Local Word Embeddings for Query Expansion based on Co-Authorship and Citations

Word embedding techniques have gained a lot of interest
from natural language processing researchers recently and they are valuable resource in identifying a list of semantically related terms for a
search query. These related terms build a natural addition for query expansion, but might mismatch when the application domains use different
jargon. Using the Skip-Gram algorithm of Word2Vec, terms are selected
only from a specific subset of the corpus, which is extended by documents
from co-authorship and citations. We demonstrate that locally-trained
word embeddings with this extension provides a valuable augmentation
and can improve retrieval performance. First result suggest that query
expansion and word embeddings could also benefit from other related
information.