Bilingual (EN-PT) corpus acquired from website (https://ec.europa.eu/commission/presscorner/) of the EU portal (14th May 2020).
Bilingual (EN-PT) corpus acquired from website (https://ec.europa.eu/commission/presscorner/) of the EU portal (8th July 2020).
Bilingual (EN-PT) corpus acquired from Wikipedia on health and COVID-19 domain (2nd May 2020)
Bilingual (EN-PT) corpus acquired from website (https://ec.europa.eu/*coronavirus-response) of the EU portal (20th May 2020).
The Brands.Br corpus was built from a fraction of B2W-Reviews01 corpus. We use a set of 252 samples selected by B2W to be enriched. In Brands.Br corpus we want to solve two main challenges in product reviews corpus. The first: it is very common to find customer reviews referring to distinct thing...
The LX-WordSim-353 was created from WordSim-353 (Agirre et al., 2009). As the name suggests, this data set contains 353 pairs of words. Both words in each pair can have different morphosyntactic categories. The data set is made of nouns, adjectives, verbs and named entities, and has no multiwords...
The LX-SimLex-999 was created from SimLex-999 (Hill et al., 2015) which, in turn, was based in the University of South Florida Free Association Database (USF) (Nelson et al., 2014). There were strict guidelines to create SimLex-999. Both words in each pair have the same morphosyntactic category ...
The LX-Rare Word Similarity Data set was created from Stanford Rare Word (RW) Similarity data set (Luong et al., 2013). This list contains 2 034 words (1 017 pairs of words). All the words were extracted from Wikipedia and from WordNet (Miller, 1995), a lexical database where the concepts are gro...
A collection of language resources for the evaluation of distributional semantic models of Portuguese: LX-SimLex-999: http://metashare.metanet4u.eu/go2/lx-simlex-999 LX-Rare Word Similarity Data set: http://metashare.metanet4u.eu/go2/lx-rare-word-similarity-dataset LX-WordSim-353: h...
The test set described in was used as the basis for the assessment of word embeddings. An example entry in this data set would read: ‘Berlin Germany Lisbon Portugal’. With these four words relations – as in this example – one can test semantic analogies by using any of the possible combinations o...