Return-Path: X-Original-To: apmail-spark-issues-archive@minotaur.apache.org Delivered-To: apmail-spark-issues-archive@minotaur.apache.org Received: from mail.apache.org (hermes.apache.org [140.211.11.3]) by minotaur.apache.org (Postfix) with SMTP id C64E81775C for ; Tue, 16 Feb 2016 04:47:18 +0000 (UTC) Received: (qmail 77704 invoked by uid 500); 16 Feb 2016 04:47:18 -0000 Delivered-To: apmail-spark-issues-archive@spark.apache.org Received: (qmail 77631 invoked by uid 500); 16 Feb 2016 04:47:18 -0000 Mailing-List: contact issues-help@spark.apache.org; run by ezmlm Precedence: bulk List-Help: List-Unsubscribe: List-Post: List-Id: Delivered-To: mailing list issues@spark.apache.org Received: (qmail 77155 invoked by uid 99); 16 Feb 2016 04:47:18 -0000 Received: from arcas.apache.org (HELO arcas) (140.211.11.28) by apache.org (qpsmtpd/0.29) with ESMTP; Tue, 16 Feb 2016 04:47:18 +0000 Received: from arcas.apache.org (localhost [127.0.0.1]) by arcas (Postfix) with ESMTP id 2F16F2C1F5C for ; Tue, 16 Feb 2016 04:47:18 +0000 (UTC) Date: Tue, 16 Feb 2016 04:47:18 +0000 (UTC) From: "Qian Huang (JIRA)" To: issues@spark.apache.org Message-ID: In-Reply-To: References: Subject: [jira] [Commented] (SPARK-4036) Add Conditional Random Fields (CRF) algorithm to Spark MLlib MIME-Version: 1.0 Content-Type: text/plain; charset=utf-8 Content-Transfer-Encoding: 7bit X-JIRA-FingerPrint: 30527f35849b9dde25b450d4833f0394 [ https://issues.apache.org/jira/browse/SPARK-4036?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15148099#comment-15148099 ] Qian Huang commented on SPARK-4036: ----------------------------------- Hi, I have created a spark package, http://spark-packages.org/package/hqzizania/crf-spark. I co-work on it with hujiayin. [~josephkb] It can be said that this package is a spark-based re-implementation of crf++. It has the same limit as crf++ has, like feature generator design and only for "segmenting/labeling sequential data". But it basically meets the requirement of NLP and can run in parallel for big data. Welcome to try it. If you encounter bugs, feel free to submit an issue or pull request. > Add Conditional Random Fields (CRF) algorithm to Spark MLlib > ------------------------------------------------------------ > > Key: SPARK-4036 > URL: https://issues.apache.org/jira/browse/SPARK-4036 > Project: Spark > Issue Type: New Feature > Components: MLlib > Reporter: Guoqiang Li > Assignee: Kai Sasaki > Attachments: CRF_design.1.pdf, crf-spark.zip, dig-hair-eye-train.model, features.hair-eye, sample-input, sample-output > > > Conditional random fields (CRFs) are a class of statistical modelling method often applied in pattern recognition and machine learning, where they are used for structured prediction. > The paper: > http://www.seas.upenn.edu/~strctlrn/bib/PDF/crf.pdf -- This message was sent by Atlassian JIRA (v6.3.4#6332) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscribe@spark.apache.org For additional commands, e-mail: issues-help@spark.apache.org