Learn More
Traditional information retrieval models treat the query as a bag of words, assuming that the occurrence of each query term is independent of the positions and occurrences of others. Several of these traditional models have been extended to incorporate positional information, most often through the inclusion of phrases. This has shown improvements in(More)
— This paper addresses a relatively new text categorization problem: classifying a political blog as either 'liberal' or 'conservative', based on its political leaning. Instead of simply using " Bag of Words " features (BoW) as in previous work, we have explored subjectivity manifested in blogs and used subjectivity information thus found to help build(More)
In this paper, we address a relatively new and interesting text categorization problem: classify a political blog as either <i>liberal</i> or <i>conservative</i>, based on its political leaning. Our subjectivity analysis based method is twofold: 1) we identify subjective sentences that contain at least two strong subjective clues based on the General(More)
After more than 30 years of research in information retrieval, the dominant paradigm remains the " bag-of-words " , in which query terms are considered independent of their coocurrences with each other. Although there has been some work on incorporating phrases or other syntactic information into IR, such attempts have given modest and inconsistent(More)
We hypothesize that the variance in volume of high-velocity queries over time can be explained by observing that these queries are formulated in response to events in the world that users are interested in. Based on it, this paper describes a system, ZED, which automatically finds explanations for high velocity queries, by extracting descriptions of(More)
  • 1