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From "ASF GitHub Bot (JIRA)" <j...@apache.org>
Subject [jira] [Commented] (TIKA-2720) A parser to output universal sentence encodings to text
Date Sun, 02 Sep 2018 23:03:00 GMT

    [ https://issues.apache.org/jira/browse/TIKA-2720?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16601698#comment-16601698

ASF GitHub Bot commented on TIKA-2720:

chrismattmann commented on a change in pull request #248: Fix for TIKA-2720 [WIP]
URL: https://github.com/apache/tika/pull/248#discussion_r214555761

 File path: tika-dl/src/test/java/org/apache/tika/dl/text/sentencoder/TFUniSentEncoderTest.java
 @@ -0,0 +1,134 @@
+package org.apache.tika.dl.text.sentencoder;
 Review comment:
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> A parser to output universal sentence encodings to text
> -------------------------------------------------------
>                 Key: TIKA-2720
>                 URL: https://issues.apache.org/jira/browse/TIKA-2720
>             Project: Tika
>          Issue Type: New Feature
>          Components: tika-dl
>            Reporter: Thejan Wijesinghe
>            Priority: Major
>             Fix For: 2.0
> This parser encodes a text into high dimensional vectors that can be used for text classification,
semantic similarity, clustering and other natural language tasks. The model is trained and
optimized for greater-than-word length text, such as sentences, phrases or short paragraphs.
It is trained on a variety of data sources and a variety of tasks with the aim of dynamically
accommodating a wide variety of natural language understanding tasks. The input is variable
length English text and the output is a 512 dimensional vector.

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