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Merge branch 'place_extraction'

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	analyze.py
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Arne Schlüter 2015-01-17 17:38:41 +01:00
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analyze.py
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<<<<<<< HEAD
import requests, json
=======
import re
import string
from nltk.tag.stanford import POSTagger
tagger = POSTagger('./stanford-postagger-full-2014-10-26/models/german-fast.tagger',
'./stanford-postagger-full-2014-10-26/stanford-postagger-3.5.0.jar',
'UTF-8')
punctuation_regex = re.compile("[%s]" % re.escape(string.punctuation))
def get_potential_places(article_place, article_body):
"""
Returns a list of potential places as tuples with their part-of-speech tags
for later filtering
"""
place_pos = tagger.tag(punctuation_regex.sub(" ", article_place).split())
text_pos = tagger.tag(punctuation_regex.sub(" ", article_body).split())
# extract the places out of the full text
places = [place_pos]
is_matching = False
current_match = []
for tuple in text_pos:
if is_matching:
# when we're matching, the phrases we're looking for look like
# "Im S-Bahnhof Wedding"... the tags below mean
if tuple[1] in ("ART", "ADJA", "NN", "NE", "CARD"):
current_match.append(tuple)
else:
# we stop the match, so append the current match
places.append(current_match)
current_match = []
# whe we're looking at a preposition again, just start new match
if tuple[1] not in ("APPR", "APPRART"):
is_matching = False
else:
# start matching when we have a preposition
if tuple[1] in ("APPR", "APPRART"):
is_matching = True
return places
def improve_potential_places(pos_tuples):
"""
Improves the matches' quality so we don't have to look up the lat-lng of so
many mismatches
"""
better_tuples = []
for tuple_list in pos_tuples:
# first, exluce empty lists
if tuple_list:
cleaner_list = []
index = -1
for tuple in tuple_list:
index += 1
# exclude articles ("the", "a"), they only introduce noise, but
# keep the list as a whole
if tuple[1] == "ART":
continue
# if we have numbers in the middle of our phrase, probably the
# whole list is not useful (as opposed to e.g. Krügerstr. 22)
if tuple[1] == "CARD" and index < len(tuple_list):
cleaner_list = []
break
cleaner_list.append(tuple)
if cleaner_list:
better_tuples.append(cleaner_list)
return better_tuples
>>>>>>> place_extraction
def get_district(article_headline):
"""

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stanford-postagger-full-2014-10-26/LICENSE.txt Normal file
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Stanford POS Tagger, v3.5.0 - 2014-10-26
Copyright (c) 2002-2012 The Board of Trustees of
The Leland Stanford Junior University. All Rights Reserved.
Original tagger author: Kristina Toutanova
Code contributions: Christopher Manning, Dan Klein, William Morgan,
Huihsin Tseng, Anna Rafferty, John Bauer
Major rewrite for version 2.0 by Michel Galley.
Current release prepared by: John Bauer
This package contains a Maximum Entropy part of speech tagger.
A Part-Of-Speech Tagger (POS Tagger) is a piece of software that reads
text in some language and assigns parts of speech to each word (and
other tokens), such as noun, verb, adjective, etc. Generally
computational applications use more fine-grained POS tags like
'noun-plural'. This software is a Java implementation of the log-linear
part-of-speech (POS) taggers described in:
Kristina Toutanova and Christopher D. Manning. 2000. Enriching the
Knowledge Sources Used in a Maximum Entropy Part-of-Speech
Tagger. Proceedings of the Joint SIGDAT Conference on Empirical Methods
in Natural Language Processing and Very Large Corpora (EMNLP/VLC-2000),
Hong Kong.
Kristina Toutanova, Dan Klein, Christopher Manning, and Yoram
Singer. 2003. Feature-Rich Part-of-Speech Tagging with a Cyclic
Dependency Network. In Proceedings of HLT-NAACL 2003 pages 252-259.
The system requires Java 1.6+ to be installed. About 60 MB of memory is
required to run a trained tagger, depending on the OS, tagging model
chosen, etc. (i.e., you may need to give to java an option like java
-mx120m). Plenty of memory is needed to train a tagger. It depends on
the complexity of the model but at least 1GB is recommended (java
-mx1g). Two trained tagger models for English are included with the
tagger, along with some caseless versions, and we provide models for
some other languages. The tagger can be retrained on other languages
based on POS-annotated training text.
If you really want to use this software under Java 1.4, look into RetroWeaver:
http://retroweaver.sourceforge.net/
QUICKSTART
-----------------------------------------------
The Stanford POS Tagger is designed to be used from the command line or
programmatically via its API.
There is a GUI interface, but it is for
demonstration purposes only; most features of the tagger can only be
accessed via the command line. To run the demonstration GUI you should
be able to use any of the following 3 methods:
1)
java -mx200m -classpath stanford-postagger.jar edu.stanford.nlp.tagger.maxent.MaxentTaggerGUI models/wsj-0-18-left3words.tagger
2)
Providing your system gives java enough memory by default, you can also
run it by either double-clicking the stanford-postagger.jar file, or
giving the command:
java -jar stanford-postagger.jar
3) Running the appropriate script for your operating system:
stanford-postagger-gui.bat
./stanford-postagger-gui.sh
To run the tagger from the command line, you can start with the provided
script appropriate for you operating system:
./stanford-postagger.sh models/wsj-0-18-left3words.tagger sample-input.txt
stanford-postagger models\wsj-0-18-left3words.tagger sample-input.txt
The output should match what is found in sample-output.txt
The tagger has three modes: tagging, training, and testing. Tagging
allows you to use a pretrained model (two English models are included)
to assign part of speech tags to unlabeled text. Training allows you to
save a new model based on a set of tagged data that you provide.
Testing allows you to see how well a tagger performs by tagging labeled
data and evaluating the results against the correct tags.
Many options are available for training, tagging, and testing. These
options can be set using a properties file. To start, you can generate a
default properties file by:
java -classpath stanford-postagger.jar edu.stanford.nlp.tagger.maxent.MaxentTagger -genprops > myPropsFile.prop
This will create the file myPropsFile.prop with descriptions of each
option for the tagger and the default values for these options
specified. Any properties you can specify in a properties file can be
specified on the command line or vice versa. For further information,
please consult the Javadocs (start with the entry for MaxentTagger,
which includes a table of all options which may be set to configure the
tagger and descriptions of those options).
To tag a file using the pre-trained bidirectional model
=======================================================
java -mx300m -classpath stanford-postagger.jar edu.stanford.nlp.tagger.maxent.MaxentTagger -model models/wsj-0-18-bidirectional-distsim.tagger -textFile sample-input.txt > sample-tagged.txt
Tagged output will be printed to standard out, which you can redirect
as above. Note that the bidirectional model is slightly more accurate
but significantly slower than the left3words model.
To train a simple model
=======================
java -classpath stanford-postagger.jar edu.stanford.nlp.tagger.maxent.MaxentTagger -prop propertiesFile -model modelFile -trainFile trainingFile
To test a model
===============
java -classpath stanford-postagger.jar edu.stanford.nlp.tagger.maxent.MaxentTagger -prop propertiesFile -model modelFile -testFile testFile
CONTENTS
-----------------------------------------------
README.txt
This file.
LICENSE.txt
Stanford POS Tagger is licensed under the GNU General Public License (v2+).
stanford-postagger.jar
stanford-postagger-YYYY-MM-DD.jar
This is a JAR file containing all the Stanford classes necessary to
run the Stanford POS Tagger. The two jar files are identical. You can use
either the one with a version (date) indication or without, as you prefer.
src
A directory containing the Java 1.5 source code for the Stanford POS
Tagger distribution.
build.xml, Makefile
Files for building the distribution (with ant and make, respectively)
models
A directory containing trained POS taggers; the taggers end in ".tagger"
and the props file used to make the taggers end in ".props". The
".props" files cannot be directly used on your own machine as they use
paths on the Stanford NLP machines, but they may serve as examples for
your own properties files. Included in the full version are other
English taggers, a German tagger, an Arabic tagger, and a Chinese
tagger. If you chose to download the smaller version of the tagger,
you have only two English taggers (left3words is faster but slightly
less accurate than bidirectional-distsim) - feel free to download any
other taggers you need from the POS tagger website. More information
about the models can be found in the README-Models.txt file in this
directory.
sample-input.txt
A sample text file that you can tag to demonstrate the tagger.
sample-output.txt
Tagged output of the tagger (using the left3words model)
stanford-postagger-gui.sh
stanford-postagger-gui.bat
Scripts for invoking the GUI demonstration version of the tagger.
stanford-postagger.sh
stanford-postagger.bat
Scripts for running the command-line version of the tagger.
javadoc
Javadocs for the distribution. In particular, look at the javadocs
for the class edu.stanford.nlp.tagger.maxent.MaxentTagger.
TaggerDemo.java
A sample file for how to call the tagger in your own program. You
should be able to compile and run it with:
javac -cp stanford-postagger.jar TaggerDemo.java
java -cp ".:stanford-postagger.jar" TaggerDemo models/wsj-0-18-left3words.tagger sample-input.txt
(If you are on Windows, you need to replace the ":" with a ";" in the
-cp argument, and should use a "\" in place of the "/" in the filename....)
THANKS
-----------------------------------------------
Thanks to the members of the Stanford Natural Language Processing Lab
for great collaborative work on Java libraries for natural language
processing.
http://nlp.stanford.edu/javanlp/
CHANGES
-----------------------------------------------
2014-10-26 3.5.0 Upgrade to Java 1.8; add annotators for
dependency parsing and relation extraction
2014-10-26 3.5.0 Upgrade to Java 1.8; add annotators for
dependency parsing and relation extraction
2014-08-27 3.4.1 Add Spanish models
2014-06-16 3.4 Using CC tagset for French
2014-01-04 3.3.1 Bugfix release
2013-11-12 3.3.0 Add imperatives to English training data
2013-06-19 3.2.0 Decrease size and improve speed of tagger
models for all languages
2013-04-04 3.1.5 Speed improvements, ctb7 model, -nthreads
option
2012-11-11 3.1.4 Updated Chinese model
2012-07-09 3.1.3 Minor bug fixes
2012-05-22 3.1.2 Updated for compatibility with other releases
2012-03-09 3.1.1 Caseless models added
2012-01-06 3.1.0 French tagger added, tagging speed improved
2011-09-14 3.0.4 Updated for compatibility with other releases
2011-06-15 3.0.3 Updated for compatibility with other releases
2011-05-15 3.0.2 Can read training files in TSV format
2011-04-17 3.0.1 Improved German and Arabic models
Compatible with other Stanford releases
2010-05-21 3.0.0 Re-entrant
LICENSE
-----------------------------------------------
Stanford POS Tagger
Copyright (c) 2002-2010 The Board of Trustees of
The Leland Stanford Junior University. All Rights Reserved.
This program is free software; you can redistribute it and/or
modify it under the terms of the GNU General Public License
as published by the Free Software Foundation; either version 2
of the License, or (at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program; if not, write to the Free Software
Foundation, Inc., 59 Temple Place - Suite 330, Boston, MA 02111-1307, USA.
For more information, bug reports, fixes, contact:
Christopher Manning
Dept of Computer Science, Gates 1A
Stanford CA 94305-9010
USA
Support/Questions: java-nlp-user@lists.stanford.edu
Licensing: java-nlp-support@lists.stanford.edu
http://www-nlp.stanford.edu/software/tagger.shtml
CONTACT
-----------------------------------------------
For questions about the Stanford POS tagger, please feel free to contact
the Stanford JavaNLP user community at the mailing list
java-nlp-user@lists.stanford.edu. You need to be a member of this
mailing list to be able to post to it. Join the list either by emailing
java-nlp-user-join@lists.stanford.edu (leave the subject and message
body empty) or by using the web interface at:
https://mailman.stanford.edu/mailman/listinfo/java-nlp-user
This is the best list to post to in order to ask questions, make
announcements, or for discussion among Stanford JavaNLP tool users. We
provide assistance on a best-effort basis. You can also look at the list
archives via https://mailman.stanford.edu/pipermail/java-nlp-user/. For
licensing questions, please see the tagger webpage or contact Stanford
JavaNLP at java-nlp-support@lists.stanford.edu.

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import java.io.BufferedReader;
import java.io.FileReader;
import java.util.List;
import edu.stanford.nlp.ling.Sentence;
import edu.stanford.nlp.ling.TaggedWord;
import edu.stanford.nlp.ling.HasWord;
import edu.stanford.nlp.tagger.maxent.MaxentTagger;
class TaggerDemo {
private TaggerDemo() {}
public static void main(String[] args) throws Exception {
if (args.length != 2) {
System.err.println("usage: java TaggerDemo modelFile fileToTag");
return;
}
MaxentTagger tagger = new MaxentTagger(args[0]);
List<List<HasWord>> sentences = MaxentTagger.tokenizeText(new BufferedReader(new FileReader(args[1])));
for (List<HasWord> sentence : sentences) {
List<TaggedWord> tSentence = tagger.tagSentence(sentence);
System.out.println(Sentence.listToString(tSentence, false));
}
}
}

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import java.io.BufferedReader;
import java.io.FileInputStream;
import java.io.InputStreamReader;
import java.io.OutputStreamWriter;
import java.io.PrintWriter;
import java.util.List;
import edu.stanford.nlp.ling.Sentence;
import edu.stanford.nlp.ling.TaggedWord;
import edu.stanford.nlp.ling.HasWord;
import edu.stanford.nlp.ling.CoreLabel;
import edu.stanford.nlp.process.CoreLabelTokenFactory;
import edu.stanford.nlp.process.DocumentPreprocessor;
import edu.stanford.nlp.process.PTBTokenizer;
import edu.stanford.nlp.process.TokenizerFactory;
import edu.stanford.nlp.tagger.maxent.MaxentTagger;
/** This demo shows user-provided sentences (i.e., {@code List<HasWord>})
* being tagged by the tagger. The sentences are generated by direct use
* of the DocumentPreprocessor class.
*
* @author Christopher Manning
*/
class TaggerDemo2 {
private TaggerDemo2() {}
public static void main(String[] args) throws Exception {
if (args.length != 2) {
System.err.println("usage: java TaggerDemo2 modelFile fileToTag");
return;
}
MaxentTagger tagger = new MaxentTagger(args[0]);
TokenizerFactory<CoreLabel> ptbTokenizerFactory = PTBTokenizer.factory(new CoreLabelTokenFactory(),
"untokenizable=noneKeep");
BufferedReader r = new BufferedReader(new InputStreamReader(new FileInputStream(args[1]), "utf-8"));
PrintWriter pw = new PrintWriter(new OutputStreamWriter(System.out, "utf-8"));
DocumentPreprocessor documentPreprocessor = new DocumentPreprocessor(r);
documentPreprocessor.setTokenizerFactory(ptbTokenizerFactory);
for (List<HasWord> sentence : documentPreprocessor) {
List<TaggedWord> tSentence = tagger.tagSentence(sentence);
pw.println(Sentence.listToString(tSentence, false));
}
// print the adjectives in one more sentence. This shows how to get at words and tags in a tagged sentence.
List<HasWord> sent = Sentence.toWordList("The", "slimy", "slug", "crawled", "over", "the", "long", ",", "green", "grass", ".");
List<TaggedWord> taggedSent = tagger.tagSentence(sent);
for (TaggedWord tw : taggedSent) {
if (tw.tag().startsWith("JJ")) {
pw.println(tw.word());
}
}
pw.close();
}
}

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<!-- build.xml file for ant for JavaNLP -->
<!-- A "project" describes a set of targets that may be requested
when Ant is executed. The "default" attribute defines the
target which is executed if no specific target is requested,
and the "basedir" attribute defines the current working directory
from which Ant executes the requested task. This is normally
set to the current working directory.
-->
<project name="JavaNLP" default="compile" basedir=".">
<property name="build.home" value="${basedir}/classes"/>
<property name="build.tests" value="${basedir}/classes"/>
<property name="docs.home" value="${basedir}/docs"/>
<property name="src.home" value="${basedir}/src"/>
<property name="javadoc.home" value="${basedir}/javadoc"/>
<!-- ==================== Compilation Control Options ==================== -->
<!--
These properties control option settings on the Javac compiler when it
is invoked using the <javac> task.
compile.debug Should compilation include the debug option?
compile.deprecation Should compilation include the deprecation option?
compile.optimize Should compilation include the optimize option?
compile.source Source version compatibility
compile.target Target class version compatibility
-->
<property name="compile.debug" value="true"/>
<property name="compile.deprecation" value="false"/>
<property name="compile.optimize" value="true"/>
<property name="compile.source" value="1.8" />
<property name="compile.target" value="1.8" />
<!-- ==================== All Target ====================================== -->
<!--
The "all" target is a shortcut for running the "clean" target followed
by the "compile" target, to force a complete recompile.
-->
<target name="all" depends="clean,compile"
description="Clean build and dist directories, then compile"/>
<!-- ==================== Clean Target ==================================== -->
<!--
The "clean" target deletes any previous "build" and "dist" directory,
so that you can be ensured the application can be built from scratch.
-->
<target name="clean" description="Delete old classes">
<delete dir="${build.home}/edu"/>
</target>
<!-- ==================== Classpath Targets ==================================== -->
<!--
Sets the classpath for this project properly. We now always use the
lib dir within javanlp.
-->
<target name="classpath" description="Sets the classpath">
<path id="compile.classpath">
<!-- <fileset dir="${basedir}/lib">
<include name="*.jar"/>
<exclude name="javanlp*"/>
</fileset> -->
</path>
</target>
<!-- ==================== Compile Target ================================== -->
<!--
The "compile" target transforms source files (from your "src" directory)
into object files in the appropriate location in the build directory.
This example assumes that you will be including your classes in an
unpacked directory hierarchy under "/WEB-INF/classes".
-->
<target name="compile" depends="prepare,classpath"
description="Compile Java sources">
<!-- Compile Java classes as necessary -->
<mkdir dir="${build.home}"/>
<javac srcdir="${src.home}"
destdir="${build.home}"
debug="${compile.debug}"
encoding="utf-8"
deprecation="${compile.deprecation}"
optimize="${compile.optimize}"
source="${compile.source}"
target="${compile.target}"
includeantruntime="false">
<classpath refid="compile.classpath"/>
<compilerarg value="-Xmaxerrs"/>
<compilerarg value="20"/>
<!-- <compilerarg value="-Xlint"/> -->
</javac>
<!-- Copy application resources -->
<!--
<copy todir="${build.home}/WEB-INF/classes">
<fileset dir="${src.home}" excludes="**/*.java"/>
</copy>
-->
</target>
<!-- ==================== Jar Target ================================== -->
<!--
The "jar" target recreates the jar file, which you may want to do if
you take apart the source jar and change something.
-->
<property name="jar.output" value="stanford-postagger.jar" />
<property name="jar.mainclass" value="edu.stanford.nlp.tagger.maxent.MaxentTaggerGUI" />
<target name="jar" depends="compile" description="Build a jar file">
<jar destfile="${jar.output}">
<fileset dir="${build.home}"/>
<manifest>
<attribute name="Main-Class" value="${jar.mainclass}"/>
</manifest>
</jar>
</target>
<!-- ==================== Javadoc Target ================================== -->
<!--
The "javadoc" target creates Javadoc API documentation for the Java
classes included in your application. Normally, this is only required
when preparing a distribution release, but is available as a separate
target in case the developer wants to create Javadocs independently.
-->
<target name="javadoc" depends="compile"
description="Create Javadoc API documentation">
<mkdir dir="${javadoc.home}"/>
<javadoc sourcepath="${src.home}"
destdir="${javadoc.home}"
maxmemory="768m"
author="true"
source="1.6"
Overview="${src.home}/edu/stanford/nlp/overview.html"
Doctitle="Stanford JavaNLP API Documentation"
Windowtitle="Stanford JavaNLP API"
packagenames="*">
<bottom><![CDATA[<FONT SIZE=2><A HREF=\"http://nlp.stanford.edu\">Stanford NLP Group</A></FONT>]]></bottom>
<link href="http://java.sun.com/j2se/1.6.0/docs/api/"/>
</javadoc>
</target>
<!-- ==================== Prepare Target ================================== -->
<!--
The "prepare" target is used to create the "build" destination directory,
and copy the static contents of your web application to it. If you need
to copy static files from external dependencies, you can customize the
contents of this task.
Normally, this task is executed indirectly when needed.
-->
<target name="prepare">
<!-- Create build directories as needed -->
<mkdir dir="${build.home}"/>
</target>
</project>

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Stanford POS Tagger, v3.5.0 - 2014-10-26
Copyright (c) 2002-2012 The Board of Trustees of
The Leland Stanford Junior University. All Rights Reserved.
This document contains (some) information about the models included in
this release and that may be downloaded for the POS tagger website at
http://nlp.stanford.edu/software/tagger.shtml . If you have downloaded
the full tagger, all of the models mentioned in this document are in the
downloaded package in the same directory as this readme. Otherwise,
included in the download are two
English taggers, and the other taggers may be downloaded from the
website. All taggers are accompanied by the props files used to create
them; please examine these files for more detailed information about the
creation of the taggers.
For English, the bidirectional taggers are slightly more accurate, but
tag much more slowly; choose the appropriate tagger based on your
speed/performance needs.
English taggers
---------------------------
wsj-0-18-bidirectional-distsim.tagger
Trained on WSJ sections 0-18 using a bidirectional architecture and
including word shape and distributional similarity features.
Penn Treebank tagset.
Performance:
97.28% correct on WSJ 19-21
(90.46% correct on unknown words)
wsj-0-18-left3words.tagger
Trained on WSJ sections 0-18 using the left3words architecture and
includes word shape features. Penn tagset.
Performance:
96.97% correct on WSJ 19-21
(88.85% correct on unknown words)
wsj-0-18-left3words-distsim.tagger
Trained on WSJ sections 0-18 using the left3words architecture and
includes word shape and distributional similarity features. Penn tagset.
Performance:
97.01% correct on WSJ 19-21
(89.81% correct on unknown words)
english-left3words-distsim.tagger
Trained on WSJ sections 0-18 and extra parser training data using the
left3words architecture and includes word shape and distributional
similarity features. Penn tagset.
english-bidirectional-distsim.tagger
Trained on WSJ sections 0-18 using a bidirectional architecture and
including word shape and distributional similarity features.
Penn Treebank tagset.
wsj-0-18-caseless-left3words-distsim.tagger
Trained on WSJ sections 0-18 left3words architecture and includes word
shape and distributional similarity features. Penn tagset. Ignores case.
english-caseless-left3words-distsim.tagger
Trained on WSJ sections 0-18 and extra parser training data using the
left3words architecture and includes word shape and distributional
similarity features. Penn tagset. Ignores case.
Chinese tagger
---------------------------
chinese-nodistsim.tagger
Trained on a combination of CTB7 texts from Chinese and Hong Kong
sources.
LDC Chinese Treebank POS tag set.
Performance:
93.46% on a combination of Chinese and Hong Kong texts
(79.40% on unknown words)
chinese-distsim.tagger
Trained on a combination of CTB7 texts from Chinese and Hong Kong
sources with distributional similarity clusters.
LDC Chinese Treebank POS tag set.
Performance:
93.99% on a combination of Chinese and Hong Kong texts
(84.60% on unknown words)
Arabic tagger
---------------------------
arabic.tagger
Trained on the *entire* ATB p1-3.
When trained on the train part of the ATB p1-3 split done for the 2005
JHU Summer Workshop (Diab split), using (augmented) Bies tags, it gets
the following performance:
96.26% on test portion according to Diab split
(80.14% on unknown words)
French tagger
---------------------------
french.tagger
Trained on the French treebank.
German tagger
---------------------------
german-hgc.tagger
Trained on the first 80% of the Negra corpus, which uses the STTS tagset.
The Stuttgart-Tübingen Tagset (STTS) is a set of 54 tags for annotating
German text corpora with part-of-speech labels, which was jointly
developed by the Institut für maschinelle Sprachverarbeitung of the
University of Stuttgart and the Seminar für Sprachwissenschaft of the
University of Tübingen. See:
http://www.ims.uni-stuttgart.de/projekte/CQPDemos/Bundestag/help-tagset.html
This model uses features from the distributional similarity clusters
built over the HGC.
Performance:
96.90% on the first half of the remaining 20% of the Negra corpus (dev set)
(90.33% on unknown words)
german-dewac.tagger
This model uses features from the distributional similarity clusters
built from the deWac web corpus.
german-fast.tagger
Lacks distributional similarity features, but is several times faster
than the other alternatives.
Performance:
96.61% overall / 86.72% unknown.

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## tagger training invoked at Tue Jul 08 16:25:43 PDT 2014 with arguments:
model = german-fast.tagger
arch = words(-2,2),order(1),prefix(1),suffix(10)
wordFunction =
trainFile = format=TREES,trf=edu.stanford.nlp.trees.international.negra.NegraPennTreeReaderFactory,/u/nlp/data/GermanACL08/negra/negra_1.mrg
closedClassTags =
closedClassTagThreshold = 40
curWordMinFeatureThresh = 2
debug = false
debugPrefix =
tagSeparator = _
encoding = iso-8859-1
iterations = 100
lang = german
learnClosedClassTags = false
minFeatureThresh = 2
openClassTags =
rareWordMinFeatureThresh = 10
rareWordThresh = 5
search = owlqn
sgml = false
sigmaSquared = 0.0
regL1 = 0.75
tagInside =
tokenize = true
tokenizerFactory =
tokenizerOptions = asciiQuotes
verbose = false
verboseResults = true
veryCommonWordThresh = 250
xmlInput = null
outputFile =
outputFormat = slashTags
outputFormatOptions =
nthreads = 1

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A passenger plane has crashed shortly after take-off from Kyrgyzstan's
capital, Bishkek, killing a large number of those on board. The head of
Kyrgyzstan's civil aviation authority said that out of about 90
passengers and crew, only about 20 people have survived. The Itek Air
Boeing 737 took off bound for Mashhad, in north-eastern Iran, but turned
round some 10 minutes later.

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A_DT passenger_NN plane_NN has_VBZ crashed_VBN shortly_RB after_IN take-off_NN from_IN Kyrgyzstan_NNP 's_POS capital_NN ,_, Bishkek_NNP ,_, killing_VBG a_DT large_JJ number_NN of_IN those_DT on_IN board_NN ._.
The_DT head_NN of_IN Kyrgyzstan_NNP 's_POS civil_JJ aviation_NN authority_NN said_VBD that_IN out_IN of_IN about_IN 90_CD passengers_NNS and_CC crew_NN ,_, only_RB about_IN 20_CD people_NNS have_VBP survived_VBN ._.
The_DT Itek_NNP Air_NNP Boeing_NNP 737_CD took_VBD off_RP bound_VBN for_IN Mashhad_NNP ,_, in_IN north-eastern_JJ Iran_NNP ,_, but_CC turned_VBD round_NN some_DT 10_CD minutes_NNS later_RB ._.

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:: runs the POS tagger (toy) GUI
:: usage stanford-postagger-gui
java -mx200m -cp "stanford-postagger.jar;" edu.stanford.nlp.tagger.maxent.MaxentTaggerGUI

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#!/bin/sh
java -mx200m -cp 'stanford-postagger.jar:' edu.stanford.nlp.tagger.maxent.MaxentTaggerGUI

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:: usage: stanford-postagger model textFile
:: e.g., stanford-postagger models\english-left3words-distsim.tagger sample-input.txt
java -mx300m -cp "stanford-postagger.jar;" edu.stanford.nlp.tagger.maxent.MaxentTagger -model %1 -textFile %2

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#!/bin/sh
#
# usage: ./stanford-postagger.sh model textFile
# e.g., ./stanford-postagger.sh models/english-left3words-distsim.tagger sample-input.txt
java -mx300m -cp 'stanford-postagger.jar:' edu.stanford.nlp.tagger.maxent.MaxentTagger -model $1 -textFile $2

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from nltk.tag.stanford import POSTagger
from models import Article
from analyze import *
tagger = POSTagger('./stanford-postagger-full-2014-10-26/models/german-fast.tagger',
'./stanford-postagger-full-2014-10-26/stanford-postagger-3.5.0.jar',
'UTF-8')
for article in Article.select().limit(100):
potential = get_potential_places(article.place, article.description)
places = improve_potential_places(potential)
print(article.place)
print(article.description)
print()
print("Potential: " + str(potential))
print("Improved: " + str(places))