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) <[EMAIL PROTECTED]> 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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