Recently, Le and Mikolov (2014) proposed doc2vec as an extension to word2vec (Mikolov et al., 2013a) to learn document-level embeddings.
While the celebrated Word2Vec technique yields semantically rich representations for individual words, there has been relatively less success in extending to generate unsupervised sentences or documents embeddings.
Finally, although not trained for embedding sentences and words, it also achieves competitive performance on crosslingual sentence and word retrieval tasks.
Most current approaches to metaphor identification use restricted linguistic contexts, e. g. by considering only a verb's arguments or the sentence containing a phrase.
Visualizing graph embeddings annotated with predictions of potentially suicidal individuals shows the integrated model could classify such individuals even if they are positioned far from the support group.
However, the existing graph-based works can neither capture the contextual word relationships within each document nor fulfil the inductive learning of new words.