Translation Acquisition Using Synonym Sets
Daniel Andrade, Masaki Tsuchida, Takashi Onishi and Kai Ishikawa
We propose a new method for translation acquisition which uses a set of
synonyms to acquire translations from comparable corpora.
The motivation is that, given a certain query term, it is often possible for a
user to specify one or more synonyms.
Using the resulting set of query terms has the advantage that we can overcome
the problem that a single query term's context vector does not always reliably
represent a terms meaning due to the context vector's sparsity.
Our proposed method uses a weighted average of the synonyms' context vectors,
that is derived by inferring the mean vector of the von Mises-Fisher
We evaluate our method, using the synsets from the cross-lingually aligned
Japanese and English WordNet.
The experiments show that our proposed method significantly improves
translation accuracy when compared to a previous method for smoothing context
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