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5% |
7% |
8% |
5% |
3% |
6% |
? |
? |
Table 4: Error reduction for the two development data sets when using extra information like |
gazetteers (G), unannotated data (U) or externally |
developed named entity recognizers (E). The lines |
have been sorted by the sum of the reduction percentages for the two languages. |
with extra information compared to while using only |
the available training data. The inclusion of extra named entity recognition systems seems to have |
worked well (Florian et al., 2003). Generally the systems that only used gazetteers seem to gain more |
than systems that have used unannotated data for |
other purposes than obtaining capitalization information. However, the gain differences between the |
two approaches are most obvious for English for |
which better gazetteers are available. With the exception of the result of Zhang and Johnson (2003), |
there is not much difference in the German results |
between the gains obtained by using gazetteers and |
those obtained by using unannotated data. |
3.4 |
Performances |
A baseline rate was computed for the English and the |
German test sets. It was produced by a system which |
only identified entities which had a unique class in |
the training data. If a phrase was part of more than |
one entity, the system would select the longest one. |
All systems that participated in the shared task have |
outperformed the baseline system. |
For all the Fβ=1 rates we have estimated significance boundaries by using bootstrap resampling |
(Noreen, 1989). From each output file of a system, |
250 random samples of sentences have been chosen |
and the distribution of the Fβ=1 rates in these samples is assumed to be the distribution of the performance of the system. We assume that performance |
A is significantly different from performance B if A |
is not within the center 90% of the distribution of B. |
The performances of the sixteen systems on the |
two test data sets can be found in Table 5. For English, the combined classifier of Florian et al. (2003) |
achieved the highest overall Fβ=1 rate. However, |
the difference between their performance and that |
of the Maximum Entropy approach of Chieu and Ng |
(2003) is not significant. An important feature of the |
best system that other participants did not use, was |
the inclusion of the output of two externally trained |
named entity recognizers in the combination process. |
Florian et al. (2003) have also obtained the highest |
Fβ=1 rate for the German data. Here there is no significant difference between them and the systems of |
Klein et al. (2003) and Zhang and Johnson (2003). |
We have combined the results of the sixteen system in order to see if there was room for improvement. We converted the output of the systems to |
the same IOB tagging representation and searched |
for the set of systems from which the best tags for |
the development data could be obtained with majority voting. The optimal set of systems was determined by performing a bidirectional hill-climbing |
search (Caruana and Freitag, 1994) with beam size 9, |
starting from zero features. A majority vote of five |
systems (Chieu and Ng, 2003; Florian et al., 2003; |
Klein et al., 2003; McCallum and Li, 2003; Whitelaw |
and Patrick, 2003) performed best on the English |
development data. Another combination of five systems (Carreras et al., 2003b; Mayfield et al., 2003; |
McCallum and Li, 2003; Munro et al., 2003; Zhang |
and Johnson, 2003) obtained the best result for the |
German development data. We have performed a |
majority vote with these sets of systems on the related test sets and obtained Fβ=1 rates of 90.30 for |
English (14% error reduction compared with the best |
system) and 74.17 for German (6% error reduction). |
4 |
Concluding Remarks |
We have described the CoNLL-2003 shared task: |
language-independent named entity recognition. |
Sixteen systems have processed English and German |
named entity data. The best performance for both |
languages has been obtained by a combined learning system that used Maximum Entropy Models, |
transformation-based learning, Hidden Markov Models as well as robust risk minimization (Florian et al., |
2003). Apart from the training data, this system also |
employed gazetteers and the output of two externally |
trained named entity recognizers. The performance |
of the system of Chieu et al. (2003) was not significantly different from the best performance for English and the method of Klein et al. (2003) and the |
approach of Zhang and Johnson (2003) were not significantly worse than the best result for German. |
Eleven teams have incorporated information other |
than the training data in their system. Four of them |
have obtained error reductions of 15% or more for |
English and one has managed this for German. The |
resources used by these systems, gazetteers and externally trained named entity systems, still require a |
lot of manual work. Systems that employed unannotated data, obtained performance gains around 5%. |
The search for an excellent method for taking advantage of the fast amount of available raw text, remains |
open. |
Acknowledgements |
Tjong Kim Sang is financed by IWT STWW as a |
researcher in the ATraNoS project. De Meulder is |
supported by a BOF grant supplied by the University |
of Antwerp. |
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