Algorithm

Natural language

Linguistics

Philosophy

Machine translation

Biology

Identification (biology)

Interpretation (philosophy)

Word-sense disambiguation

Economics

Computer science

Botany

Embedding

WordNet

Natural language processing

Word embedding

Task (project management)

Literal (mathematical logic)

Management

Artificial intelligence

Sentence

Programming language

Word (group theory)

Metaphor

Speech recognition

Preprocessor

Metaphoric expressions are widespread in natural language, posing a significant challenge for various natural language processing tasks such as Machine Translation. Current word embedding based metaphor identification models cannot identify the exact metaphorical words within a sentence. In this paper, we propose an unsupervised learning method that identifies and interprets metaphors at word-level without any preprocessing, outperforming strong baselines in the metaphor identification task. Our model extends to interpret the identified metaphors, paraphrasing them into their literal counterparts, so that they can be better translated by machines. We evaluated this with two popular translation systems for English to Chinese, showing that our model improved the systems significantly.

Word Embedding and WordNet Based Metaphor Identification and Interpretation