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From "jian chen" <chenjian1...@gmail.com>
Subject Re: [jira] Updated: (LUCENE-725) NovelAnalyzer - wraps your choice of Lucene Analyzer and filters out all "boilerplate" text
Date Wed, 21 Mar 2007 02:45:06 GMT
Hi, Mark,

Your program is very helpful. I am trying to understand your code but it
seems would take longer to do that than simply asking you some questions.

1) What is the sliding window used for? It is that the Analyzer remembers
the previously seen N tokens, and N is the window size?

2) As the Analyzer does text parsing, is it that the patterns happened
before (in the previous N token window) is used and any such pattern in the
latest N token window is recognized?

Could you provide some more insights how your algorithm works by removing
duplicate snippets of text from many documents?

Thanks and really appreciate your help.

Jian


On 3/20/07, Mark Harwood (JIRA) <jira@apache.org> wrote:
>
>
>      [
> https://issues.apache.org/jira/browse/LUCENE-725?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel]
>
> Mark Harwood updated LUCENE-725:
> --------------------------------
>
>     Attachment: NovelAnalyzer.java
>
> Updated version can now process any number of documents and remove
> "boilerplate" text tokens such as copyright notices etc.
> New version automatically maintains only a sliding window of content in
> which it searches for duplicate paragraphs enabling it to process unlimited
> numbers of documents.
>
> > NovelAnalyzer - wraps your choice of Lucene Analyzer and filters out all
> "boilerplate" text
> >
> -------------------------------------------------------------------------------------------
> >
> >                 Key: LUCENE-725
> >                 URL: https://issues.apache.org/jira/browse/LUCENE-725
> >             Project: Lucene - Java
> >          Issue Type: New Feature
> >          Components: Analysis
> >            Reporter: Mark Harwood
> >            Priority: Minor
> >         Attachments: NovelAnalyzer.java, NovelAnalyzer.java
> >
> >
> > This is a class I have found to be useful for analyzing small (in the
> hundreds) collections of documents and  removing any duplicate content such
> as standard disclaimers or repeated text in an exchange of  emails.
> > This has applications in sampling query results to identify key phrases,
> improving speed-reading of results with similar content (eg email
> threads/forum messages) or just removing duplicated noise from a search
> index.
> > To be more generally useful it needs to scale to millions of documents -
> in which case an alternative implementation is required. See the notes in
> the Javadocs for this class for more discussion on this
>
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