university of illinois tools for unstructured text nicar 2012 loretta auvil, uiuc chase davis, cir...
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University of Illinois
Tools for Unstructured TextNICAR 2012
Loretta Auvil, UIUCChase Davis, CIR
February 2012
University of Illinois
Hottest Analytics
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Overview
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Sites that List Tools
• DiRT, Digital Research Tools• http://digitalresearchtools.pbworks.com
• Kdnuggets• http://www.kdnuggets.com/software/text.html
• text-processing.com
• Discussion Groups• Text Analytics on linkedin
• http://www.linkedin.com/groups/Text-Analytics-115439• Visual Analytics on linkedin
• http://www.linkedin.com/groups/Visual-Analytics-80552
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Bad News
• There is no open and freely available tool that is going to solve all your problems!!!
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Good News
• There is a variety of tools that can be beneficial and must be used in combination to accomplish the goal!!!
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Natural Language Processing (NLP)• Tokenization
• Part of speech tagging
• Stemming
• Stop word removal
• Other transformations
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NLP Tools• NLTK
• http://www.nltk.org/
• http://text-processing.com
• OpenNLP• http://incubator.apache.org/opennlp/
• Stanford CoreNLP• http://nlp.stanford.edu/software/corenlp.shtml
• Mallet• http://mallet.cs.umass.edu/
• GATE
• http://gate.ac.uk/
• LingPipe
• http://alias-i.com/lingpipe/
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Entity Extraction
• Finding entities, like People, Locations, Time, etc
• Some have ability to add your own entities (with seed terms)
• Tools
• OpenNLP
• Stanford CoreNLP
• OpenCalais
• GATE
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Journalism Application
• Structuring unstructured data
• Social networks of entities
• Clustering
• Plotting data on time line
• Plotting locations on a map
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Information Extraction
• Automatically identifies and extracts binary relationships from English sentences
• TextRunner
• NACTEM, MEDIE• http://www.nactem.ac.uk/medie/
• ReVerb
• http://reverb.cs.washington.edu/
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Information Extraction: ReVerb
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Question and Answer
• Parse the question to determine what type of information needs to be returned
• Leveraging approaches like the information extraction for retrieving the results
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Journalism Application
• Engagement! Help users find facts relevant to their situations.
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Document Classification
• Starts with a training set
• Predicts what class a document belongs
• Leveraging pure data mining approaches like Naïve Bayes, Decision Trees, Neural Networks
• Tools• NLTK• Mallet• Weka• Rapid Miner• GATE• Meandre
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Journalism Application
• IBM's ManyBills project• Identifies the topic of each section in a Congressional bill for
the purposes of identifying outliers. • For example, if a Congressman proposes a bill about the
environment, but it has a section deep down about banking regulation, ManyBills would identify that as an outlier and highlight it.
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Document Similarity/Clustering
• TF-IDF (Term frequency * inverse document frequency)
• Overview project (AP)• Tools
• GATE• Rapid Miner
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Journalism Application
• Identifying copycat legislation from year to year
• Clustering documents to find trends
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Topic Modeling
• Exploratory approach to find patterns by finding words that frequently occur together
• Document can have multiple topics• Words can exist in multiple topics• Tools
• Mallet uses LDA (latent Dirichlet allocation)• Other implementations as well…
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Topic Exploration
• Topical Guide: • http://tg.byu.edu/
• Tmve (Topic Model Visualization Engine) • http://code.google.com/p/tmve/
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Topical Guide
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Tmve
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Journalism Application
• Reporting tool for making sense of corpus• Isolating topics allows the user to focus only on the documents
in a corpus that are relevant. • There exists a clear potential for more data visualization.
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Automatic Summarization• Identifies sentences from among the documents
• Identifies common information conveyed across all the documents and then reformulates new sentences expressing that information
• Aims to combine the main themes with completeness, readability, and conciseness
• Lots of algorithms, but not really software tools to download to run on your collection
• Meandre implements a HITS algorithm that identifies sentences but does not reformat them
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Journalism Application
• Newsblaster• Summarizing all the news on the web• Every night, the system crawls a series of Web sites, downloads
articles, groups them together into "clusters" about the same topic, and summarizes each cluster.
• http://newsblaster.cs.columbia.edu/
• Ultimate Research Assistant• http://ultimate-research-assistant.com
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Sentiment Analysis
• NLTK• http://www.text-processing.com/demo/sentiment
• APIs• AlchemyAPI• Open Dover• Lexalytics• Saplo
• Meandre (concept tracking)
• Sentiment Analysis Symposium• http://sentimentsymposium.com/agenda.html• May 8, 2012 in New York
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Journalism Application
• Tracking Twitter sentiment about political candidates
• Comparing the tone of political statements over time or between candidates
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Analysis Frameworks
• Meandre• http://seasr.org/meandre
• DocumentCloud• www.documentcloud.org/
• Rapid Miner
• http://rapid-i.com/
• Weka• http://www.cs.waikato.ac.nz/ml/weka/index.html
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Locations
Components
Flows
• Web-based UI• Components and
flows are retrieved from server
• Additional locations of components and flows can be added to server
• Create flow using a graphical drag and drop interface
• Change property values
• Execute the flow
Meandre Workbench
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Meandre Services from Firefox Plugin
Tag Cloud Analysis
Readability Analysis
Automatic Summarization
Network Analysis
Location Entity to Google Map
Date Entity to Simile Timeline
Example: Zotero, SEASR, Protovis, Google Maps,
Simile
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Topic Modeling
Uses Mallet Topic Modeling to cluster nouns from almost 4000 documents from 19th century with 10 segments per document
Example below is clustering the Bible and shows 8 topics with at most 200 keywords for that topic
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Concept Mapping
Sentiment Analysissix core emotions (Love, Joy, Surprise, Anger, Sadness, Fear)
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Correlation Analysis
• Corrected OCR errs with spellchecking