A large web corpus (over 10 billion tokens) licensed under CreativeCommons license family in 50+ languages that has been extracted from CommonCrawl, the largest publicly available general Web crawl to date with about 2 billion crawled URLs.
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)
This corpora is part of Deliverable 5.5 of the European Commission project QTLeap FP7-ICT-2013.4.1-610516 (http://qtleap.eu).
The texts are sentences from the Europarl parallel corpus (Koehn, 2005). We selected the monolingual sentences from parallel corpora for the following pairs: Bulgarian-English, Czech-English, Portuguese-English and Spanish-English. The English corpus is comprised by the English side of the Spanish-English corpus.
Basque is not in Europarl. In addition, it contains the Basque and English sides of the GNOME corpus.
The texts have been automatically annotated with NLP tools, including Word Sense Disambiguation, Named Entity Disambiguation and Coreference resolution. Please check deliverable D5.6 in http://qtleap.eu/deliverables for more information.
Embeddings from word2vec model described in "From Diachronic to Contextual Lexical Semantic Change: Introducing Semantic Difference Keywords (SDKs) for Discourse Studies". Full reference TBC.