An Architecture for Integrated Retrieval over Multiple Electronic Dictionaries

  1. 1. Jon Patrick

    Department of Information Systems - Massey University

  2. 2. Jun Zhang

    Department of Information Systems - Massey University

  3. 3. Xabier Artola-Zubillaga

    Lengoaia eta Sistema Informatikoak - University of the Basque Country

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1. Introduction
Dictionaries in electronic form are rapidly emerging, although at the moment they more closely
mimic their paper predecessor than exploit the
added advantages available through computer devices. In particular, extensive cross-referencing
and the rapid access of material relevant to a user’s
needs can now be delivered at a reasonable cost.
However, as user familiarity and competence
grows, so does the demand for more elaborate and
sophisticated access. It is now possible to acknowledge the need to support and deliver elaborate
information systems that enable a user to access
multiple dictionaries of many types and languages
in a single software environment, much in the
same way as that provided by Mosaic on the World
Wide Web. Such an ambitious project needs an
upper level design that caters for the wide diversity
of needs for potential users across a variety of
hardware platforms and software environments
with broad geographical dispersion. We define the
requirements of this project as having four broad
categories which in turn can be broken into smaller
tasks, many of which can be tackled independently. These categories are:
(1) the conversion of paper dictionaries to an
electronic form in a way that provides for the
automatic extraction of their implicit structure and
for converting the information into lexical databases (Boguraev & Briscoe eds., 1989),
(2) to automate the production of different database structures as required for different computational requirements, for example, lexical databases
vis-a-vis dictionary databases vis-a-vis multi-lingual databases whilst at the same time fulfilling
the requirements of category (1) above.
(3) to develop techniques for the generation and
integration of conventional dictionaries with multi-media dictionaries and encyclopaedic information.
(4) the provision of inter-connecting semantic
links between the lexical units of an unlimited
number of electronic dictionaries and similar documents to enable browsing and search trails
across the various documents
This paper has the aims of:
(i) defining an overarching architecture to support
the requirements of categories 1-4 above,
(ii) to introduce a generalized dictionary query
(iii) to introduce a new type of database organization for mono-, cross- and multi-lingual dictionaries, that allows a relaxation of demands for a strict
physical organisation of the data so as to facilitate
(iv) to report on the implementation of the new
organization in (iii) above for Basque-English and
English-Basque cross-lingual dictionaries.
2. Description of some of the problems
Electronic versions of dictionaries for human use
are emerging everywhere. The search for new
ways of representing and retrieving dictionary
knowledge represents a challenge. There is a large
distance between an electronic dictionary that is
merely a word-processor formatted text file and an
intelligent dictionary help system such as that
described in (Agirre et al., 1993, 1994), for example, where the user is allowed, among other sophisticated access functions, to retrieve words based
on semantic constraints formulated in a specially
conceived language.
Moreover, lexical databases (LDB) for natural
language processing (NLP) share many of the
features that dictionaries have, but they also need
many other kinds of information that are necessary
when the goal is the automatic treatment of the
To gather these two kinds of resources into a single
system is desirable:
NLP needs real-size LDB’s that have to be built
with computer support. Much research has been
carried out in the last few years (Boguraev &
Briscoe eds., 1989) in the sense of taking advantage of conventional dictionaries in machine-readable form to extract from them information needed for NLP tasks. Hence, computational linguists
need on-line access to dictionaries when working
on their LDB’s.
Lexicographers can take advantage of information
coded in LDB’s when doing their work: much
more formal and systematic ways are used in
coding the information for NLP than when com-
piling information for the human reader. In addition, multi-lingual societies require bilingual
cross-lingual and multi-lingual dictionaries. The
language industry needs multi-lingual tools (online dictionaries, writing aids, machine-assisted
translation tools, etc.), that obviously require the
use of cross- and multi-lingual databases. We define a cross- lingual dictionary as one which provides a match between words and terms in one
language with those in another language, whereas
a multi-lingual dictionary has a head word in one
language and the definindum in one or more other
Therefore, the problem posed in this paper is related mainly to lexical and dictionary knowledge
representation and retrieval issues, for either NLP
applications or human users, and takes into account the need to deal with a great diversity of
electronic lexical sources. The solution proposed
is intended to gather, in a single integrated computer architecture, all the aspects mentioned above
as well as some initial work on practical solutions.
3. Variety and complexity of source
Currently, a diversity of lexical resources can be
found in electronic form, including Roget’s Thesaurus and different dictionaries (monolingual
and/or bilingual) of different languages and of
different types (explanatory dictionaries with definitions, synonym dictionaries, thesauri, etc.).
Moreover, lexical databases developed mainly for
NLP, which contain information needed for the
automatic treatment of different languages, are
also available.
Obviously, this variety of sources contains a great
diversity and complexity in formats in which this
information is actually stored from plain texts,
marked up or not, to conventional database management systems or even sophisticated knowledgebased systems. This high diversity of formats or
representations provides for very differentiated
levels of retrieval functionality. Furthermore, the
potential for candidate dictionaries to be in geographically disparate locations must be considered. The need to provide for reusability of these
sources is evident.
Therefore, the requirements of a system that
would serve as a basis for all these sources include
a flexible architecture which provides a Generalized Lexical and Dictionary Description Language
(GLDDL) along with a powerful query language
for retrieval.
4. Architecture of the system
The architecture proposed in this paper aims to
provide a solution to the problem of gathering
such a diversity of lexical and dictionary sources
into a well-integrated system. Its most important
characteristics are the integration of all the different sources or tools in a single federation of
databases (the term database is here employed in
its widest sense), and the conception of a generalized lexical and dictionary description language
that would provide a platform for the exchange of
information between the databases and the users.
Two aspects are discussed here: the federation
itself, and the query language.
A Federation of Heterogeneous Lexical & Dictionary Databases (FHLDDB). It is not conceivable
to compel the providers of the different lexical and
dictionary sources to convert their information
bases to a single standard, and it is actually unnecessary and indeed unworkable. Our proposal
aims to accept any source in any format, and
integrate them in a so-called “federation of heterogeneous lexical and dictionary databases”. We
distinguish here the term Lexical Database (LDB),
that stands for those databases built as support for
NLP tasks and, so, source of a variety of computational lexica, from the term Dictionary Database
(DDB), that stands for the computer implementations of dictionaries for human use, be they explanatory dictionaries, synonym dictionaries, thesauri, bilingual dictionaries, etc. The structure of each
database in the federation has to be described by
means of the generalized lexical and dictionary
description language mentioned above.
Universal Query Language: to design a suitable
query language that, based on the common lexical
description language, will allow the end user, either human or program, to communicate with the
federated system.
5. A Generalized Lexical & Dictionary
Description Language (GLDDL)
A GLDDL is a common description language for
lexical and dictionary knowledge that, placed on
top of the FHLDDB, will facilitate the exchange
of information between the physical information
stores and the end user.
To that end, some pilot studies on real dictionaries
and lexical databases, such as the huge one built
in Japan (EDR, 93), must be considered. Surveys
of lexicographers could also be considered. Moreover, the standards being proposed for the representation of lexical and dictionary knowledge in
different projects recently finished or still ongoing
must be analysed, e.g. as Acquilex I (Common
Lexical Entry) and II, Genelex, Comlex, Multilex,
Eagles, Cambridge Language Survey, etc. (Copestake, 1992; Sperberg-McQueen & Burnard eds.,
The TEI Guidelines have been drawn up through
a lengthy study of these projects and arrived at an
integration of most of the aforementioned studies.
Although the TEI guidelines were drawn up for
essentially encoding paper dictionaries, we propo222
se that the TEI standard for Feature Structures
encoding be the first version of the GLDDL for
terms and their definitions. We expect that in time
and with more experience this version will be
5.1. A Representation Formalism for a
A GLDDL must be implemented with a general
representation formalism. Feature Structures (FS)
as definable in an Object-Oriented DBMS are
general purpose data structures that have been
used in many systems to encode linguistic information. There exists a well-developed theoretical
framework for them, and their applicability to
encode the information found in dictionaries, or in
lexical databases for NLP, is quite natural (Ide et
al., 93).
We present here a different proposal to feature
structures and other approaches. We start from the
point that a TEI-like standard supplies a comprehensive set of terminology and operationalisable
definitions and criteria for us to describe entries in
a lexical or dictionary database. One of the limitations in describing a document as complex and
diverse as a dictionary is the rich variety of organisational structures used by lexicographers from
entry to entry. However, such diversity almost
always has its own idiosyncratic organization that
enables a set of parse rules to be defined for the
structures of the greater part of the dictionary.
We advocate that the dictionary entrie’s structures
should not be dismantled just to pack them into a
formal database schema whether it be relational or
feature-structured as each have their own limitations. Rather we propose to keep the original entries intact as a single one field string in a database
record just as they appear in a paper dictionary.
However, in our system we parse the entries into
their component structures and store in a separate
field in the database record the parse node number
(equivalent to its feature name) for each structure
and pointers into the record where each feature
commences. This strategy requires a parser to be
written for every dictionary and the parse structure
states to be identified in terms of the LDDL. The
resulting database organization we call a Parse
State Structure (PSS).
This strategy has a number of benefits over previous solutions to lexical and dictionary database
(i) The data structure is indefinitely recursive and
hence more flexible,
(ii) It is not possible to waste space due to empty
schema fields in the database records.
(iii) The particular schema for a database does not
need to be learnt for either human or machine
(iv) The database is directly portable between
DBMSs without restructuring although indexing
mechanisms will need to be recompiled,
(v) A TEI marked up version of the database can
be automatically generated at any time as it is a
structural subset of the parse tree and parse states
map directly to the TEI nomenclature in the LDDL.
5.2. Generalized Lexical & Dictionary Query
Language (GLDQL)
When a query from the user is presented to the
system, it must be addressed to one or several units
in the FHLDDB. These queries, expressed in, say,
SQL or Object-SQL for units resident in relational
or object-oriented databases, or in specific query
languages designed for the specific lexical stores,
must then be broadcast to the retrieval modules of
each unit. Once the answers are obtained in a
variety of forms, they must be translated and possibly unified, into the GLDDL, in order to provide
a unified answer. A translation process must take
place when translating an answer from the system
into the answer ultimately presented to the user.
The PSS offers a number of advantages for querying a single database or a federation of databases.
The basic form of a Generalized Lexical & Dictionary Query Language (GLDQL), which mimics
SQL is:
SELECT <select_list>
FROM <dictionary_list>
WHERE <predicate>
where select_list is the list of structural elements
(features, TEI attributes) required to be extracted
from each entry; dictionary_list is a list of dictionaries to be searched; and, predicate is the set of
restriction conditions as formed by SQL-like logical expressions that is applied to any features in an
If such a query is broadcast to a federation of
databases it would have to be mapped to the
DBMS structure and data definition terminology
for each database that had to respond to the request. A mapping scheme has been defined to
operate with the SQL of the Oracle DBMS.
6. Work to date
We have stored the entries for each of a BasqueEnglish (Aulestia, 1990) and an English-Basque
(Aulestia & White, 1990) dictionary in their own
Oracle Databases. Implementing the PSS inside a
standard DBMS – like Oracle gives advantages
such as, all querying functionalities provided by
Oracle can be used to provide the intermediate
results, for example, wild card matching in conditions; database security is automatically handled
and all user interface software available with the
DBMS is readily accessible.
Included in Table 1 are some statistics for the two
dictionaries that we have processed. The source
files were created from an OCR process from the
paper dictionaries and therefore contained errors
that have not yet been corrected. The parsing hit
rate should therefore increase significantly when
the erroneous records are corrected and resubmitted to the parser. The figures show that a source
file of 4 Kbytes and 43,545 entries can be loaded
in 60 minutes into an Oracle DBMS running on a
Sparc Server 1000. Although this is a long period
for a small file it represents only an initial overhead for creating the database and a large variety of
indices on many attributes. This also explains the
10-fold increase in the size of the Oracle database
compared to the source file. However, once the
database is created the efficiency of the data representation is demonstrated in that the whole dictionary can be written to a TEI conformant file in
4 minutes. We also have found that response to
queries over a large number of fields or wild card
patterns in the definindum field is very nearly
immediate. Individual entries can be requested in
a Marked-up TEI conformant format. To test our
claims of transportability a sample component of
the databases has been provided to another site and
readily installed without problem.
Aulestia &White
Source File Size
(ASCII text)
3,966 Kb 1,666 Kb
Parsable Data
File Size
3,353 Kb 1,473 Kb
Parsable Entry
43,545 22,663
Parsing Hit
90.5% 92.7%
Space Used in
44,520 Kb 25,780 Kb
Time Consumed
to Load
60 minutes 40 minutes
Time to Create
TEI File
4 minutes 3 minutes
TEI File Size 10,275 Kb 7,386 Kb
Table 1. Processing specifications for 2 dictionary databases organised in a Parse State Structure mounted
on a SPARC server 1000.
Although we have tried to make our model independent of a specific dictionary, some attributes
with a closed set of values, e.g. part of speech
(pos), still need to be mapped to the standard
nomenclature of a local dictionary. Therefore a
mapping table is necessary for each dictionary so
that comparison of the value sets of the same
feature across different dictionaries will be possible. For example, in one dictionary the intransitive
verb is abbreviated as ’vi’ while in another dictionary it is ’v.i.’, hence the condition D1.pos =
D2.pos has to be checked based on the mapping
tables provided by each dictionary.
The development of this project requires us to
complete a federation of a variety of dictionary
sources. Currently we have the following list of
dictionaries in electronic form, whose structure is
represented in a TEI(FS)-like way:
– EDBL (Lexical Database of Basque for NLP
applications, stored in a relational database),
– LPPL (Le Plus Petit Larousse, ordinary dictionary of French, stored in a relational database),
– HLEH (Hauta-Lanerako Euskal Hiztegia,
dictionary of Basque automatically parsed
and encoded following the TEI guidelines for
7. Conclusion
This design accomplishes:
• a higher level of architectural descriptions of
a federation of lexical and dictionary databases,
• a unique proposal for parallel retrieval with
integrated response to the user,
• and a description of an implementation of a
Lexical & Dictionary Definition Language
(LDDL) using a Parse State Structure (PSS),
that is more useful than a mark-up language
or a fixed database schema for cross communicating between different platforms which
support a Generalized Lexical & Dictionary
Query Language (GLDQL).
Our architecture will support a variety of classes
of users including the general public, translators,
lexicographers, etc. and it provides for universal
access to electronic dictionaries without attempting to enforce the dictionary publisher to convert
existing paper or electronic dictionaries into a
particular schema structure or DBMS platform,
hence lowering co-operation and general accessibility.
Finally, it provides for a database architecture
suitable for readily coalescing and cross-referencing a variety of source materials, including
mono-, cross- and multi-lingual dictionaries, thesauri, phrase books, encyclopaedic information, etc.
8. References
Agirre E., Arregi X., Artola X., Diaz de Ilarraza
A., Evrard F., Sarasola K. Intelligent Dictionary Help System. Proc. 9th Symposium on
Languages for special Purposes. Bergen (Norway), 1993.
Agirre E., Arregi X., Artola X., Diaz de Ilarraza
A., Sarasola, K. Lexical Knowledge Representation in an Intelligent Dictionary Help System. Proc. of COLING’94, 544–550. Kyoto
(Japan), 1994.
Aulestia G. Basque-English Dictionary, University of Nevada Press, Reno, 1990.
Aulestia G. & White L. English-Basque Dictionary, University of Nevada Press, Reno, 1990.
Boguraev B., Briscoe T. eds., Computational Lexicography for Natural Language Processing.
New York: Longman, 1989.
Copestake A. The ACQUILEX LKB: representation issues in semi-automatic acquisition of
large lexicons, Proceedings 3rd. Conference
on Applied Natural Language Processing
(Trento, Italia), 88–95. 1992.
EDR Electronic Dictionary Technical Guide. Japan Electronic Dictionary Research Institute,
Ltd. TR-042, 1993.
Ide N., Le Maitre J., Veronis J. Outline of a Model
for Lexical Databases. Information Processing
and Management, vol. 29, no. 2, pp. 159–186,
Sperberg-McQueen C.M., Burnard L. eds., Guidelines for Electronic Text Encoding and Interchange. Chicago, Oxford: TEI P3 Text
Encoding Initiative, 1994.

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Conference Info

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Bergen, Norway

June 25, 1996 - June 29, 1996

147 works by 190 authors indexed

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Conference website:

Series: ACH/ICCH (16), ALLC/EADH (23), ACH/ALLC (8)

Organizers: ACH, ALLC

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  • Language: English
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