Evaluating KGR10 Polish word embeddings in the recognition of temporal expressions using BiLSTM-CRF.
Table 4: KGR10 word embeddings created at WUST, G4.19, with the information about embedding ID (EP – previous, EC – current), original embedding name, dimension, binary format, method of creation (Skipgram, CBOW), softmax approximation method (hs – hierarchical softmax, ns – negative sampling).
['E_ID', 'Name', 'Dim.', 'Bin.', 'Meth.', 'App.']
We created a new Polish word embeddings models using the KGR10 corpus. We built 16 models of word embeddings using the implementation of CBOW and Skip-gram methods in the FastText tool The internal encoding solution based on embeddings of n-grams composing each word makes it possible to obtain FastText vector representations, also for words which were not processed during the creation of the model. A vector representation is associated with character n-gram and each word is represented as the sum of its n-gram vector representations. Previous solutions ignored the morphology of words and were assigning a distinct vector to each word. Authors observed that using word representations trained with subword information outperformed the plain Skip-gram model and the improvement was most significant for morphologically rich Slavic languages such as Czech (8% reduction of perplexity over SG) and Russian (13% reduction) We expected that word embeddings created that way for Polish should also provide such improvements.