Multilingual adaptations of ANNIE, a reusable information extraction tool Diana Maynard, Hamish Cunningham Dept of Computer Science University of Sheffield Sheffield, Si 4DP, UK diana@dc
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Multilingual adaptations of ANNIE, a reusable information
extraction tool
Diana Maynard, Hamish Cunningham
Dept of Computer Science University of Sheffield Sheffield, Si 4DP, UK
diana@dcs.shef.ac.uk
Abstract
In this demo we will present GATE, an
archi-tecture and framework for language
engineer-ing, and ANNIE, an information extraction
sys-tem developed within it We will demonstrate
how ANNIE has been adapted to perform NE
recognition in different languages, including
In-dic and Slavonic languages as well as Western
European ones, and how the resources can be
reused for new applications and languages
1 Introduction
GATE' is an architecture, development
envi-ronment, and framework for building systems
that process human language (Cunningham et
al., 2002; Maynard et al., 2002) It has been
in development at the University of Sheffield
since 1995, and has been used for many R&D
projects, including Information Extraction in
multiple languages and media, and for multiple
tasks and clients GATE is available freely, as
an open source system, under the GNU library
licence, and has been downloaded by around
2500 sites worldwide The core architecture and
some applications developed within GATE have
been previously demonstrated (Cunningham et
al., 2002); however, this demonstration will
fo-cus on the multilingual aspects of GATE, and
adaptations of its IE system for different
lan-guages
Version 2 of GATE has a large number of
added features from the previous version, such
as:
• comprehensive multilingual support via
Unicode
'This work has been supported by the Engineering
and Physical Sciences Research Council (EPSRC)
un-der grants GR/K25267 and GR/M31699, and by several
smaller grants.
• tools for performance evaluation
• support for manual annotation
• reusable visualisation components
• database support (Oracle, PostgresQL)
• support for distributed resources from the Web
• comprehensive document format support (SGML, XML, HTML, RDF, email, plain text)
Figure 1: Unicode text in Gate2
2 Processing Resources
GATE provides a baseline set of reusable and extendable language processing components for common NLP tasks, known collectively as AN-NIE (A Nearly New Information Extraction System) These include a Unicode tokeniser, sentence splitter, POS tagger, gazetteer, seman-tic tagger, name coreferencer (orthomatcher)
Trang 2IMMFis AnnotatiooidA
Din surnarui Nr.336/1111110.2002
• Gnidul calatoriilorfa4a vita in ball Bilandul activitadii poobor buzodani in anul 111.(partea b•Polidistii au posibOtatea legislativa de aid indeplini mai blue
sarcinileii misnimle cc le revin• A incep-ut cnn nou an de lupta cu infractorii: Printre
primii la calatorli fara vita, 5eribedn Nadine din a font prinsa pa aerop-ort incercand nO fuga de datorii neonorate de circa Un het din biserici font intuit tocmai de pazni-cul care l-a prins/S-a spart biblioleca Casei de culkira dar hodii RIJ s-au afire de nici o carte/Explozie la sonda 304 Grajdana •Jurnal rutier
- Ultima zi din an sub semnul imprudendei Somne papilare" de Col Nicotae Rutaru • "kitrnet pas cu pas" de Adrian firlacdru4-Tanasescu•invataturi din arhivele brancovenes.ti•
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and pronominal coreferencer For more details,
see (Cunningham et al., 2002) ANNIE
cur-rently produces precision and recall figures for
named entity recognition of around 90%,
de-pending on the text type
An online demo of ANNIE is available at
http://gate.ac.uk/a,nnie/index.jsp A set of
movies demonstrating document and corpus
loading, processing and storing, manual
an-notation of documents and corpora,
creat-ing, runncreat-ing, saving and restoring
applica-tions and viewing their results is available at
http: / /www.gat e ac uk/ demos / movies ht ml
3 Multilingual support - the GATE
Unicode Kit
GATE is one of the few architectures to
sup-port multilingual processing, using Unicode as
its default text encoding A Unicode enabled
two main issues: the capability to display text
and the ability to enter text in other languages
than the default one
It also provides a means of entering text in
a variety of languages and scripts, using virtual
keyboards where the language is not supported
by the underlying operating platform (Java
it-self does not support input in many languages
covered by Unicode, although it supports
Uni-code representation) Figure 1 depicts text in
various scripts displayed in GATE The
facili-ties have been developed as part of the EMILLE
project (Baker et al., 2002), designed to
con-struct a 63 million word corpus of South Asian
languages There are currently 28 languages
supported in GATE, and more are planned for
the future Since GATE is an open architecture,
new virtual keyboards can easily be defined by
users and added to the system
Apart from the input methods, GUK also
provides a simple Unicode-aware text editor
which is important because not all platforms
provide one by default or the users may not
know which one of the already installed
edi-tors is Unicode-aware Besides providing text
visualisation and editing facilities, the GUK
ed-itor also performs encoding conversion
opera-tions The editor has proved a useful tool
dur-ing the development and testdur-ing of GATE in a
cross-platform environment, while the ability to
handle Unicode enables applications developed
within GATE to be easily ported to new lan-guages
4 The future isn't English
Robust tools for multilingual information ex-traction are becoming increasingly sought af-ter now that we have capabilities for processing texts in different languages and scripts While the default IE system is English-specific, some
of the modules can be reused directly (e.g the Unicode-based tokeniser can handle Indo-European languages), and/or easily customised for new languages (Pastra et al., 2002) So far, ANNIE has been adapted to do IE in Bulgarian, Romanian, Bengali, Greek, Spanish, Swedish, German, Italian, and French, and we are cur-rently porting it to Arabic, Chinese and
Figure 3: Romanian news text annotated in GATE
4.1 NE in Slavonic languages The Bulgarian NE recogniser (Paskaleva et al., 2(02) was built using three main processing re-sources: a tokeniser, a gazetteer and a semantic grammar built using JAPE There was no POS tagger available in Bulgarian, and consequently
we had no need of a sentence splitter either The main changes to the system were in terms 2http://www.des.shefac.uk/research/groups/n1p/muse/
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CI Language Resources
= A Text EiG Processing Resources
BG Names t" ■ Unicode Tokeniser
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Messages I di BO system Text BO I
Emol- apgslee aerompe 8 METO4HEI EBFIOna AHM,I9 e H8 crepes reproahuln m chpaagnn ca e Sanagaa Espana.
tR - I'M K IHNHI X8M
D - r M Cunningham
Dr. M Cunningham D-rAHrenoaa
Lookup ,Default, 71 78 trninorType=country, majorType=location}
Lookup ,Default> 60 68 trninorType=country, majorType=location}
Lookup ,Default> 37 43 trninorType=country, majorType=location}
Lookup ,Default> 0 8 trninorType=country, majorType=location}
Person ,Default> 210 216 tkInd=personName, rule=KallnaPersonl Person ,Default> 173 179 tkInd=personName, rule=HamishPerson}
Person ,Default> 148 154 tkInd=personName, rule=HamishPerson}
Person ,Default> 108 113 tkind=personName, rule.HamishPersonBet Person ,Default> 266 270 tkind=personName, rule=GaliaPersont Person ,Default> 248 253 tkind=personName, rule=GaliaPersont Person ,Default> 230 236 tkind=personName, rule=KalinaPersonl
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Figure 2: Bulgarian named entities in GATE
of the gazetteer lists (e.g lists of first names,
days of the week, locations etc were tailored for
Bulgarian), and in terms of some of the pattern
matching rules in the grammar For example,
Bulgarian makes far more use of morphology
than English does, e.g 91% of Bulgarian
sur-names could be directly recognised using
mor-phological information The lack of a POS
tag-ger meant that many rules had to be specified in
terms of orthographic features rather than parts
of speech Figure 2 shows some Bulgarian text
annotated in GATE
Since the structure of the Bulgarian and
Russian languages is quite similar, we
antic-ipate that converting the Bulgarian system
to Russian will be fairly straightforward, and
will involve mostly replacing and/or updating
gazetteer lists at least to obtain comparable
results
4.2 NE in Romanian
2002) was developed from ANNIE in a
simi-lar way to the Bulgarian one, using a tokeniser,
gazetteer and a JAPE semantic grammar
Fig-ure 3 shows some Romanian text annotated in
GATE
Romanian is a more flexible language than
English in terms of word order; it is also
agglu-tinative e.g definite articles attach to nouns, making a definite and indefinite form of both common and proper nouns
As with Bulgarian, the tokeniser did not need
to be modified, while the gazetteer lists and
which were fairly minor For both Bulgar-ian and RomanBulgar-ian, the modifications necessary were easily implemented by a native speaker who did not require any other specialist skills beyond a basic grasp of the JAPE language and the GATE architecture No Java skills or other programming knowledge was necessary The Gate Unicode kit was invaluable in enabling the preservation of the diacritics in Romanian, by saving them with UTF-8 encoding
4.3 NE in other languages ANNIE has also been adapted to perform NE recognition on English, French and German
of which is shown in Figure 4 Since French and German are more similar to English in many ways than e.g Slavonic languages, it was very easy to adapt the gazetteers and grammars ac-cordingly
3http://www.dcs.shcf.ac.uk/nlp/amitics
Trang 4File
AMITIES: Amities, how can I help, je vous ecoute, was lcann ich ftir Sie tun?
USER: Guten Tag, Ich mochte gem wissen, movie! ich out metnem Konto hate
AMITIES: Darf ich bitte Ihre Kontonummer haben?
USER: Mon numero est 5522333344945555
AMITIES: Pouvez-vous me donner votre adresse et votre date de naissance, s'il
vous plait?
USER: I live at 547 Ratchet Lane, and my date of birth is the 18th of May, 1969
AMITIES: Thank you Here is the information about your account.
Figure 4: AMITIES multilingual dialogue
4.4 Surprise languages
We are currently investigating methods of
adapting ANNIE to new languages with the
minimum of resources and time Our previous
experiments with languages other than English
have demonstrated that we can get reasonable
results in around 2 person months using a
na-tive speaker and hand-coded semantic tagging
rules, without requiring resources such as
dictio-naries or POS taggers for that language We are
also planning participation in the TIDES-based
"surprise language experiment", which requires
various NLP tasks such as IE, IR,
summarisa-tion and MT to be carried out in a month on
a surprise language, the nature of which will
not be known in advance The open and
flex-ible architecture of GATE, and the separation
of linguistic data from processing makes it an
ideal environment within which to perform such
a task Any available linguistic resources such
as dictionaries and POS taggers can be simply
plugged into the model, but if these are not
available we can simply modify other
compo-nents as necessary
5 Conclusion
In this demo we have shown how an existing set
of IE tools has been modified to a diverse set
of languages with minimum overhead The
ad-vantage of having such low-overhead portability
is that it enables quick deployment of IE tools
with acceptable performance, which, even if not
to bootstrap the creation of IE-annotated
cor-pora and/or facilitate the training of learning
tools for adaptive IE In addition, some
adap-tive IE tools are now using the ANNIE
corn-ponents to provide them with richer linguistic information (Ciravegna et al., 2002)
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