KSU KDD: Word Sense Induction by Clustering in Topic Space
Table 2: V-measure and F-score on SemEval-1
['[EMPTY]', 'All', 'Verbs', 'Nouns']
Next, we evaluated the performance of our system on SemEval-1 WSI task data. Since no training data was provided for this task, we used an un-annotated version of the test instances to create the LDA topic model. For each target word (verb or noun), we trained the topic model on its given test instances. Then we used the generated model’s inferencer to find the topics distribution of each one of them. These distributions are then clustered in the topic space using the K-means algorithm and the cosine similarity measure was used to evaluate the distances between these distributions.