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Assessment_1_NLP303

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Introduction to the CoNLL-2003 Shared Task:
Language-Independent Named Entity Recognition
Erik F. Tjong Kim Sang and Fien De Meulder
CNTS - Language Technology Group
University of Antwerp
{erikt,fien.demeulder}@uia.ua.ac.be
Abstract
We describe the CoNLL-2003 shared task:
language-independent named entity recognition. We give background information on
the data sets (English and German) and
the evaluation method, present a general
overview of the systems that have taken
part in the task and discuss their performance.
of the 2003 shared task have been offered training
and test data for two other European languages:
English and German. They have used the data
for developing a named-entity recognition system
that includes a machine learning component. The
shared task organizers were especially interested in
approaches that made use of resources other than
the supplied training data, for example gazetteers
and unannotated data.
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1
Introduction
Named entities are phrases that contain the names
of persons, organizations and locations. Example:
[ORG U.N. ] official [PER Ekeus ] heads for
[LOC Baghdad ] .
This sentence contains three named entities: Ekeus
is a person, U.N. is a organization and Baghdad is
a location. Named entity recognition is an important task of information extraction systems. There
has been a lot of work on named entity recognition,
especially for English (see Borthwick (1999) for an
overview). The Message Understanding Conferences
(MUC) have offered developers the opportunity to
evaluate systems for English on the same data in a
competition. They have also produced a scheme for
entity annotation (Chinchor et al., 1999). More recently, there have been other system development
competitions which dealt with different languages
(IREX and CoNLL-2002).
The shared task of CoNLL-2003 concerns
language-independent named entity recognition. We
will concentrate on four types of named entities:
persons, locations, organizations and names of
miscellaneous entities that do not belong to the previous three groups. The shared task of CoNLL-2002
dealt with named entity recognition for Spanish and
Dutch (Tjong Kim Sang, 2002). The participants
Data and Evaluation
In this section we discuss the sources of the data
that were used in this shared task, the preprocessing
steps we have performed on the data, the format of
the data and the method that was used for evaluating
the participating systems.
2.1
Data
The CoNLL-2003 named entity data consists of eight
files covering two languages: English and German1 .
For each of the languages there is a training file, a development file, a test file and a large file with unannotated data. The learning methods were trained with
the training data. The development data could be
used for tuning the parameters of the learning methods. The challenge of this year’s shared task was
to incorporate the unannotated data in the learning
process in one way or another. When the best parameters were found, the method could be trained on
the training data and tested on the test data. The
results of the different learning methods on the test
sets are compared in the evaluation of the shared
task. The split between development data and test
data was chosen to avoid systems being tuned to the
test data.
The English data was taken from the Reuters Corpus2 . This corpus consists of Reuters news stories
1
Data files (except the words) can be found on
http://lcg-www.uia.ac.be/conll2003/ner/
2
http://www.reuters.com/researchandstandards/
English data
Training set
Development set
Test set
Articles
946
216
231
Sentences
14,987
3,466
3,684