words and more words: challenges of big data by prof. edie rasmussen

Words and More Words: Challenges of Big (Text) Data

Edie Rasmussen Visiting Professor, Nanyang Technological University

Professor, University of British Columbia

WKWSCI

SYMPOSIUM

2014 Big Data, Big Ideas for Smarter Communities

Outline

• The Rise of Big Text Data

• Challenges for Text Data

• Research Opportunities

– Counting and Culturomics

– Extracting Meaning from Text

2 WKWSCI SYMPOSIUM 2014

Big Data, Big Ideas for Smarter Communities

The Rise of Big Text Data

• Before there was Big Data, there were large bibliographic databases:

– Dialog: ~180 scholarly databases

– Lexis/Nexis: 5 billion documents (business/law/news)

– Citation Indexes: > 40 million records

• IR techniques designed for rapid access to very large (text) databases

• Swanson: “Undiscovered public knowledge” (1987)

WKWSCI SYMPOSIUM 2014 Big Data, Big Ideas for Smarter Communities

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Current Text Sources

• Digitized Legacy Materials – Google Books, Hathi Trust (11 million volumes, 500 TB)

• The Web

• Search Logs (over 2 million queries per minute)

• Wikipedia (~4.5 million English articles)

• Blogs (The Blogosphere)

• Twitter (The Twitterverse)

• Test Collections – Smaller

– Experimentally more robust



Challenges of Text

• Legacy Text/Digitization Costs • Quality (OCR Errors; Metadata Errors) • Availability (Access, Copyright, Privacy) • Reliability

– Algorithmic dependencies – Creator trustworthiness

• Authorship Issues (Identification, Authority) • Lack of Structure • Lack of Context • Ambiguity of human language • Breadth vs. Depth

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Processing Text

• Tokenizing, stopping, stemming

• Statistics of text: term values (tf*idf)

• “Bag of Words” approach

• Other evidence: network structures

• Similarity calculations

• Creating ranked lists

• Note: Probabilistic rather than Deterministic

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Counting and the Rise of Culturomics

• “Culturomics is the application of high-throughput data collection and analysis to the study of human culture”

• Database of >5 million digitized books (~4%)

• Michel et al. (Science, 2011): “Quantitative analysis of culture using millions of digitized books”

• Google’s N-Gram Viewer

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Using the N-Gram Viewer

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typhoid

gout

1800 2000 1900

HIV

cholera


How Far Will Counting Take us?

• Many limitations (e.g. incomplete data set)

• Some surprisingly sophisticated analyses:

– Size of English lexicon

– Change in word usage (irregular verbs) over time

– Cultural turnover (inventions)

– The nature (duration) of fame

– Patterns of censorship (“suppression index”)



Critiques of Culturomics

• “The death of theory”

• “…second-rate scholars will use the Google Books corpus to churn out gigabytes of uninformative graphs and insignificant conclusions.” (Nunberg, 2011)

• Books as a representation of human history

• A “time sink”



Social Media as Big Data

• ‘Internet Minute’

– 320+ new Twitter accounts

– 100,000 new Tweets

– 2+ million search queries

– 6 new Wikipedia articles

– 30 hours of video uploaded (Source: Intel http://www.intel.com/content/www/us/en/communications/internet-minute-infographic.html)



http://www.intel.com/content/www/us/en/communications/internet-minute-infographic.html








TM: Topic Detection and Tracking

• Tracking a story line over time

• News wire input, identify new story, find subsequent instances

• Story segmentation, First story detection, Clustering of like stories

• Interesting to news, business, security analysts



TM: Sentiment Analysis/Opinion Mining

• Rich data from Blogs and Tweets

• Basically a classification problem (SVM, Naïve Bayes, etc.) - > positive, negative, neutral

• Involves Entity Extraction, NLP, sentiment vocabularies

• Of interest to government and businesses

• See Stanford SA of movie reviews: http://nlp.stanford.edu:8080/sentiment/rntnDemo.html



http://nlp.stanford.edu:8080/sentiment/rntnDemo.html

http://nlp.stanford.edu:8080/sentiment/rntnDemo.html

TM: Trends and Predictions

• Can Tweets and Search Logs be used to predict the future?

• Google Flu Trends, Google Dengue Trends – Correlated with Search Terms

• Network analysis on Tweets on Arab Spring

• Assessing tone of global news data to predict national stability, location of terrorists, etc. (Leetaru)

• Predicting opinions (recommender systems)

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TM: Question Answering

• Combines multiple sources of evidence:

– Question type identification

– Information retrieval of candidate text

– Natural language processing

– Entity extraction

– Hypothesis generation and scoring (confidence)

– Ranking hypotheses



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Watson, 2011

Hans Peter Luhn, 1952

Watson, 2011

Structuring Research: “Digging Into Data” Program

• Addresses: “how "big data" changes the research landscape for the humanities and social sciences”

• 3 rounds of international research funding • Canada, US, UK, plus Netherlands • Team approach: scholars, scientists, information

professionals • Requires international teams; funding from at

least two countries • Wide range of datasets made available • http://www.diggingintodata.org/

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http://www.diggingintodata.org/

http://www.diggingintodata.org/

Thank you!



words and more words: challenges of big data by prof. edie rasmussen

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