The corpus presented consists of job ads in Spanish related to Engineering positions in Peru.
The documents were preprocessed and annotated for POS tagging, NER, and topic modeling tasks.
The corpus is divided in two components:
- POS tagging/ NER training data: Consisting of 800 job ads, each one tokenized and manually annotated with POS tag information (EAGLE format) and Entity Label in BIO format.
- Topic modeling training data: containing 9000 documents stripped from stopwords. Comes in two formats:
* Whole text documents: containing all the information originally posted in the ad.
* Extracted chunks documents: containing chunks extracted by custom NER models (expected skills, tasks to perform, and preferred major), as described in Improving Topic Coherence Using Entity Extraction Denoising (to appear)
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
Corpus of Czech educational texts for readability studies, with paraphrases, measured reading comprehension, and a multi-annotator subjective rating of selected text features based on the Hamburg Comprehensibility Concept
Corpus of Czech educational texts for readability studies, with paraphrases, measured reading comprehension, and a multi-annotator subjective rating of selected text features based on the Hamburg Comprehensibility Concept
The January 2018 release of the ParaCrawl is the first version of the corpus. It contains parallel corpora for 11 languages paired with English, crawled from a large number of web sites. The selection of websites is based on CommonCrawl, but ParaCrawl is extracted from a brand new crawl which has much higher coverage of these selected websites than CommonCrawl. Since the data is fairly raw, it is released with two quality metrics that can be used for corpus filtering. An official "clean" version of each corpus uses one of the metrics. For more details and raw data download please visit: http://paracrawl.eu/releases.html
Wikipedia plain text data obtained from Wikipedia dumps with WikiExtractor in February 2018.
The data come from all Wikipedias for which dumps could be downloaded at [https://dumps.wikimedia.org/]. This amounts to 297 Wikipedias, usually corresponding to individual languages and identified by their ISO codes. Several special Wikipedias are included, most notably "simple" (Simple English Wikipedia) and "incubator" (tiny hatching Wikipedias in various languages).
For a list of all the Wikipedias, see [https://meta.wikimedia.org/wiki/List_of_Wikipedias].
The script which can be used to get new version of the data is included, but note that Wikipedia limits the download speed for downloading a lot of the dumps, so it takes a few days to download all of them (but one or a few can be downloaded fast).
Also, the format of the dumps changes time to time, so the script will probably eventually stop working one day.
The WikiExtractor tool [http://medialab.di.unipi.it/wiki/Wikipedia_Extractor] used to extract text from the Wikipedia dumps is not mine, I only modified it slightly to produce plaintext outputs [https://github.com/ptakopysk/wikiextractor].