<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>est</author><text>As a native Chinese speaker, this comes so natural.&lt;p&gt;pork = pig + meat&lt;p&gt;So the year of 2015 is the year of ram&amp;#x2F;sheep&amp;#x2F;goat, in Chinese they literally means&lt;p&gt;Ram = male ∪  caprinae&lt;p&gt;sheep = wool ∪ caprinae&lt;p&gt;goat = mountain ∪ caprinae&lt;p&gt;basically, word composition is pretty common in analytic language like Chinese, but kinda new idea in fusional languages like English.</text><parent_chain></parent_chain></comment><story><title>A Word Is Worth a Thousand Vectors</title><url>http://technology.stitchfix.com/blog/2015/03/11/word-is-worth-a-thousand-vectors/</url></story>
<instructions>Your goal is to analyze the following comment and estimate how highly it will be upvoted by the Hacker News community.</instructions><comment><author>imh</author><text>I&amp;#x27;ve always wondered about doing this in non-flat spaces. Like if I add the &amp;quot;7100 miles west&amp;quot; vector to the &amp;quot;California&amp;quot; point, I get Turkmenistan. If I add &amp;quot;7100 miles west&amp;quot; again, I get back near &amp;quot;California.&amp;quot; Similarly, adding the &amp;quot;not&amp;quot; vector twice might get you back where you started in a word embedding. Anyone know if anyone is working on this? It could be tricky because &amp;quot;7100 miles west&amp;quot; lives in the tangent space to the space &amp;quot;California&amp;quot; lives in, but that in itself could be an interesting thing to study in the context of words.</text><parent_chain></parent_chain></comment><story><title>A Word Is Worth a Thousand Vectors</title><url>http://technology.stitchfix.com/blog/2015/03/11/word-is-worth-a-thousand-vectors/</url></story>