| Sumario: | Abstract. Building bilingual dictionaries from Wikipedia has been extensively studied in the area of computation linguistics. These dictionaries play a crucial role in Natural Language Processing(NLP) applications like Cross-Lingual Information Retrieval, Machine Translation and Named Entity Recognition. To build these dictionaries, most of the existing approaches use information present in Wikipedia titles, info-boxes and categories. Interestingly, not many use the structural properties of a document like sections, subsections, etc. In this work we exploit the structural properties of documents to build a bilingual English-Hindi dictionary. The main intuition behind this approach is that documents in different languages discussing the same topic are likely to have similar structural elements. Though we present our experiments only for Hindi, our approach is language independent and can be easily extended to other languages. The major contribution of our work is that the dictionary contains translation and transliteration of words which include Named Entities to a large extent. We evaluate our dictionary using manually computed precision. We generated a massive list of 72k tokens using our approach with 0.75 precision.
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