external translations will be the same
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Lexicon Validation (continued)
• But the most frequent ones can be validated either way
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Lexicon Validation (continued)
• The problem is, of course, exhaustivity, especially with the long tails
of the distribution
•
My approach will be stratified sampling (suggestions welcome)
–
–
–
–
–
–
For a total sample of 1000, take
200 from top pairs with frequency >= 5
200 pairs from frequency =4
200 pairs from frequency = 3
200 pairs from frequency = 2
600 pairs from frequency = 1
NII - Bilingual Technical Lexicon from NTCIRSic-
If Google translate does such a good job with
technical terms, why bother with this project?
• Google technology is proprietary – reverse engineering their
dictionary would violate intellectual property
• Developers may wish to have more control over the use of the
lexicon for MT, CLIR or other purposes
• Existing available dictionaries are very limited
• The lexicon may have translations not found in G-translate
• This lexicon can be used for further research in correspondences
between the Japanese and English language
• In particular for transliteration development and matching
techniques for Katakana to English
• The lexicon is a great source for Katakana (David Evans extracted
20,871 distinct Katakana terms from NTCIR-2 Gakkai alone)
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What are the Limitations
to the Lexicon Use?
• The lexicon has limited domain coverage – major areas
of electrical engineering, information technology,
architecture for NTCIR-1 and 2 gakkai collections
• The lexicon is statistically derived, and thus noisy
– Probably excellent for research into transliteration and
matching
– Spelling variants can be matched for English spelling errors
– The utility of the ‘long tail’ still needs to be investigated
• BUT, it is (to my knowledge) the first freely available
Japanese-English technical lexicon
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Lexicon implications for
Transliteration/Matching research*
• Transliteration research, especially between Japanese and English,
was jump-started in 1997 with Kevin Knight and Jon Graehl’s paper
“Machine Transliteration (ACL 1997)”
• Transliteration & Back-transliteration
• Transliteration:
– Translating proper names, technical terms, etc. based on phonetic
equivalents
– Complicated for language pairs with different alphabets & sound
inventories
– E.g. “computer” --> “konpyuutaa” コンピュータ
• Back-transliteration
– E.g. “konpyuuta” --> “computer”
– Inversion of a lossy process
• Knight & Graehl developed a probabilistic finite-state machine
model for transliteration and back-transliteration
*(slide adapted from U Washington seminar on web)
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Application areas of
Transliteration/Matching research
• Cross-language search where search terms in the source language
are not in the translation dictionary, e.g. person and place names
– English-French is easy because of so many cognates (see Buckley et al
1997, Using Clustering and SuperConcepts Within SMART), Buckley
“We regard French as just misspelled English.”
– Savoy & Rasolofo 2002: Report on the TREC 11 Experiment: Arabic,
Named Page and Topic Distillation Searches. performed
EnglishArabic search by first Romanizing the entire Arabic corpus
(done in Malta where Arabic is represented with Latin alphabet).
• Multilingual, multidocument summarization (Newsblaster,
NewsExplorer) – see Steinberger & Pouliquien 2007 Cross-lingual
Named Entity Recognition.
• Machine Translation for finding out-of-vocabulary words to
augment the translation dictionaries
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Transliteration versus Romanization
• Transliteration is a process of phonetic mapping from one
language to another
– Transliteration research uses machine learning to find the best
phonetic representation to maximize matching over a training set
– Fairly easy for Latin alphabet languages
– Less easy for Cyrillic script languages (but often 1-1 reverseable)
– More difficult for foreign scripts with different phonetic bases
– Next to impossible for Chinese
• Romanization is rule-based mapping from a non-Latin script to the
Latin alphabet
– Romanization has been around for a long time
– Hepburn created Japanese Romanization in 1887. Hepburn for
コンピュータ is kompyuta There is a Hepburn module in the Perl archive
CPAN (utf-8). Dr. Apel at NII has Hepburn Romanization software written
in Perl (eucJP) .
Library of Congress has Romanization rules for dozens of languages,
which are used in cataloging of non-English books
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Can Romanization be rescued?
• My research question is whether this vast amount of existing
Romanization can be used for search across languages using
approximate string matching, for example using Edit distance:
– Edit distance between computer and kompyuta (コンピュータ ) is 5
– Edit distance between fish (E) and fisch (DE) is 1, between fish and
frisch (E) is 2, between fresh (E) and frisch (DE) is also 2,
– “Edit distance is a terrible cross-lingual matching method” Martin
Braschler, University of Zurich
• Can we use older Rule-based phonetic matching methods?
– Soundex (patented 1918) for telephone lookup
– Phonix developed by Gadd (Fisching fore Werds: Phonetic Retrieval of
Written Text in Information Systems, 1988)
– a little background on approximate matching is in order
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Well-known Approximate String Matching Methods
•
Edit distance: number of insertion, deletions and replacements needed to
transform one string to its matching string
•
Q-grams – number of substrings of length q which the string has in
common with its matching string
– Simply counting q-grams is not adequate because it is ignores string length
(Fred has as many common q-grams with itself as with Frederick)
– Ukonian proposed a length-based distance metric (Approximate string matching
with q-grams and maximal matches, 1992)
– |Gs| + |Gt| - 2|Gs ∩ Gt| where Gs is the set of q-grams in string s
•
Q-gram methods were used by Robertson & Willett for searching historical
English (Searching for historical word-forms in a database of 17th-century
English text using spelling-correction methods, 1992)
•
Modified q-gram method, targeted s-grams (where 2-grams are allowed to
include a character skip were investigated by Pirkola et al for crosslanguage search between Finnish, Swedish and German (Targeted s-gram
matching: a novel n-gram matching technique for cross- and mono-lingual
word form variants, 2002) - would resolve the fish fisch match.
NII - Bilingual Technical Lexicon from NTCIRSic-
Classical Phonetic Matching Methods
• The classical phonetic matching algorithms (originally developed
for telephone operators to do lookup for similar-sounding names)
operate by shrinking (removing vowels) and mapping consonants
to a canonical subset, as well as truncation
For example if computer  kmptr and kompyuta  kmpt and a
maximum of 4 leading characters are retained, you have a match
• Soundex – map {a e I o u y h w}  0, {b p f v}  1, {c g j k q s x z} 
2, {d t}  3, {l}  4, {m,n}  5, {r}  6
– Replace all but the first letter by phonetic map
– Eliminate adjacent repetitions of codes
– Eliminate all occurrences of code 0
– Truncate to the first 4 characters of the result
• Phonix – similar to Soundex, but slightly different coding, preceded
by 160 letter-group transformations (x ecs, tjV chV at start of w)
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Zobel-Dart Combined Methods
• In 1995 and 1996 Justin Zobel and Phillip Dart (University of
Melbourne) did a substantial experimental evaluations of multiple
approximate string matching methods on large corpora
– (Finding Approximate Matches in Large Lexicons, 1995, and Phonetic
String Matching: Lessons from Information Retrieval, 1996)
– Used IR methods (recall/precision) to evaluate match effectiveness
– Concluded that Phonix and Soundex have terrible performance, but
Phonix finds matches which other methods don’t
– Developed Phonix+ (modified Phonix without truncation) and ZobelDart algorithm which
• combined Phonix+ with edit distance for a better performing matching
• rewarded matches at the beginning of the strings
• Zobel-Dart has not been applied to cross-lingual term matching
• String matching may also be useful to correct English spelling
errors in the NTCIR lexicon
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Discussion of Potential Next Steps
•
Further processing of the Kaken sub-collection to remove common
prefixes and hence reduce the number of singly occurring pairs (may be
useful for chemical compounds)
– kaken-j-0860020200|KYWE| La-Ce geochronometer | La-Ba geochronometer |
&lt;^(138)Ce&gt; isotope tracer | Re-Os geochronometer | Decay constant of
&lt;^(138)La&gt; | REE pattern | Ce anomaly |KYWD| La-Ce年代測定法 | La-Ba年
代測定法 | 【^(138)Ce】同位体トレーサ | Re-Os年代測定法 | 【^(138)La】の壊変定数 |
REEパターン | Ce異常 | 希土鉱物
– kaken-j-0891105400|KYWE| Re-Os geochronometer | sulfide ore minerals | ICP
mass spectrometer | isotopic equilibrium | mass discrimination | gas-mist
merging introduction | molybdenite |KYWD| Re-Os年代測定法 | 硫化鉱物 | ICPMS | 同位体平衡 | 質量差別効果 | 蒸気発生-混合導入法 | モリブデナイト
– Google translates 年代測定法 as “age determination method”
• Application of Statistical MT (Giza++) to the Kaken sub-collection
to enhance the lexicon and cross-validate the keyword matching
methods
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Discussion of Potential Next Steps
• Assessing the overlap of the sub-collection lexicons and merging
into a single lexicon
• Subsetting the lexicon by levels of quality
• Creation of a Katakana subset of the lexicon
– With or without Romanization
• And, of course, public release of the lexicon (with an
accompanying paper for LREC 2008)
–
–
–
–
Downloadable file(s)
Online search version
Has user interface and display issues for chemical formulae
Professor H Satoh has pointed me to
http://homepage3.nifty.com/xymtex/fujitas/rd/choshoe.html#FBOOK6
NII - Bilingual Technical Lexicon from NTCIRSic-
Fini (終えられる)
• I enjoyed my time at NII and hope to return
• Thank you very much
• 本当にありがとう (I hope Google translate is correct)
NII - Bilingual Technical Lexicon from NTCIRSic-
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Lexicon creation methodology - Metadata Research Program Home