Chared is a software tool which can detect character encoding of a text document provided the language of the document is known. The language of the text has to be specified as an input parameter so that the corresponding language model can be used. The package contains models for a wide range of languages (currently 57 --- covering all major languages). Furthermore, it provides a training script to learn models for additional languages using a set of user supplied sample html pages in the given language. The detection algorithm is based on determining similarity of byte trigrams vectors. In general, chared should be more accurate than other character encoding detection tools with no language constraints. This is an important advantage allowing precise character decoding needed for building large textual corpora. The tool has been used for building corpora in American Spanish, Arabic, Czech, French, Japanese, Russian, Tajik, and six Turkic languages consisting of 70 billions tokens altogether. Chared is an open source software, licensed under New BSD License and available for download (including the source code) at http://code.google.com/p/chared/. The research leading to this piece of software was published in POMIKÁLEK, Jan a Vít SUCHOMEL. chared: Character Encoding Detection with a Known Language. In Aleš Horák, Pavel Rychlý. RASLAN 2011. 5. vyd. Brno, Czech Republic: Tribun EU, 2011. od s. 125-129, 5 s. ISBN 978-80-263-0077-9. and PRESEMT, Lexical Computing Ltd
Sentence-parallel corpus made from English and Czech Wikipedias based on translated articles from English into Czech.
The work done is described in the paper: ŠTROMAJEROVÁ, Adéla, Vít BAISA a Marek BLAHUŠ. Between Comparable and Parallel: English-Czech Corpus from Wikipedia. In RASLAN 2016 Recent Advances in Slavonic Natural Language Processing. Brno: Tribun EU, 2016. s. 3-8, 6 s. ISBN 978-80-263-1095-2.
jusText is a heuristic based boilerplate removal tool useful for cleaning documents in large textual corpora. The tool has been implemented in Python, licensed under New BSD License and made an open source software (available for download including the source code at http://code.google.com/p/justext/). It is successfully used for cleaning large textual corpora at Natural language processing centre at Faculty of informatics, Masaryk university Brno and it's industry partners. The research leading to this piece of software was published in author's Ph.D. thesis "Removing Boilerplate and Duplicate Content from Web Corpora". The boilerplate removal algorithm is able to remove most of non-grammatical sentences from a web page like navigation, advertisements, tables, short notes and so on. It has been shown it overperforms or at least keeps up with it's competitors (according to comparison with participants of Cleaneval competition in author's Ph.D. thesis). The precise removal of unwanted content and scalability of the algorithm has been demonstrated while building corpora of American Spanish, Arabic, Czech, French, Japanese, Russian, Tajik, and six Turkic languages consisting --- over 20 TB of HTML pages were processed resulting in corpora of 70 billions tokens altogether. and PRESEMT, Lexical Computing Ltd
onion (ONe Instance ONly) is a tool for removing duplicate parts from large collections of texts. The tool has been implemented in Python, licensed under New BSD License and made an open source software (available for download including the source code at http://code.google.com/p/onion/). It is being successfuly used for cleaning large textual corpora at Natural language processing centre at Faculty of informatics, Masaryk university Brno and it's industry partners. The research leading to this piece of software was published in author's Ph.D. thesis "Removing Boilerplate and Duplicate Content from Web Corpora". The deduplication algorithm is based on comparing n-grams of words of text. The author's algorithm has been shown to be more suitable for textual corpora deduplication than competing algorithms (Broder, Charikar): in addition to detection of identical or very similar (95 %) duplicates, it is able to detect even partially similar duplicates (50 %) still achieving great performace (further described in author's Ph.D. thesis). The unique deduplication capabilities and scalability of the algorithm were been demonstrated while building corpora of American Spanish, Arabic, Czech, French, Japanese, Russian, Tajik, and six Turkic languages consisting --- several TB of text documents were deduplicated resulting in corpora of 70 billions tokens altogether. and PRESEMT, Lexical Computing Ltd
The ILRB has been created by two cooperating teams - by the team of the Institute of Czech Language, Czech Academy of Sciences and the team of the NLP Centre at the Faculty of Informatics, Masaryk University (2004-2008).
The tool consists of two sections: wordlist and reference (explanatory) one. Comments and remarks are welcome and should be send to the address poradna@ujc.cas.cz.
1. Wordlist section
It contains more than 60 000 dictionary entries and is based on the glossary of the School Rules of Czech Orthography, the Dictionary of the Literary Czech and selected entries from the New Dictionary of Words of Foreign Origin and Dictionary of Neologisms. The entries typically include information that is asked about frequently by the users. Also inflectional forms of the particular words forms are offered in the form of tables thanks to the morphological analyzer ajka created at the Faculty of Informatics, MU. The dictionary part is linked to the explanatory one through the hypertext links.
2. Reference section
It comprises the explanations about linguistic phenomena described in the Rules of Czech Orthography and contemporary Czech grammars, frequently and repeatedly asked by the users turning to the Linguistic Advisory Line in the Institute of Czech Language. In the offered explanations some typical spelling problems are dealt with including the appropriate recommendations. The ILRB is regularly updated and completed, new expressions are added and made more precise. and Academy of Sciences of the Czech Republic in project 1ET200610406 and Ministry of Education, Youth and Sports in projects LM2010013, LC536 and 2C06009.