Using latent Dirichlet allocation (LDA) in Apache Lucene
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Use latent Dirichlet allocation (LDA) in Apache Lucene


Stephen W. Thomas <>


lucene-lda allows users to build indexes and perform queries using latent Dirichlet allocation (LDA), an advanced topic model, within the Lucene framework.

lucene-lda was originally developed as part of a research project that compared the performance of the Vector Space Model (VSM), which is Lucene's default IR model, with the performance of LDA. The context was bug localization, where the goal is to determine the similarity between bug reports and source code files. However, lucene-lda is general enough that other contexts can be considered: as long as there are (a) input documents to be searched and (b) queries to be executed.

lucene-lda can work in two different ways:

  • You have already executed LDA on the input corpus, and you feed to the resultant topics and topic memberships to lucene-lda. In this case, lucene-lda will internalize the topics and topic memberships while building the index and executing the queries. (You can even input multiple LDA executions, for example if you have run LDA with different parameters. Here, you specify and query time which set of parameters you would like to use.) Specifically, you need to specify four files, for each parameter LDA parameter combination:
    • vocab.dat: a Vx1 list of terms in the corpus
    • words.dat: a KxV matrix (white-space delimited) that specifies the membership of each word in each topic.
    • files.dat: A Dx3 matrix (white-space) that lists the original file names that LDA was executed on. The first and third columns are ignored; the second column should contain the file name.
    • theta.dat: A DxK matrix (white-space) tat specifies the topic membership of each file in each topic.

In the above, V is the number of terms; K is the number of topics; and D is the number of documents. The order of the terms in vocab.dat should match the order in words.dat; the same is true for the filenames in files.dat and theta.dat.

  • You have not yet run LDA on the input corpus, and you feed only the raw documents to lucene-lda. In this case, lucene-lda will first execute LDA on the documents (using MALLET), and then build the index using the resultant topics and topic memberships. (NOTE: this scenario is not yet implemented.)

In either case, you can specify at query time if you want to use the VSM model or LDA model for executing a particular query. lucene-lda will then return a ranked list of documents that best match the given query.

lucene-lda assumes that any complicated preprocessing of the documents or queries has already been performed. See [] for a nice preprocessor.


The main design goal was to use LDA, not VSM, to compute the similarity between a query and a document. To understand how I achieved this, a bit of background is required:

By default, Lucene uses a slight variant of the Vector Space Model (VSM) to compute the similarity between a query and each document in the index. (There are some bells and whistles that are available, but this is the general idea.) The basic formulation of the similarity comes from the cosine distance between two vectors: one for the document, and one for the query. The numbers in the vectors are the term weights of each term in the document and query.

LDA works very differently. In the LDA model, similarity is computed using conditional probability, which not only involves the terms of the query and document, but also the topics in the query and documents. Basically, we needed a way to store which topics are in each document in Lucene. To do so, we use Payloads to cleverly encode the topics in each document at index time. Then, at query time, we do the following.

  • Determine which topics are in the query, based on the terms in the query
  • Create a Payload query based on these topics
  • Lucene will then find all documents that contain these topics.
  • We ignore the actual relevancy returned by Lucene, and instead use the contents of the Payload to compute the relevancy ourselves, and re-rank the results.

Two notes about similarity:

  • In the above process, performance is actually fast for computing conditional probability, since we are only computing it for those documents that have some of the topics in the query, as opposed to every document in the index.

  • We have created an LDAHelper() class that holds necessary values related to LDA, such as the theta and phi matrices returned by LDA. These values are necessary to compute conditional probability, but are impractical to store along with every document in the index. Currently, these values are written to disk during indexing as a separate "LDA index", and then read into memory again at query time. A potential improvement is to add these matrices to the Lucene index somehow, in a space and time efficient manner.


Use on the command line:

bin/indexDirectory [--help] <inDir> <outIndexDir> <outLDAIndex> [--fileCodes <fileCodes>] [--ldaConfig ldaConfig1,ldaConfig2,...,ldaConfigN ]

bin/queryWithVSM [--help] <indexDir> <queryDir> <resultsDir> [--weightingCode <weightingCode>] [--scoringCode <scoringCode>] 

bin/queryWithLDA [--help] <indexDir> <LDAIndexDir> <queryDir> <resultsDir> [--K <K>] [--scoringCode <scoringCode>]

The above scripts simply call the corresponding Java classes, after setting the classpath as needed.


Simply type:

ant jar
ant test


lucene-lda depends on Apache Lucene, MALLET, Apache Commons, Apache log4j, JSAP, and JUnit. All are included in the lib/ directory.


Copyright (C) 2012 by Stephen W. Thomas