Dataset Viewer
text
stringlengths 0
203
|
|---|
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.
|
2
|
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
|
End of preview. Expand
in Data Studio
README.md exists but content is empty.
- Downloads last month
- 5