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Posted to issues@opennlp.apache.org by "Boris Galitsky (Created) (JIRA)" <ji...@apache.org> on 2011/11/16 21:09:51 UTC
[jira] [Created] (OPENNLP-387) Demonstration on how similarity
component improves search accuracy. A query is run through Bing search API,
and less relevant hits are sorted out by similarity measure between the
query and the snippet
Demonstration on how similarity component improves search accuracy. A query is run through Bing search API, and less relevant hits are sorted out by similarity measure between the query and the snippet
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Key: OPENNLP-387
URL: https://issues.apache.org/jira/browse/OPENNLP-387
Project: OpenNLP
Issue Type: Improvement
Components: Similarity
Affects Versions: 1.6.0
Reporter: Boris Galitsky
Assignee: Boris Galitsky
Right now we use a fancy domain to demonstrate the usability of similarity component, such as content generation based on web mining. I believe it also makes sense to use a simpler case such as search relevance improvement by use of similarity component. We get search candidates by TF*IDF and then apply OpenNLP parsing and then Similarity component to assess relevance of a snippet to an answer.
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[jira] [Commented] (OPENNLP-387) Demonstration on how similarity
component improves search accuracy. A query is run through Bing search API,
and less relevant hits are sorted out by similarity measure between the
query and the snippet
Posted by "Joern Kottmann (Commented) (JIRA)" <ji...@apache.org>.
[ https://issues.apache.org/jira/browse/OPENNLP-387?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13152743#comment-13152743 ]
Joern Kottmann commented on OPENNLP-387:
----------------------------------------
HitBaseComparable is also Java 6, please change your compiler to Java 5.
> Demonstration on how similarity component improves search accuracy. A query is run through Bing search API, and less relevant hits are sorted out by similarity measure between the query and the snippet
> ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
>
> Key: OPENNLP-387
> URL: https://issues.apache.org/jira/browse/OPENNLP-387
> Project: OpenNLP
> Issue Type: Improvement
> Components: Similarity
> Affects Versions: 1.6.0
> Reporter: Boris Galitsky
> Assignee: Boris Galitsky
> Attachments: patch.OPENNLP-387.txt
>
> Original Estimate: 48h
> Remaining Estimate: 48h
>
> Right now we use a fancy domain to demonstrate the usability of similarity component, such as content generation based on web mining. I believe it also makes sense to use a simpler case such as search relevance improvement by use of similarity component. We get search candidates by TF*IDF and then apply OpenNLP parsing and then Similarity component to assess relevance of a snippet to an answer.
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[jira] [Commented] (OPENNLP-387) Demonstration on how similarity
component improves search accuracy. A query is run through Bing search API,
and less relevant hits are sorted out by similarity measure between the
query and the snippet
Posted by "Joern Kottmann (Commented) (JIRA)" <ji...@apache.org>.
[ https://issues.apache.org/jira/browse/OPENNLP-387?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13152754#comment-13152754 ]
Joern Kottmann commented on OPENNLP-387:
----------------------------------------
Applied your patch.
Also please take care to comply to our code conventions. Instead of tabs we use two spaces to indent. Lines should not be too long. We didn't define a hard limit, but most lines are 80 to 100 chars long.
> Demonstration on how similarity component improves search accuracy. A query is run through Bing search API, and less relevant hits are sorted out by similarity measure between the query and the snippet
> ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
>
> Key: OPENNLP-387
> URL: https://issues.apache.org/jira/browse/OPENNLP-387
> Project: OpenNLP
> Issue Type: Improvement
> Components: Similarity
> Affects Versions: 1.6.0
> Reporter: Boris Galitsky
> Assignee: Boris Galitsky
> Attachments: patch.OPENNLP-387.txt
>
> Original Estimate: 48h
> Remaining Estimate: 48h
>
> Right now we use a fancy domain to demonstrate the usability of similarity component, such as content generation based on web mining. I believe it also makes sense to use a simpler case such as search relevance improvement by use of similarity component. We get search candidates by TF*IDF and then apply OpenNLP parsing and then Similarity component to assess relevance of a snippet to an answer.
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[jira] [Updated] (OPENNLP-387) Demonstration on how similarity
component improves search accuracy. A query is run through Bing search API,
and less relevant hits are sorted out by similarity measure between the
query and the snippet
Posted by "Boris Galitsky (Updated) (JIRA)" <ji...@apache.org>.
[ https://issues.apache.org/jira/browse/OPENNLP-387?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Boris Galitsky updated OPENNLP-387:
-----------------------------------
Attachment: patch.OPENNLP-387.txt
It is now illustrated how to build search engine based on similarity. 3 new tests are added, including the one which goes to Bing for search and then re-sorts results based on similarity. Some fixes for the cases of problematic parsings are done.
> Demonstration on how similarity component improves search accuracy. A query is run through Bing search API, and less relevant hits are sorted out by similarity measure between the query and the snippet
> ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
>
> Key: OPENNLP-387
> URL: https://issues.apache.org/jira/browse/OPENNLP-387
> Project: OpenNLP
> Issue Type: Improvement
> Components: Similarity
> Affects Versions: 1.6.0
> Reporter: Boris Galitsky
> Assignee: Boris Galitsky
> Attachments: patch.OPENNLP-387.txt
>
> Original Estimate: 48h
> Remaining Estimate: 48h
>
> Right now we use a fancy domain to demonstrate the usability of similarity component, such as content generation based on web mining. I believe it also makes sense to use a simpler case such as search relevance improvement by use of similarity component. We get search candidates by TF*IDF and then apply OpenNLP parsing and then Similarity component to assess relevance of a snippet to an answer.
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[jira] [Commented] (OPENNLP-387) Demonstration on how similarity
component improves search accuracy. A query is run through Bing search API,
and less relevant hits are sorted out by similarity measure between the
query and the snippet
Posted by "Joern Kottmann (Commented) (JIRA)" <ji...@apache.org>.
[ https://issues.apache.org/jira/browse/OPENNLP-387?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13152742#comment-13152742 ]
Joern Kottmann commented on OPENNLP-387:
----------------------------------------
Reviewed patch.OPENNLP-387.txt. A couple of files are missing the AL header. All java files must have this header.
I will add them, please ensure to set up your IDE to always create this header, when you create a new file.
> Demonstration on how similarity component improves search accuracy. A query is run through Bing search API, and less relevant hits are sorted out by similarity measure between the query and the snippet
> ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
>
> Key: OPENNLP-387
> URL: https://issues.apache.org/jira/browse/OPENNLP-387
> Project: OpenNLP
> Issue Type: Improvement
> Components: Similarity
> Affects Versions: 1.6.0
> Reporter: Boris Galitsky
> Assignee: Boris Galitsky
> Attachments: patch.OPENNLP-387.txt
>
> Original Estimate: 48h
> Remaining Estimate: 48h
>
> Right now we use a fancy domain to demonstrate the usability of similarity component, such as content generation based on web mining. I believe it also makes sense to use a simpler case such as search relevance improvement by use of similarity component. We get search candidates by TF*IDF and then apply OpenNLP parsing and then Similarity component to assess relevance of a snippet to an answer.
--
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