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Language Computer Corporation:  Text Extraction Profile
Language Computer Corporation:  Text Extraction Profile
Language Computer Corporation:  Text Extraction Profile
Language Computer Corporation:  Text Extraction Profile
Language Computer Corporation:  Text Extraction Profile
Language Computer Corporation:  Text Extraction Profile
Language Computer Corporation:  Text Extraction Profile
Language Computer Corporation:  Text Extraction Profile
Language Computer Corporation:  Text Extraction Profile
Language Computer Corporation:  Text Extraction Profile
Language Computer Corporation:  Text Extraction Profile
Language Computer Corporation:  Text Extraction Profile
Language Computer Corporation:  Text Extraction Profile
Language Computer Corporation:  Text Extraction Profile
Language Computer Corporation:  Text Extraction Profile
Language Computer Corporation:  Text Extraction Profile
Language Computer Corporation:  Text Extraction Profile
Language Computer Corporation:  Text Extraction Profile
Language Computer Corporation:  Text Extraction Profile
Language Computer Corporation:  Text Extraction Profile
Language Computer Corporation:  Text Extraction Profile
Language Computer Corporation:  Text Extraction Profile
Language Computer Corporation:  Text Extraction Profile
Language Computer Corporation:  Text Extraction Profile
Language Computer Corporation:  Text Extraction Profile
Language Computer Corporation:  Text Extraction Profile
Language Computer Corporation:  Text Extraction Profile
Language Computer Corporation:  Text Extraction Profile
Language Computer Corporation:  Text Extraction Profile
Language Computer Corporation:  Text Extraction Profile
Language Computer Corporation:  Text Extraction Profile
Language Computer Corporation:  Text Extraction Profile
Language Computer Corporation:  Text Extraction Profile
Language Computer Corporation:  Text Extraction Profile
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Language Computer Corporation: Text Extraction Profile

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An overview of Language Computer Corporation\'s text extraction capabilities.

An overview of Language Computer Corporation\'s text extraction capabilities.

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  • 1. Language Computer Corporation: Knowledge Supremacy through Customizable Text Extraction Products Andrew Hickl, CEO / President Language Computer Corporation December 2008
  • 2. Language Computer Corporation (LCC) “Boutique” provider of next‐generation natural language processing  • software solutions for Government and commercial customers Founded 1995 • Based in Richardson, Texas • 25 developers and researchers • Strong track record:  top marks at more than 20 different Government  • evaluations since 1999 Question Answering (TREC, 1999‐2008) – Summarization (DUC, 2003‐2008) – Information Extraction (ACE, 2005‐2006) – Textual Inference (RTE, 2006‐2008) –
  • 3. A Brief History of LCC 1996‐2004:  Closed‐Domain Information Extraction • MUC / Tipster (precursors to ACE) – Grammar‐ or rule‐based systems – Entity Extraction (100+ types, English) – Relationship Extraction (50+ types, English) – Event Extraction (5‐8 types, English) – 1999 – : First Automatic Question‐Answering Systems • TREC Question Answering evaluations – Factoid:  What is Britney Spears’s middle name? – Complex:  What impact did Hurricane Gustav have on the Dallas economy? – Yes/No:  Did Lindsay Lohan’s album reach #1? – How‐To:  How do I file an extension on my 2008 Federal Income Taxes? – Why:  Why did John McCain name Sarah Palin as his running mate? –
  • 4. A Brief History of LCC 2002 – : Wide‐Coverage Entity Extraction System • – Used a maximum entropy‐based framework to categorize more than 350 different name categories in text • English:  368 types • French, Spanish, German, Dutch, Russian, Japanese:  ~100 types • Arabic, Chinese, Farsi, Korean:  ~50 types – Dependent on sources of training data 2004 – :  First Open‐Domain, Customizable Event Extraction System • Used active learning to leverage feedback gathered from a user – Allows users to define event extractors for any event of interest – Deployed for other languages:  English, Arabic, Chinese, Korean, Farsi – Completely ontology‐independent –
  • 5. A Brief History of LCC 2007 – :  First Customizable Information Extraction Systems • – Allows users to define extractors for any entity, attribute, relationship, or   sentiment / attitude expressed in text – Used active learning to leverage feedback gathered from a user – Leverages automatic candidate generation techniques to find new instances  for extractor training – Deployed for other languages:  English, Arabic, Chinese – Completely ontology‐independent 2007 – :  Truly Domain‐Independent Extraction • – Allows extractors to maintain high levels of performance, regardless of  training  or testing domain – Reduces “overfitting” to particular domain – Reduces “tag spam”:  overtagging of certain (frequent) categories in out‐of‐ domain documents
  • 6. A Brief History of LCC 2008 – :  First Automatic Dossier / Infobox Generation System • – Learns what attributes and relationships are inherently relevant for an entity  from information stored in unstructured text – Generates either Wikipedia‐style infoboxes or prose descriptions  (a.k.a. “dossiers”) for each entity – Capable of analogizing from existing structured data resources or learning  from feedback provided by users 2008 – :  Robust Textual Inference for NLP Applications • – Deployed state‐of‐the‐art system for recognizing textual entailment to  validate content stored in large databases – Developed temporal inference systems capable of accurately timestamping  events mentioned in text / message traffic
  • 7. Our Mission Provide customers with knowledge supremacy necessary to support analytic operations in any domain. Make it easy (and cost‐effective) to unlock knowledge from collections  of unstructured text in any language or domain. Develop “game changing” search and discovery tools which  turn knowledge into value. Build the premier information extraction brand.
  • 8. Key Delineators Scalable.    • – LCC’s entity, relationship, attribute, and event extraction tools provide access to  more types of information than any other provider. Customizable. • – LCC’s customization framework allows content providers to add value to existing  repositories quickly – and cheaply. Flexible. • – LCC’s learning‐based extraction tools won’t degrade when run on “new” types of  documents. Deployable. • – LCC offers distributable and parallelizable components which can be run  in any environment – big or small. Integrate‐able. • – LCC’s products are designed to interoperate with a customer’s existing text and  knowledge management tools. Reliable. • – 10+ years of excellence in providing USG customer with high‐tech NLP solutions that just work.
  • 9. How do you achieve knowledge supremacy? Wide Coverage (enough for most applications) • Customizable (in minutes, or less) • Trainable (by application builders or end‐users) • Domain Portable (with next to no human intervention)  • Fast (enough to index TBs of text) • Manageable (demonstrated value‐add) • Challenge: Is it possible to build an extraction system  which can learn hundreds of types?
  • 10. Solving (part of) the Coverage Problem: CiceroLite LCC’s wide‐coverage named entity recognizer, CiceroLite, categorizes 8  • high‐frequency NE classes with over 90% precision and recall. But it’s capable of much more: the English language version of CiceroLite  can also categorize 368 different NE classes, including:
  • 11. How do you achieve knowledge supremacy? Wide Coverage (enough for most applications) • Customizable (in minutes, or less) • Trainable (by application builders or end‐users) • Domain Portable (with next to no human intervention) • Fast (enough to index TBs of text) • Manageable (demonstrated value‐add) • Challenge: Is it possible to build an extraction system  which can allow users to create new extractors?
  • 12. Introducing… CiceroCustom CiceroCustom can be used to extract nearly any type of entity, attribute,  • relationship, or event information from text without the need for hand‐ crafted rules or pre‐specified extraction templates.  Three steps to customized information extraction: • – Step 1. Use CiceroCustom to define a customized extractor which specifies  that the type of information that a user is most interested in. – Step 2.   Use the CiceroCustom GUI to “train” each extractor: • Mark instances as “relevant” or “irrelevant” • Correct annotations supplied by CiceroCustom • Accurate results seen after < 15 minutes of training – Step 3. Use extractors to extract information from new texts
  • 13. Traditional Text Extraction vs. CiceroCustom Traditional Extraction CiceroCustom Ontology Required? Fixed set of templates User‐defined templates Techniques used? Heuristics / Classifiers Active Learning Information considered? Limited to information found in  Inter‐ and Intra‐ sentential  a single sentence extraction Access to discourse information? N/A Automatic Discourse Parsing Domain portability? Domain‐Dependent Domain‐Independent Applicable to new genres? Performance degrades when  Robust performance across  applied to new genres document genres Representation of information? Fixed, Immutable Dynamically created Discovery of new, essential  User Automatic information? Coreference? User Automatic Level of expertise required? Extraction Experts Any End User Time to create extractors? Days, Weeks Minutes, Hours
  • 14. CiceroCustom: Innovations First open‐domain extraction system that can be customized in minutes • Active learning‐based framework makes it possible for novices to train high‐performance extractors  – in under an hour Extractors can be refined / split / fused as needs change – State‐of‐the‐art inference‐based instance fusion • State‐of‐the‐art temporal, spatial, and textual inference components make it possible to fuse partial  – representations into coherent instances that can be used operationally Automatic Discovery of Essential Information Related to Candidates • Rich semantic substrate helps extraction models identify all of the information needed for extraction – First Extraction System to Leverage Multiple Semantic Parsers • Combines dependency information from PropBank, NomBank, and FrameNet to automatically  – create semantic representations for entities, attributes, relationships, or events of interest First work done leveraging semantic parsing for extraction done at LCC:  (Surdeanu et al. 2003) – State‐of‐the‐Art Discourse Parsing • Identification of relations between sentences or events provides for greater recall of extractors – Extraction can go beyond a single sentence –
  • 15. How do you achieve knowledge supremacy? Wide Coverage (enough for most applications) • Customizable (in minutes, or less) • Trainable (by application builders or end‐users) • Domain Portable (with next to no human intervention) • Fast (enough to index TBs of text) • Manageable (demonstrated value‐add) •
  • 16. What does it mean to be “domain portable”? Performance of most learning‐based extraction systems (entity, event,  • etc.) suffers when trained and tested on different types of documents • Most IE systems suffer degradation of > ‐30% when ported to new  domains (e.g. newswire  message traffic) LCC is pioneering new unsupervised and lightly‐supervised approaches to  • reduce the amount of degradation observed when testing on out‐of‐ domain documents With ~15 minutes of input from a user,  LCC reduces extractor error by an average of 25%.
  • 17. How do you achieve knowledge supremacy? Wide Coverage (enough for most applications) • Customizable (in minutes, or less) • Trainable (by application builders or end‐users) • Domain Portable (with next to no human intervention) • Fast (enough to index TBs of text) • Manageable (demonstrated value‐add) •
  • 18. Performance Profile: 2 GHz, single core, 2 GB RAM
  • 19. How do you achieve knowledge supremacy? Wide Coverage (enough for most applications) • Customizable (in minutes, or less) • Trainable (by application builders or end‐users) • Domain Portable (with next to no human intervention) • Fast (enough to index TBs of text) • Manageable (demonstrated value‐add) •
  • 20. LCC Text Processing Cycle Question Answering Open APIs Semantic Search Web Services Keyword Expansion Java RMI Analytic Info Output Need Geocoding Predictive Analysis Spatial Inference Situational Analysis Socio-Cultural Analysis Timestamping Awareness Dossier Generation Temporal Inference Data  Processing Collection Data Ingestion & Indexing Named Entity Recognition Information Extraction Coreference Resolution
  • 21. Dossier Generation (2009) Need for tools which can automatically assemble • high‐quality knowledge resources from information  extracted from text LCC is developing an integrated, unsupervised  • Dossier Generation capability which can assemble  relevant entity profiles (either as unstructured text or  Intellipedia‐style structured infoboxes) Hundreds of Entity, Relation, Attribute, Events – Implicit Relations from Data Mining Systems – Normalized Dates / Times / Locations – Learning‐based relevance detection algorithms capable  – of learning what’s relevant for each individual or  category of individuals
  • 22. Database Validation (2009) Content Validation Information The attack took place in the morning. Retrieval The attack killed 2 caretakers. The attack damaged 50 cars. The attack damaged 20 buildings. Commitment The mosque was in Mariengasse. Extraction Anas Shakfeh said the attack was a protest by rightest circles against the Islam conference.
  • 23. Knowledge Acquisition for Link Analysis (2009) Entity Extraction Relationship Extraction Event Extraction Untyped Dependency Extraction Model Semantic Feedback Triples Weights, Entailment Graph Pruning Validation Population Candidate Graph Relations Edges Inference Enrichment
  • 24. LCC Services Custom End‐to‐End Application Development • Custom Component Development • Corporate R&D • Production Services • Data Verification Services • Support and Maintenance •
  • 25. Who is LCC’s customer base? Target Markets • Government, Intelligence, and Defense – Commodity Search Providers – Company, Credit, and Financial Information – News and Trade Publishers – General Aggregators and Distributors – Pharma – Emerging Markets • Legal  – CRM – Supply Chain Management – Business Intelligence Providers – Healthcare –
  • 26. Who are LCC’s partners? Strategic Partners Technology Partners • • – Application Developers (with  Extraction Providers – complementary S&D interests) Data Mining Providers – – Visualization Developers Database Providers – – Commodity Search Providers Inference Providers – – Mobile App Developers Integration Partners Channel Partners • • – Large Government integrators  – Content Providers with access to customers,  • News systems of record • Education – Large software vendors with  • Financial interest in extraction technology • Business Intelligence
  • 27. CiceroLite High‐performance named entity  • recognition for multiple  languages Foreign Languages: • – English (3/2009: > 1000 types) – Spanish, French, Dutch, German,  Russian, Japanese (~100 types) – Arabic, Chinese, Farsi, Korean  (~50 types) Available as server or standalone  • application
  • 28. PinPoint Geocoding of more than 10M  • place names Absolute Expressions – Relative Expressions – Street Addresses – Latitude / Longitude or MGRS – Timestamping for events and  • event‐denoting nominals – Absolute Expressions – Relative Expressions – Duration Estimation Available as a server app only •
  • 29. CiceroCustom Open‐domain, customizable: • – Entity – Attribute – Relationship and – Event Extraction Foreign language support: • – Arabic, Chinese Available as a server or  • standalone application
  • 30. IndexManager Distributable annotation and  • indexing that’s compatible with  all of LCC’s products • Can index annotations from  multiple providers into single  open‐standard index format Document formats supported:  • .xml, .html, .pdf, .doc, .ppt, .txt,  e‐mail, etc. Available as a server or a desktop  • application
  • 31. Sentiment Tracking Identifies sentiment, opinions,  • and other subjective attitudes  held by individuals towards any of  a set of “target” products or  issues. Only available for English • Only available as a server app • Can be run with LCC’s indexes – • or any standard Apache Lucene  index.
  • 32. Ferret State‐of‐the‐art question  • answering for factoid, list, and  complex questions Foreign Language Support: • – English, Arabic, Chinese, Farsi,  Korean, Turkish, Spanish, French,  Dutch, German, Japanese Available as a server or  • standalone application • Can be run with LCC’s indexes – or any standard Apache Lucene  index.
  • 33. GistTexter Summarization for document  • clusters or search results Foreign Language Support: • – English, Arabic, Chinese, Farsi,  and Korean Available as a server or  • standalone application • Can be run with LCC’s indexes – or any standard Apache Lucene  index.
  • 34. For More Information For more information, contact us: • – Andrew Hickl, CEO/President andy@languagecomputer.com tel:  (972) 231‐0052, Extension 114 cel:  (858) 366‐8424 Websites: • – Corporate:  http://www.languagecomputer.com – Labs:  http://labs.languagecomputer.com – Online Demos:  http://www.getferret.com

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