  Distributional models that learn rich semantic word representations are a
success story of recent NLP research. However, developing models that learn
useful representations of phrases and sentences has proved far harder. We
propose using the definitions found in everyday dictionaries as a means of
bridging this gap between lexical and phrasal semantics. Neural language
embedding models can be effectively trained to map dictionary definitions
(phrases) to (lexical) representations of the words defined by those
definitions. We present two applications of these architectures: "reverse
dictionaries" that return the name of a concept given a definition or
description and general-knowledge crossword question answerers. On both tasks,
neural language embedding models trained on definitions from a handful of
freely-available lexical resources perform as well or better than existing
commercial systems that rely on significant task-specific engineering. The
results highlight the effectiveness of both neural embedding architectures and
definition-based training for developing models that understand phrases and
sentences.
