Learning to Describe Phrases with Local and Global Contexts
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When reading a text, it is common to become stuck on unfamiliar words and phrases, such as polysemous words with novel senses, rarely used idioms, Internet slang, or emerging entities. At first, we attempt to figure out the meaning of those expressions from their context, and ultimately we may consult a dictionary for their definitions. However, rarely-used senses or emerging entities are not always covered by the hand-crafted definitions in existing dictionaries, which can cause problems in text comprehension. This paper undertakes a task of describing (or defining) a given expression (word or phrase) based on its usage contexts, and presents a novel neural-network generator for expressing its meaning as a natural language description. Experimental results on four datasets (including WordNet, Oxford and Urban Dictionaries, non-standard English, and Wikipedia) demonstrate the effectiveness of our method over previous methods for definition generation[Noraset+17; Gadetsky+18; Ni+17].
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