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Text Mining Tools 22C:196 Text Retrieval & Text Mining Seminar Tools WordNet MxTerminator Lingpipe Stanford TP Tools Stanford-NER SVM Light Rainbow Toolkit Manjal WordNet http://wordnet.princeton.edu/ English lexical database Developed at Princeton Univ. by George A. Miller, etc.

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text mining tools

Text Mining Tools

22C:196

Text Retrieval & Text Mining Seminar

tools
Tools
  • WordNet
  • MxTerminator
  • Lingpipe
  • Stanford TP Tools
    • Stanford-NER
  • SVM Light
  • Rainbow Toolkit
  • Manjal
wordnet
WordNet
  • http://wordnet.princeton.edu/
  • English lexical database
  • Developed at Princeton Univ. by George A. Miller, etc.
  • Organized as Synsets
    • Cognitive synonym sets
  • Synsets for Nouns, Verbs, Adjectives and Adverbs
wordnet4
WordNet
  • Synsets interlinked via lexical and conceptual-sematic relations
  • Network of meaningfully related concepts and words
  • Available online and can also be freely downloaded
  • Perl and Java packages available to interface with WordNet
wordnet5
WordNet
  • WordNet 2.0 on sulu and geordi
  • Command line interface
  • Example
    • /usr/local/WordNet-2.0/bin/wn <w> -over
      • Provides overview of various senses
    • /usr/local/WordNet-2.0/bin/wn <w> -synsn
      • Provides list of synonyms
mxterminator
MxTerminator
  • http://www.id.cbs.dk/~dh/corpus/tools/MXTERMINATOR.html
  • Java sentence boundary detection tool
  • Algorithm described in
    • J.C. Reynar and A. Ratnaparkhi. A Maximum Entropy Approach to Identifying Sentence Boundaries. 1997.
mxterminator7
MxTerminator
  • Installed on sulu and geordi
  • Command-line interface
    • Requires two parameters
      • Trained model directory
      • Text File to parse
    • Syntax
      • /usr/local/mxterminator/mxterminator ‘modeldir’ < ‘textfile’
  • Comes with pre-trained model
    • /usr/local/mxterminator/eos.project
mxterminator8
MxTerminator
  • New models can be trained
    • trainmxterminator <projectdir> <traindata>
      • <projectdir> is newly created model directory
      • <traindata> is training data with one sentence per line
  • Package also includes mxpost
    • part-of-speech tagger
    • /usr/local/mxterminator/mxpost ‘modeldir < ‘wordfile’
      • Pre-built model - /usr/local/mxterminator/tagger.project
      • wordfile - contains words; one sentence per line
lingpipe
LingPipe
  • http://www.alias-i.com/lingpipe/
  • Suite of Java libraries for different kinds of analyses
    • Sentence detection
    • Part-of-speech tagging
    • Named-entity extraction
    • Phrase extraction
    • Entity co-reference
    • Spell checker
    • Clustering
    • Chinese language support
lingpipe10
LingPipe
  • Also contains tools for database text mining
    • Directly work-off a database such as MySQL
  • Package contains demos, tutorials, pre-trained models and javadoc
  • Widely used in text mining community
    • Especially for general and biomedical named-entity recognition
  • Website has links to blogs and developer discussion forum
stanford tp tools
Stanford TP Tools
  • http://nlp.stanford.edu/software/index.shtml
  • Variety of text processing tools
  • Made available by Stanford NLP group
  • All tools are implemented in Java
  • Freely downloadable
stanford tp tools12
Stanford TP Tools
  • Parser
  • POS Tagger
  • Named Entity Recognizer
  • Chinse word segmenter
  • Classifier
  • Tregex and Tsurgeon
    • Matching patterns in trees
stanford ner
Stanford-NER
  • Based on CRFs
  • Contains demo programs
  • 4 pre-built models
    • 3 class basic model trained on US and UK Newswire data from CoNLL, MUC and ACE
      • Labels PERSON, ORGANIZATION and LOCATION
    • 4 class model trained on CoNLL training data
      • Additionally labels MISC
    • 2 more accurate distsim versions of above models
stanford ner14
Stanford-NER
  • Example
    • java -mx600m -cp ./stanford-ner.jar:. stanfordNER ner-eng-ie.crf-3-all2006-distsim.ser.gz “text”
      • Advanced distsim model
  • Example
    • java -mx300m -cp stanford-ner.jar edu.stanford.nlp.ie.crf.CRFClassifier -textFile sample.txt
      • Default basic model
svmlight
SVMLight
  • http://svmlight.joachims.org/
  • C support-vector-machine implementation by Thorsten Joachims
  • Does classification, regression and ranking
  • Many other functions
    • Estimate error-rate and precision and recall directly
  • Freely downloadable
    • Instructions on website
svmlight16
SVMLight
  • Contains 2 main executable files
    • svm_learn (learn model from training set)
    • svm_classify (classify test set)
  • Input file contains weighted term vectors
    • Strategy: index doc files using Lucene or SMART and obtain term vectors
    • Example: -1 1:0.43 3:0.12 9284:0.2

+1 1:0.20 3:0.14 9284:0.97

  • Use different kernel functions
    • Support for linear and non-linear kernels
svmlight17
SVMLight
  • Syntax:
    • svm_learn [options] example_file model_file
    • svm_classify [options] example_file model_file output_file
  • Example data included in distribution
rainbow toolkit
Rainbow Toolkit
  • http://www.cs.cmu.edu/~mccallum/bow/rainbow/
  • Part of the Bow toolkit
    • http://www.cs.cmu.edu/~mccallum/bow/
  • Text Classification tool
  • Supports 4 classification methods
    • Naïve Bayes (default)
    • TFIDF/Rocchio
    • K-nearest neighbor
    • Probabilistic Indexing
rainbow toolkit19
Rainbow Toolkit
  • Building a model
    • rainbow -d ./model --index <modeldir> --use-stemming --skip-html
    • <modeldir> contains individual folders (with text files) for each class
    • Model is stored in./model
  • Test model
    • rainbow -d ~/model --test-set=0.4 --test=3
      • Train-test split is 0.6/0.4; 3 iterations
rainbow toolkit20
Rainbow Toolkit
  • Test model
    • rainbow -d ~/model --test-set=0.5 --test=1
      • Specify test set
      • Half chosen randomly
    • rainbow -d ~/model --test-files <testdir>
      • Classify previously unseen files in<testdir>
rainbow toolkit21
Rainbow Toolkit
  • Formatted output
    • rainbow-stats
    • Example
      • rainbow -d ./model --test-set=0.4 --test=2 | rainbow-stats
    • Confusion matrix, Percent accuracy, Std. error,
manjal
Manjal
  • Online demo