<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>CoordinateKit CRF</title><link>/</link><description>Recent content on CoordinateKit CRF</description><generator>Hugo</generator><language>en-US</language><atom:link href="/index.xml" rel="self" type="application/rss+xml"/><item><title>Getting Started</title><link>/docs/0.1/getting-started/</link><pubDate>Mon, 01 Jan 0001 00:00:00 +0000</pubDate><guid>/docs/0.1/getting-started/</guid><description>&lt;p&gt;Conditional random fields are a type of machine learning that is used to apply a tag to tokens within a sequence when the surrounding tokens influence the tag. Conceptually, CRFs process sequences of tokens and apply tags to each token. A set of features are associated with each token, and the relationships of those features to each other are used to assign scores to each possible tag and assign the best tag to each token. Typical features include the length of the token, punctuation in the token, and tokens from the preceding and following tokens.&lt;/p&gt;</description></item></channel></rss>