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cost saved by the free queries and relevance judgments may make it worthwhile for you.
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NOTE
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The above bullet points are paraphrased from a presentation given by Grant Ingersoll at ApacheCon 2007. Although this information is quite similar to that given on the TREC website, we want to make sure to recognize him.
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Lucene includes utility classes in the contribution package, which we ll be discussing in chapter 13, to utilize TREC information to judge system relevance. You could utilize this to help with generating queries for your application. The benchmark tools are located at lucene_install_directory/contrib/benchmark. You ll have to unjar the documentation in the lucene-benchmark-javadoc.jar file. Once you have this uncompressed, start by reading the org.apache.lucene.benchmark.quality package file. It contains a complete example of how to quantify search quality. A discussion of these utilities was absent in the first edition of Lucene in Action but is included as one of the appendixes in the second edition due out in March 2009. Information retrieval systems are optimized quantitatively by continuously measuring the effect of system changes through the previous procedures. Once this process is completed, you might ask whether there is any other way that relevant document frequency can be enhanced while still keeping the retrieved document count constant. The document feedback process was formulated to help answer this problem. Basically, it allows the user to determine what is relevant and what is not. Let s examine that process now.
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12.4.3 Document feedback: tell me what you want!
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The process of increasing the relevance of returned documents is known as relevance or document feedback. This methodology, available since the 1960s, assumes that since users usually know little about the details of a particular document repository s makeup, they have difficulty assembling an initial query that produces the results they re looking for. Think about it. When you call up your favorite search engine in your browser to search for a topic, do you really have any idea what s on the web Because of this user-repository mismatch, the initial query should be treated as only a preliminary step in obtaining useful information. The user can then examine the results of this first-round query. Terms from documents that are judged relevant can then be reformulated and applied to new, additional queries, thereby increasing the relevance of the newly returned documents. At the same time, terms in documents considered less relevant can be downplayed in the new queries. The effect of this process after several iterations is to cause more relevant documents to be returned. An alternative to this manual intervention is known as pseudo or blind relevance feedback. This method does normal retrieval to find an initial set of documents and automatically considers a subset of a certain number of the top documents returned
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as relevant. Let s say that the top four returned documents in each query round are to be considered relevant documents. The top three terms from each of these documents are to be utilized in the additional queries, and this entire process is to be repeated for a certain number of iterations. At the end of these iterations, the results should be sufficient for the user. Lucene is capable of emulating this process. Through the use of term vectors, a developer can examine individual terms in documents returned from a query and develop a framework to query for more documents that contain those terms. Term vectors are collections of term-frequency pairs. To utilize term vectors, you must enable them during indexing so they are stored with documents. To enable them, the org.apache.lucene.document.Field class has several overloaded constructors that turn on the storage of term vectors:
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Field(String name, Reader reader, Field.TermVector termVector) Field(String name, String value, Field.Store store, Field.Index index, Field.TermVector termVector) Field(String name, TokenStream tokenStream, Field.TermVector termVector)
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The org.apache.lucene.document.Field.TermVector class contains the five static values that termVector can have in these methods. These values are given in table 12.4.
Table 12.4 The Field.TermVector values and their definition Value Definition Store the term vectors of each document. Do not store term vectors. Store the term vector plus token offset information. Store the term vector plus token position information. Store the term vector plus token position and offset information.
Field.TermVector.YES Field.TermVector.NO Field.TermVector.WITH_OFFSETS Field.TermVector.WITH_POSITIONS Field.TermVector.WITH_POSITIONS_OFFSETS
Notice that in addition to being able to store term frequency counts, it is also possible to store a term s position information and offset information. We ll talk about these shortly. Let s look at what term vectors can do for us, by examining the additional information we can retrieve from a result. The code in listing 12.16 shows not only the annotation declaration inside the ElectricalProperties class but also the code that builds the index consisting of ElectricalProperties documents.
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