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Subcharacter Information in Japanese Embeddings: When Is It Worth It?
Languages with logographic writing systems present a difficulty for traditional character-level models.
Subword-level Composition Functions for Learning Word Embeddings
Subword-level information is crucial for capturing the meaning and morphology of words, especially for out-of-vocabulary entries.
Investigating Different Syntactic Context Types and Context Representations for Learning Word Embeddings
The number of word embedding models is growing every year.
The (too Many) Problems of Analogical Reasoning with Word Vectors
This paper explores the possibilities of analogical reasoning with vector space models.
Word Embeddings, Analogies, and Machine Learning: Beyond king - man + woman = queen
Solving word analogies became one of the most popular benchmarks for word embeddings on the assumption that linear relations between word pairs (such as \textit{king}:\textit{man} :: \textit{woman}:\textit{queen}) are indicative of the quality of the embedding.
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