Abstract
This paper proposes a simple test for compositionality (i.e., literal usage) of a word or phrase in a context-specific way. The test is computationally simple, relying on no external resources and only uses a set of trained word vectors. Experiments show that the proposed method is competitive with state of the art and displays high accuracy in context-specific compositionality detection of a variety of natural language phenomena (idiomaticity, sarcasm, metaphor) for different datasets in multiple languages. The key insight is to connect compositionality to a curious geometric property of word embeddings, which is of independent interest.
Original language | English (US) |
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Pages | 3202-3208 |
Number of pages | 7 |
State | Published - 2017 |
Event | 31st AAAI Conference on Artificial Intelligence, AAAI 2017 - San Francisco, United States Duration: Feb 4 2017 → Feb 10 2017 |
Other
Other | 31st AAAI Conference on Artificial Intelligence, AAAI 2017 |
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Country/Territory | United States |
City | San Francisco |
Period | 2/4/17 → 2/10/17 |
ASJC Scopus subject areas
- Artificial Intelligence