* the terms of that query (this is correct for some of the queries), * A document may match a multi term query without containing all * a document is multiplied by the boost of that query term query-boost(q). * each query term, hence the contribution of a query term to the score of * At search time users can specify boosts to each query, sub-query, and * the separate additions (or parts) of that field within the document. * and so the boost of that field is the multiplication of the boosts of * The same field can be added to a document during indexing several times, * and in addition to document boost there are also document fields boosts. * field, document length normalization is by the length of the certain field, * Lucene is field based, hence each query term applies to a single * For this, the score of each document is also multiplied by its boost value * important than others, by assigning a document boost. * At indexing, users can specify that certain documents are more * factor is used, which normalizes to a vector equal to or larger * To avoid this problem, a different document length normalization * But for a document which contains no duplicated paragraphs, * especially if that paragraph is made of distinct terms. a document made by duplicating a certain paragraph 10 times, * For some documents removing this info is probably ok, * it removes all document length information. * Normalizing V(d) to the unit vector is known to be problematic in that * Lucene refines VSM score for both search quality and usability: * V(q) by its euclidean norm is normalizing it to a unit vector. * the normalized weighted vectors, in the sense that dividing * Note: the above equation can be viewed as the dot product of * of the weighted query vectors V(q) and V(d): * VSM score of document d for query q is the * number of index documents containing term t. * idf(t) similarly varies with the inverse of the * (when one increases so does the other) and * Tf(t,x) varies with the number of occurrences of term t in x * for given term t and document (or query) x, * but for now, for completion, let's just say that
* Tf and Idf are described in more detail below, * but Tf-idf values are believed to produce search results of high quality, * VSM does not require weights to be Tf-idf values, * where each distinct index term is a dimension, * weighted vectors in a multi-dimensional space, * In VSM, documents and queries are represented as * documents "approved" by BM are scored by VSM. * Vector Space Model (VSM) of Information Retrieval. * Boolean model (BM) of Information Retrieval * (the latter is connected directly with Lucene classes and methods). * from which, finally, evolves Lucene's Practical Scoring Function * then derive from it Lucene's Conceptual Scoring Formula,
* underlying information retrieval models to (efficient) implementation. * The following describes how Lucene scoring evolves from * Introduction To Information Retrieval, Chapter 6. * Overriding computation of these components is a convenient * TFIDFSimilarity defines the components of Lucene scoring. * Implementation of Similarity} with the Vector Space Model.
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