Abstract
We present a method for translating semantic relationships between languages where relationships are defined as pattern clusters. Given a pattern set which represents a semantic relationship, we use the web to extract sample term pairs of this relationship. We automatically translate the obtained term pairs using multilingual dictionaries and disambiguate the translated pairs using web counts. Finally we discover the set of most relevant target language patterns for the given relationship. The obtained pattern set can be utilized for extraction of new relationship examples for the target language. We evaluate our method on 11 diverse target languages. To assess the quality of the discovered relationships, we use an automatically generated cross-lingual SAT analogy test, WordNet relationships, and concept-specific relationships, achieving high precision. The proposed framework allows fully automated cross-lingual relationship mining and construction of multilingual pattern dictionaries without relying on parallel corpora.
| Original language | English |
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| Pages | 241-249 |
| Number of pages | 9 |
| State | Published - 2010 |
| Event | 23rd International Conference on Computational Linguistics, Coling 2010 - Beijing, China Duration: 23 Aug 2010 → 27 Aug 2010 |
Conference
| Conference | 23rd International Conference on Computational Linguistics, Coling 2010 |
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| Country/Territory | China |
| City | Beijing |
| Period | 23/08/10 → 27/08/10 |