Apache lucene scoreing code#

However, most of us wonвЂ™t be in the situation that we can or want to come up with a entirely new scoring model so we are likely using one of the already existing scoring models available in Lucene. Once we have indexed our documents we can proceed to implement our Custom Similarity to incorporate the page rank into the document score. Adding custom boost / score values as IndexDocValues Lucene scoring uses a combination of the Vector Space Model (VSM) of Information Retrieval and the Boolean model to determine how relevant a given Document is. Objects / Expert: Common scoring functionality for different types of queries.

Apache lucene scoreing code#

IndexDocValuesField valuesField = new IndexDocValuesField('pageRank') ДЇigure 1. Here is the source code for .Scorer.java. store the PageRank into a IndexDocValues field, Figure 1. First of all you need to get your data into Lucene ie. toString public String toString() Overrides: toString in class Object Skip. (int doc, float score, int shardIndex) Constructs a ScoreDoc. idf inverse document frequency measure of how.

tf term frequency in document measure of how often a term appears in the document. The factors involved in Lucene's scoring algorithm are as follows: 1. Constructors Constructor and Description ScoreDoc (int doc, float score) Constructs a ScoreDoc. Posted by Kelvin on at 08:46 pm Tagged as: crawling, Lucene / Solr / Elasticsearch / Nutch. Imagine you indexed websites and calculated a pagerank but LuceneвЂ™s index-time boosting mechanism is not flexible enough for you, you could use IndexDocValues to store the page rank. Only set by rge(int, .TopDocs) Constructor Summary. LetвЂ™s look at a specific usecase вЂ“ custom boosting. Now that we have a selection of scoring models and the freedom to extend them we can tailor the scoring function exactly to our needs. Each field could use a slightly different similarity or incorporate additional scoring factors like IndexDocValues. This refactoring basically moved the actual score calculation out of the QueryScorer into a Similarity to allow implementing alternative scoring within a single method.В Lucene 4.0 also comes with a new SimilarityProvider which lets you define a Similarity per field. LuceneвЂ™s central scoring class SimilarityВ has been extended to return dedicated Scorers like ExactDocScorer and SloppyDocScorerВ to calculate the actual score.

With Lucene 4.0 this is history! David NemeskeyВ and Robert Muir added an extensible API as well as index based statistics like Sum of Total Term Frequency or Sum of Document Frequency per FieldВ to provide multiple scoring models. If you found yourself in a situation where another scoring model worked better for your usecase you basically had two choices you either override all existing Scorers in Queries and implement your own model provided you have all the statistics available or you switch to some other search engine providing alternative models or extension points. Prior to this and in all Lucene versions released so far the Vector-Space Model was tightly bound into Lucene. During GoogleSummerOfCode 2011В David Nemeskey, PhD student, proposed to improve LuceneвЂ™s scoring architecture and implement some state-of-the-art ranking models with the new framework.