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https://issues.apache.org/jira/browse/TIKA-2720?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16601688#comment-16601688
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ASF GitHub Bot commented on TIKA-2720:
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ThejanW commented on issue #248: Fix for TIKA-2720 [WIP]
URL: https://github.com/apache/tika/pull/248#issuecomment-417964712
**The set of test sentences are as follows, (consider the "About.." part as
the topic of the particular sentence group)**
About age
> How old are you?
> What is your age?
> How old did you turn?
> When is your birthday?
About smart phones
> The Samsung Galaxy S10 has the potential to be the most exciting phone of
2019
> Android beats iOS in smartphone loyalty, study finds
> IPhone X includes a 5.8-inch edge-to-edge display which covers the entire
front of the phone.
> Apple became the world’s first trillion-dollar public company
About weather
> With roads covered with slippery snow and ice, can challenge even the most
experienced driver.
> Heavy rain slammed the mid-Atlantic United States on Monday, delaying
flights, forming sinkholes
> News showed, violent floodwaters surging down main Streets
> Recently a lot of hurricanes have hit the US
> Multiple lines of scientific evidence show that the climate system is
warming
About health
> An ounce of prevention is worth a pound of cure
> Green tea contains bioactive compounds that improve health
> Yoga has been shown to help people reduce anxiety
> Is paleo better than keto?
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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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