Nasreen Abdul Jaleel

Learn More
For the TREC 2004 Novelty track, UMass participated in all four tasks. Although finding relevant sentences was harder this year than last, we continue to show marked improvements over the baseline of calling all sentences relevant, with a variant of tfidf being the most successful approach. We achieve 5–9% improvements over the base-line in locating novel(More)
Out of vocabulary <i>(OOV)</i> words are problematic for cross language information retrieval. One way to deal with OOV words when the two languages have different alphabets, is to <i>transliterate</i> the unknown words, that is, to render them in the orthography of the second language. In the present study, we present a simple statistical technique to(More)
As participants in the TIDES Surprise language exercise, researchers at the University of Massachusetts helped collect Hindi--English resources and developed a cross-language information retrieval system. Components included normalization, stop-word removal, transliteration, structured query translation, and language modeling using a probabilistic(More)
• In the HARD track, we developed document metadata to correspond to query metadata requirements; implemented clarification forms based on query expansion, passage retrieval, and clustering; and retrieved variable length passages deemed most likely to be relevant. This work is discussed at length in Section 1. • In the QA track, we focused on retrieving(More)
  • 1