Conventional word sense induction (WSI) methods usually represent each
instance with discrete linguistic features or cooccurrence features, and train
a model for each polysemous word individually. In this work, we propose to
learn sense embeddings for the WSI task. In the training stage, our method
induces several sense centroids (embedding) for each polysemous word. In the
testing stage, our method represents each instance as a contextual vector, and
induces its sense by finding the nearest sense centroid in the embedding space.
The advantages of our method are (1) distributed sense vectors are taken as the
knowledge representations which are trained discriminatively, and usually have
better performance than traditional count-based distributional models, and (2)
a general model for the whole vocabulary is jointly trained to induce sense
centroids under the mutlitask learning framework. Evaluated on SemEval-2010 WSI
dataset, our method outperforms all participants and most of the recent
state-of-the-art methods. We further verify the two advantages by comparing
with carefully designed baselines.

本文提出一种基于 sense embeddings 方法的词义感知 (word sense induction) 算法，通过 Sense 聚类和 Contextual 向量来辨别多义词的词义，相对于传统基于分布式模型的方法，我们的算法可以更好地处理语义相似性，通过对 SemEval-2010 数据进行的实验表明，本算法的效果优于所有参赛者及最近的大部分最新方法。