flexible text mining using interactive information extraction david milward...
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Text mining vs. Data Mining
• Text mining– getting nuggets of information
from text
– extracting relationships
– structured results to feed into data mining, visualisation or databases
company activity companySanofi bid AventisRoche partner Antisoma
• Data mining– getting new knowledge from databases
– suggesting new relationships, trends, patterns
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Text Data Mining
• Emphasizes finding new knowledge from text
• Typically knowledge that is implicit within multiple documents
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What is the relationship to IR?
• IR finds the most relevant documents
• Text mining finds information from within documents, or across documents– What drugs are used for psoriasis treatment?
– Who are associated directly or indirectly with the Board of Exxon?
• There is overlap …– we often search to answer a question, not to find a
document
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Traditional Information Extraction
• Uses natural language processing to distinguish– Sanofi bid for Aventis – Aventis bid for Sanofi
• Provides structured results for easy review and analysis
• Uses normalised terminology to allow integration with databases e.g.
– Preferred term: Sanofi, – Synonyms: Sanofi Pasteur, Sanofi Synthelabo, Sanofi Synthélabo …
• But:– typically limited to patterns on a single sentence– constructing, testing and running queries can take days
• Appropriate if you always have the same question e.g. want to run over a newsfeed every night
company activity companySanofi bid AventisRoche partner Antisoma
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I2E: Interactive Information Extraction
• A new concept• Encompasses
– keywords → documents– patterns → relationships (structured output)
• Queries ranging from:– General Motors – General Motors & acquisition in the same
document– Automotive companies & acquisitions in the
same sentence– What companies is General Motors
associated with?
• Not limited to patterns within sentences e.g.– Merger and acquisition activity in
documents mentioning Japan
• Fast, scalable, versatile
I2EInformation ExtractionInformation Extraction
NLPNLP
Taxonomies/ Ontologies
Taxonomies/ Ontologies
Text SearchText Search
Structured Output
Structured Output
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Linguistic Processing
We find that p42mapk phosphorylates c-Myb on serine and threonine .
Purified recombinant p42 MAPK was found to phosphorylate Wee1 .
sentences
• Groups words into meaningful units
• Morphology allows search for different forms of words
morphology -
different forms
noun phrases
match entities
verb groups
match actions
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Monitoring Merger and Acquisition Activity
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Company Positions
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Using I2E in the Life Sciences
• Good resources– Scientific abstracts are readily
available in XML
– Large number of existing taxonomies/terminologies
• Very large scale– 16 million abstracts relevant to life
sciences. Growing ???? a year
– Large numbers of internal reports and full-text articles
– Internal documents often > 1000 pages, may be PDF images
– Taxonomies/terminologies are large, often deeply structured e.g.
• 350K nodes, ??? synonyms
– Still need to augment terminology for specific areas
• Relatively large scale– 17 million abstracts
– Large numbers of internal reports and full-text articles
– Internal documents can be >1000 pages, may be PDF images
– Taxonomies/terminologies are large, often deeply structured
> 100K concepts
> 400K synonyms
– Still need to augment terminology for specific areas
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Examples of Pharma Questions
• R&D
– Which proteins interact with metabolite X?
– What are the reaction kinetics for canonical pathway Y?
– What attributes are common to sets of biomarker genes
– What are the known associations between expressed genes and environmental factors.
– What dosages of compound B cause adverse reactions?
• Competitive Intelligence
– Which companies are working on technology C?
– What compounds are available for in-licensing in a disease area?
– Which research groups are my competitors collaborating with?
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Linking Drugs to Adverse Events
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Measurements
• Extraction of numerical parameters, – e.g. amounts, dosages, concentrations
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Benefits of Flexible Text Mining
• The ideal final query may use – co-occurrence of terms within a document or sentence
– a precise linguistic pattern
– a mixture of both
• It depends on– the nature of the task
– the availability of terminologies
– the kind of documents (news vs. science, abstract vs. full text)
– the time available to check results
• Flexibility to mix different techniques is also critical for fast development of queries– e.g. start with broad queries to explore the “results space”,
then home in
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Fast query creation
I2E: Better Results, Faster
Fast return of results
Fast review and analysis
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BCL2 CDKN1A DMPK EPHB2 INS MAP2K1 MAPK1 MAPK3 MAPK7 RB1 STK3 VIM
suppress
regulate
phosphorylate
mediate
interact
inhibit
induce
inactivate
co-express
block
bind
activate
[c] Reln
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Impact of I2E
• Significant reduction in time spent searching/reading the literature– weeks reduced to days or hours
• Structure the unstructured to – provide systematic and comprehensive review of
information content
– enable integration with traditional structured data
– allow complex analysis of literature derived information
– generate hypotheses, gain insight