text: In addition to the frequency of terms in a document collection, the distribution of terms plays an important role in determining the relevance of documents. In this paper, a new approach for representing term positions in documents is presented. The approach allows an efficient evaluation of term-positional information at query evaluation time. Three applications are investigated: a function-based ranking optimization representing a user-defined document region, a query expansion technique based on overlapping the term distributions in the top-ranked documents, and cluster analysis of terms in documents. Experimental results demonstrate the effectiveness of the proposed approach.
  @cite_4: In most existing retrieval models, documents are scored primarily based on various kinds of term statistics such as within-document frequencies, inverse document frequencies, and document lengths. Intuitively, the proximity of matched query terms in a document can also be exploited to promote scores of documents in which the matched query terms are close to each other. Such a proximity heuristic, however, has been largely under-explored in the literature; it is unclear how we can model proximity and incorporate a proximity measure into an existing retrieval model. In this paper,we systematically explore the query term proximity heuristic. Specifically, we propose and study the effectiveness of five different proximity measures, each modeling proximity from a different perspective. We then design two heuristic constraints and use them to guide us in incorporating the proposed proximity measures into an existing retrieval model. Experiments on five standard TREC test collections show that one of the proposed proximity measures is indeed highly correlated with document relevance, and by incorporating it into the KL-divergence language model and the Okapi BM25 model, we can significantly improve retrieval performance.
  @cite_7: Based on the idea that the closer the query terms in a document are, the more relevant this document is, we propose a mathematical model of information retrieval based on a fuzzy proximity degree of term occurences. Our model is able to deal with Boolean queries, but contrary to the traditional extensions of the basic Boolean information retrieval model, it does not explicitly use a proximity operator. A single parameter allows to control the proximity degree required. With conjunctive queries, setting this parameter to low values requires a proximity at the phrase level and with high values, the required proximity can continuously be relaxed to the sentence or paragraph levels. We conducted some experiments and present the results.
  @cite_3: 
summary: An early approach to apply term-positional data in IR is the work of Attar and Fraenkel @cite_3 . The authors propose different models to generate clusters of terms related to a query (searchonyms) and use these clusters in a local feedback process. In their experiments they confirm that metrical methods based on functions of the distance between terms are superior to methods based merely on weighted co-occurrences of terms. There are several other approaches that use metrical information @cite_7 @cite_4 .