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The output of a word-sense induction algorithm is a clustering of contexts in which the target word occurs or a clustering of words related to the target word. Three main methods have been proposed in the literature: [1] [2] Context clustering; Word clustering; Co-occurrence graphs
Senseval-2 – evaluated word sense disambiguation systems on three types of tasks (the all-words, lexical-sample and the translation task) Senseval-3 – included tasks for word sense disambiguation, as well as identification of semantic roles, multilingual annotations, logic forms, subcategorization acquisition.
For each context window, MSSA calculates the centroid of each word sense definition by averaging the word vectors of its words in WordNet's glosses (i.e., short defining gloss and one or more usage example) using a pre-trained word-embedding model. These centroids are later used to select the word sense with the highest similarity of a target ...
In linguistics, a word sense is one of the meanings of a word. For example, a dictionary may have over 50 different senses of the word "play", each of these having a different meaning based on the context of the word's usage in a sentence, as follows: We went to see the play Romeo and Juliet at the theater.
In computational linguistics the Yarowsky algorithm is an unsupervised learning algorithm for word sense disambiguation that uses the "one sense per collocation" and the "one sense per discourse" properties of human languages for word sense disambiguation. From observation, words tend to exhibit only one sense in most given discourse and in a ...
Once a vocal Trump critic, David Sacks is set to join the next White House to work on topics at the forefront of tech and global economy policy debates.
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Word2vec is a group of related models that are used to produce word embeddings.These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words.
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