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Posted to commits@uima.apache.org by sc...@apache.org on 2009/09/22 20:56:38 UTC
svn commit: r817768 [1/2] - in /incubator/uima/sandbox/trunk/Tagger: ./
docs/ docs/html/ docs/html/hmmTaggerUsersGuide/
docs/html/hmmTaggerUsersGuide/css/ docs/html/images/
docs/html/images/callouts/ docs/html/images/hmmTaggerUsersGuide/ docs/pdf/
Author: schor
Date: Tue Sep 22 18:56:30 2009
New Revision: 817768
URL: http://svn.apache.org/viewvc?rev=817768&view=rev
Log:
UIMA-1583 add new property needed for doc build, save docs for website, update pom to share more from parents
Added:
incubator/uima/sandbox/trunk/Tagger/docs/
incubator/uima/sandbox/trunk/Tagger/docs/html/
incubator/uima/sandbox/trunk/Tagger/docs/html/hmmTaggerUsersGuide/
incubator/uima/sandbox/trunk/Tagger/docs/html/hmmTaggerUsersGuide/css/
incubator/uima/sandbox/trunk/Tagger/docs/html/hmmTaggerUsersGuide/css/stylesheet-html.css
incubator/uima/sandbox/trunk/Tagger/docs/html/hmmTaggerUsersGuide/hmmTaggerUsersGuide.html
incubator/uima/sandbox/trunk/Tagger/docs/html/images/
incubator/uima/sandbox/trunk/Tagger/docs/html/images/blank.png (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/callouts/
incubator/uima/sandbox/trunk/Tagger/docs/html/images/callouts/1.gif (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/callouts/1.png (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/callouts/10.gif (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/callouts/10.png (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/callouts/11.gif (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/callouts/11.png (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/callouts/12.gif (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/callouts/12.png (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/callouts/13.gif (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/callouts/13.png (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/callouts/14.gif (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/callouts/14.png (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/callouts/15.gif (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/callouts/15.png (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/callouts/2.gif (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/callouts/2.png (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/callouts/3.gif (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/callouts/3.png (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/callouts/4.gif (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/callouts/4.png (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/callouts/5.gif (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/callouts/5.png (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/callouts/6.gif (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/callouts/6.png (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/callouts/7.gif (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/callouts/7.png (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/callouts/8.gif (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/callouts/8.png (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/callouts/9.gif (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/callouts/9.png (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/caution.gif (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/caution.png (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/caution.svg
incubator/uima/sandbox/trunk/Tagger/docs/html/images/caution.tif (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/draft.png (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/hmmTaggerUsersGuide/
incubator/uima/sandbox/trunk/Tagger/docs/html/images/home.gif (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/home.png (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/home.svg
incubator/uima/sandbox/trunk/Tagger/docs/html/images/important.gif (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/important.png (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/important.svg
incubator/uima/sandbox/trunk/Tagger/docs/html/images/important.tif (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/next.gif (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/next.png (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/next.svg
incubator/uima/sandbox/trunk/Tagger/docs/html/images/note.gif (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/note.png (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/note.svg
incubator/uima/sandbox/trunk/Tagger/docs/html/images/note.tif (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/prev.gif (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/prev.png (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/prev.svg
incubator/uima/sandbox/trunk/Tagger/docs/html/images/tip.gif (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/tip.png (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/tip.svg
incubator/uima/sandbox/trunk/Tagger/docs/html/images/tip.tif (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/toc-blank.png (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/toc-minus.png (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/toc-plus.png (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/up.gif (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/up.png (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/up.svg
incubator/uima/sandbox/trunk/Tagger/docs/html/images/warning.gif (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/warning.png (with props)
incubator/uima/sandbox/trunk/Tagger/docs/html/images/warning.svg
incubator/uima/sandbox/trunk/Tagger/docs/html/images/warning.tif (with props)
incubator/uima/sandbox/trunk/Tagger/docs/pdf/
incubator/uima/sandbox/trunk/Tagger/docs/pdf/hmmTaggerUsersGuide.pdf (with props)
Modified:
incubator/uima/sandbox/trunk/Tagger/build_documentation.xml
incubator/uima/sandbox/trunk/Tagger/pom.xml
Modified: incubator/uima/sandbox/trunk/Tagger/build_documentation.xml
URL: http://svn.apache.org/viewvc/incubator/uima/sandbox/trunk/Tagger/build_documentation.xml?rev=817768&r1=817767&r2=817768&view=diff
==============================================================================
--- incubator/uima/sandbox/trunk/Tagger/build_documentation.xml (original)
+++ incubator/uima/sandbox/trunk/Tagger/build_documentation.xml Tue Sep 22 18:56:30 2009
@@ -24,6 +24,7 @@
<project name="HMM Tagger User's Guide" default="all" basedir=".">
<property name="book_name" value="hmmTaggerUsersGuide"/>
+ <property name="artifactId" value="Tagger"/>
<import file="${basedir}/../SandboxDocs/sandbox_build.xml"/>
Added: incubator/uima/sandbox/trunk/Tagger/docs/html/hmmTaggerUsersGuide/css/stylesheet-html.css
URL: http://svn.apache.org/viewvc/incubator/uima/sandbox/trunk/Tagger/docs/html/hmmTaggerUsersGuide/css/stylesheet-html.css?rev=817768&view=auto
==============================================================================
--- incubator/uima/sandbox/trunk/Tagger/docs/html/hmmTaggerUsersGuide/css/stylesheet-html.css (added)
+++ incubator/uima/sandbox/trunk/Tagger/docs/html/hmmTaggerUsersGuide/css/stylesheet-html.css Tue Sep 22 18:56:30 2009
@@ -0,0 +1,302 @@
+/*
+ Licensed to the Apache Software Foundation (ASF) under one
+ or more contributor license agreements. See the NOTICE file
+ distributed with this work for additional information
+ regarding copyright ownership. The ASF licenses this file
+ to you under the Apache License, Version 2.0 (the
+ "License"); you may not use this file except in compliance
+ with the License. You may obtain a copy of the License at
+
+ http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing,
+ software distributed under the License is distributed on an
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+*/
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Added: incubator/uima/sandbox/trunk/Tagger/docs/html/hmmTaggerUsersGuide/hmmTaggerUsersGuide.html
URL: http://svn.apache.org/viewvc/incubator/uima/sandbox/trunk/Tagger/docs/html/hmmTaggerUsersGuide/hmmTaggerUsersGuide.html?rev=817768&view=auto
==============================================================================
--- incubator/uima/sandbox/trunk/Tagger/docs/html/hmmTaggerUsersGuide/hmmTaggerUsersGuide.html (added)
+++ incubator/uima/sandbox/trunk/Tagger/docs/html/hmmTaggerUsersGuide/hmmTaggerUsersGuide.html Tue Sep 22 18:56:30 2009
@@ -0,0 +1,547 @@
+<html><head>
+ <meta http-equiv="Content-Type" content="text/html; charset=ISO-8859-1">
+ <title>Tagger Annotator Documentation</title><link rel="stylesheet" href="css/stylesheet-html.css" type="text/css"><meta name="generator" content="DocBook XSL Stylesheets V1.72.0"></head><body bgcolor="white" text="black" link="#0000FF" vlink="#840084" alink="#0000FF"><div class="book" lang="en" id="d0e2"><div class="titlepage"><div><div><h1 class="title"><a name="d0e2"></a>Tagger Annotator Documentation</h1></div><div><div class="authorgroup"><h3 class="corpauthor">Authors: The Apache UIMA Development Community</h3></div></div><div><span class="productname">Apache UIMA Sandbox<br></span></div><div><p class="releaseinfo">Version 2.3.0</p></div><div><p class="copyright">Copyright © 2008, 2009 The Apache Software Foundation</p></div><div><div class="legalnotice"><a name="d0e15"></a><p> </p><p><b>Incubation Notice and Disclaimer. </b>Apache UIMA is an effort undergoing incubation at the Apache Software Foundation (ASF).
+ Incubation is required of all newly accepted projects until a further review indicates that
+ the infrastructure, communications, and decision making process have stabilized in a manner
+ consistent with other successful ASF projects. While incubation status is not necessarily
+ a reflection of the completeness or stability of the code,
+ it does indicate that the project has yet to be fully endorsed by the ASF.</p><p> </p><p> </p><p><b>License and Disclaimer. </b>The ASF licenses this documentation
+ to you under the Apache License, Version 2.0 (the
+ "License"); you may not use this documentation except in compliance
+ with the License. You may obtain a copy of the License at
+
+ </p><div class="blockquote"><blockquote class="blockquote"><p>
+ <a xmlns:xlink="http://www.w3.org/1999/xlink" href="http://www.apache.org/licenses/LICENSE-2.0" target="_top">http://www.apache.org/licenses/LICENSE-2.0</a>
+ </p></blockquote></div><p>
+
+ Unless required by applicable law or agreed to in writing,
+ this documentation and its contents are distributed under the License
+ on an
+ "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+ KIND, either express or implied. See the License for the
+ specific language governing permissions and limitations
+ under the License.
+ </p><p> </p><p> </p><p><b>Trademarks. </b>All terms mentioned in the text that are known to be trademarks or
+ service marks have been appropriately capitalized. Use of such terms
+ in this book should not be regarded as affecting the validity of the
+ the trademark or service mark.
+ </p></div></div></div><hr></div><div class="toc"><p><b>Table of Contents</b></p><dl><dt><span class="preface"><a href="#sandbox.tagger.introduction">Introduction</a></span></dt><dt><span class="chapter"><a href="#sandbox.tagger.prerequisites">1. Prerequisites</a></span></dt><dt><span class="chapter"><a href="#sandbox.tagger.processingOverview">2. Processing Overview</a></span></dt><dt><span class="chapter"><a href="#sandbox.tagger.annotatorDescriptor">3. Annotator Descriptor</a></span></dt><dd><dl><dt><span class="section"><a href="#sandbox.tagger.annotatorDescriptor.configParam">3.1. Configuration Parameters</a></span></dt><dt><span class="section"><a href="#sandbox.tagger.annotatorDescriptor.capabilities">3.2. Capabilities</a></span></dt></dl></dd><dt><span class="chapter"><a href="#sandbox.tagger.unittest">4. Functionality Test</a></span></dt><dt><span class="chapter"><a href="#sandbox.tagger.tagger">5. Overview of the Tagger package</a></span></dt><dt><span class
="chapter"><a href="#sandbox.tagger.training">6. Training Own Models</a></span></dt><dt><span class="chapter"><a href="#sandbox.tagger.evaluation">7. Evaluation</a></span></dt><dt><span class="appendix"><a href="#sandbox.tagger.theory">A. Theory Behind</a></span></dt><dt><span class="glossary"><a href="#d0e638">Glossary</a></span></dt><dt><span class="bibliography"><a href="#d0e661">Bibliography</a></span></dt></dl></div><div class="preface" lang="en" id="sandbox.tagger.introduction"><div class="titlepage"><div><div><h2 class="title"><a name="sandbox.tagger.introduction"></a>Introduction</h2></div></div></div><p>
+ Tagger Annotator is an Apache UIMA statistical analysis
+ engine that annotates tokens with corresponding grammatical
+ types (parts of speech, or just POS). The tagger is a
+ standard hidden Markov model (HMM) tagger.
+ </p></div><div class="chapter" lang="en" id="sandbox.tagger.prerequisites"><div class="titlepage"><div><div><h2 class="title"><a name="sandbox.tagger.prerequisites"></a>Chapter 1. Prerequisites</h2></div></div></div><p>
+ The UIMA HMM Tagger annotator assumes that sentences and
+ tokens have already been annotated in the CAS with Sentence
+ and Token annotations respectively (see e.g.
+ <code class="code">Whitespace Tokenizer Annotator</code>
+ ).
+
+ Further, the tagger requires a parameter file which
+ specifies a number of necessary parameters for tagging
+ procedure (see
+ <a href="#sandbox.tagger.annotatorDescriptor.configParam" title="3.1. Configuration Parameters">Section 3.1, “Configuration Parameters”</a>
+ ).
+
+ Two trained models for English and German are included in
+ the package (in the
+ <code class="code">resources</code>
+ folder). Other models can be trained outside of the UIMA
+ framework (see
+ <a href="#sandbox.tagger.training" title="Chapter 6. Training Own Models">Chapter 6, <i xmlns:xlink="http://www.w3.org/1999/xlink">Training Own Models</i></a>
+ ).
+ </p></div><div class="chapter" lang="en" id="sandbox.tagger.processingOverview"><div class="titlepage"><div><div><h2 class="title"><a name="sandbox.tagger.processingOverview"></a>Chapter 2. Processing Overview</h2></div></div></div><p>
+ The algorithm iterates over sentences and tokens in turn to
+ accumulate a list of words. These are then sent to a
+ processing engine of HMM tagger. For each
+ <code class="code">Token</code>
+ , the
+ <code class="code">posTag</code>
+ field is updated with the corresponding part of speech (e.g.
+ <code class="code">posTag = "NN"</code>
+ where
+ <code class="code">NN</code>
+ stands for
+ <span class="emphasis"><em>common noun</em></span>
+ ).
+ </p></div><div class="chapter" lang="en" id="sandbox.tagger.annotatorDescriptor"><div class="titlepage"><div><div><h2 class="title"><a name="sandbox.tagger.annotatorDescriptor"></a>Chapter 3. Annotator Descriptor</h2></div></div></div><p>
+ Two descriptors are employed to configure tagger's
+ functionality:
+ </p><div class="itemizedlist"><ul type="disc"><li><p>
+ <code class="code">HmmTagger.xml</code>
+ - is a primitive analysis engine descriptor,
+ which defines the tagger basic functionality and
+ can be combined in an aggregate analysis engine
+ with an arbitrary tokenizer. This descriptor
+ cannot be used on itself as the tagger alone
+ does not perfom tokenization.
+ </p></li><li><p>
+ <code class="code">HmmTaggerTAE.xml</code>
+ - is an aggregate analysis engine whose only
+ function is to combine UIMA
+ <code class="code">Whitespace Tokenizer Annotator</code>
+ with
+ <code class="code">HMM Tagger Annotator</code>
+ and is thereby a "ready to use" tagging
+ descriptor.
+ </p></li></ul></div><p>
+ </p><div class="section" lang="en"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="sandbox.tagger.annotatorDescriptor.configParam"></a>3.1. Configuration Parameters</h2></div></div></div><p>
+ The HMM tagger annotator (
+ <code class="code">HmmTagger.xml</code>
+ ) requires the following configuration parameters:
+ </p><p>
+ </p><div class="itemizedlist"><ul type="disc"><li><p>
+ <code class="code">NGRAM_SIZE</code>
+ - this parameter is an Integer, defining
+ whether a bi- or trigram model should be
+ used for tagging (default is N=3).
+ </p><pre class="programlisting"><span class="emphasis"><em> <configurationParameters>
+ <configurationParameter>
+ <name>NGRAM_SIZE</name>
+ <type>Integer</type>
+ <multiValued>false</multiValued>
+ <mandatory>true</mandatory>
+ </configurationParameter>
+ </configurationParameters>
+ <configurationParameterSettings>
+ <nameValuePair>
+ <name>NGRAM_SIZE</name>
+ <value>
+ <integer>3</integer>
+ </value>
+ </nameValuePair>
+ </configurationParameterSettings></em></span></pre><p>
+ </p></li><li><p>
+ <code class="code">ModelFile</code>
+ - binary file containing the statistical model which should be used for tagging is defined as an external resource
+</p><pre class="programlisting"><span class="emphasis"><em>
+ <externalResources>
+ <externalResource>
+ <name>ModelFile</name>
+ <description>HMM Tagger model file</description>
+ <fileResourceSpecifier>
+ <fileUrl>file:german/TuebaModel.dat</fileUrl>
+ </fileResourceSpecifier>
+ <implementationName>org.apache.uima.examples.tagger.ModelResource</implementationName>
+ </externalResource>
+ </externalResources></em></span></pre><p>
+
+Thus, one can easily use a different model by changing the <code class="code">fileUrl</code> line:
+<code class="code">file:german/TuebaModel.dat</code>.
+(NB. <span class="emphasis"><em>New models must be located in the <code class="code">resources</code> folder</em></span>.)
+After these two parameters have been set, the tagger is ready to use.
+
+ </p></li></ul></div><p>
+ </p></div><div class="section" lang="en"><div class="titlepage"><div><div><h2 class="title" style="clear: both"><a name="sandbox.tagger.annotatorDescriptor.capabilities"></a>3.2. Capabilities</h2></div></div></div><p>
+ As the tagger inherits tokenization indexes from the CAS,
+ <code class="code">uima.SentenceAnnotation</code> and <code class="code">uima.TokenAnnotation</code> with their
+ <code class="code">begin</code> and <code class="code">end</code> features respectively have to be defined as
+ input capabilities in the HMM Tagger annotator descriptor. <code class="code">Token</code> receives
+ also an additional <code class="code">posTag</code> feature as an output capability.
+ </p><p>
+ </p><pre class="programlisting"><span class="emphasis"><em><capabilities>
+ <capability>
+ <inputs>
+ <type>org.apache.uima.TokenAnnotation</type>
+ <type allAnnotatorFeatures="true">org.apache.uima.SentenceAnnotation</type>
+ <feature>org.apache.uima.TokenAnnotation:end</feature>
+ <feature>org.apache.uima.TokenAnnotation:begin</feature>
+ </inputs>
+ <outputs>
+ <type>org.apache.uima.TokenAnnotation</type>
+ <feature>org.apache.uima.TokenAnnotation:posTag</feature>
+ <feature>org.apache.uima.TokenAnnotation:end</feature>
+ <feature>org.apache.uima.TokenAnnotation:begin</feature>
+ </outputs>
+ </capability>
+ </capabilities></em></span></pre><p>
+ </p></div></div><div class="chapter" lang="en" id="sandbox.tagger.unittest"><div class="titlepage"><div><div><h2 class="title"><a name="sandbox.tagger.unittest"></a>Chapter 4. Functionality Test</h2></div></div></div><p>
+ The <code class="code">TaggerTest</code> is a JUnit test file (available in the <code class="code">test</code> folder),
+ which provides an opportunity to test provided models for English and German,
+ as well as the basic functionality of the tagger. In order to check whether
+ the tagger's configuration is correct, just run this file as JUnit Test and you should get the following output:
+
+ </p><pre class="programlisting">Tesing German Model...
+The used model is:resources/german/TuebaModel.dat
+61646 distinct words in the model
+Number of part-of-speech tags used: 54
+These are: [$(, $,, $., ADJA, ADJD, ADV, APPO, APPR, APPRART, APZR, ART, CARD, ... ]
+Testing German trigram tagger..
+[Jerry, liebt, Wansley, .]
+expected: [NE, VVFIN, NE, $.]
+tagger output: [NE, VVFIN, NE, $.]
+Very Good!
+==========================================================
+Tesing English Model...
+The used model is:resources/english/BrownModel.dat
+56012 distinct words in the model
+Number of part-of-speech tags used: 473
+These are: [', '', (, ), *, ,, --, ., :, ``, abl, abn, abx, ap, ap$, at, be, bed, ...]
+Testing English trigram tagger...
+[Jerry, loves, Wansley, .]
+expected: [np, vbz, np, .]
+tagger output: [np, vbz, np, .]
+Very Good!</pre><p>
+
+ </p></div><div class="chapter" lang="en" id="sandbox.tagger.tagger"><div class="titlepage"><div><div><h2 class="title"><a name="sandbox.tagger.tagger"></a>Chapter 5. Overview of the Tagger package</h2></div></div></div><p>
+ The package <code class="code">org.apache.uima.examples.tagger</code> contains:
+ </p><div class="itemizedlist"><ul type="disc"><li><p>
+ two interfaces:
+ </p><div class="orderedlist"><ol type="1"><li><p>
+ <code class="code">IModelResource</code>
+ - model resource interface
+ </p></li><li><p>
+ <code class="code">Tagger</code>
+ - general tagger interface, in case one would want to integrate further tagger types.
+ </p></li></ol></div><p>
+
+ </p></li><li><p> three classes:
+ </p><div class="orderedlist"><ol type="1"><li><p>
+ <code class="code">HMMTagger</code>
+ - hidden Markov model tagger for UIMA, that is using Viterbi algorithm to compute the most
+ probable part-of-speech sequence for a given list of tokens.
+ </p></li><li><p>
+ <code class="code">Viterbi</code>
+ - implementation of the Viterbi Algorithm. This class makes up the core of the tagger.
+ </p></li><li><p>
+ <code class="code">ModelResource.java</code>
+ - implementation of the <code class="code">IModelResource</code>
+ </p></li></ol></div><p>
+
+ </p></li></ul></div><p>
+ </p></div><div class="chapter" lang="en" id="sandbox.tagger.training"><div class="titlepage"><div><div><h2 class="title"><a name="sandbox.tagger.training"></a>Chapter 6. Training Own Models</h2></div></div></div><p>
+ Though we decide not to include training directly into UIMA framework, one can easily
+ train other models for different pre-annotated corpora outside of the UIMA using <code class="code">ModelGeneration</code> class,
+ available in the subpackage <code class="code">org.apache.uima.examples.tagger.trainAndTest</code>.
+ This subpackage includes some further files needed for training of own models:
+
+
+ </p><div class="itemizedlist"><ul type="disc"><li><p>
+ <code class="code">MappingInterface</code>
+ - defines mapping for a tagset. For example, one may wish to map a more detailed tagset
+ to a less distinctive one (i.e. tell a program to tag all verbs as just <code class="code">VERB</code>
+ instead of differentiating between <code class="code">verb infinitive</code>, <code class="code">verb imperative</code>, etc.
+
+ Two sample implementations for <code class="code">MappingInterface</code> are included,
+ namely <code class="code">TagMappingBrown</code> (mapping reducing Brown corpus tagset from more than 400 tags to 93) and
+ <code class="code">GrobMappingTueba</code>(mapping German STTS tagset from 54 tags to basic 11 categories plus special symbols and punctuation)
+ </p></li><li><p>
+ <code class="code">ModelGeneration</code>
+ - trains an N-gram model for the tagger, iterating over a List of <code class="code">Token</code>s.
+ Writes the resulting model to a binary file. At the moment,
+ only bi-and trigram models are supported. Further N-grams can be easily integrated.
+ <code class="code">ModelGeneration</code> is not concerned with the fact,
+ whether the training corpus is given as a single file or as a directory containing a number of files,
+ as this is a <code class="code">CORPUS_READER</code> implementation issue. Two supplied readers include both a reader
+ for a corpus as a single file (<code class="code">TT_FormatReader</code>code>) or as a directory (<code class="code">BrownReader</code>code>)
+ </p></li><li><p>
+ Interface <code class="code">CorpusReader</code>
+ - should be used to implement corpus readers for own corpora; the objective
+ of the reader is to take charge of the preprocessing and transform tokenized units
+ (usually <span class="emphasis"><em>words</em></span>) into a List of <code class="code">Token</code> objects.
+ Two sample implementations of <code class="code">CorpusReader</code> are included:
+
+ </p><div class="orderedlist"><ol type="1"><li><p>
+ <code class="code">BrownReader</code>
+ - for the Brown corpus from the nltk distribution (nltk.sourceforge.net)
+ </p></li><li><p>
+ <code class="code">TT_FormatReader</code>
+ - for the corpora in TreeTagger format, i.e. one word per line
+ with tags separated from the words by tabs.
+ </p></li></ol></div><p>
+
+ </p></li></ul></div><p>
+ </p><p>
+ To train a new model, one should adjust a number of parameters in the <code class="code">"tagger.properties"</code> file,
+ which is in Java properties file format (see <a href="#properties.file">tagger.properties file</a>). After the parameters are set, you just need to run
+ <code class="code">ModelGeneration.java</code>
+ <a name="properties.file"></a></p><pre class="programlisting"><span class="emphasis"><em>######## This is the default tagger.properties file
+######## This file is used for training and testing only,
+######## The configuration for tagging is directly tuned in the descriptor "HmmTagger.xml"
+
+
+########################## BOTH FOR TRAINING AND EVALUATION ################################
+
+######## THESE ARE THE DEFAULT MODEL FILES FOR GERMAN AND ENGLISH
+######## You can either uncomment one of them, if you want to replace given models with your own one,
+
+#MODEL_FILE = resources/german/TuebaModel.dat
+#MODEL_FILE = resources/english/BrownModel.dat
+
+######## or specify a completely different name
+MODEL_FILE =
+
+######## If mapping of tags is desired, uncomment the following
+#DO_MAPPING = true
+
+
+####### EXAMPLES OF MAPPING CLASSES
+
+## Basic mapping for the Brown corpus (nltk distribution) tagset: to get 93 tags out of 473
+#MAPPING = org.apache.uima.examples.tagger.TagMappingBrown
+## Basic mapping for STTS tagset: from 54 tags onto the basic ca. 15 classes plus punctuation
+#MAPPING = org.apache.uima.examples.tagger.GrobMappingTueba
+
+## If you implement your own mapping, you should specify here in the same manner as above a java-path to the class
+MAPPING =
+
+####### FILE CONTAINING TRAINING CORPUS:
+####### can be in specified either as an absolute or as a relative path
+####### e.g. FILE = ../../tueba_tigerFormat.txt or FILE = C:/Data/tueba.txt
+FILE =
+
+######## If corpus is in a different format and cannot be read with the provided READERS,
+######## you should specify here a java-path to the class (s. examples below)
+
+#CORPUS_READER = org.apache.uima.examples.tagger.trainAndTest.TT_FormatReader
+#CORPUS_READER = org.apache.uima.examples.tagger.trainAndTest.BrownReader
+CORPUS_READER =
+
+################# ONLY FOR EVALUATION ###############################
+
+######### GOLD STANDARD CORPUS FILE:
+######### can be specified as an absolute or as a relative path
+## e.g. GOLD_STANDARD = ../../tueba_tigerFormat.txt or GOLD_STANDARD = C:/Data/tueba.txt
+GOLD_STANDARD =
+
+######### Here we specify whether one intends to test a bi- or a trigram model (default is a trigram model)
+N=3
+</em></span>
+ </pre><p>
+
+ </p></div><div class="chapter" lang="en" id="sandbox.tagger.evaluation"><div class="titlepage"><div><div><h2 class="title"><a name="sandbox.tagger.evaluation"></a>Chapter 7. Evaluation</h2></div></div></div><p>
+ To evaluate performance if a "gold standard" corpus is available, one can use the following provided file:
+ </p><div class="itemizedlist"><ul type="disc"><li><p>
+ <code class="code">TaggerEvaluation.java</code>
+ - can be used to evaluate the tagger and/or new models on a manually annotated corpus.
+ </p></li></ul></div><p>
+ </p><p>
+ <code class="code">HMMTagger</code> was evaluated for English and German. For English, it was trained on 80% of the Brown corpus
+ (180,000 tokens) and tested on the rest unseen 20%. The achieved accuracy was about 96%, test corpus contained 4.5% of unknown tokens.
+ </p><p>
+ For German, it achieves between 95% and 96% accuracy when trained and tested on the same type of corpus, i.e. with 80% of corpus used for training and 20% for testing.
+ The accuracy goes a bit down when tagging is performed for different types of corpora than the training one, mostly due to the growing number of unknown words.
+ </p></div><div class="appendix" lang="en" id="sandbox.tagger.theory"><div class="titlepage"><div><div><h2 class="title"><a name="sandbox.tagger.theory"></a>Appendix A. Theory Behind</h2></div></div></div><p>
+ This chapter is just a sketch of the statistical model
+ undelying the tagger.
+
+ Hidden Markov Models (HMMs) are the mainstay of the
+ applications employing statistical modeling in any form,
+ like speech recognition and production systems, signal
+ processing, part of speech tagging.
+
+ A Hidden Markov Model is a probabilistic function of a
+ Markov process. A Markov process is a process that fulfills
+ Markov assumptions.
+
+
+ Markov assumptions are:
+ </p><div class="itemizedlist"><ul type="disc"><li><p>
+ <code class="code">limited horizon</code>
+ - Markov processes are states without memory,
+ except for condition of the current state.
+ Though we usually consider sequences of
+ variables that are not independent of each
+ other, it often suffices to know the value of
+ the current situation without going deep into
+ the past happenings. As [
+ <a xmlns:xlink="http://www.w3.org/1999/xlink" href="#schuetze">ManningSchuetze99</a>
+ ] put it, we do not really need to know, how
+ many books were in the library last week or last
+ year in order to predict how many books there
+ will be tomorrow. It is often enough to know the
+ current situation. Thereby, future states in the
+ Markov process are independent of the past, they
+ only depend on the present. Let
+ <span class="mathphrase">
+ X = (X
+ <sub>1</sub>
+ , ..., X
+ <sub>T</sub>
+ )
+ </span>
+ be a sequence of random variables taking the
+ values from the finite state space
+ <span class="mathphrase">
+ S = (s
+ <sub>1</sub>
+ , ..., s
+ <sub>N</sub>
+ )
+ </span>
+ , then a limited horizon property could be
+ formalized by:
+ </p><div class="informalequation"><span class="mathphrase">
+ P(X
+ <sub>t+1</sub>
+ = s
+ <sub>k</sub>
+ |X
+ <sub>1</sub>
+ , ..., X
+ <sub>t</sub>
+ ) = P(X
+ <sub>t+1</sub>
+ = s
+ <sub>k</sub>
+ |X
+ <sub>t</sub>
+ )
+ </span></div><p>
+
+
+ </p></li><li><p>
+ <code class="code">time invariance</code>
+ </p><p>
+ The probabilities do not change over time, i.e.
+ if we know that the probability of observing a
+ rainbow after the rain is equal to 90\%, we know
+ that it should be true for today as well as for
+ tomorrow.
+ </p></li></ul></div><p>
+ </p><p>
+
+ If
+ <code class="code">X</code>
+ conforms to these two properties, then it is said to be a
+ Markov chain.
+
+ One can describe a Markov chain by a transition matrix:
+ </p><div class="informalequation"><span class="mathphrase">
+ A = a
+ <sub>i,j</sub>
+ = P(X
+ <sub>t+1</sub>
+ = s
+ <sub>j</sub>
+ |X
+ <sub>t</sub>
+ =s
+ <sub>i</sub>
+ )
+ </span></div><p>
+
+
+ </p><div class="informalequation"><span class="mathphrase">
+ - with a
+ <sub>i,j</sub>
+ >= 0 (for all
+ <span class="emphasis"><em>i,j</em></span>
+ ) and the sum of all transition probabilities from
+ state
+ <span class="emphasis"><em>i</em></span>
+ (a
+ <sub>i,j</sub>
+ ) should be equal to 1 (for all
+ <span class="emphasis"><em>i</em></span>
+ )
+ </span></div><p>
+
+ </p><p>
+
+ Markov models can be used whenever one needs to model the
+ probability of a linear sequence of variables.
+
+ One distinguishes Visible Markov Models (VMM) vs. Hidden
+ Markov Models. The difference is that when we work with
+ "visible" events, we can directly estimate the corresponding
+ probabilities (which is the case if training corpus is
+ available to train own models for HMM taggers).
+
+ Finding a sequence of part of speech tags (i.e. Viterbi part
+ of the tagger) in contrast is a hidden Markov model as the
+ states (tags) are not directly observable.
+ </p><p>
+ <span class="emphasis"><em>The goal of HMM - based tagger</em></span>
+ is to find part of speech tags ( = hidden states) that
+ generate a sequence of words ( = observable states). Most of
+ the known implementations of POS taggers are viewing text as
+ being produced by a hidden Markov model, so that tagging can
+ be regarded as a Markov process deciding which states the
+ system went through to generate a given text.
+
+ </p><p>
+ <span class="emphasis"><em>General Form of HMM</em></span>
+ </p><p>
+ A HMM is a five-tuple:
+ <span class="mathphrase">(S, O, π, A, B)</span>
+ </p><div class="informalexample"><p>where:</p><p>
+ </p><div class="itemizedlist"><ul type="disc"><li><p>
+ <code class="code">S</code>
+ - the set of states (here: parts of
+ speech)
+ </p></li><li><p>
+ <code class="code">K</code>
+ - the set of observations (here: words)
+ </p></li><li><p>
+ <code class="code">π</code>
+ - initial state probabilities
+ </p></li><li><p>
+ <code class="code">A</code>
+ - state transitions probabilities
+ </p></li><li><p>
+ <code class="code">B</code>
+ - symbol emissions probabilities
+ </p></li></ul></div><p>
+ </p></div><p>
+ </p><p>
+ Further,
+ <code class="code">
+ X
+ <sub>t</sub>
+ </code>
+ (state sequence) and
+ <code class="code">
+ O
+ <sub>t</sub>
+ </code>
+ (output sequence) are given.
+
+ Tagging procedure is then the following:
+ </p><div class="informalexample"><div class="orderedlist"><ol type="1"><li><p>
+ <code class="code">t := 1</code>
+ </p></li><li><p>
+ <code class="code">
+ Start in state s
+ <sub>i</sub>
+ with probability π
+ <sub>i</sub>
+ (i.e., X
+ <sub>1</sub>
+ = i)
+ </code>
+ </p></li><li><p>
+ <code class="code">forever do:</code>
+ </p><div class="itemizedlist"><ul type="disc"><li><p>
+ <code class="code">
+ Move from s
+ <sub>i</sub>
+ to s
+ <sub>j</sub>
+ with probability a
+ <sub>i,j</sub>
+ (i.e. X
+ <sub>t+1</sub>
+ = j)
+ </code>
+ </p></li><li><p>
+ <code class="code">
+ Emit observation symbol o
+ <sub>t</sub>
+ = k with probability b
+ <sub>i,j,k</sub>
+ </code>
+ </p></li><li><p>
+ <code class="code">t := T+1</code>
+ </p></li></ul></div></li><li><p>
+ <code class="code">end</code>
+ </p></li></ol></div></div><p>
+</p><p>
+ Despite their limitations, HMM-s are one of the most
+ successful techniques in natural language processing and are
+ widely used, especially in sequence tagging applications.
+ The best statistical taggers all perform at about the same
+ level of accuracy.
+ </p></div><div class="glossary" id="d0e638"><div class="titlepage"><div><div><h2 class="title"><a name="d0e638"></a>Glossary</h2></div></div></div><div class="glossdiv"><h3 class="title">HMM</h3><dl><dt><a name="hmm"></a>Hidden Markov Model</dt><dd><p></p></dd></dl></div><div class="glossdiv"><h3 class="title">POS</h3><dl><dt><a name="pos"></a>Part of Speech</dt><dd><p></p></dd></dl></div></div><div class="bibliography" id="d0e661"><div class="titlepage"><div><div><h2 class="title"><a name="d0e661"></a>Bibliography</h2></div></div></div><div class="biblioentry"><a name="schuetze"></a><p>[ManningSchuetze99] <span class="authorgroup"><span class="firstname">Christopher</span> <span class="surname">Manning</span> and <span class="firstname">Hinrich</span> <span class="surname">Schuetze</span>. </span><span class="title"><i>
+ Foundations of Statistical Natural Language Processing
+ </i>. </span><span class="copyright">Copyright © 1999. </span><span class="publisher"><span class="publishername">MIT Press. </span></span></p></div></div></div></body></html>
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