  Cross-Language Information Retrieval (CLIR) has become an important problem
to solve in the recent years due to the growth of content in multiple languages
in the Web. One of the standard methods is to use query translation from source
to target language. In this paper, we propose an approach based on word
embeddings, a method that captures contextual clues for a particular word in
the source language and gives those words as translations that occur in a
similar context in the target language. Once we obtain the word embeddings of
the source and target language pairs, we learn a projection from source to
target word embeddings, making use of a dictionary with word translation
pairs.We then propose various methods of query translation and aggregation. The
advantage of this approach is that it does not require the corpora to be
aligned (which is difficult to obtain for resource-scarce languages), a
dictionary with word translation pairs is enough to train the word vectors for
translation. We experiment with Forum for Information Retrieval and Evaluation
(FIRE) 2008 and 2012 datasets for Hindi to English CLIR. The proposed word
embedding based approach outperforms the basic dictionary based approach by 70%
and when the word embeddings are combined with the dictionary, the hybrid
approach beats the baseline dictionary based method by 77%. It outperforms the
English monolingual baseline by 15%, when combined with the translations
obtained from Google Translate and Dictionary.
