  Word vector representations are central to deep learning natural language
processing models. Many forms of these vectors, known as embeddings, exist,
including word2vec and GloVe. Embeddings are trained on large corpora and learn
the word's usage in context, capturing the semantic relationship between words.
However, the semantics from such training are at the level of distinct words
(known as word types), and can be ambiguous when, for example, a word type can
be either a noun or a verb. In question answering, parts-of-speech and named
entity types are important, but encoding these attributes in neural models
expands the size of the input. This research employs holographic compression of
pre-trained embeddings, to represent a token, its part-of-speech, and named
entity type, in the same dimension as representing only the token. The
implementation, in a modified question answering recurrent deep learning
network, shows that semantic relationships are preserved, and yields strong
performance.
