Improving Automatic Categorization of Technical vs. Laymen Medical Words using FastText Word Embeddings

На основе исходных векторных представлений слов, а также векторных представлений слов типа PCA, PCA-1, PCA-n были построены векторные

We compare the usage of Aff2Vec word embeddings in the ranking and query expansion task against both vanilla embedding models (like word2vec and GloVe) and enriched models like

The best result for task 2 is obtained with word embedding trained on the same data, but the best result for China data is ob- tained when we used word embedding from another

Rangel et al. in the author profiling task at PAN 2013 contest [10] describe the identification of age and gender using multilingual dataset, consisting of English and

In this paper, we study usage of recently developed word embeddings, which contain context information for words together with other linguistic and non-linguistic features, for

Bold black values are the best F-scores overall; blue bold values are the best F-scores obtained by using a single combination method in the word-based word embeddings scenario

Similar to the task in 2017, the bag of words ensemble again obtained the best results in the depression subtask, while the CNN based on the self-trained fastText embeddings (BCSGD)

As shown in Table 1, CWE overall outperforms skipgram and cbow on word similarity and word analogy tasks, since it leverages certain subword information – character-level