Your SlideShare is downloading. ×
Upcoming SlideShare
Loading in...5

Thanks for flagging this SlideShare!

Oops! An error has occurred.

Saving this for later? Get the SlideShare app to save on your phone or tablet. Read anywhere, anytime – even offline.
Text the download link to your phone
Standard text messaging rates apply
1 Like
No Downloads
Total Views
On Slideshare
From Embeds
Number of Embeds
Embeds 0
No embeds

Report content
Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

No notes for slide
  • Check attribution Lexicon: lookup Classify candidates Sliding window – when candidats not known Boundary model – window+classification in one Finite state machine for complete path Grammers
  • All of these provide a form of API for integration with other code
  • Transcript

    • 1. Towards Web-Scale Information Extraction Eugene Agichtein Mathematics & Computer Science Emory University [email_address] http:// /~eugene/
    • 2. The Value of Text Data
      • “ Unstructured” text data is the primary form of human-generated information
        • Blogs, web pages, news, scientific literature, online reviews, …
        • Semi-structured data (database generated): see Prof. Bing Liu’s KDD webinar:
        • The techniques discussed here are complimentary to structured object extraction methods
      • Need to extract structured information to effectively manage, search, and mine the data
      • Information Extraction: mature, but active research area
        • Intersection of Computational Linguistics, Machine Learning, Data mining, Databases, and Information Retrieval
        • Traditional focus on accuracy of extraction
    • 3. Example: Answering Queries Over Text For years, Microsoft Corporation CEO Bill Gates was against open source. But today he appears to have changed his mind. "We can be open source. We love the concept of shared source," said Bill Veghte , a Microsoft VP . "That's a super-important shift for us in terms of code access.“ Richard Stallman , founder of the Free Software Foundation , countered saying… Name Title Organization Bill Gates CEO Microsoft Bill Veghte VP Microsoft Richard Stallman Founder Free Soft.. PEOPLE Select Name From PEOPLE Where Organization = ‘Microsoft’ Bill Gates Bill Veghte (from William Cohen’s IE tutorial, 2003)
    • 4. Outline
      • Information Extraction Tasks
        • Entity tagging
        • Relation extraction
        • Event extraction
      • Scaling up Information Extraction
        • Focus on scaling up to large collections (where data mining can be most beneficial)
        • Other dimensions of scalability
    • 5. Information Extraction Tasks
      • Extracting entities and relations: this talk
        • Entities : named (e.g., Person) and generic (e.g., disease name)
        • Relations : entities related in a predefined way (e.g., Location of a Disease outbreak, or a CEO of a Company)
        • Events : can be composed from multiple relation tuples
      • Common extraction subtasks:
        • Preprocess : sentence chunking, syntactic parsing, morphological analysis
        • Create rules or extraction patterns : hand-coded, machine learning, and hybrid
        • Apply extraction patterns or rules to extract new information
        • Postprocess and integrate information
          • Co-reference resolution, deduplication, disambiguation
    • 6. Entity Tagging
      • Identifying mentions of entities (e.g., person names, locations, companies) in text
        • MUC (1997): Person, Location, Organization, Date/Time/Currency
        • ACE (2005): more than 100 more specific types
      • Hand-coded vs. Machine Learning approaches
      • Best approach depends on entity type and domain:
        • Closed class (e.g., geographical locations, disease names, gene & protein names): hand coded + dictionaries
        • Syntactic (e.g., phone numbers, zip codes): regular expressions
        • Semantic (e.g., person and company names): mixture of context, syntactic features, dictionaries, heuristics, etc.
        • “ Almost solved” for common/typical entity types
    • 7. Example: Extracting Entities from Text
        • Useful for data warehousing, data cleaning, web data integration
      1 4089 Whispering Pines Nobel Drive San Diego CA 92122 Address Citation Ronald Fagin, Combining Fuzzy Information from Multiple Systems , Proc. of ACM SIGMOD , 2002 House number Building Road City Zip State Year 2002 S 4 Conference Proc. of ACM SIGMOD S 3 Title Combining Fuzzy Information from Multiple Systems S 2 Author Ronald Fagin S 1 Label( s i ) Sequence Segment( s i )
    • 8. Hand-Coded Methods
      • Easy to construct in some cases
        • e.g., to recognize prices, phone numbers, zip codes, conference names, etc.
      • Intuitive to debug and maintain
        • Especially if written in a “high-level” language:
        • Can incorporate domain knowledge
      • Scalability issues:
        • Labor-intensive to create
        • Highly domain-specific
        • Often corpus-specific
        • Rule-matches can be expensive
      [IBM Avatar] ContactPattern  RegularExpression(Email.body,”can be reached at”)
    • 9. Machine Learning Methods
      • Can work well when lots of training data easy to construct
      • Can capture complex patterns that are hard to encode with hand-crafted rules
        • e.g., determine whether a review is positive or negative
        • extract long complex gene names
        • Non-local dependencies
      The human T cell leukemia lymphotropic virus type 1 Tax protein represses MyoD-dependent transcription by inhibiting MyoD-binding to the KIX domain of p300.“ [From AliBaba]
    • 10. Representation Models [Cohen and McCallum, 2003] Any of these models can be used to capture words, formatting or both. Lexicons Alabama Alaska … Wisconsin Wyoming Sliding Window Classify Pre-segmented Candidates Finite State Machines Context Free Grammars Boundary Models Abraham Lincoln was born in Kentucky. member? Abraham Lincoln was born in Kentucky. Abraham Lincoln was born in Kentucky . Classifier which class? … and beyond Abraham Lincoln was born in Kentucky. Classifier which class? Try alternate window sizes: Classifier which class? BEGIN END BEGIN END BEGIN Abraham Lincoln was born in Kentucky. Most likely state sequence? Abraham Lincoln was born in Kentucky. NNP V P NP V NNP NP PP VP VP S Most likely parse?
    • 11. Popular Machine Learning Methods
      • Naive Bayes
      • SRV [Freitag 1998], Inductive Logic Programming
      • Rapier [Califf and Mooney 1997]
      • Hidden Markov Models [Leek 1997]
      • Maximum Entropy Markov Models [McCallum et al. 2000]
      • Conditional Random Fields [Lafferty et al. 2001]
      • Scalability
        • Can be labor intensive to construct training data
        • At run time, complex features can be expensive to construct or process (batch algorithms can help: [ Chandel et al. 2006] )
      For details: [Feldman, 2006 and Cohen, 2004]
    • 12. Some Available Entity Taggers
      • ABNER:
        • Linear-chain conditional random fields (CRFs) with orthographic and contextual features.
      • Alias-I LingPipe
      • MALLET:
        • Collection of NLP and ML tools, can be trained for name entity tagging
      • MinorThird :
        • Tools for learning to extract entities, categorization, and some visualization
      • Stanford Named Entity Recognizer:
        • CRF-based entity tagger with non-local features
    • 13. Alias-I LingPipe ( )
      • Statistical named entity tagger
        • Generative statistical model
          • Find most likely tags given lexical and linguistic features
          • Accuracy at (or near) state of the art on benchmark tasks
      • Explicitly targets scalability:
        • ~100K tokens/second runtime on single PC
        • Pipelined extraction of entities
        • User-defined mentions, pronouns and stop list
          • Specified in a dictionary, left-to-right, longest match
        • Can be trained/bootstrapped on annotated corpora
    • 14. Outline
      • Overview of Information Extraction
        • Entity tagging
        • Relation extraction
        • Event extraction
      • Scaling up Information Extraction
        • Focus on scaling up to large collections (where data mining and ML techniques shine)
        • Other dimensions of scalability
    • 15. Relation Extraction Examples
      • Extract tuples of entities that are related in predefined way
      Disease Outbreaks relation Relation Extraction „ We show that CBF-A and CBF-C interact with each other to form a CBF-A-CBF-C complex and that CBF-B does not interact with CBF-A or CBF-C individually but that it associates with the CBF-A-CBF-C complex.“ [From AliBaba] Zaire Ebola May 1995 U.S. Pneumonia Feb. 1995 July 1995 Jan. 1995 Date Location Disease Name U.K. Mad Cow Disease Ethiopia Malaria May 19 1995 , Atlanta -- The Centers for Disease Control and Prevention, which is in the front line of the world's response to the deadly Ebola epidemic in Zaire , is finding itself hard pressed to cope with the crisis… CBF-A CBF-C CBF-B CBF-A-CBF-C complex interact complex associates
    • 16. Relation Extraction Approaches
      • Knowledge engineering
      • Experts develop rules, patterns:
        • Can be defined over lexical items: “<company> located in <location>”
        • Over syntactic structures: “((Obj <company>) (Verb located) (*) (Subj <location>))”
      • Sophisticated development/debugging environments:
        • Proteus, GATE
      • Machine learning
      • Supervised : Train system over manually labeled data
        • Soderland et al. 1997, Muslea et al. 2000, Riloff et al. 1996, Roth et al 2005, Cardie et al 2006, Mooney et al. 2005, …
      • Partially-supervised : train system by bootstrapping from “seed” examples:
        • Agichtein & Gravano 2000, Etzioni et al., 2004, Yangarber & Grishman 2001, …
        • “ Open” ( no seeds ): Sekine et al. 2006, Cafarella et al. 2007, Banko et al. 2007
      • Hybrid or interactive systems:
        • Experts interact with machine learning algorithms (e.g., active learning family) to iteratively refine/extend rules and patterns
        • Interactions can involve annotating examples, modifying rules, or any combination
    • 17. Open Information Extraction [Banko et al., IJCAI 2007]
      • Self-Supervised Learner:
        • All triples in a sample corpus ( e 1 , r, e 2 ) are considered potential “tuples” for relation r
        • Positive examples: candidate triplets generated by a dependency parser
        • Train classifier on lexical features for positive and negative examples
      • Single-Pass Extractor:
        • Classify all pairs of candidate entities for some (undetermined) relation
        • Heuristically generate a relation name from the words between entities
      • Redundancy-Based Assessor:
        • Estimate probability that entities are related from co-occurrence statistics
      • Scalability
        • Extraction/Indexing
          • No tuning or domain knowledge during extraction, relation inclusion determined at query time
          • 0.04 CPU seconds pre sentence, 9M web page corpus in 68 CPU hours
          • Every document retrieved, processed (parsed, indexed, classified) in a single pass
        • Query-time
          • Distributed index for tuples by hashing on the relation name text
      • Related efforts : [Cucerzan and Agichtein 2005], [Pasca et al. 2006], [Sekine et al. 2006], [Rozenfeld and Feldman 2006], …
    • 18. Event Extraction
      • Similar to Relation Extraction, but:
        • Events can be nested
        • Significantly more complex (e.g., more slots) than relations/template elements
        • Often requires coreference resolution, disambiguation , deduplication , and inference
      • Example: an integrated disease outbreak event [Hatunnen et al. 2002]
    • 19. Event Extraction: Integration Challenges
      • Information spans multiple documents
        • Missing or incorrect values
        • Combining simple tuples into complex events
        • No single key to order or cluster likely duplicates while separating them from similar but different entities.
        • Ambiguity: distinct physical entities with same name (e.g., Kennedy)
      • Duplicate entities, relation tuples extracted
        • Large lists with multiple noisy mentions of the same entity/tuple
        • Need to depend on fuzzy and expensive string similarity functions
        • Cannot afford to compare each mention with every other.
      • See Part II of KDD 2006 Tutorial “Scalable Information Extraction and Integration” -- scaling up integration: /
    • 20. Other Information Extraction Tutorials
      • See these tutorials for more details:
        • R. Feldman, Information Extraction – Theory and Practice, ICML 2006
        • W. Cohen, A. McCallum, Information Extraction and Integration: an Overview, KDD 2003 http://
    • 21. Summary: Accuracy of Extraction Tasks
      • Errors cascade (errors in entity tag cause errors in relation extraction)
      • This estimate is optimistic:
        • Primarily for well-established (tuned) tasks
        • Many specialized or novel IE tasks (e.g. bio- and medical- domains) exhibit lower accuracy
        • Accuracy for all tasks is significantly lower for non-English
      [Feldman, ICML 2006 tutorial]
    • 22. Multilingual Information Extraction
      • Active research area, beyond the scope of this talk. Nevertheless, a few (incomplete) pointers are provided.
      • Closely tied to machine translation and cross-language information retrieval efforts.
      • Language-independent named entity tagging and related tasks at CoNLL:
        • 2006: multi-lingual dependency parsing ( )
        • 2002, 2003 shared tasks: language independent Named Entity Tagging ( )
      • Global Autonomous Language Exploitation program (GALE):
      • Interlingual Annotation of Multilingual Text Corpora (IAMTC)
        • Tools and data for building MT and IE systems for six languages
      • REFLEX project: NER for 50 languages
        • Exploit for training temporal correlations in weekly aligned corpora
      • Cross-Language Information Retrieval (CLEF)
    • 23. Outline
      • Overview of Information Extraction
        • Entity tagging
        • Relation extraction
        • Event Extraction
      • Scaling up Information Extraction
        • Focus on scaling up to large collections (where data mining and ML techniques shine)
        • Other dimensions of scalability
    • 24. Scaling Information Extraction to the Web
      • Dimensions of Scalability
        • Corpus size :
          • Applying rules/patterns is expensive
          • Need efficient ways to select/filter relevant documents
        • Document accessibility :
          • Deep web: documents only accessible via a search interface
          • Dynamic sources: documents disappear from top page
        • Source heterogeneity :
          • Coding/learning patterns for each source is expensive
          • Requires many rules (expensive to apply)
        • Domain diversity:
          • Extracting information for any domain, entities, relationships
          • Some recent progress (e.g., see slide 17)
          • Not the focus of this talk
    • 25. Scaling Up Information Extraction
      • Scan-based extraction
        • Classification/filtering to avoid processing documents
        • Sharing common tags/annotations
      • General keyword index -based techniques
        • QXtract, KnowItAll
      • Specialized indexes
        • BE/KnowItNow, Linguist’s Search Engine
      • Parallelization/distributed processing
        • IBM WebFountain, UIMA, Google’s Map/Reduce
    • 26. Efficient Scanning for Information Extraction
      • 80/20 rule: use few simple rules to capture majority of the instances [Pantel et al. 2004]
      • Train a classifier to discard irrelevant documents without processing [Grishman et al. 2002]
        • ( e.g., the Sports section of NYT is unlikely to describe disease outbreaks)
      • Share base annotations (entity tags) for multiple extraction tasks
      Extraction System Text Database
      • Extract output tuples
      • Process documents
      • Retrieve docs from database
      filtered … Output Tuples Classifier
      • Filter documents
    • 27. Exploiting Keyword and Phrase Indexes
      • Generate queries to retrieve only relevant documents
      • Data mining problem!
      • Some methods in literature:
        • Traversing Query Graphs [Agichtein et al. 2003]
        • Iteratively refine queries [Agichtein and Gravano 2003]
        • Iteratively partition document space [Etzioni et al., 2004]
      • Case studies: QXtract , KnowItAll
    • 28. Simple Strategy: Iterative Set Expansion
      • Execution time = |Retrieved Docs| * (R + P) + |Queries| * Q
      Extraction System Text Database
      • Extract tuples from docs
      • Process retrieved documents
      • Query database with seed tuples
      Time for retrieving a document Time for answering a query Time for processing a document Query Generation
      • Augment seed tuples with new tuples
      (e.g., [ Ebola AND Zaire ]) (e.g., <Malaria, Ethiopia>) … Output Tuples
    • 29. Reachability via Querying t 1 retrieves document d 1 that contains t 2 t 1 t 2 t 3 t 4 t 5 Upper recall limit : determined by the size of the biggest connected component Reachability Graph Tuples Documents t 1 t 2 t 3 t 4 t 5 d 1 d 2 d 3 d 4 d 5 <SARS, China> <Ebola, Zaire> <Malaria, Ethiopia> <Cholera, Sudan> <H5N1, Vietnam> [Agichtein et al. 2003b]
    • 30. Reachability Graph for DiseaseOutbreaks DiseaseOutbreaks, New York Times 1995
    • 31. Getting Around Reachability Limits
      • KnowItAll [Etzioni et al., WWW 2004]:
        • Add keywords to partition documents into retrievable disjoint sets
        • Submit queries with parts of extracted instances
      • QXtract [Agichtein and Gravano 2003a]
        • General queries with many matching documents
        • Assumes many documents retrievable per query
    • 32. QXtract [Agichtein and Gravano 2003]
      • Get document sample with “likely negative” and “likely positive” examples.
      • Label sample documents using information extraction system as “oracle.”
      • Train classifiers to “recognize” useful documents.
      • Generate queries from classifier model/rules.
      Query Generation Information Extraction Seed Sampling Classifier Training Queries User-Provided Seed Tuples
    • 33. Using Generic Indexes: Summary
      • Order of magnitude speed up in runtime
      • But: keyword indexes are approximate (so the queries are not precise)
      • Require many documents to retrieve
      • Can we do better?
    • 34. Index Structures for Information Extraction
      • Bindings Engine [Cafarella and Etzioni 2005]
      • Indexing and querying entities: [K. Chakrabarti et al. 2006 ]
      • IBM Avatar project:
        • http:// /
      • Other indexing schemes:
        • Linguist’s search engine (P. Resnik):
        • FREE: Indexing regular expressions: [Cho and Rajagolapan, ICDE 2002]
        • Indexing and querying linguistic information in XML: [Bird et al., 2006]
    • 35.
      • Variabilized search query language
      • Integrates variable/type data with inverted index, minimizing query seeks
        • Index <NounPhrase>, <Adj-Term> terms
      • Key idea: neighbor index
        • At each position in the index, store “neighbor text” – both lexemes and tags
      • Query: “ cities such as <NounPhrase> ”
      Bindings Engine (BE) [Cafarella and Etzioni 2005] neighbor 1 str 1 #docs … pos 0 pos 1 docid #docs-1 pos #docs-1 #posns pos 0 pos 1 … pos #pos-1 docid 0 docid 1 #posns pos 0 neighbor 0 pos 1 neighbor 1 … pos #pos-1 … #neighbors blk_offset 19 12 Result: in document 19: “I love cities such as Philadelphia .” neighbor 0 str 0 words such philadelphia nickels mayors give friendly cities billy as NP right Philadelphia 3 <offset> AdjT left such
    • 36. Related Approach: [ K. Chakrabarti et al. 2006]
      • Support “relationship” keyword queries over indexed entities
      • Top-K support for early processing termination
    • 37. Workload-Driven Indexing [ S. Chakrabarti et al. 2006] Indexing Thousands of Entity Types
    • 38. Workload-Driven Indexing (continued)
    • 39. Parallelization/Adaptive Processing
      • Parallelize processing:
        • WebFountain [Gruhl et al. 2004]
        • UIMA architecture
        • Map/Reduce
    • 40. IBM WebFountain
      • Dedicated share-nothing 256-node cluster
      • Blackboard annotation architecture
      • Data pipelined and streamed past each “augmenter” to add annotations
      • Merge and index annotations
      • Index both tokens and annotations
      • Between 25K-75K entities per second
      [Gruhl et al. 2004]
    • 41. UIMA (IBM Research)
      • Unstructured Information Management Architecture (UIMA)
        • http:// /UIMA/
      • Open component software architecture for development, composition, and deployment of text processing and analysis components.
      • Run-time framework allows to plug in components and applications and run them on different platforms. Supports distributed processing, failure recovery, …
      • Scales to millions of documents – incorporated into IBM OmniFind, grid computing-ready
      • The UIMA SDK ( freely available ) includes a run-time framework, APIs, and tools for composing and deploying UIMA components.
      • Framework source code also available on Sourceforge:
    • 42. Map/Reduce ( [ Dean & Ghemawat, OSDI 2004 ])
    • 43. Map/Reduce (continued)
      • General framework
        • Scales to 1000s of machines
        • Recently implemented in Nutch and other open source efforts
      • “ Maps” nicely to information extraction
        • Map phase:
          • Parse individual documents
          • Tag entities
          • Propose candidate relation tuples
        • Reduce phase
          • Merge multiple mentions of same relation tuple
          • Resolve co-references, duplicates
    • 44. Summary
      • Presented a brief overview of information extraction
      • Techniques and ideas to scale up information extraction
        • Scan-based techniques (limited impact)
        • Exploiting general indexes (limited accuracy)
        • Building specialized index structures (most promising)
      • Scalability is a data mining problem
        • Querying graphs  link discovery
        • Workload mining for index optimization
        • Automatically optimizing for specific text mining application
        • Considerations for building integrated data mining systems
    • 45. References and Supplemental Materials
      • References, slides, and additional information available at:
      • http:// -webinar/
      • Will also post answers to questions