This paper describes an experimental comparison of three unsupervised learning algorithms that distinguish the sense of an ambiguous word in untagged text. The methods described in this paper, McQuitty's similarity analysis, Ward's minimum-variance method, and the EM algorithm, assign each instance of an ambiguous word to a known sense definition based solely on the values of automatically identifiable features in text. These methods and feature sets are found to be more successful in disambiguating nouns rather than adjectives or verbs. Overall, the most accurate of these procedures is McQuitty's similarity analysis in combination with a high dimensional feature set.
An early application of clustering to word--sense disambiguation is described in @cite_29 . There words are represented in terms of the co-occurrence statistics of four letter sequences. This representation uses 97 features to characterize a word, where each feature is a linear combination of letter four-grams formulated by a singular value decomposition of a 5000 by 5000 matrix of letter four-gram co-occurrence frequencies. The weight associated with each feature reflects all usages of the word in the sample. A context vector is formed for each occurrence of an ambiguous word by summing the vectors of the contextual words (the number of contextual words considered in the sum is unspecified). The set of context vectors for the word to be disambiguated are then clustered, and the clusters are manually sense tagged.
@cite_29
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In this paper, we present a novel approach for automatically learning a compact and yet discriminative appearance-based human action model. A video sequence is represented by a bag of spatiotemporal features called video-words by quantizing the extracted 3D interest points (cuboids) from the videos. Our proposed approach is able to automatically discover the optimal number of video-word clusters by utilizing maximization of mutual information(MMI). Unlike the k-means algorithm, which is typically used to cluster spatiotemporal cuboids into video words based on their appearance similarity, MMI clustering further groups the video-words, which are highly correlated to some group of actions. To capture the structural information of the learnt optimal video-word clusters, we explore the correlation of the compact video-word clusters. We use the modified correlogram, which is not only translation and rotation invariant, but also somewhat scale invariant. We extensively test our proposed approach on two publicly available challenging datasets: the KTH dataset and IXMAS multiview dataset. To the best of our knowledge, we are the first to try the bag of video-words related approach on the multiview dataset. We have obtained very impressive results on both datasets.