Upload W03-0419.txt
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W03-0419.txt
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1 |
+
Introduction to the CoNLL-2003 Shared Task:
|
2 |
+
Language-Independent Named Entity Recognition
|
3 |
+
Erik F. Tjong Kim Sang and Fien De Meulder
|
4 |
+
CNTS - Language Technology Group
|
5 |
+
University of Antwerp
|
6 |
+
{erikt,fien.demeulder}@uia.ua.ac.be
|
7 |
+
|
8 |
+
Abstract
|
9 |
+
We describe the CoNLL-2003 shared task:
|
10 |
+
language-independent named entity recognition. We give background information on
|
11 |
+
the data sets (English and German) and
|
12 |
+
the evaluation method, present a general
|
13 |
+
overview of the systems that have taken
|
14 |
+
part in the task and discuss their performance.
|
15 |
+
|
16 |
+
of the 2003 shared task have been offered training
|
17 |
+
and test data for two other European languages:
|
18 |
+
English and German. They have used the data
|
19 |
+
for developing a named-entity recognition system
|
20 |
+
that includes a machine learning component. The
|
21 |
+
shared task organizers were especially interested in
|
22 |
+
approaches that made use of resources other than
|
23 |
+
the supplied training data, for example gazetteers
|
24 |
+
and unannotated data.
|
25 |
+
|
26 |
+
2
|
27 |
+
1
|
28 |
+
|
29 |
+
Introduction
|
30 |
+
|
31 |
+
Named entities are phrases that contain the names
|
32 |
+
of persons, organizations and locations. Example:
|
33 |
+
[ORG U.N. ] official [PER Ekeus ] heads for
|
34 |
+
[LOC Baghdad ] .
|
35 |
+
This sentence contains three named entities: Ekeus
|
36 |
+
is a person, U.N. is a organization and Baghdad is
|
37 |
+
a location. Named entity recognition is an important task of information extraction systems. There
|
38 |
+
has been a lot of work on named entity recognition,
|
39 |
+
especially for English (see Borthwick (1999) for an
|
40 |
+
overview). The Message Understanding Conferences
|
41 |
+
(MUC) have offered developers the opportunity to
|
42 |
+
evaluate systems for English on the same data in a
|
43 |
+
competition. They have also produced a scheme for
|
44 |
+
entity annotation (Chinchor et al., 1999). More recently, there have been other system development
|
45 |
+
competitions which dealt with different languages
|
46 |
+
(IREX and CoNLL-2002).
|
47 |
+
The shared task of CoNLL-2003 concerns
|
48 |
+
language-independent named entity recognition. We
|
49 |
+
will concentrate on four types of named entities:
|
50 |
+
persons, locations, organizations and names of
|
51 |
+
miscellaneous entities that do not belong to the previous three groups. The shared task of CoNLL-2002
|
52 |
+
dealt with named entity recognition for Spanish and
|
53 |
+
Dutch (Tjong Kim Sang, 2002). The participants
|
54 |
+
|
55 |
+
Data and Evaluation
|
56 |
+
|
57 |
+
In this section we discuss the sources of the data
|
58 |
+
that were used in this shared task, the preprocessing
|
59 |
+
steps we have performed on the data, the format of
|
60 |
+
the data and the method that was used for evaluating
|
61 |
+
the participating systems.
|
62 |
+
2.1
|
63 |
+
|
64 |
+
Data
|
65 |
+
|
66 |
+
The CoNLL-2003 named entity data consists of eight
|
67 |
+
files covering two languages: English and German1 .
|
68 |
+
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
|
69 |
+
the training data. The development data could be
|
70 |
+
used for tuning the parameters of the learning methods. The challenge of this year’s shared task was
|
71 |
+
to incorporate the unannotated data in the learning
|
72 |
+
process in one way or another. When the best parameters were found, the method could be trained on
|
73 |
+
the training data and tested on the test data. The
|
74 |
+
results of the different learning methods on the test
|
75 |
+
sets are compared in the evaluation of the shared
|
76 |
+
task. The split between development data and test
|
77 |
+
data was chosen to avoid systems being tuned to the
|
78 |
+
test data.
|
79 |
+
The English data was taken from the Reuters Corpus2 . This corpus consists of Reuters news stories
|
80 |
+
1
|
81 |
+
|
82 |
+
Data files (except the words) can be found on
|
83 |
+
http://lcg-www.uia.ac.be/conll2003/ner/
|
84 |
+
2
|
85 |
+
http://www.reuters.com/researchandstandards/
|
86 |
+
|
87 |
+
English data
|
88 |
+
Training set
|
89 |
+
Development set
|
90 |
+
Test set
|
91 |
+
|
92 |
+
Articles
|
93 |
+
946
|
94 |
+
216
|
95 |
+
231
|
96 |
+
|
97 |
+
Sentences
|
98 |
+
14,987
|
99 |
+
3,466
|
100 |
+
3,684
|
101 |
+
|
102 |
+
Tokens
|
103 |
+
203,621
|
104 |
+
51,362
|
105 |
+
46,435
|
106 |
+
|
107 |
+
English data
|
108 |
+
Training set
|
109 |
+
Development set
|
110 |
+
Test set
|
111 |
+
|
112 |
+
LOC
|
113 |
+
7140
|
114 |
+
1837
|
115 |
+
1668
|
116 |
+
|
117 |
+
MISC
|
118 |
+
3438
|
119 |
+
922
|
120 |
+
702
|
121 |
+
|
122 |
+
ORG
|
123 |
+
6321
|
124 |
+
1341
|
125 |
+
1661
|
126 |
+
|
127 |
+
PER
|
128 |
+
6600
|
129 |
+
1842
|
130 |
+
1617
|
131 |
+
|
132 |
+
German data
|
133 |
+
Training set
|
134 |
+
Development set
|
135 |
+
Test set
|
136 |
+
|
137 |
+
Articles
|
138 |
+
553
|
139 |
+
201
|
140 |
+
155
|
141 |
+
|
142 |
+
Sentences
|
143 |
+
12,705
|
144 |
+
3,068
|
145 |
+
3,160
|
146 |
+
|
147 |
+
Tokens
|
148 |
+
206,931
|
149 |
+
51,444
|
150 |
+
51,943
|
151 |
+
|
152 |
+
German data
|
153 |
+
Training set
|
154 |
+
Development set
|
155 |
+
Test set
|
156 |
+
|
157 |
+
LOC
|
158 |
+
4363
|
159 |
+
1181
|
160 |
+
1035
|
161 |
+
|
162 |
+
MISC
|
163 |
+
2288
|
164 |
+
1010
|
165 |
+
670
|
166 |
+
|
167 |
+
ORG
|
168 |
+
2427
|
169 |
+
1241
|
170 |
+
773
|
171 |
+
|
172 |
+
PER
|
173 |
+
2773
|
174 |
+
1401
|
175 |
+
1195
|
176 |
+
|
177 |
+
Table 1: Number of articles, sentences and tokens in
|
178 |
+
each data file.
|
179 |
+
|
180 |
+
Table 2: Number of named entities per data file
|
181 |
+
2.3
|
182 |
+
|
183 |
+
between August 1996 and August 1997. For the
|
184 |
+
training and development set, ten days’ worth of data
|
185 |
+
were taken from the files representing the end of August 1996. For the test set, the texts were from December 1996. The preprocessed raw data covers the
|
186 |
+
month of September 1996.
|
187 |
+
The text for the German data was taken from the
|
188 |
+
ECI Multilingual Text Corpus3 . This corpus consists
|
189 |
+
of texts in many languages. The portion of data that
|
190 |
+
was used for this task, was extracted from the German newspaper Frankfurter Rundshau. All three of
|
191 |
+
the training, development and test sets were taken
|
192 |
+
from articles written in one week at the end of August 1992. The raw data were taken from the months
|
193 |
+
of September to December 1992.
|
194 |
+
Table 1 contains an overview of the sizes of the
|
195 |
+
data files. The unannotated data contain 17 million
|
196 |
+
tokens (English) and 14 million tokens (German).
|
197 |
+
2.2
|
198 |
+
|
199 |
+
Data preprocessing
|
200 |
+
|
201 |
+
The participants were given access to the corpus after some linguistic preprocessing had been done: for
|
202 |
+
all data, a tokenizer, part-of-speech tagger, and a
|
203 |
+
chunker were applied to the raw data. We created
|
204 |
+
two basic language-specific tokenizers for this shared
|
205 |
+
task. The English data was tagged and chunked by
|
206 |
+
the memory-based MBT tagger (Daelemans et al.,
|
207 |
+
2002). The German data was lemmatized, tagged
|
208 |
+
and chunked by the decision tree tagger Treetagger
|
209 |
+
(Schmid, 1995).
|
210 |
+
Named entity tagging of English and German
|
211 |
+
training, development, and test data, was done by
|
212 |
+
hand at the University of Antwerp. Mostly, MUC
|
213 |
+
conventions were followed (Chinchor et al., 1999).
|
214 |
+
An extra named entity category called MISC was
|
215 |
+
added to denote all names which are not already in
|
216 |
+
the other categories. This includes adjectives, like
|
217 |
+
Italian, and events, like 1000 Lakes Rally, making it
|
218 |
+
a very diverse category.
|
219 |
+
3
|
220 |
+
|
221 |
+
http://www.ldc.upenn.edu/
|
222 |
+
|
223 |
+
Data format
|
224 |
+
|
225 |
+
All data files contain one word per line with empty
|
226 |
+
lines representing sentence boundaries. At the end
|
227 |
+
of each line there is a tag which states whether the
|
228 |
+
current word is inside a named entity or not. The
|
229 |
+
tag also encodes the type of named entity. Here is
|
230 |
+
an example sentence:
|
231 |
+
U.N.
|
232 |
+
official
|
233 |
+
Ekeus
|
234 |
+
heads
|
235 |
+
for
|
236 |
+
Baghdad
|
237 |
+
.
|
238 |
+
|
239 |
+
NNP
|
240 |
+
NN
|
241 |
+
NNP
|
242 |
+
VBZ
|
243 |
+
IN
|
244 |
+
NNP
|
245 |
+
.
|
246 |
+
|
247 |
+
I-NP
|
248 |
+
I-NP
|
249 |
+
I-NP
|
250 |
+
I-VP
|
251 |
+
I-PP
|
252 |
+
I-NP
|
253 |
+
O
|
254 |
+
|
255 |
+
I-ORG
|
256 |
+
O
|
257 |
+
I-PER
|
258 |
+
O
|
259 |
+
O
|
260 |
+
I-LOC
|
261 |
+
O
|
262 |
+
|
263 |
+
Each line contains four fields: the word, its partof-speech tag, its chunk tag and its named entity
|
264 |
+
tag. Words tagged with O are outside of named entities and the I-XXX tag is used for words inside a
|
265 |
+
named entity of type XXX. Whenever two entities of
|
266 |
+
type XXX are immediately next to each other, the
|
267 |
+
first word of the second entity will be tagged B-XXX
|
268 |
+
in order to show that it starts another entity. The
|
269 |
+
data contains entities of four types: persons (PER),
|
270 |
+
organizations (ORG), locations (LOC) and miscellaneous names (MISC). This tagging scheme is the
|
271 |
+
IOB scheme originally put forward by Ramshaw and
|
272 |
+
Marcus (1995). We assume that named entities are
|
273 |
+
non-recursive and non-overlapping. When a named
|
274 |
+
entity is embedded in another named entity, usually
|
275 |
+
only the top level entity has been annotated.
|
276 |
+
Table 2 contains an overview of the number of
|
277 |
+
named entities in each data file.
|
278 |
+
2.4
|
279 |
+
|
280 |
+
Evaluation
|
281 |
+
|
282 |
+
The performance in this task is measured with Fβ=1
|
283 |
+
rate:
|
284 |
+
Fβ =
|
285 |
+
|
286 |
+
(β 2 + 1) ∗ precision ∗ recall
|
287 |
+
(β 2 ∗ precision + recall)
|
288 |
+
|
289 |
+
(1)
|
290 |
+
|
291 |
+
Florian
|
292 |
+
Chieu
|
293 |
+
Klein
|
294 |
+
Zhang
|
295 |
+
Carreras (a)
|
296 |
+
Curran
|
297 |
+
Mayfield
|
298 |
+
Carreras (b)
|
299 |
+
McCallum
|
300 |
+
Bender
|
301 |
+
Munro
|
302 |
+
Wu
|
303 |
+
Whitelaw
|
304 |
+
Hendrickx
|
305 |
+
De Meulder
|
306 |
+
Hammerton
|
307 |
+
|
308 |
+
lex
|
309 |
+
+
|
310 |
+
+
|
311 |
+
+
|
312 |
+
+
|
313 |
+
+
|
314 |
+
+
|
315 |
+
+
|
316 |
+
+
|
317 |
+
+
|
318 |
+
+
|
319 |
+
+
|
320 |
+
+
|
321 |
+
+
|
322 |
+
+
|
323 |
+
+
|
324 |
+
|
325 |
+
pos
|
326 |
+
+
|
327 |
+
+
|
328 |
+
+
|
329 |
+
+
|
330 |
+
+
|
331 |
+
+
|
332 |
+
+
|
333 |
+
+
|
334 |
+
+
|
335 |
+
+
|
336 |
+
+
|
337 |
+
+
|
338 |
+
+
|
339 |
+
+
|
340 |
+
|
341 |
+
aff
|
342 |
+
+
|
343 |
+
+
|
344 |
+
+
|
345 |
+
+
|
346 |
+
+
|
347 |
+
+
|
348 |
+
+
|
349 |
+
+
|
350 |
+
+
|
351 |
+
+
|
352 |
+
+
|
353 |
+
+
|
354 |
+
+
|
355 |
+
-
|
356 |
+
|
357 |
+
pre
|
358 |
+
+
|
359 |
+
+
|
360 |
+
+
|
361 |
+
+
|
362 |
+
+
|
363 |
+
+
|
364 |
+
+
|
365 |
+
+
|
366 |
+
+
|
367 |
+
+
|
368 |
+
+
|
369 |
+
+
|
370 |
+
-
|
371 |
+
|
372 |
+
ort
|
373 |
+
+
|
374 |
+
+
|
375 |
+
+
|
376 |
+
+
|
377 |
+
+
|
378 |
+
+
|
379 |
+
+
|
380 |
+
+
|
381 |
+
+
|
382 |
+
+
|
383 |
+
+
|
384 |
+
+
|
385 |
+
-
|
386 |
+
|
387 |
+
gaz
|
388 |
+
+
|
389 |
+
+
|
390 |
+
+
|
391 |
+
+
|
392 |
+
+
|
393 |
+
+
|
394 |
+
+
|
395 |
+
+
|
396 |
+
+
|
397 |
+
+
|
398 |
+
+
|
399 |
+
|
400 |
+
chu
|
401 |
+
+
|
402 |
+
+
|
403 |
+
+
|
404 |
+
+
|
405 |
+
+
|
406 |
+
+
|
407 |
+
+
|
408 |
+
+
|
409 |
+
+
|
410 |
+
|
411 |
+
pat
|
412 |
+
+
|
413 |
+
+
|
414 |
+
+
|
415 |
+
+
|
416 |
+
+
|
417 |
+
-
|
418 |
+
|
419 |
+
cas
|
420 |
+
+
|
421 |
+
+
|
422 |
+
+
|
423 |
+
+
|
424 |
+
+
|
425 |
+
-
|
426 |
+
|
427 |
+
tri
|
428 |
+
+
|
429 |
+
+
|
430 |
+
+
|
431 |
+
+
|
432 |
+
-
|
433 |
+
|
434 |
+
bag
|
435 |
+
+
|
436 |
+
+
|
437 |
+
-
|
438 |
+
|
439 |
+
quo
|
440 |
+
+
|
441 |
+
+
|
442 |
+
-
|
443 |
+
|
444 |
+
doc
|
445 |
+
+
|
446 |
+
-
|
447 |
+
|
448 |
+
Table 3: Main features used by the the sixteen systems that participated in the CoNLL-2003 shared task
|
449 |
+
sorted by performance on the English test data. Aff: affix information (n-grams); bag: bag of words; cas:
|
450 |
+
global case information; chu: chunk tags; doc: global document information; gaz: gazetteers; lex: lexical
|
451 |
+
features; ort: orthographic information; pat: orthographic patterns (like Aa0); pos: part-of-speech tags; pre:
|
452 |
+
previously predicted NE tags; quo: flag signing that the word is between quotes; tri: trigger words.
|
453 |
+
with β=1 (Van Rijsbergen, 1975). Precision is the
|
454 |
+
percentage of named entities found by the learning
|
455 |
+
system that are correct. Recall is the percentage of
|
456 |
+
named entities present in the corpus that are found
|
457 |
+
by the system. A named entity is correct only if it
|
458 |
+
is an exact match of the corresponding entity in the
|
459 |
+
data file.
|
460 |
+
|
461 |
+
3
|
462 |
+
|
463 |
+
Participating Systems
|
464 |
+
|
465 |
+
Sixteen systems have participated in the CoNLL2003 shared task. They employed a wide variety of
|
466 |
+
machine learning techniques as well as system combination. Most of the participants have attempted
|
467 |
+
to use information other than the available training data. This information included gazetteers and
|
468 |
+
unannotated data, and there was one participant
|
469 |
+
who used the output of externally trained named entity recognition systems.
|
470 |
+
3.1
|
471 |
+
|
472 |
+
Learning techniques
|
473 |
+
|
474 |
+
The most frequently applied technique in the
|
475 |
+
CoNLL-2003 shared task is the Maximum Entropy
|
476 |
+
Model. Five systems used this statistical learning
|
477 |
+
method. Three systems used Maximum Entropy
|
478 |
+
Models in isolation (Bender et al., 2003; Chieu and
|
479 |
+
Ng, 2003; Curran and Clark, 2003). Two more
|
480 |
+
systems used them in combination with other techniques (Florian et al., 2003; Klein et al., 2003). Maximum Entropy Models seem to be a good choice for
|
481 |
+
|
482 |
+
this kind of task: the top three results for English
|
483 |
+
and the top two results for German were obtained
|
484 |
+
by participants who employed them in one way or
|
485 |
+
another.
|
486 |
+
Hidden Markov Models were employed by four of
|
487 |
+
the systems that took part in the shared task (Florian et al., 2003; Klein et al., 2003; Mayfield et al.,
|
488 |
+
2003; Whitelaw and Patrick, 2003). However, they
|
489 |
+
were always used in combination with other learning
|
490 |
+
techniques. Klein et al. (2003) also applied the related Conditional Markov Models for combining classifiers.
|
491 |
+
Learning methods that were based on connectionist approaches were applied by four systems. Zhang
|
492 |
+
and Johnson (2003) used robust risk minimization,
|
493 |
+
which is a Winnow technique. Florian et al. (2003)
|
494 |
+
employed the same technique in a combination of
|
495 |
+
learners. Voted perceptrons were applied to the
|
496 |
+
shared task data by Carreras et al. (2003a) and
|
497 |
+
Hammerton used a recurrent neural network (Long
|
498 |
+
Short-Term Memory) for finding named entities.
|
499 |
+
Other learning approaches were employed less frequently. Two teams used AdaBoost.MH (Carreras
|
500 |
+
et al., 2003b; Wu et al., 2003) and two other groups
|
501 |
+
employed memory-based learning (De Meulder and
|
502 |
+
Daelemans, 2003; Hendrickx and Van den Bosch,
|
503 |
+
2003). Transformation-based learning (Florian et
|
504 |
+
al., 2003), Support Vector Machines (Mayfield et al.,
|
505 |
+
2003) and Conditional Random Fields (McCallum
|
506 |
+
|
507 |
+
and Li, 2003) were applied by one system each.
|
508 |
+
Combination of different learning systems has
|
509 |
+
proven to be a good method for obtaining excellent
|
510 |
+
results. Five participating groups have applied system combination. Florian et al. (2003) tested different methods for combining the results of four systems and found that robust risk minimization worked
|
511 |
+
best. Klein et al. (2003) employed a stacked learning system which contains Hidden Markov Models,
|
512 |
+
Maximum Entropy Models and Conditional Markov
|
513 |
+
Models. Mayfield et al. (2003) stacked two learners
|
514 |
+
and obtained better performance. Wu et al. (2003)
|
515 |
+
applied both stacking and voting to three learners.
|
516 |
+
Munro et al. (2003) employed both voting and bagging for combining classifiers.
|
517 |
+
3.2
|
518 |
+
|
519 |
+
Features
|
520 |
+
|
521 |
+
The choice of the learning approach is important for
|
522 |
+
obtaining a good system for recognizing named entities. However, in the CoNLL-2002 shared task we
|
523 |
+
found out that choice of features is at least as important. An overview of some of the types of features
|
524 |
+
chosen by the shared task participants, can be found
|
525 |
+
in Table 3.
|
526 |
+
All participants used lexical features (words) except for Whitelaw and Patrick (2003) who implemented a character-based method. Most of the systems employed part-of-speech tags and two of them
|
527 |
+
have recomputed the English tags with better taggers (Hendrickx and Van den Bosch, 2003; Wu et al.,
|
528 |
+
2003). Othographic information, affixes, gazetteers
|
529 |
+
and chunk information were also incorporated in
|
530 |
+
most systems although one group reports that the
|
531 |
+
available chunking information did not help (Wu et
|
532 |
+
al., 2003) Other features were used less frequently.
|
533 |
+
Table 3 does not reveal a single feature that would
|
534 |
+
be ideal for named entity recognition.
|
535 |
+
3.3
|
536 |
+
|
537 |
+
External resources
|
538 |
+
|
539 |
+
Eleven of the sixteen participating teams have attempted to use information other than the training
|
540 |
+
data that was supplied for this shared task. All included gazetteers in their systems. Four groups examined the usability of unannotated data, either for
|
541 |
+
extracting training instances (Bender et al., 2003;
|
542 |
+
Hendrickx and Van den Bosch, 2003) or obtaining
|
543 |
+
extra named entities for gazetteers (De Meulder and
|
544 |
+
Daelemans, 2003; McCallum and Li, 2003). A reasonable number of groups have also employed unannotated data for obtaining capitalization features for
|
545 |
+
words. One participating team has used externally
|
546 |
+
trained named entity recognition systems for English
|
547 |
+
as a part in a combined system (Florian et al., 2003).
|
548 |
+
Table 4 shows the error reduction of the systems
|
549 |
+
|
550 |
+
Zhang
|
551 |
+
Florian
|
552 |
+
Chieu
|
553 |
+
Hammerton
|
554 |
+
Carreras (a)
|
555 |
+
Hendrickx
|
556 |
+
De Meulder
|
557 |
+
Bender
|
558 |
+
Curran
|
559 |
+
McCallum
|
560 |
+
Wu
|
561 |
+
|
562 |
+
G
|
563 |
+
+
|
564 |
+
+
|
565 |
+
+
|
566 |
+
+
|
567 |
+
+
|
568 |
+
+
|
569 |
+
+
|
570 |
+
+
|
571 |
+
+
|
572 |
+
+
|
573 |
+
+
|
574 |
+
|
575 |
+
U
|
576 |
+
+
|
577 |
+
+
|
578 |
+
+
|
579 |
+
+
|
580 |
+
-
|
581 |
+
|
582 |
+
E
|
583 |
+
+
|
584 |
+
-
|
585 |
+
|
586 |
+
English
|
587 |
+
19%
|
588 |
+
27%
|
589 |
+
17%
|
590 |
+
22%
|
591 |
+
12%
|
592 |
+
7%
|
593 |
+
8%
|
594 |
+
3%
|
595 |
+
1%
|
596 |
+
?
|
597 |
+
?
|
598 |
+
|
599 |
+
German
|
600 |
+
15%
|
601 |
+
5%
|
602 |
+
7%
|
603 |
+
8%
|
604 |
+
5%
|
605 |
+
3%
|
606 |
+
6%
|
607 |
+
?
|
608 |
+
?
|
609 |
+
|
610 |
+
Table 4: Error reduction for the two development data sets when using extra information like
|
611 |
+
gazetteers (G), unannotated data (U) or externally
|
612 |
+
developed named entity recognizers (E). The lines
|
613 |
+
have been sorted by the sum of the reduction percentages for the two languages.
|
614 |
+
with extra information compared to while using only
|
615 |
+
the available training data. The inclusion of extra named entity recognition systems seems to have
|
616 |
+
worked well (Florian et al., 2003). Generally the systems that only used gazetteers seem to gain more
|
617 |
+
than systems that have used unannotated data for
|
618 |
+
other purposes than obtaining capitalization information. However, the gain differences between the
|
619 |
+
two approaches are most obvious for English for
|
620 |
+
which better gazetteers are available. With the exception of the result of Zhang and Johnson (2003),
|
621 |
+
there is not much difference in the German results
|
622 |
+
between the gains obtained by using gazetteers and
|
623 |
+
those obtained by using unannotated data.
|
624 |
+
3.4
|
625 |
+
|
626 |
+
Performances
|
627 |
+
|
628 |
+
A baseline rate was computed for the English and the
|
629 |
+
German test sets. It was produced by a system which
|
630 |
+
only identified entities which had a unique class in
|
631 |
+
the training data. If a phrase was part of more than
|
632 |
+
one entity, the system would select the longest one.
|
633 |
+
All systems that participated in the shared task have
|
634 |
+
outperformed the baseline system.
|
635 |
+
For all the Fβ=1 rates we have estimated significance boundaries by using bootstrap resampling
|
636 |
+
(Noreen, 1989). From each output file of a system,
|
637 |
+
250 random samples of sentences have been chosen
|
638 |
+
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
|
639 |
+
A is significantly different from performance B if A
|
640 |
+
is not within the center 90% of the distribution of B.
|
641 |
+
The performances of the sixteen systems on the
|
642 |
+
|
643 |
+
two test data sets can be found in Table 5. For English, the combined classifier of Florian et al. (2003)
|
644 |
+
achieved the highest overall Fβ=1 rate. However,
|
645 |
+
the difference between their performance and that
|
646 |
+
of the Maximum Entropy approach of Chieu and Ng
|
647 |
+
(2003) is not significant. An important feature of the
|
648 |
+
best system that other participants did not use, was
|
649 |
+
the inclusion of the output of two externally trained
|
650 |
+
named entity recognizers in the combination process.
|
651 |
+
Florian et al. (2003) have also obtained the highest
|
652 |
+
Fβ=1 rate for the German data. Here there is no significant difference between them and the systems of
|
653 |
+
Klein et al. (2003) and Zhang and Johnson (2003).
|
654 |
+
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
|
655 |
+
the same IOB tagging representation and searched
|
656 |
+
for the set of systems from which the best tags for
|
657 |
+
the development data could be obtained with majority voting. The optimal set of systems was determined by performing a bidirectional hill-climbing
|
658 |
+
search (Caruana and Freitag, 1994) with beam size 9,
|
659 |
+
starting from zero features. A majority vote of five
|
660 |
+
systems (Chieu and Ng, 2003; Florian et al., 2003;
|
661 |
+
Klein et al., 2003; McCallum and Li, 2003; Whitelaw
|
662 |
+
and Patrick, 2003) performed best on the English
|
663 |
+
development data. Another combination of five systems (Carreras et al., 2003b; Mayfield et al., 2003;
|
664 |
+
McCallum and Li, 2003; Munro et al., 2003; Zhang
|
665 |
+
and Johnson, 2003) obtained the best result for the
|
666 |
+
German development data. We have performed a
|
667 |
+
majority vote with these sets of systems on the related test sets and obtained Fβ=1 rates of 90.30 for
|
668 |
+
English (14% error reduction compared with the best
|
669 |
+
system) and 74.17 for German (6% error reduction).
|
670 |
+
|
671 |
+
4
|
672 |
+
|
673 |
+
Concluding Remarks
|
674 |
+
|
675 |
+
We have described the CoNLL-2003 shared task:
|
676 |
+
language-independent named entity recognition.
|
677 |
+
Sixteen systems have processed English and German
|
678 |
+
named entity data. The best performance for both
|
679 |
+
languages has been obtained by a combined learning system that used Maximum Entropy Models,
|
680 |
+
transformation-based learning, Hidden Markov Models as well as robust risk minimization (Florian et al.,
|
681 |
+
2003). Apart from the training data, this system also
|
682 |
+
employed gazetteers and the output of two externally
|
683 |
+
trained named entity recognizers. The performance
|
684 |
+
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
|
685 |
+
approach of Zhang and Johnson (2003) were not significantly worse than the best result for German.
|
686 |
+
Eleven teams have incorporated information other
|
687 |
+
|
688 |
+
than the training data in their system. Four of them
|
689 |
+
have obtained error reductions of 15% or more for
|
690 |
+
English and one has managed this for German. The
|
691 |
+
resources used by these systems, gazetteers and externally trained named entity systems, still require a
|
692 |
+
lot of manual work. Systems that employed unannotated data, obtained performance gains around 5%.
|
693 |
+
The search for an excellent method for taking advantage of the fast amount of available raw text, remains
|
694 |
+
open.
|
695 |
+
|
696 |
+
Acknowledgements
|
697 |
+
Tjong Kim Sang is financed by IWT STWW as a
|
698 |
+
researcher in the ATraNoS project. De Meulder is
|
699 |
+
supported by a BOF grant supplied by the University
|
700 |
+
of Antwerp.
|
701 |
+
|
702 |
+
References
|
703 |
+
Oliver Bender, Franz Josef Och, and Hermann Ney.
|
704 |
+
2003. Maximum Entropy Models for Named Entity Recognition. In Proceedings of CoNLL-2003.
|
705 |
+
Andrew Borthwick. 1999. A Maximum Entropy Approach to Named Entity Recognition. PhD thesis,
|
706 |
+
New York University.
|
707 |
+
Xavier Carreras, Lluı́s Màrquez, and Lluı́s Padró.
|
708 |
+
2003a. Learning a Perceptron-Based Named Entity Chunker via Online Recognition Feedback. In
|
709 |
+
Proceedings of CoNLL-2003.
|
710 |
+
Xavier Carreras, Lluı́s Màrquez, and Lluı́s Padró.
|
711 |
+
2003b. A Simple Named Entity Extractor using
|
712 |
+
AdaBoost. In Proceedings of CoNLL-2003.
|
713 |
+
Rich Caruana and Dayne Freitag. 1994. Greedy Attribute Selection. In Proceedings of the Eleventh
|
714 |
+
International Conference on Machine Learning,
|
715 |
+
pages 28–36. New Brunswick, NJ, USA, Morgan
|
716 |
+
Kaufman.
|
717 |
+
Hai Leong Chieu and Hwee Tou Ng. 2003. Named
|
718 |
+
Entity Recognition with a Maximum Entropy Approach. In Proceedings of CoNLL-2003.
|
719 |
+
Nancy Chinchor, Erica Brown, Lisa Ferro, and Patty
|
720 |
+
Robinson. 1999. 1999 Named Entity Recognition
|
721 |
+
Task Definition. MITRE and SAIC.
|
722 |
+
James R. Curran and Stephen Clark. 2003. Language Independent NER using a Maximum Entropy Tagger. In Proceedings of CoNLL-2003.
|
723 |
+
Walter Daelemans, Jakub Zavrel, Ko van der Sloot,
|
724 |
+
and Antal van den Bosch. 2002. MBT: MemoryBased Tagger, version 1.0, Reference Guide. ILK
|
725 |
+
Technical Report ILK-0209, University of Tilburg,
|
726 |
+
The Netherlands.
|
727 |
+
|
728 |
+
Fien De Meulder and Walter Daelemans. 2003.
|
729 |
+
Memory-Based Named Entity Recognition using
|
730 |
+
Unannotated Data. In Proceedings of CoNLL2003.
|
731 |
+
Radu Florian, Abe Ittycheriah, Hongyan Jing, and
|
732 |
+
Tong Zhang. 2003. Named Entity Recognition
|
733 |
+
through Classifier Combination. In Proceedings of
|
734 |
+
CoNLL-2003.
|
735 |
+
James Hammerton. 2003. Named Entity Recognition with Long Short-Term Memory. In Proceedings of CoNLL-2003.
|
736 |
+
Iris Hendrickx and Antal van den Bosch. 2003.
|
737 |
+
Memory-based one-step named-entity recognition:
|
738 |
+
Effects of seed list features, classifier stacking, and
|
739 |
+
unannotated data. In Proceedings of CoNLL-2003.
|
740 |
+
Dan Klein, Joseph Smarr, Huy Nguyen, and Christopher D. Manning. 2003. Named Entity Recognition with Character-Level Models. In Proceedings
|
741 |
+
of CoNLL-2003.
|
742 |
+
James Mayfield, Paul McNamee, and Christine Piatko. 2003. Named Entity Recognition using Hundreds of Thousands of Features. In Proceedings of
|
743 |
+
CoNLL-2003.
|
744 |
+
Andrew McCallum and Wei Li. 2003. Early results
|
745 |
+
for Named Entity Recognition with Conditional
|
746 |
+
Random Fields, Feature Induction and WebEnhanced Lexicons. In Proceedings of CoNLL2003.
|
747 |
+
Robert Munro, Daren Ler, and Jon Patrick.
|
748 |
+
2003. Meta-Learning Orthographic and Contextual Models for Language Independent Named Entity Recognition. In Proceedings of CoNLL-2003.
|
749 |
+
Eric W. Noreen. 1989. Computer-Intensive Methods
|
750 |
+
for Testing Hypotheses. John Wiley & Sons.
|
751 |
+
|
752 |
+
English test
|
753 |
+
Florian
|
754 |
+
Chieu
|
755 |
+
Klein
|
756 |
+
Zhang
|
757 |
+
Carreras (a)
|
758 |
+
Curran
|
759 |
+
Mayfield
|
760 |
+
Carreras (b)
|
761 |
+
McCallum
|
762 |
+
Bender
|
763 |
+
Munro
|
764 |
+
Wu
|
765 |
+
Whitelaw
|
766 |
+
Hendrickx
|
767 |
+
De Meulder
|
768 |
+
Hammerton
|
769 |
+
Baseline
|
770 |
+
|
771 |
+
Precision
|
772 |
+
88.99%
|
773 |
+
88.12%
|
774 |
+
85.93%
|
775 |
+
86.13%
|
776 |
+
84.05%
|
777 |
+
84.29%
|
778 |
+
84.45%
|
779 |
+
85.81%
|
780 |
+
84.52%
|
781 |
+
84.68%
|
782 |
+
80.87%
|
783 |
+
82.02%
|
784 |
+
81.60%
|
785 |
+
76.33%
|
786 |
+
75.84%
|
787 |
+
69.09%
|
788 |
+
71.91%
|
789 |
+
|
790 |
+
Recall
|
791 |
+
88.54%
|
792 |
+
88.51%
|
793 |
+
86.21%
|
794 |
+
84.88%
|
795 |
+
85.96%
|
796 |
+
85.50%
|
797 |
+
84.90%
|
798 |
+
82.84%
|
799 |
+
83.55%
|
800 |
+
83.18%
|
801 |
+
84.21%
|
802 |
+
81.39%
|
803 |
+
78.05%
|
804 |
+
80.17%
|
805 |
+
78.13%
|
806 |
+
53.26%
|
807 |
+
50.90%
|
808 |
+
|
809 |
+
Fβ=1
|
810 |
+
88.76±0.7
|
811 |
+
88.31±0.7
|
812 |
+
86.07±0.8
|
813 |
+
85.50±0.9
|
814 |
+
85.00±0.8
|
815 |
+
84.89±0.9
|
816 |
+
84.67±1.0
|
817 |
+
84.30±0.9
|
818 |
+
84.04±0.9
|
819 |
+
83.92±1.0
|
820 |
+
82.50±1.0
|
821 |
+
81.70±0.9
|
822 |
+
79.78±1.0
|
823 |
+
78.20±1.0
|
824 |
+
76.97±1.2
|
825 |
+
60.15±1.3
|
826 |
+
59.61±1.2
|
827 |
+
|
828 |
+
German test
|
829 |
+
Florian
|
830 |
+
Klein
|
831 |
+
Zhang
|
832 |
+
Mayfield
|
833 |
+
Carreras (a)
|
834 |
+
Bender
|
835 |
+
Curran
|
836 |
+
McCallum
|
837 |
+
Munro
|
838 |
+
Carreras (b)
|
839 |
+
Wu
|
840 |
+
Chieu
|
841 |
+
Hendrickx
|
842 |
+
De Meulder
|
843 |
+
Whitelaw
|
844 |
+
Hammerton
|
845 |
+
Baseline
|
846 |
+
|
847 |
+
Precision
|
848 |
+
83.87%
|
849 |
+
80.38%
|
850 |
+
82.00%
|
851 |
+
75.97%
|
852 |
+
75.47%
|
853 |
+
74.82%
|
854 |
+
75.61%
|
855 |
+
75.97%
|
856 |
+
69.37%
|
857 |
+
77.83%
|
858 |
+
75.20%
|
859 |
+
76.83%
|
860 |
+
71.15%
|
861 |
+
63.93%
|
862 |
+
71.05%
|
863 |
+
63.49%
|
864 |
+
31.86%
|
865 |
+
|
866 |
+
Recall
|
867 |
+
63.71%
|
868 |
+
65.04%
|
869 |
+
63.03%
|
870 |
+
64.82%
|
871 |
+
63.82%
|
872 |
+
63.82%
|
873 |
+
62.46%
|
874 |
+
61.72%
|
875 |
+
66.21%
|
876 |
+
58.02%
|
877 |
+
59.35%
|
878 |
+
57.34%
|
879 |
+
56.55%
|
880 |
+
51.86%
|
881 |
+
44.11%
|
882 |
+
38.25%
|
883 |
+
28.89%
|
884 |
+
|
885 |
+
Fβ=1
|
886 |
+
72.41±1.3
|
887 |
+
71.90±1.2
|
888 |
+
71.27±1.5
|
889 |
+
69.96±1.4
|
890 |
+
69.15±1.3
|
891 |
+
68.88±1.3
|
892 |
+
68.41±1.4
|
893 |
+
68.11±1.4
|
894 |
+
67.75±1.4
|
895 |
+
66.48±1.5
|
896 |
+
66.34±1.3
|
897 |
+
65.67±1.4
|
898 |
+
63.02±1.4
|
899 |
+
57.27±1.6
|
900 |
+
54.43±1.4
|
901 |
+
47.74±1.5
|
902 |
+
30.30±1.3
|
903 |
+
|
904 |
+
Lance A. Ramshaw and Mitchell P. Marcus.
|
905 |
+
1995. Text Chunking Using Transformation-Based
|
906 |
+
Learning. In Proceedings of the Third ACL Workshop on Very Large Corpora, pages 82–94. Cambridge, MA, USA.
|
907 |
+
|
908 |
+
Table 5: Overall precision, recall and Fβ=1 rates obtained by the sixteen participating systems on the
|
909 |
+
test data sets for the two languages in the CoNLL2003 shared task.
|
910 |
+
|
911 |
+
Helmut Schmid. 1995. Improvements in Part-ofSpeech Tagging with an Application to German.
|
912 |
+
In Proceedings of EACL-SIGDAT 1995. Dublin,
|
913 |
+
Ireland.
|
914 |
+
|
915 |
+
Casey Whitelaw and Jon Patrick. 2003. Named Entity Recognition Using a Character-based Probabilistic Approach. In Proceedings of CoNLL-2003.
|
916 |
+
|
917 |
+
Erik F. Tjong Kim Sang. 2002. Introduction to the
|
918 |
+
CoNLL-2002 Shared Task: Language-Independent
|
919 |
+
Named Entity Recognition. In Proceedings of
|
920 |
+
CoNLL-2002, pages 155–158. Taipei, Taiwan.
|
921 |
+
C.J. van Rijsbergen. 1975. Information Retrieval.
|
922 |
+
Buttersworth.
|
923 |
+
|
924 |
+
Dekai Wu, Grace Ngai, and Marine Carpuat. 2003.
|
925 |
+
A Stacked, Voted, Stacked Model for Named Entity Recognition. In Proceedings of CoNLL-2003.
|
926 |
+
Tong Zhang and David Johnson. 2003. A Robust
|
927 |
+
Risk Minimization based Named Entity Recognition System. In Proceedings of CoNLL-2003.
|
928 |
+
|
929 |
+
|