Local-Global Vectors to Improve Unigram Terminology Extraction
PRESENTER:
Ehsan Amjadian
Carleton University
ABSTRACT:
We explore a novel method that integrates efficient
distributed representations with terminology extraction. We show that the
information from a small number of observed
instances can be combined with local and global word embeddings to
remarkably improve the term extraction results on unigram terms. To do so,
we pass the terms extracted by other tools
to a filter made of the local-global embeddings and a classifier which in
turn decides whether or not a term candidate is a term. The filter can
also be used as a hub to merge different term
extraction tools into a single higher-performing system. We compare
filters that use the skipgram architecture and filters that employ the
CBOW architecture for the task at hand.
This is joint work with Diana Inkpen, T.Sima Paribakht, and Farahnaz Faez,
published in the proceedings of the CompuTerm Workshop 2016.