Is there a way to itterate through all of the terms held against a particular document in a Lucene.NET index?
Basically I want to be able to retrieve a Document from the Index based on it's ID and then find the frequency with which each Term is used in that Document. Does anyone know a way to do this?
I can find the number of Documents that match a particular Term but not the Terms contained within a particular Document.
Many thanks,
Tim
In Lucene Java, at least, one of the options when indexing a document is storing the term frequency vector. The term frequency vector is simply a list of all the terms in a given field of a document, and how often each of those terms was used. Getting the term frequency vector at runtime involves calling a method in the IndexReader with the Lucene ID of the document in question.
Related
I'm new to AWS CloudSearch and have set up my first domain. It only has one basic text index field.
I've tried a number of simple searches and – more often than not – I get different relevance scores across documents where it seems they should be the same. Even searching for one simple word, which matches exactly once in a number of documents, often produces different scores.
Is this supposed to happen? If so, why?
This is normal. Document length is one factor that will affect this. Think about it: finding your query in a 5 word document indicates a better match than finding your query in a 1000 word document.
The current version of CloudSearch uses Solr/Lucene, an Apache project, so you can dig into the internals to your heart's content if you'd like to learn more. Here is the Similarity which discusses the underlying scoring algorithm in Lucene.
As your app matures, you may want to look into custom ranking of your results. CloudSearch provides this capability as well as a tool for comparing the results according to different rankers. You aren't able to customize the base document relevance score but you can boost it according to different fields, etc.
I am trying to change the scoring in apache lucene 5.3, and for my formula I need the document length (the number of tokens in the document). I understood from answers to similar question, you don't have an easy way to do it. because lucene doesn't keep it at the index. so I thought maybe while indexing I will create an Map from docID to the document length, and then use it in query evaluation. But, I have no idea where I should put this map and where I will update it.
You are exactly right, storing this when the document is indexed is the best approach. The place to store it is in the norm (not to be confused with the queryNorm, that's something different). Norms provide a single value stored with the field, which is made available at query time for scoring.
In your Similarity implementation, this should go into the ComputeNorm method, which exposes the information you need through the FieldInvertState, particularly FieldInvertState.getLength(). Norms are made available at search time through LeafReader.GetNormValues.
If you are extending TFIDFSimilarity, instead, you just need to implement the encodeNormValue, decodeNormValue and lengthNorm methods.
I am working with large number of documents and have a set of predefined categories/tags(could be phrases) that would be present in the text of the documents either in the exact or inexact form.
I want to assign each document to exactly one tag among the tags that is closest to its text.
Please give me some directions as to what should I do to address this problem.
You can look at the lucene search engine that tags the documents while indexing. Northernlight search engine used to do a similar task mentioned by you in their searching methodology. You can have a look at its implementation in order to get an idea.
I have a set of text documents in java . I have to identify the most important document (just as what an expert would identify) using a computer.
eg. I have 10 books on java , the system identifies Java complete reference as the most important document or the most relevant.(based on similarities with the wikipedia page about java)
One method would be to have a reference document and find similarities between this document and the set of documents at hand (as mentioned in the previous example). And provide a result saying the one which has maximum similarity is the most important docuemnt .
I want to identify other more efficient methods of performing this.
please suggest other methods for finding the relevant document (in a unsupervised way if possible) .
I think another mechanism would be, have a dictionary of words and ranking map associated with each document.
For example, in Java complete reference book case, there will be a dictionary of keywords and its ranking.
Java-10
J2ee-5
J2SDK-10
Java5-10 etc.,
Note:If your documents are dynamic streams and names also dynamic, I am not sure how to handle it.
Association mining seems to give good results for retrieving related terms in text corpora. There are several works on this topic including well-known LSA method. The most straightforward way to mine associations is to build co-occurrence matrix of docs X terms and find terms that occur in the same documents most often. In my previous projects I implemented it directly in Lucene by iteration over TermDocs (I got it by calling IndexReader.termDocs(Term)). But I can't see anything similar in Solr.
So, my needs are:
To retrieve the most associated terms within particular field.
To retrieve the term, that is closest to the specified one within particular field.
I will rate answers in the following way:
Ideally I would like to find Solr's component that directly covers specified needs, that is, something to get associated terms directly.
If this is not possible, I'm seeking for the way to get co-occurrence matrix information for specified field.
If this is not an option too, I would like to know the most straightforward way to 1) get all terms and 2) get ids (numbers) of documents these terms occur in.
You can export a Lucene (or Solr) index to Mahout, and then use Latent Dirichlet Allocation. If LDA is not close enough to LSA for your needs, you can just take the correlation matrix from Mahout, and then use Mahout to take the singular value decomposition.
I don't know of any LSA components for Solr.
Since there are still no answers to my questions, I have to write my own thoughts and accept it. Nevertheless, if someone propose better solution, I'll happily accept it instead of mine.
I'll go with co-occurrence matrix, since it is the most principal part of association mining. In general, Solr provides all needed functions for building this matrix in some way, though they are not as efficient as direct access with Lucene. To construct matrix we need:
All terms or at least the most frequent ones, because rare terms won't affect result of association mining by their nature.
Documents where these terms occur, again, at least top documents.
Both these tasks may be easily done with standard Solr components.
To retrieve terms TermsComponent or faceted search may be used. We can get only top terms (by default) or all terms (by setting max number of terms to take, see documentation of particular feature for details).
Getting documents with the term in question is simply search for this term. The weak point here is that we need 1 request per term, and there may be thousands of terms. Another weak point is that neither simple, nor faceted search do not provide information about the count of occurrences of the current term in found document.
Having this, it is easy to build co-occurrence matrix. To mine association it is possible to use other software like Weka or write own implementation of, say, Apriori algorithm.
You can get the count of occurrences of the current term in found document in the following query:
http://ip:port/solr/someinstance/select?defType=func&fl=termfreq(field,xxx),*&fq={!frange l=1}termfreq(field,xxx)&indent=on&q=termfreq(field,xxx)&sort=termfreq(field,xxx) desc&wt=json