Utilizing Neural Networks and Linguistic Metadata for Early Detection of Depression Indications in Text Sequences
TABLE V: Semantic, Syntactic, and Total Accuracies on the Word Analogy Dataset for 300-Dimensional Word Embeddings
['Model', 'Sem.', 'Syn.', 'Total']
The dataset was originally created to evaluate the word embeddings of word2vec and consists of 8.869 semantic as well as 10.675 syntactic analogy questions: Given an analogy (e.g. Athens and Greece) and a third word (e.g. Oslo), word embeddings have to return the fourth word (Norway in this case) as closest vector to the result of vec(‘‘Greece′′)−vec(‘‘Athens′′)+vec(‘‘Oslo′′) according to cosine similarity. While the results are far from the ones obtained by the state-of-the-art fastText models trained on Wikipedia and news articles, especially the result in the syntactic category illustrates that even the training on these much less formal documents has lead to a decent model of the English language.