The task of Word Sense Disambiguation (WSD) consists of associating words in context with their most suitable entry in a pre-defined sense inventory. The de-facto sense inventory for English in WSD is WordNet. For example, given the word “mouse” and the following sentence:
“A mouse consists of an object held in one's hand, with one or more buttons.”
we would assign “mouse” with its electronic device sense (the 4th sense in the WordNet sense inventory).
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Since vectors of the same word type can vary depending on the respective context, they implicitly provide a model for word sense disambiguation (WSD).
In this demonstration we present SupWSD, a Java API for supervised Word Sense Disambiguation (WSD).
In word sense disambiguation (WSD), knowledge-based systems tend to be much more interpretable than knowledge-free counterparts as they rely on the wealth of manually-encoded elements representing word senses, such as hypernyms, usage examples, and images.
We introduce a new task, visual sense disambiguation for verbs: given an image and a verb, assign the correct sense of the verb, i. e., the one that describes the action depicted in the image.
In this paper, we report a knowledge-based method for Word Sense Disambiguation in the domains of biomedical and clinical text.