An Intelligent Multilingual Information Browsing and Retrieval 
System Using Information Extraction 
Chinatsu Aone and Nicholas Charocopos and James Gorlinsky 
Systems Research and Applications Corporation (SRA) 
4300 Fair Lakes Court 
Fairfax, VA 22033 
aonec@sra.com 
Abstract 
In this paper, we describe our multilingual 
(or cross-linguistic) information browsing 
and retrieval system, which is aimed at 
monolingual users who are interested in in- 
formation from multiple language sources. 
The system takes advantage of information 
extraction (IE) technology in novel ways 
to improve the accuracy of cross-linguistic 
retrieval and to provide innovative meth- 
ods for browsing and exploring multilin- 
gual document collections. The system in- 
dexes texts in different languages (e.g., En- 
glish and Japanese) and allows the users to 
retrieve relevant texts in their native lan- 
guage (e.g., English). The retrieved text 
is then presented to the users with proper 
names and specialized domain terms trans- 
lated and hyperlinked. Moreover, the sys- 
tem allows interactive information discov- 
ery from a multilingual document collec- 
tion. 
1 Introduction 
More and more multilingual information is available 
on-line every day. The World Wide Web (WWW), 
for example, is becoming a vast depository of mul- 
tilingual information. However, monolingual users 
can currently access information only in their na- 
tive language. For example, it is not easy for a 
monolingual English speaker to locate necessary in- 
formation written in Japanese. The users would not 
know the query terms in Japanese even if the search 
engine accepts Japanese queries. In addition, even 
when the users locate a possibly relevant text in 
Japanese, they will have little idea about what is 
in the text. Outputs of off-the-shelf machine trans- 
lation (MT) systems are often of low-quality, and 
even "high-end" MT systems have problems partic- 
ularly in translating proper names and specialized 
domain terms, which often contain the most critical 
information to the users. 
In this paper, we describe our multilingual (or 
cross-linguistic) information browsing and retrieval 
system, which is aimed at monolingual users who 
are interested in information from multiple language 
sources. The system takes advantage of information 
extraction (IE) technology in novel ways to improve 
the accuracy of cross-linguistic retrieval and to pro- 
vide innovative methods for browsing and exploring 
multilingual document collections. The system in- 
dexes texts in different languages (e.g., English and 
Japanese) and allows the users to retrieve relevant 
texts in their native language (e.g., English). The 
retrieved text is then presented to the users with 
proper names and specialized domain terms trans- 
lated and hyperlinked. The system also allows the 
user in their native language to browse and discover 
information buried in the database derived from the 
entire document collection. 
2 System Description 
The system consists of the Indexing Module, the 
Client Module, the Term Translation Module, and 
the Web Crawler. The Indexing Module creates and 
loads indices into a database while the Client Module 
allows browsing and retrieval of information in the 
database through a Web browser-based graphical 
user interface (GUI). The Term Translation Mod- 
ule is bi-directional; it dynamically translates user 
queries into target foreign languages and the indexed 
terms in retrieved documents into the user's native 
language. The Web Crawler can be used to add tex- 
tual information from the WWW; it fetches pages 
from user-specified Web sites at specified intervals, 
and queues them up for the Indexing Module to in- 
gest regularly. 
For our current application, the system indexes 
names of people, entities, and locations, and scien- 
tific and technical (S~zT) terms in both English and 
Japanese texts, and allows the user to query and 
browse the database in English. When Japanese 
texts are retrieved, indexed terms are translated into 
English. 
This system is designed to expand to other lan- 
332 
guages besides English and Japanese and other do- 
mains beyond S&T terms. Moreover, the English- 
centric browsing and retrieval mode can be switched 
according to the users' language preference so that, 
for example, a Japanese user can query and browse 
English documents in Japanese. 
2.1 The Intelligent Indexing Module 
The Indexing Module indexes names of people, enti- 
ties, and locations and a list of scientific and techni- 
cal (S~zT) terms using state-of-the-art IE technol- 
ogy. It uses different configurations of the same 
fast indexing engine called NameTag TM for differ- 
ent languages. Two separate configurations ("index- 
ing servers") are used for English and Japanese, and 
how the English and Japanese indexing servers work 
is described in (Krupka, 1995; Aone, 1996). 
In the Sixth Message Understanding Conference 
(MUC-6), the English system was benchmarked 
against the Wall Street Journal blind test set for 
the name tagging task, and achieved a 96% F- 
measure, which is a combination of recall and preci- 
sion measures (Adv, 1995),. Our internal testing 
of the Japanese system against blind test sets of 
various Japanese newspaper articles indicates that 
it achieves from high-80 to low-90% accuracy, de- 
pending on the types of corpora. Indexing names 
in Japanese texts is usually more challenging than 
English for two main reasons. First, there is no case 
distinction in Japanese, whereas English names in 
newspapers are capitalized, and capitalization is a 
very strong clue for English name tagging. Sec- 
ond, Japanese words are not separated by spaces and 
therefore must be segmented into separate words be- 
fore the name tagging process. As segmentation is 
not 100% accurate, segmentation errors can some- 
times cause name tagging rules not to fire or to mis- 
fire. 
Indexing of names is particularly useful in the 
Japanese case as it can improve overall segmenta- 
tion and thus indexing accuracy. In English, since 
words are separated by spaces, there is no issue of in- 
dexing accuracy for individual words. On the other 
hand, in languages like Japanese, where word bound- 
aries are not explicitly marked by spaces, indexing 
accuracy of individual words depends on accuracy 
of word segmentation. However, most segmentation 
algorithms are more likely to make errors on names, 
as these are less likely to be in the lexicons. Name 
tagging can reduce such errors by identifying names 
as single units. 
Both indexing servers are "intelligent" because 
they identify and disambiguate names with high 
speed and accuracy. They identify names in texts 
dynamically rather than relying on finite lists of 
names. Thus, they can identify names which they 
have never seen before. In addition, they can dis- 
ambiguate types of names so that a person named 
"Washington" is distinguished from a place called 
Washington, and a company "Apple" can be dis- 
tinguished from a common noun "apple." In addi- 
tion, they can generate aliases of names automat- 
ically (e.g., "ANA" for "All Nippon Airline") and 
link variants of names within a document. 
As the indexing servers process texts, the in- 
dexed terms are stored in a relational database 
with their semantic type information (person, entity, 
place, S&:T term) and alias information along with 
such meta data as source, date, language, and fre- 
quency information. The system can use any ODBC 
(Open DataBase Connectivity)-compliant database, 
and form-based Boolean queries from the Client 
Module, similar to those seen in any Web search 
engine, are translated into standard SQL queries 
automatically. We have decided to use commercial 
databases for our applications as we are not only in- 
dexing strings of terms but also adding much richer 
information on indexed terms available through the 
use of IE technology. Furthermore, we plan to apply 
data-mining algorithms to the resulting databases 
to conduct advanced data analysis and knowledge 
discovery. 
2.2 The Client Module 
The Client Module lets the user both retrieve and 
browse information in the database through the Web 
browser-based GUI. In the query mode (cf. Fig- 
ure 1), a form-based Boolean query issued by a user 
is automatically translated into an SQL query, and 
the English terms in the query are sent to the Term 
Translation Module. The Client Module then re- 
trieves documents which match either the original 
English query or the translated Japanese query. As 
the indices are names and terms which may con- 
sist of multiple words (e.g, "Bill Clinton," "personal 
computer"), the query terms are delimited in sep- 
arate boxes in the form, making sure no ambiguity 
occurs in both translation and retrieval. The user 
has the choice of selecting the sources (e.g, Washing- 
ton Post, Nikkei Newspaper, Web pages), languages 
(e.g., English, Japanese, or both), and specific date 
ranges of documents to constrain queries. 
In the browsing mode, the Client Module allows 
the user to browse the information in the database 
in various ways. As an overview of the database con- 
tent, the Client Module lets the user browse the top 
25 and 50 most frequent entity, person, and loca- 
tion names and S&T terms in the database (cf. Fig- 
ure 4). Once the user selects a particular document 
for viewing, the client sends the document to an ap- 
propriate (i.e., English or Japanese) indexing server 
for creating hyperlinks for the indexed terms and in 
the case of a Japanese document, sends the indexed 
terms to the Term Translation Module to translate 
the Japanese terms into English. The result that the 
user browses is a document each of whose indexed 
terms are hyperlinked to other documents contain- 
ing the same indexed terms (cf. Figure 2). Since hy- 
333 
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Figure 1: The Search Screen 
334 
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Figure 2: Translated and Hyperlinked Terms 
perlinking is based on the original or translated En- 
glish terms, the user can follow the links to both En- 
glish and Japanese documents transparently. In ad- 
dition, the Client Module is integrated with a com- 
mercial MT system for rough translation. A docu- 
ment which the user is browsing can be translated 
on the fly by clicking the TRANSLATE button. 
2.3 The Term Translation Module 
The Term Translation Module is used by the Client 
Module bi-directionally in two different modes. It 
translates English query terms into Japanese in the 
query mode and translates Japanese indexed terms 
into English for viewing of a retrieved Japanese text 
in the browsing mode. 
This translation module is sensitive to the seman- 
tic types of terms it is translating to resolve trans- 
lation ambiguity. Thus, if a term can be translated 
in one way for one type and in another way for an- 
other type, the Term Translation Module can output 
appropriate translations based on the type informa- 
tion. For example, in translating Japanese text into 
English, a single kanji (Chinese) character standing 
for England can be also a first name of a Japanese 
personal name, which should be translated to "Hide" 
and not "England." In translating an English query 
into Japanese, a company "Apple" should be trans- 
lated into a transliteration in katakana and not into 
a Japanese word meaning a fruit apple. 
The Term Translation Module uses various re- 
sources and methods to translate English and 
Japanese names. We use automated methods as 
much as possible to reduce the cost of creating a 
large name lexicon manually. 
First, this module is unique in that it creates on 
the fly English translations of hiragana names and 
personal names. Hiragana names are transliterated 
into English using the hiragana-to-romaji mapping 
rules. Japanese personal names are translated by 
finding a combination of first and last names which 
spans the input) Then, each of the name parts is 
translated using the Japanese-English first and last 
name lexicons. 
In addition, in order to develop a large lexicon 
of English names and their Japanese translations, 
which are transliterated into katakana, we have au- 
tomatically generated katakana names from pho- 
netic transcriptions of English names. We have 
written rules which maps phonetic transcriptions to 
katakana letters, and generated possible Japanese 
katakana translations for given English names. As 
transliterations of the same English names may dif- 
fer, multiple katakana translations may be generated 
for single English names3 
The remaining terms are currently translated us- 
ing the English-Japanese translation lexicons, and 
we are expanding the lexicons by utilizing on-line 
resources and corpora and a translation aiding tool. 
3 Utilizing IE in Multilingual 
Information Access 
The system applies information extraction technol- 
ogy (Adv, 1995) to index names accurately and ro- 
bustly. In this section, we describe how we have in- 
corporated this technology to improve multilingual 
information access in several innovative ways. 
3.1 Query Disambiguation 
As described in Section 2.1, the Indexing Module not 
only identifies names of people, entities and locations 
but also disambiguates types among themselves and 
between names and non-names. Thus, if the user is 
searching for documents with the location "Wash- 
ington (not a person or a company named "Wash- 
ington"), a person "Clinton" (not a location), or an 
entity "Apple" (not fruit), the system allows the user 
to specify, through the GUI, the type of each query 
term (cf. Figure 1). This ability to disambiguate 
types of queries not only constrains the search and 
hence improves retrieval precision but also speeds 
1The Japanese Indexing Module does not specify if 
an identified name is a first name, a last name, or a 
combination of first and last name. Since there is no 
space between first and last names in Japanese, this must 
be automatically determined. 
2This is still an experimental effort, and we have not 
evaluated the quality of generated translations quantita- 
tively yet. 
335 
up the search time considerably especially when the 
database is very large. 
3.2 Translation Disambiguation 
In developing the system, we have intentionally 
avoided an approach where we first translate foreign- 
language documents into English and index the 
translated English texts (Fluhr, 1995; Kay, 1995; 
Oard and Dorr, 1996). In (Aone et al., 1994), we 
have shown that, in an application of extracting in- 
formation from foreign language texts and present- 
ing the results in English, the "MT first, IE second" 
approach was less accurate than the approach in the 
reverse order, i.e., "IE first, MT second". In partic- 
ular, translation quality of names by even the best 
MT systems is poor. 
There are two cases where an MT system fails to 
translate names. First, it fails to recognize where 
a name starts and ends in a text string. This is a 
non-trivial problem in languages such as Japanese 
where words are not segmented by spaces and there 
is no capitalization convention. Often, an MT sys- 
tem "chops up" names into words and translates 
each word individually. For example, among the 
errors we have encountered, an MT system failed 
to recognize a person name "Mori Hanae" in kanji 
characters, segmented it into three words "mori," 
"hana," and "e" and translated them into "forest," 
"England" and "blessing," respectively. 
Another common MT system error is where the 
system fails to make a distinction between names 
and non-names. This distinction is very important 
in getting correct translations as names are usu- 
ally translated very differently from non-names. For 
example, a personal name "Dole" in katakana was 
translated into a common noun "doll" as the two 
have the same katakana string in Japanese. Abbre- 
viated country names for Japan and United States in 
single kanji characters, which often occurs in news- 
papers, were sometimes translated by an MT system 
into their literal kanji meanings, "day" and "rice," 
respectively. 
Our system avoids these common but serious 
translation errors by taking advantage of the Index- 
ing Module's ability to identify and disambiguate 
names. In translating terms from Japanese to En- 
glish in the browsing mode, the Indexing Module 
identifies names correctly, avoiding the first type 
of translation errors. Then, the Term Translation 
Module utilizes type information obtained by the In- 
dexing Module to decide which translation strategies 
to use, thus overcoming the second type of error. 
3.3 Intelligent Query Expansion and 
Hyperlinking 
As described in Section 2.1, the Indexing Module 
automatically identifies aliases of names and keeps 
track of such alias links in the database. For exam- 
ple, if "International Business Machine" and "IBM" 
appears in the same document, the system records 
in the database that they are aliases. 
The system uses this information in automatically 
expanding terms for query expansion and hyper- 
linking. At the query time, when the user types 
"IBM" and chooses the alias option in the search 
screen (see Figure 1), the query is automatically ex- 
panded to include its variant names both in English 
and Japanese, e.g., "International Business Ma- 
chine," "International Business Machine Corp." and 
Japanese translations for "IBM" and their aliases 
in Japanese. This is especially useful in retriev- 
ing Japanese documents because typically the user 
would not know various ways to say "IBM" in 
Japanese. The automated query expansion thus 
improves retrieval recall without manually creating 
alias lexicons. 
The same alias capability is also used in hyper- 
linking indexed terms in browsing a document. For 
example, when a user follows a hyperlink "United 
States," it takes the user to a collection of documents 
which contains the English term "United States" 
and its aliases (e.g., "US," "U.S.A." etc.), and the 
Japanese translations of "United States" and their 
aliases. The result is a truly transparent multilin- 
gual document browsing and access capability. 
3.4 Information Discovery 
One of the biggest advantages of introducing IE tech- 
nology into information access systems is the ability 
to create rich structured data which can be analyzed 
for "buried" information. Our multilingual capabil- 
ity enables the merging of possibly complementary 
data from both English and Japanese sources and 
enriching the available information. 
Currently thesystem offers the user several ways 
to explore and discover hidden information. Our 
search capability allows interactive information dis- 
covery methods. For example, using the query inter- 
face, the user can in effect ask "Which company was 
mentioned along with Intel in regard to micropro- 
cessors?" and the system will return all the articles 
which mentions "Intel," "microprocessors," and one 
or more company names. The user might see that 
NexGen and Cyrix often occurs with Intel and find 
out that they are competitors of Intel in this field. 
Or the user might ask "Who is related to "Shinshin- 
tou Party," a Japanese political party, and the user 
can find out all the people associated with this party. 
This type of search capabilities cannot be offered by 
typical information retrieval systems as they treat 
words as just strings and do not distinguish their 
semantic attributes. 
Furthermore, as we discussed earlier in Sec- 
tion 2.2, browsing documents by following hyper- 
links allows a user to discover related information 
effectively. For example, when the user searches for 
documents on "NEC Corp.", selects one of the re- 
turned documents, and finds another company name 
336 
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.... : : : " .~ . . s.~t~. : ~: ..: .. 
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• ~ ";"~;~°m" : ~ ~ ':: :~0~: !:.~ : : :-.i+ 
:' \] 
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Figure 3: Person Names Co-occurring with Peru 
:IEI:\]NI : + 
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. . . : ~ .tTm ~\] :Toe f~} \[~'~p ~\] \[To_~.~\] . \[Tm.5~\] 
Figure 4: The 25 and 50 Most Frequent Names 
"Toshiba" mentioned in this document, the user can 
establish an immediate connection and follow the 
link from "Toshiba" to other English and Japanese 
documents which contain that term. 
In addition, for each indexed term, the user can 
explore co-occurring persons, entities, places and 
technology. For example, Figure 3 shows a list of 
people co-occurring with the place "Peru." It lists 
the Japanese prime minister and the Peruvian pres- 
ident at the top (as the Japanese embassy hostage 
incident occurred recently.) 
4 The System Tour 
In this section, we give a tour of the system. Figure 4 
shows the main Browse screen where the user can 
browse the top 25 or 50 names of people, entities, 
locations, and S&:T terms. This can provide the 
user with a snapshot of what is in the database and 
what types of information are likely to be available. 
By following the top 50 entity name link, the user 
sees the list of entity names in order of frequency 
(cf. Figure 5). The Subtype column in the screen 
indicates more detailed types of the entity (e.g., or- 
ganization, company, facility, etc.) From this screen, 
the user can go to a list of all English and Japanese 
documents which mention, for example, "Bank of 
Japan" by clicking the link (cf. Figure 6). The list 
provides information on the title, length, source, lan- 
guage, and date of each article. 
: :;.:e~wo~:~,,."~.~=,,~ ::; ii~o~,~. • ::....11+ : :/. :i; 
:;ii .:.~,~im~:~mN,,~^~i ~ i~, i:. i 
..... i~*~mmm~p:A~:.' i ..~=~,.~,, ~. 14. : : :.: 
• :i:. ? " ~:::: L T. • :: - : 
:: : ' .e~A++r~:oeJ+~^~ :.:,: :a,;~=.. : i{. ~. : 
Figure 5: Top 50 Entity Names 
337 
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-. . • . 
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Figure 6: Documents Containing "Bank of Japan" 
• " " ! 
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who is in the mid~ of vifiting:the United States on the 30th. cony•wed with t 
American I~aident Clinton at Whitelumse, Conferred c0ncermng Middle 
Eutetn peace negotiation ~md the ~'Torist meamre which are aagnant with 
strong sroup neva item -~ ~' ~ adminiRr atio n start of h reel. 
In joint press ~nfenmce after converting Is for s. rose ~ president," in order 
for our ~dOrt to mcceed, AmQican rOleis indisl~ns~ble, "that doing, in order 
to Pull back hrael m ~ proton, it made that it um~ht the Ix~ifive 
mediation ofth e United States clear. 
Vis-a-vis thh~ az forFre~ident ointo~; ,~ at .for ut, at you ¢gpreu; that it 
: agreed,b~_ thefact thtt the prenmt Middle Easter n peace prvcm which 
expands Pale~inian pr~Ional autonomy b firmlymaintained "in the 
::filmre,it'includes thettart of Syl.i.an. L~anon and Isr~e~ which ereleft 
.negotiatlon; ~d~~ur.~ble thh~ entirely" that dete/'minatio n wu Shown, 
• is, lint" it d~:not e~ape tsome di~conthm ance and ~tagnadon," that 
attendant up0a hraeli ~nistration allefntlion it did.did not show the 
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Ononelhand bethlead¢~ did opinion ~¢changeconcerning te~oHs t " . 
,precenU.'~n t~; :u, f0r :~ ra~e-:~' P~de~C* u: far Problem 0f tefrorit~n,. 
:~tarted from Mi(lfil¢.Eas~ region "that doii~, if.indm~e peace of the 
• Middle EMt a~cm alizes~ to conclude it. probiblY:iS p0~ible.95 % ~ te~rorin 
\]activkyof~e~dd'~thatycu~l~ei~d.... :: : .... 
:::(~y~t~10i:~): : :: " -~ '. ' . : • . .... ,. - .... : . ./ .... . 
Figure 7: Translation by a Commercial MT system 
In the main Search screen (cf. Figure 1), the user 
types in each query term, including multi-words like 
"personal computer," in each numbered box. The 
user can formulate a Boolean query using the box 
numbers and boolean operators. If not specified, the 
query terms are joined by "OR". When the Alias 
button is on, query terms are expanded to include 
their aliases. The Type menu allows the user to dis- 
ambiguate types of query terms. In the Language 
box, the user has the choice of selecting documents 
in English, Japanese, or both. In addition, the user 
can constrain sources and the date range of docu- 
ments, and also sort the results by date, title, and 
sources. 
As discussed in Section 2.2, when the user selects a 
Japanese article, they can optionally send the article 
to a commercial MT system for rough translation by 
pushing the TRANSLATE button (cf. Figure2). Fig- 
ure 7 shows the translation result for the Japanese 
document in Figure 2. 
5 Summary 
We have described an advanced multilingual cross- 
linguistic information browsing and retrieval sys- 
tem which takes advantage of information extraction 
technology in unique ways. In addition to its basic 
capability of allowing a user to send Boolean queries 
in English against English and Japanese documents 
and to view the results in semi- and fully translated 
forms, the system has many innovative capabilities. 
It can disambiguate query terms to increase preci- 
sion, expand query terms automatically using aliases 
to increase recall, and improve translation accuracy 
significantly by finding and disambiguating names 
accurately. Moreover, the system allows interactive 
information discovery from a multilingual document 
collection by combining IE and MT technologies. 
The Indexing Module is currently running on a 
Sun platform and is designed to scale for a multi-user 
operational environment. The Web browser-based 
user interface will work in any Web browser sup- 
porting HTML 3.0 on any platform which the Web 
browser supports, and this ensures a large user base. 
The system is customizable in several ways. For our 
current application, the system indexes names and 
S&T terms, but for other applications we can cus- 
tomize the system to index different types of names 
and terms. For example, the system can be cus- 
tomized to index product names and financial terms 
for a business application. Its ODBC-compliance 
makes porting of databases from one vendor to an- 
other very easy. Finally, the system does not as- 
sume any particular language combination or target 
language. Thus, this system can also be used for 
Japanese monolingual users who want to query and 
browse in Japanese a set of documents written in 
English, Japanese, and Spanish. 
338 

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