
Penta and Hexa Valued Representation of Neutrosophic Information
Starting from the primary representation of neutrosophic information, na...
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MUSE: Modularizing Unsupervised Sense Embeddings
This paper proposes to address the word sense ambiguity issue in an unsu...
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Geometry of Polysemy
Vector representations of words have heralded a transformational approac...
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Large deviation principles induced by the Stiefel manifold, and random multidimensional projections
Given an ndimensional random vector X^(n) , for k < n, consider its kd...
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MultiContext Term Embeddings: the Use Case of Corpusbased Term Set Expansion
In this paper, we present a novel algorithm that combines multicontext ...
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Measuring diachronic sense change: new models and Monte Carlo methods for Bayesian inference
In a bagofwords model, the senses of a word with multiple meanings, e....
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Did William Shakespeare and Thomas Kyd Write Edward III?
William Shakespeare is believed to be a significant author in the anonym...
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Automatic Ambiguity Detection
Most work on sense disambiguation presumes that one knows beforehand  e.g. from a thesaurus  a set of polysemous terms. But published lists invariably give only partial coverage. For example, the English word tan has several obvious senses, but one may overlook the abbreviation for tangent. In this paper, we present an algorithm for identifying interesting polysemous terms and measuring their degree of polysemy, given an unlabeled corpus. The algorithm involves: (i) collecting all terms within a kterm window of the target term; (ii) computing the interterm distances of the contextual terms, and reducing the multidimensional distance space to two dimensions using standard methods; (iii) converting the twodimensional representation into radial coordinates and using isotonic/antitonic regression to compute the degree to which the distribution deviates from a singlepeak model. The amount of deviation is the proposed polysemy index
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