'Entity recognition' presentation slideshows

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Search And Text Analysis

Search And Text Analysis

Search And Text Analysis. An Introduction to Java-based Open Source Tools and Techniques Grant Ingersoll October 15, 2008 Charlotte JUG. Overview. Background Taming Text Importance Foundations Language Basics Obtaining Text Tools for Search and Text Analysis Concepts Demos Resources.

By Mia_John
(389 views)

Information Extraction Referatsthemen

Information Extraction Referatsthemen

Information Extraction Referatsthemen. CIS, LMU München Winter Semester 2013-2014 Dr. Alexander Fraser, CIS. Information Extraction – Reminder. Vorlesung Learn the basics of Information Extraction (IE) Klausur – only on the Vorlesung ! Seminar Deeper understanding of IE topics

By iman
(168 views)

No Free Lunch: Brute Force vs Locality-Sensitive Hashing for Cross-Lingual Pairwise Similarity

No Free Lunch: Brute Force vs Locality-Sensitive Hashing for Cross-Lingual Pairwise Similarity

No Free Lunch: Brute Force vs Locality-Sensitive Hashing for Cross-Lingual Pairwise Similarity. Ferhan Ture 1 Tamer Elsayed 2 Jimmy Lin 1,3. 1 Department of Computer Science, University of Maryland

By cliff
(170 views)

LINGUISTICA GENERALE E COMPUTAZIONALE

LINGUISTICA GENERALE E COMPUTAZIONALE

LINGUISTICA GENERALE E COMPUTAZIONALE. SENTIMENT ANALYSIS. FACTS AND OPINIONS. Two main types of textual information on the Web: FACTS and OPINIONS Current search engines search for facts (assume they are true) Facts can be expressed with topic keywords .

By amandla
(147 views)

Domain Adaptation with Structural Correspondence Learning

Domain Adaptation with Structural Correspondence Learning

Domain Adaptation with Structural Correspondence Learning. John Blitzer. Joint work with. Shai Ben-David, Koby Crammer, Mark Dredze, Ryan McDonald, Fernando Pereira. Statistical models, multiple domains. politics blogs. tech blogs. Different Domains of Text.

By livvy
(159 views)

Text Mining Applications for Literature Curation

Text Mining Applications for Literature Curation

Text Mining Applications for Literature Curation. Kimberly Van Auken WormBase Consortium Textpresso Gene Ontology Consortium. WormBase: A Database for C. elegans and Other Nematodes. www.wormbase.org. Curating Diverse Data Types . Aggregation Behavior. Which worms aggregate

By vea
(113 views)

Inferring Hidden Relationships from Biological Literature with Multi-level Context T erms

Inferring Hidden Relationships from Biological Literature with Multi-level Context T erms

Inferring Hidden Relationships from Biological Literature with Multi-level Context T erms. Introduction. Literature Based Discovery (LBD). PKC1. 3. 8. Alzheimer. Insulin. CATS. 5. 9. Drug repositioning. 4. 2. SOS2. Swanson’s ABC model.

By keren
(113 views)

Content architecture and website design

Content architecture and website design

Content architecture and website design. WordPress MEETUP J anuary 4 th 2012 Ruby’s Inn & Convention Center, Missoula Rose Lockwood rose@roselockwood.com. Information/Content Architecture …we’re all librarians now…. Information/Content Architecture:

By hisa
(128 views)

SI485i : NLP

SI485i : NLP

SI485i : NLP. Set 12 Features and Prediction. What is NLP, really?. Many of our tasks boil down to finding intelligent features of language. We do lots of machine learning over features NLP researchers also use linguistic insights, deep language processing, and semantics.

By emmett
(149 views)

LINDEN : Linking Named Entities with Knowledge Base via Semantic Knowledge

LINDEN : Linking Named Entities with Knowledge Base via Semantic Knowledge

LINDEN : Linking Named Entities with Knowledge Base via Semantic Knowledge. Date : 2013/03 /25 Resource : WWW 2012 Advisor : Dr. Jia -Ling Koh Speaker : Wei Chang. Outline. Introduction Approach Experiment Conclusion. A Real W orld Entity with Different Name. New York City.

By brant
(210 views)

REACTION Workshop 2011.01.06 Task 1 – Progress Report & Plans Lisbon , PT and Austin , TX

REACTION Workshop 2011.01.06 Task 1 – Progress Report & Plans Lisbon , PT and Austin , TX

REACTION Workshop 2011.01.06 Task 1 – Progress Report & Plans Lisbon , PT and Austin , TX. Mário J. Silva University of Lisbon , Portugal. Grants (paid by Reaction). Sílvio Moreira (BI: Oct 1, 2010 – March 31, 2011 ) João Ramalho (BIC: Jan 1, 2011 – April 31, 2011). Mining resources.

By conan
(131 views)

Real-time Text Mining for the Biomedical Literature a collaboration between Discovery Net & myGrid

Real-time Text Mining for the Biomedical Literature a collaboration between Discovery Net & myGrid

Real-time Text Mining for the Biomedical Literature a collaboration between Discovery Net & myGrid. Rob Gaizauskas Department of Computer Science University of Sheffield. Moustafa M. Ghanem Department of Computing Imperial College London. Outline. Context

By zhen
(111 views)

Basi di dati distribuite

Basi di dati distribuite

Basi di dati distribuite. Prof. M.T. PAZIENZA a.a. 2003-2004. INFORMATION EXTRACTION And QUESTION / ANSWERING. Information Extraction.

By genera
(107 views)

Intelligent Systems (AI-2) Computer Science cpsc422 , Lecture 19 Feb, 28, 2014

Intelligent Systems (AI-2) Computer Science cpsc422 , Lecture 19 Feb, 28, 2014

Intelligent Systems (AI-2) Computer Science cpsc422 , Lecture 19 Feb, 28, 2014. Slide Sources Raymond J. Mooney University of Texas at Austin D. Koller , Stanford CS - Probabilistic Graphical Models D. Page , Whitehead Institute, MIT. Several Figures from

By vashon
(101 views)

Information Extraction from Literature

Information Extraction from Literature

Information Extraction from Literature. Yue Lu BeeSpace Seminar Oct 24, 2007. Outline. Overview of BeeSpace v4 Entity Recognition Relation Extraction. Overview. BeeSpace V4 deeper semantic base than the current v3 system entities and relations VS mutual information Four levels

By ahanu
(94 views)

Information Extraction, Service Discovery and Semantic Services in HealthGrid Applications

Information Extraction, Service Discovery and Semantic Services in HealthGrid Applications

Information Extraction, Service Discovery and Semantic Services in HealthGrid Applications. Martin Hofmann Department of Bioinformatics. Challenges in HealthGrids. Information explosion in the Life Sciences Highly parallel experimental procedures (e.g. 30,000 genes on one microarray)

By vito
(83 views)

Encoding Extraction as Inferences

Encoding Extraction as Inferences

Encoding Extraction as Inferences. J. William Murdock 1 , Paulo Pinheiro da Silva 2 , David Ferrucci 1 , Christopher Welty 1 , Deborah McGuinness 2. 1 IBM Watson Research Center 19 Skyline Drive Hawthorne, NY 10532, USA {murdockj,ferrucci,welty}@us.ibm.com. 2 Knowledge Systems Laboratory

By oceana
(117 views)

The ILK Suite of Text Tools

The ILK Suite of Text Tools

The ILK Suite of Text Tools. Antal van den Bosch ILK Research Group Faculty of Humanties, Tilburg University http://ilk.uvt.nl Political Mashup Meeting Amsterdam, March 19, 2008. The ILK Text Tools. Text Quality Management Text normalization Spelling and grammar checking

By gale
(142 views)

A Linear Programming Formulation for Global Inference in Natural Language Tasks

A Linear Programming Formulation for Global Inference in Natural Language Tasks

A Linear Programming Formulation for Global Inference in Natural Language Tasks. Dan Roth Wen-tau Yih Department of Computer Science University of Illinois at Urbana-Champaign. Natural Language Understanding. POS Tagging. Chunking. Parsing. Word Sense Disambiguation.

By nibal
(98 views)

Human Language Technology in Musing

Human Language Technology in Musing

Human Language Technology in Musing. Horacio Saggion (U. of Sheffield) & Thierry Declerck (DFKI). Outline. Role of HLT in BI Information Extraction (IE) and Semantic Annotation IE development Overview of GATE system Ontology-based IE in Musing Identity Resolution in Musing

By honora
(176 views)

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