text: 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.
  @cite_5: There is rising interest in vector-space word embeddings and their use in NLP, especially given recent methods for their fast estimation at very large scale. Nearly all this work, however, assumes a single vector per word type ignoring polysemy and thus jeopardizing their usefulness for downstream tasks. We present an extension to the Skip-gram model that efficiently learns multiple embeddings per word type. It differs from recent related work by jointly performing word sense discrimination and embedding learning, by non-parametrically estimating the number of senses per word type, and by its efficiency and scalability. We present new state-of-the-art results in the word similarity in context task and demonstrate its scalability by training with one machine on a corpus of nearly 1 billion tokens in less than 6 hours.
summary: proposed two methods to utilize distributed representations for the WSI task. The first method learned centroid vectors by clustering all pre-computed context vectors of each target word. The other method simply adopted @cite_5 and changed context vector calculation from the average of all context word vectors to weighted average. Our work has further contributions. First, we clearly point out the two advantages of sense embedding methods: 1) joint learning under the mutli-task learning framework, 2) better knowledge representation by discriminative training, and verify them by experiments. In addition, we adopt various sense embedding methods to show that sense embedding methods are generally promising for WSI, not just one method is better than other methods. Finally, we compare our methods with recent state-of-the-art WSI methods on both supervised and unsupervised metrics.