Towards Resolving Word Ambiguity with Word Embeddings
Matthias Thurnbauer Johannes Reisinger Christoph Goller Andreas Fischer
Ambiguity is ubiquitous in natural language. Resolving ambiguous meanings is
especially important in information retrieval tasks. While word embeddings
carry semantic information, they fail to handle ambiguity well. Transformer
models have been shown to handle word ambiguity for complex queries, but they
cannot be used to identify ambiguous words, e.g. for a 1-word query.
Furthermore, training these models is costly in terms of time, hardware
resources, and training data, prohibiting their use in specialized environments
with sensitive data. Word embeddings can be trained using moderate hardware
resources. This paper shows that applying DBSCAN clustering to the latent space
can identify ambiguous words and evaluate their level of ambiguity. An
automatic DBSCAN parameter selection leads to high-quality clusters, which are
semantically coherent and correspond well to the perceived meanings of a given
word.