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The Koditex Corpus

Koditex is a synchronic, representative and reference 9-million-word corpus (excl. punctuation) compiled for the purpose of conducting a multidimensional analysis (MDA) of Czech.

Name Koditex
Positions Number of positions (tokens) 10,880,550
Number of positions (excl. punctuation) 9,139,930
Number of tokens (excl. punctuation) used in factor analysis 9,039,137
Number of word forms 509,764
Number of lemmas 205,592
Structures Number of samples <chunk> 3,428
Number of sentences <s> 719,739
Further information Reference corpus YES
Representative corpus YES
Publication year 2018

When compiling the corpus, the primary goal was for it to be as diverse and representative as possible, reflecting the variability of Czech in all of its modes and ranges of use (written, spoken, online communication) and featuring rich annotation (the texts were lemmatized, morphologically tagged using two different systems, and furthermore they were annotated for phrasemes and so-called named entities). As far as writtenness and spokenness are concerned, the Koditex is a mixed corpus.

The name Koditex is both an acronym of the Czech version of the phrase corpus of diversified texts and a tribute to Vilém Kodýtek, author of a pioneering attempt to apply MDA to Czech based on the work of D. Biber.

Corpus design

Unlike CNC's other synchronic corpora (e.g. SYN2015), the Koditex is not made up of entire texts, but rather samples from the original texts, which are marked as <chunk> within the structure.

Before sampling the assembled data for material to include in the final corpus, we decided to split texts longer than 5,000 words into contiguous chunks of 2,000–5,000 words (while respecting sentence boundaries). This decision was driven by several perceived advantages, primarily that of ensuring a higher overall diversity of the corpus in terms of registers as well as genres / text types.

At the topmost level, texts are classified into three modes of communication:

  • written language (wri),
  • spoken language (spo) and
  • web-based communication (web).

Each of the three modes is further subdivided into two or more divisions (e.g. the written mode is subdivided into fiction, non-fiction, journalism and private correspondence). Divisions then branch into classes of texts (e.g. crime novel), aiming at roughly 200,000 words per class (subject to data availability). For the written mode, we introduced an intermediate superclass level which groups several related text classes together.

Some texts had to be removed from the data set prior to performing the MDA due to technical reasons. These texts are identified in the corpus by the attribute include=“no” in their metadata. The table below summarizes the composition of the Koditex corpus, taking into account only those texts which were actually included in the MDA (i.e. bearing the attribute include=“yes”):

MODE DIVISION SUPERCLASS CLASS Tokens Text chunks
spo (spoken) int (interactive) bru (unprepared broadcast discussions) 221,812 90
eli (elicited speech/dialogue) 201,690 82
inf (informal unprepared private dialogue) 208,565 86
nin (non-interactive) wbs (written-to-be-spoken speeches) 213,201 71
web mul (multi-directional) dis (discussions) 197,948 87
fcb (Facebook posts) 199,418 91
for (forums) 200,104 85
uni (uni-directional) blo (blogs) 204,356 74
wik (cs.wikipedia.org articles) 201,691 84
wri (written) fic (fiction) nov (novels) crm (crime) 190,026 68
fan (fantasy) 189,432 69
gen (general fiction) 193,667 67
lov (romance) 189,893 70
scf (sci-fi) 188,703 68
col (short stories) 195,595 70
scr (screenplays & drama) 182,689 76
ver (poetry & lyrics) 205,837 76
nfc (non-fiction) pop (popular science) fts (formal and technical sciences) 207,607 68
hum (humanities) 204,837 74
nat (natural sciences) 204,751 71
ssc (social sciences) 203,698 68
pro (trade journals) fts (formal and technical sciences) 210,010 71
hum (humanities) 207,916 69
nat (natural sciences) 209,580 70
ssc (social sciences) 209,385 72
sci (scientific/academic) fts (formal and technical sciences) 202,932 67
hum (humanities) 204,300 71
nat (natural sciences) 206,716 72
ssc (social sciences) 205,358 67
adm (administrative texts)* 203,542 82
enc (encyclopedias) 203,957 73
mem (memoirs) 203,390 71
nmg (newspapers & magazines) lei (leisure) hou (crafts & hobbies) 207,499 68
int (interesting facts) 209,232 69
lif (lifestyle) 203,124 72
mix (supplements, Sunday magazines) 205,310 75
sct (tabloids) 201,417 73
spo (sport) 199,238 70
new (newspapers) com (op-eds, columns) 205,372 68
cul (culture) 205,690 68
eco (economic news) 211,481 70
fre (free time activities) 208,532 71
pol (politics) 206,893 70
rep (news) 206,377 70
pri (private) cor (letters)* 96,366 68
Total 9,039,137 3292

* In these classes, chunks as short as 1,000 tokens were allowed.

Texts in these classes were first aggregated (by author and time of day) and then split into chunks of 2,000–5,000 words.

Chunks

The initial idea – to have all chunks approximately of the same length (between 2,000–5,000 words) – turned out to be unrealistic for some classes, because the typical text length in these classes is shorter. This led to two types of solutions. For some classes (e.g. pri or adm), a decision was made to push the lower bound down to 1,000 words, which also hopefully mitigated the bias in favor of texts longer than customary in the given category.

In other classes (e.g. fcb), the original data consisted of a sea of fragments mostly much shorter than even 1,000 words. In these cases, an aggregation of texts was performed and chunking applied only afterwards.

The focus of Koditex is on contemporary language, the oldest pieces were published in 1990.

The majority of texts (accounting for 76% of tokens) included in the corpus are Czech originals (not translations from other languages). The only exceptions are text classes where translated material is common in Czech in general, listed in the table below (the rest of the classes are 100% Czech originals).

Class Translations (words) Originals (words) % translations
LOV 210,250 30,981 87.2%
CRM 202,921 37,677 84.3%
GEN 196,924 43,497 81.9%
FAN 188,848 52,778 78.2%
SCF 174,340 66,221 72.5%
MEM 176,000 67,731 72.2%
HUM 329,928 395,573 45.5%
NAT 324,310 401,957 44.7%
ENC 103,954 137,889 43.0%
SSC 265,640 460,324 36.6%
FTS 259,325 467,253 35.7%
VER 82,101 158,634 34.1%
WIK 49,150 192,765 20.3%

Annotation

Several layers of annotation were added to the corpus in order to facilitate operationalization of features:

  • lemmatization and morphological tagging; two systems were used: the MorphoDiTa stochastic tagger1) and a hybrid tagger combining stochastic and rule-based disambiguation2)
  • phraseme annotation by the FRANTA system3)
  • named-entity recognition using the NameTag tool4)

The following statistical models were used with MorphoDiTa and NameTag:

  • Straka, Milan & Jana Straková. 2016. Czech Models (MorfFlex CZ 161115 + PDT 3.0) for MorphoDiTa 161115. LINDAT/CLARIN digital library at the Institute of Formal and Applied Linguistics (ÚFAL), Faculty of Mathematics and Physics, Charles University. http://hdl.handle.net/11234/1-1836
  • Straka, Milan & Jana Straková. 2014. Czech Models (CNEC) for NameTag. LINDAT/CLARIN digital library at the Institute of Formal and Applied Linguistics (ÚFAL), Faculty of Mathematics and Physics, Charles University. http://hdl.handle.net/11858/00-097C-0000-0023-7D42-8

Sources of data

The vast majority of the material in the Koditex corpus draws on the resources of the Czech National Corpus (CNC); types of language data which are not collected by the CNC were acquired from other research centers. We would also like to thank Martin Prošek and Petr Kaderka from the Czech Language Institute of the Czech Academy of Sciences for providing data from the DIALOG corpus, Karel Pala and Vít Baisa from the NLPC at Masaryk University, and Josef Šlerka and his team at Socialinsider, for providing raw data for the wik class and mul division, respectively.

The Koditex corpus was created by sampling various sources and using a number of tools, all of which are cited here:

  • Benešová, Lucie, Michal Křen & Martina Waclawičová. 2013. ORAL2013.
  • Benko, Vladimír. 2015. Araneum Bohemicum Maius, version 15.04. ÚČNK FF UK.
  • Cvrček, Václav, Petr Truneček & Václav Horký. 2015. SPEECHES.
  • Čermák, František, Ana Adamovičová & Jiří Pešička. 2001. PMK.
  • Hladká, Zdeňka. 2002. BMK.
  • Hladká, Zdeňka. 2006. KSK.
  • Křen, Michal et al. 2015. SYN2015.
  • Straka, Milan & Jana Straková. 2014. Czech Models (CNEC) for NameTag. LINDAT/CLARIN ÚFAL MFF UK. http://hdl.handle.net/11858/00-097C-0000-0023-7D42-8
  • Straka, Milan & Jana Straková. 2016. Czech Models (MorfFlex CZ 161115 + PDT 3.0) for MorphoDiTa 161115. LINDAT/CLARIN ÚFAL MFF UK. http://hdl.handle.net/11234/1-1836
  • The DIALOG Corpus, version 1.2. 2015. ÚJČ AV ČR. Praha. http://ujc.dialogy.cz
  • The EUROPARL Corpus (the Proceedings of the European Parliament). http://www.europarl.eu.int/

How to cite Koditex

Zasina, A. J. – Lukeš, D. – Komrsková, Z. – Poukarová, P. – Řehořková, A.: Koditex: A corpus of diversified texts. Institute of the Czech National Corpus, Faculty of Arts, Charles University, Prague 2018. Available at WWW: www.korpus.cz

1)
Straková Jana, Milan Straka & Jan Hajič. 2014. Open-Source Tools for Morphology, Lemmatization, POS Tagging and Named Entity Recognition. In Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, 13–18. Baltimore, MD: ACL.
2)
Spoustová, Drahomíra, Jan Hajič, Jan Votrubec, Pavel Krbec & Pavel Květoň. 2007. The Best of Two Worlds: Cooperation of Statistical and Rule-Based Taggers for Czech. In Proceedings of the Workshop on Balto-Slavonic Natural Language Processing, ACL 2007. 67–74; Jelínek, Tomáš. 2008. Nové značkování v Českém národním korpusu [New tagging in the Czech National Corpus]. Naše řeč 91(1). 13–20; Petkevič, Vladimír. 2014. Problémy automatické morfologické disambiguace češtiny [Problems of automatic morphological disambiguation of Czech]. Naše řeč 97(4). 194–207.
3)
Hnátková, Milena. 2002. Značkování frazémů a idiomů v Českém národním korpusu s pomocí Slovníku české frazeologie a idiomatiky [The tagging of phraseological units and idioms in the Czech National Corpus with the aid of the Dictionary of Czech phraseology and idiomatics]. Slovo a slovesnost 63(2). 117–126.
4)
Straková Jana, Milan Straka & Jan Hajič. 2013. A New State-of-The-Art Czech Named Entity Recognizer. In Ivan Habernal & Václav Matoušek (eds.), Text, Speech and Dialogue, 68–75. Berlin & Heidelberg: Springer Verlag.