FEATURES FOR NAMED ENTITY RECOGNITION IN CZECH
LANGUAGE
Pavel Kr´al
Department of Computer Science and Engineering, University of West Bohemia, Plzeˇn, Czech Republic
Keywords:
Conditional Random Fields (CRFs), Czech News Agency, Named entity recognition.
Abstract:
This paper deals with Named Entity Recognition (NER). Our work focuses on the application for the Czech
News Agency (
ˇ
CTK). We propose and implement a Czech NER system that facilitates the data searching from
the
ˇ
CTK text news databases. The choice of the feature set is crucial for the NER task. The main contribution
of this work is thus to propose and evaluate some different features for the named entity recognition and to
create an “optimal” set of features. We use Conditional Random Fields (CRFs) as a classifier. Our system is
tested on a Czech NER corpus with nine main named entity classes. We reached 58% of the F-measure with
the best feature set which is sufficient for our target application.
1 INTRODUCTION
Named Entity Recognition has been identified as
a main research topic for automatic information re-
trieval around 1996 (Grishman and Sundheim, 1996)
and can be seen as the first step of information extrac-
tion. The objective is the identification of expressions
with special meaning such as person names, organi-
zations, times, monetary values, etc.
The named entity recognition task is composed of
the three main steps:
1. Feature set creation;
2. NE models building;
3. NE classification.
The feature set is beneficial for a successful NE
recognition (Ekbal et al., 2010). We thus apply some
features that are evaluated positively in other lan-
guages to the Czech language. Our final feature set
will be composed of the most promising ones. Condi-
tional Random Fields (CRFs) are then used for named
entity models creation and NE recognition.
The outcomes of this work will be used to improve
the
ˇ
CTK search engine. The current system allows
only the key-words search in the text news databases.
Named entity recognitionwill be integrated to the sys-
tem such facilitates to analyse well some previously
ambiguous requests and the system answers will be
more accurate.
This paper is organized as follows. The next sec-
tion presents a short review of the NER approaches.
Section 3 describes the CRF NER approach with the
particular focus on the choice of features. Section 4
evaluates the proposed approach on a Czech NE cor-
pus. In the last section, we discuss the results and
propose some future research directions.
2 RELATED WORK
Named Entity Recognition has been progressively
formalized during the Message Understanding Con-
ference series (MUC
1
) until 1998, and later on in the
IREX campaign in Japan (Satoshi and Hitoshi, 2000),
HAREM campaign in Portuguese (Santos et al.,
2006), ESTER campaign in France (Gravier, 2005),
but more importantly in the ACE
2
and CoNLL (Sang
and Erik, 2002) campaigns for English.
There are two broad approaches. The first one is
based on hand made rules and dictionaries and typ-
ically involves techniques like Context Free Gram-
mars, Finite State Automata or Regular Expressions.
The second one is based on statistical methods as
Hidden Markov Models (Zhou and Su, 2002), Maxi-
mum Entropy Models (Curran and Clark, 2003), Con-
ditional Random Fields (McCallum and Li, 2003),
Support Vector Machines (Isozaki and Kazawa, 2002)
and other. There are also hybrid systems combining
more of mentioned methods (Kozareva et al., 2007).
1
http://www-nlpir.nist.gov/related projects/muc/
2
http://www.itl.nist.gov/iad/mig/tests/ace
437
Král P..
FEATURES FOR NAMED ENTITY RECOGNITION IN CZECH LANGUAGE.
DOI: 10.5220/0003660104370441
In Proceedings of the International Conference on Knowledge Engineering and Ontology Development (KEOD-2011), pages 437-441
ISBN: 978-989-8425-80-5
Copyright
c
 2011 SCITEPRESS (Science and Technology Publications, Lda.)

Despite these efforts, the Named Entity recognition
task still faces several fundamental issues, in partic-
ular concerning the definition of the task, of named
entities and of their segmentation.
3 NAMED ENTITY
RECOGNITION APPROACH
3.1 Features
Our feature choice was inspired from the works
for the other inflecting languages as for Bulgar-
ian in (Georgiev et al., 2009), for Hindi in (Ekbal
and Bandyopadhyay, 2010) and for Arabic in (Ab-
dul Hamid and Darwish, 2010). Because few works
exist for the Czech (Kravalov´a and
ˇ
Zabokrtsk´y,
2009), only few of our features are similar to those
from the known Czech studies.
We distinguish two types of features: language in-
dependent and language dependent ones. The main
difference is that it is possible to use language in-
dependent features without any modification in any
language, however language dependant features usu-
ally perform only in one language. Note, that feature
quality for one language, do not guarantees the same
properties in the other language.
Language dependant features are based on the
properties of a given language such as syntax, words
belonging to some dictionary, etc.
3.1.1 Language Independent Features
The evaluated language independent features are de-
scribed below:
• Word and Surrounding Words (w
i
): current
word w
i
and words that are placed in the neigh-
bourhood of this current word.
• First Word (fw): true, when the current word is
the first one in the sentence, false otherwise.
• Word Length (wLen
i
): number of characters in
the word w
i
.
• Is Word Longer than? (wLenB): true , when
the word is longer than a given threshold B, false
otherwise.
• Few Occurrence Words (infreq): true, when the
word occurs fewer than a given threshold, false
otherwise.
• Previous Word Tag (tag): NE tag of the previous
word obtained during the recognition.
• Word Prefix (p
i
): i characters from the beginning
of the word.
• Word Suffix (s
i
): i characters from the end of the
word.
• Surrounding Word Prefix (c
j
p
i
): word character
prefix p
i
of the surrounding words to the word at
position j.
• Surrounding Word Suffix (c
j
s
i
): word character
suffix s
i
of the surrounding words to the word at
position j.
• Is Number? (dig): word (or its part) is composed
of digits.
• Word n-gram (N
i
): n-grams created from the
words.
• Character n-gram (chN
i
): n-grams created from
the characters.
3.1.2 Language Dependent Features
The first feature subset is based on the manual de-
fined dictionaries. The most important ones are sum-
marized further:
• World Capitals (capD): available at http://
www.mestasveta.cz/hlavni-mesta.
• World Countries (statesD): available at http://
www.mifin.cz/WU-seznam statu.doc.
• Currencies (currencyD): available at http://
www.finance.cz/bankovnictvi/meny/vse/.
• Days of the Week (daysD).
• Months of the Year (monthsD).
• Companies (compD): a list provided by the
ˇ
CTK.
• Czech Cities and Villages (CZcityD): available
at http:// www2.czso.cz/ csu/ 2009edicniplan.nsf/
p/ 1302-09.
• Numerals (numsD).
• Organizations (orgD).
• Czech First and Last Names (namesD): avail-
able at http://www.mvcr.cz/clanek/cetnost-jmen-
a-prijmeni-722752.aspx?q=Y2hudW09Mw%3d
%3d and at http://www.mvcr.cz/clanek/cetnost-
jmen-a-prijmeni-722752.aspx?q=Y2hudW09Mg
%3d%3d.
• Physical Units (unitD): available at http://
www.converter.cz.
• Word Prefixes (c
j
pD
i
) : list of word prefixes
(features are created from prefixes p
i
of the sur-
rounding words to the word at position j).
• Word Suffixes (c
j
sD
i
) : list of word suffixes (fea-
tures are created from prefixes p
i
of the surround-
ing words to the word at position j).
KEOD 2011 - International Conference on Knowledge Engineering and Ontology Development
438

The second subset is based on the Czech syntactic
properties as follows:
• Word Lemma (lemma): base form of the word
(e.g. the lemma of the English word “walking” is
“walk”).
• Part of Speech Tag (POS
i
): main part of the part
of speech (POS) tag of the word w
i
(Jan Hajic,
2005).
• Sub Category of the POS-tag (sPOS): this sub
category specifies the main POS-tag (Jan Hajic,
2005).
• Verbal Voice (voice
i
).
• Case (mc
i
): to express one or more particular syn-
tactic relationships of the word w
i
to other words
in a sentence.
• Singular vs. Plural (mn
i
): true, when the word
w
i
is singular, false otherwise.
3.2 CRF Classifier
We use conditional random fields as a classifier due to
their performances superior to the other classification
models on several NLP tasks. Laferty et al. shown
in (Lafferty et al., 2001) that CRFs avoid a fundamen-
tal limitation of maximum entropy Markov models
(MEMMs) and other discriminative Markov models
on a part-of-speech tagging task. Favre et al. com-
pared in (Favre et al., 2009) three sequence models:
Hidden-Event Language Model (HELM), factored-
HELM and Conditional Random Fields (CRF) for
comma prediction. They have shown that the best re-
sults have been obtained with CRF, although CRFs
may not scale easily to large databases.
We use thus CRFs to calculate the condi-
tional probability P(NE| f
1
, f
2
, .., f
n
) where F =<
f
1
, f
2
, .., f
n
> represents the features sequence and
NE is the sequence of the named entity classes.
4 EXPERIMENTS
We assume that tokenization task is done. Text is thus
already split into the basic text units, tokens, that cor-
respond mostly to the words.
We use a CRF classification algorithm of the Ling-
Pipe
3
tool as a background of our system. The in-
put of this tool have been modified in order to accept
the previously described features. Numerous experi-
ments have been realized. However, only these that
have a positive impact for the NER feature set cre-
ation are reported in this paper.
3
http://alias-i.com/lingpipe/
The feature selection algorithm was as follows:
1. Start (to compose the basic feature set, i.e. words
only);
2. Add one feature into the feature set;
3. Train CRF models and perform NE recognition;
4. If F-measure value is increasing, keep this feature;
remove it otherwise;
5. If F-measure is smaller than the value given, go to
step 2; finish the algorithm otherwise.
4.1 Corpus
Czech Named Entity Corpus 1.0 (Kravalov´a et al.,
2009), which is composed of some text samples from
the Czech National Corpus, is used to validatethe pro-
posed methods. It was created at the Institute of For-
mal and Applied Linguistics of Faculty of Mathemat-
ics and Physics at the Charles University in Prague.
It has been labelled manually with 12 main named
entity classes and with 61 sub-classes. The whole
corpus containing 5866 sentences (such represents
150451 words) is randomly divided into two disjoint
sets, 500 sentences for testing and the rest for training.
The corpus structure is detailed in Table 1.
Table 1: Distribution of the 12 main named entity classes in
the corpus.
Tag description Tag Sent. no.
Number as a part of the address a 195
Part of bibliographic information c 153
Geographic names g 2353
Institution names i 1380
Media names m 174
Number with a specific meaning n 537
Object names o 1196
Person names p 2710
Ordinal number, count number q 1047
Time values t 1427
Abbreviations s 747
Foreign language words f 373
Not specified ? 231
We used F-measure as an evaluation metric of our
experiments. 9 main named entity classes related to
our application is recognized.
4.2 Language Independent Features
Table 2 relates the language independent feature sets
with the experimentnumbers. The F-measure of these
experiments is reported in Table 3.
New features included into the experiments no. 2,
3 and 4 don’t improve F-measure. Therefore, these
FEATURES FOR NAMED ENTITY RECOGNITION IN CZECH LANGUAGE
439

Table 2: Composition of the evaluated language indepen-
dent feature sets with the corresponding experiment num-
bers.
Exp.
no.
Features
1 w
i
(i = −1, .., 1), fw
2 w
i
(i = −1, .., 1), fw, wLenB (B = 4)
3 w
i
(i = −2, .., 2), fw, wLenB (B = 3)
4 w
i
(i = −3, .., 3), fw, wLenB (B = 3)
5 w
i
(i = −1, .., 1), fw, wLen
i
(i = −2, .., 2), in freq
6 w
i
(i = −1, .., 1), fw, wLen
i
(i = −2, .., 2), in freq, tag
7 w
i
(i = −1, .., 1), fw, wLen
i
(i = −2, .., 2), in freq, tag, p
i
(i =
1, .., 4), s
i
(i = 1, .., 4)
8 w
i
(i = −1, .., 1), fw, wLen
i
(i = −2, .., 2), infreq, tag, p
i
(i =
1, .., 3), s
i
(i = 1, .., 3)
9 w
i
(i = −1,.., 1), fw, wLen
i
(i = −2,.., 2), infreq, tag,
c
j
p
i
( j = −1, .., 1, i = 0, .., 3), c
j
s
i
( j = −1, .., 1, i = 0, .., 3)
10 w
i
(i = −1, .., 1), fw, wLen
i
(i = −2, .., 2), in freq, tag, p
i
(i =
1, .., 3), s
i
(i = 1, .., 3), dig
Table 3: F-measure of the NER experiments with the lan-
guage independent features in %.
F-measure of the different NEs in [%]
No. a c g i n o p q t all
1 13.3 45.7 44.9 34.7 17.8 36.4 48.2 48.3 62.9 44.0
2 13.2 47.9 44.8 34.8 22.4 37.6 48.4 44.1 62.8 44.0
3 13.2 45.7 44.0 33.3 20.6 37.7 48.5 46.7 62.2 43.7
4 17.4 46.4 42.5 33.9 20.4 39.9 46.2 47.1 60.8 43.0
5 21.7 47.9 47.2 35.6 21.4 42.2 51.4 49.3 65.1 46.0
6 31.3 58.0 43.6 32.8 23.3 44.6 49.4 47.7 64.2 46.1
7 36.2 62.2 59.2 42.4 30.1 53.2 65.8 54.7 73.4 58.0
8 36.2 69.2 58.7 40.6 28.6 52.9 64.5 55.8 72.5 57.4
9 29.4 71.8 44.9 34.0 18.6 47.3 51.1 47.7 66.3 47.5
10 36.7 61.4 58.8 40.1 29.9 53.9 64.4 54.9 72.1 57.3
features are not further used. The best F-measure is
73.4% and have been obtained for the class t (time)
in the 7
th
experiment. This experiment contains also
most of the best results (for six classes). Its global
F-measure is 58% which represents a maximal value.
Additional features (see experiments no. 8, 9 and 10)
don’t improve the recognition F-measure. The final
feature set is thus chosen from experiment no. 7.
This table shows further that F-measure values for
the different NEs differ significantly. This fact may
be due to two reasons:
1. There are not enough occurrences of some NEs
in the corpus for the correct estimation of the
models (classes with enough NE occurrences are
generally recognized better that few occurrence
classes);
2. The proposed feature set in not discriminating
enough for all NEs.
4.3 Language Dependent Features
The previously created language independent feature
set (see 7
th
experiment at Table 3) is progressively
completed by the language dependent features. Ta-
ble 4 relates the features with the experiment num-
bers. Note that language independent features are
similar for all experiments and are thus not reported
in this table. The F-measure of these experiments is
reported in Table 5.
Table 4: Composition of the evaluated language dependent
feature sets with the corresponding experiment numbers.
Exp.
no.
Features
1 c
j
pD
i
( j = −2, .., 2, i = 1, .., 3), c
j
sD
i
( j = −2, .., 2, i = 1, .., 3),
numsD, POS
i
(i = −1, .., 1)
2 numsD, POS
i
(i = −1, .., 1), lemma
3 voice
i
(i = −2, .., 2)
4 sPOS
i
(i = −1, .., 1)
5 mc
i
(i = −2, .., 2), mn
i
(i = −2, .., 2)
6 mc
i
(i = −3, .., 3), mn
i
(i = −3, .., 3), orgD
7 mc
i
(i = −3, .., 3), mn
i
(i = −3, .., 3), daysD, monthsD
8 mc
i
(i = −3, .., 3), mn
i
(i = −3, .., 3), namesD
9 mc
i
(i = −3, .., 3), mn
i
(i = −3, .., 3), namesD, compD,
CZcityD, statesD
10 mc
i
(i = −3,.., 3), mn
i
(i = −3,.., 3), numsD, currencyD, unitD
Table 5: F-measure of the NER experiments with the added
language dependent features in %.
F-measure of the different NEs in [%]
No. a c g i n o p q t all
1 29.3 64.1 57.8 40.6 27.8 53.7 65.5 55.1 72.6 57.4
2 36.4 69.1 58.3 41.4 31.4 53.1 65.7 54.8 72.6 57.7
3 34.8 61.4 58.6 39.5 30.3 54.2 64.6 54.0 72.9 57.2
4 36.2 65.1 58.7 40.5 31.8 53.1 64.3 54.0 72.7 58.4
5 31.7 65.1 58.0 40.9 33.3 53.8 66.3 55.2 72.2 57.9
6 26.6 68.3 57.7 39.3 33.3 53.5 64.6 55.3 72.5 57.2
7 26.8 68.3 58.0 39.6 32.8 54.0 65.0 55.5 72.6 57.5
8 32.5 67.5 57.5 39.9 34.4 53.2 65.4 54.4 72.9 57.5
9 36.7 67.5 57.8 40.6 34.4 53.3 65.6 55.1 72.6 57.8
10 35.3 68.3 57.9 39.4 33.3 55.0 64.6 55.5 72.9 57.7
This table shows that the increase of the F-
measure is very small. The best obtained F-measure is
58.4% by the sPOS
i
(i = −1, .., 1) feature set. More-
over, the majority of the features degrades the results.
This fact may be due to the character of our NE cor-
pus which is very small and contains less NEs from
the defined dictionaries.
KEOD 2011 - International Conference on Knowledge Engineering and Ontology Development
440

5 CONCLUSIONS AND
PERSPECTIVES
The main objective of this work is to propose and im-
plement a Czech NER system that facilitates the data
searching from the
ˇ
CTK text news databases. We pre-
sented the CRF based method for automatic named
entity recognition. The particular focus was on the
NE feature selection. We have proved that the fea-
ture choice plays a crucial role for the NE recogni-
tion. We have also shown that the language indepen-
dent features are much more important than the lan-
guage dependent ones. The best obtained F-measure
is 58.4% which represents the increase of F-measure
14.4% over the baseline in absolute value.
The first perspective consists of evaluation of the
system over the
ˇ
CTK data. This includes the data an-
notation, because the current text data is not annotated
by named entities. We further propose to integrate in
the system new information coming from the other
knowledge sources, in particular from the syntactic
parsing module. Detailed syntactic features are most
often underexploited. However they bring clearly ad-
ditional information to the NE recognition.
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