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:
this https URL

提出一种基于自动感知的潜在变量模型 AutoSense，用于解决词的多个意义或含义的概率计算问题，该方法可以在不需要超参数调整和非参数归纳的情况下消除需要，使其适应不同的词语含义概率计算，同时在常用的 WSI 数据集上表现出更好的效果，并且可以在无监督的作者名称消岐任务中，得到明显改善。