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4b shows the evaluation results for using the documents’ top-k terms. We analyzed the distribution of assigned terms for the documents in our collection. Around 10% of the documents in our collection have more than 75 terms assigned. Therefore, we computed the MAP for up to the top-75 terms. Please note, we always used all documents and only limited the number of assigned terms. As in the confidence threshold experiment the best results are achieved if all terms are considered as equally important.
For example, the term beTTagaLu is segmented as ba+e+TTa+ga+La+u. The Unicode encoding supports this segmentation. The number of these segments is the length of the term. Hence, the length of the term beTTagaLu is 6. Now on, whenever we mention length in the paper, we refer to this segment length. Next, for each term we truncate these segments iteratively one by one from the back. Truncation terminates when the term reduces by a truncation factor (α) or the term length reduces below the termination threshold (tt ).
5 for tf-idf. 8 documents. The recall of TrieIR was considerably higher than that of tf-idf and WordPress. The evaluations showed that, TrieIR performed considerably better than the TF-IDF and WordPress searches. The details of the complete evaluation are available in the technical report . 3 Conclusion and Future Work This work gives new set of indexing and retrieval methods for Kannada Unicode text. Understanding of the Kannada morphologies and the Unicode encoding of Kannada text inspired the trie based indexing.
Comunidades de violencia : la persecución de las minorías en la Edad Media by NIRENBERG