270,828 research outputs found
Discriminating word senses with tourist walks in complex networks
Patterns of topological arrangement are widely used for both animal and human
brains in the learning process. Nevertheless, automatic learning techniques
frequently overlook these patterns. In this paper, we apply a learning
technique based on the structural organization of the data in the attribute
space to the problem of discriminating the senses of 10 polysemous words. Using
two types of characterization of meanings, namely semantical and topological
approaches, we have observed significative accuracy rates in identifying the
suitable meanings in both techniques. Most importantly, we have found that the
characterization based on the deterministic tourist walk improves the
disambiguation process when one compares with the discrimination achieved with
traditional complex networks measurements such as assortativity and clustering
coefficient. To our knowledge, this is the first time that such deterministic
walk has been applied to such a kind of problem. Therefore, our finding
suggests that the tourist walk characterization may be useful in other related
applications
Word Sense Determination from Wikipedia Data Using Neural Networks
Many words have multiple meanings. For example, “plant” can mean a type of living organism or a factory. Being able to determine the sense of such words is very useful in natural language processing tasks, such as speech synthesis, question answering, and machine translation. For the project described in this report, we used a modular model to classify the sense of words to be disambiguated. This model consisted of two parts: The first part was a neural-network-based language model to compute continuous vector representations of words from data sets created from Wikipedia pages. The second part classified the meaning of the given word without explicitly knowing what the meaning is. In this unsupervised word sense determination task, we did not need human-tagged training data or a dictionary of senses for each word. We tested the model with some naturally ambiguous words, and compared our experimental results with the related work by Schütze in 1998. Our model achieved similar accuracy as Schütze’s work for some words
Computational Approaches to Measuring the Similarity of Short Contexts : A Review of Applications and Methods
Measuring the similarity of short written contexts is a fundamental problem
in Natural Language Processing. This article provides a unifying framework by
which short context problems can be categorized both by their intended
application and proposed solution. The goal is to show that various problems
and methodologies that appear quite different on the surface are in fact very
closely related. The axes by which these categorizations are made include the
format of the contexts (headed versus headless), the way in which the contexts
are to be measured (first-order versus second-order similarity), and the
information used to represent the features in the contexts (micro versus macro
views). The unifying thread that binds together many short context applications
and methods is the fact that similarity decisions must be made between contexts
that share few (if any) words in common.Comment: 23 page
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