Abstract. In this paper, we present our Hindi to English and Marathi to English CLIR systems developed as part of our participation in the CLEF 2007 Ad-Hoc Bilingual task. We take a query translation based approach using bi-lingual dictionaries. Query words not found in the dictionary are transliterated using a simple rule based transliteration approach. The resultant transliteration is then compared with the unique words of the corpus to return the 'k' words most similar to the transliterated word. The resulting multiple translation/transliteration choices for each query word are disambiguated using an iterative page-rank style algorithm which, based on term-term co-occurrence statistics, produces the final translated query. Using the above approach, for Hindi, we achieve a Mean Average Precision (MAP) of 0.2366 using title and a MAP of 0.2952 using title and description. For Marathi, we achieve a MAP of 0.2163 using title.
Today, parallel corpus-based systems dominate the transliteration landscape. But the resourcescarce languages do not enjoy the luxury of large parallel transliteration corpus. For these languages, rule-based transliteration is the only viable option. In this article, we show that by properly harnessing the monolingual resources in conjunction with manually created rule base, one can achieve reasonable transliteration performance. We achieve this performance by exploiting the power of Character Sequence Modeling (CSM), which requires only monolingual resources. We present the results of our rule-based system for Hindi to English, English to Hindi, and Persian to English transliteration tasks. We also perform extrinsic evaluation of transliteration systems in the context of Cross Lingual Information Retrieval. Another important contribution of our work is to explain the widely varying accuracy numbers reported in transliteration literature, in terms of the entropy of the language pairs and the datasets involved.
Abstract. Calculational Style of Programming, while very appealing, has several practical difficulties when done manually. Due to the large number of proofs involved, the derivations can be cumbersome and errorprone. To address these issues, we have developed automated theorem provers assisted program and formula transformation rules, which when coupled with the ability to extract context of a subformula, help in shortening and simplifying the derivations. We have implemented this approach in a Calculational Assistant for Programming from Specifications (CAPS). With the help of simple examples, we show how the calculational assistant helps in taking the drudgery out of the derivation process while ensuring correctness.
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