  Word sense induction (WSI), or the task of automatically discovering multiple
senses or meanings of a word, has three main challenges: domain adaptability,
novel sense detection, and sense granularity flexibility. While current latent
variable models are known to solve the first two challenges, they are not
flexible to different word sense granularities, which differ very much among
words, from aardvark with one sense, to play with over 50 senses. Current
models either require hyperparameter tuning or nonparametric induction of the
number of senses, which we find both to be ineffective. Thus, we aim to
eliminate these requirements and solve the sense granularity problem by
proposing AutoSense, a latent variable model based on two observations: (1)
senses are represented as a distribution over topics, and (2) senses generate
pairings between the target word and its neighboring word. These observations
alleviate the problem by (a) throwing garbage senses and (b) additionally
inducing fine-grained word senses. Results show great improvements over the
state-of-the-art models on popular WSI datasets. We also show that AutoSense is
able to learn the appropriate sense granularity of a word. Finally, we apply
AutoSense to the unsupervised author name disambiguation task where the sense
granularity problem is more evident and show that AutoSense is evidently better
than competing models. We share our data and code here:
https://github.com/rktamplayo/AutoSense.
