'Entity mention' presentation slideshows

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Human Language Technology for the Semantic Web gate.ac.uk/ nlp.shef.ac.uk/

Human Language Technology for the Semantic Web gate.ac.uk/ nlp.shef.ac.uk/

Human Language Technology for the Semantic Web http://gate.ac.uk/ http://nlp.shef.ac.uk/ Hamish Cunningham Kalina Bontcheva Diana Maynard Valentin Tablan ESWS, Crete, May 2004 [This work has been supported by AKT ( http://aktors.org/ ) and SEKT ( http://sekt.semanticweb.org/ )].

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1.5k views • 130 slides
Metadata Extraction: Human Language Technology and the Semantic Web

Metadata Extraction: Human Language Technology and the Semantic Web

Metadata Extraction: Human Language Technology and the Semantic Web http://gate.ac.uk/ http://nlp.shef.ac.uk/ Hamish Cunningham Kalina Bontcheva Valentin Tablan Diana Maynard SEKT meeting, London, 21 January 2004. Gartner, December 2002:

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1.34k views • 117 slides
Metadata Extraction: Human Language Technology and the Semantic Web

Metadata Extraction: Human Language Technology and the Semantic Web

Metadata Extraction: Human Language Technology and the Semantic Web http://gate.ac.uk/ http://nlp.shef.ac.uk/ Hamish Cunningham Kalina Bontcheva Valentin Tablan Diana Maynard SEKT meeting, London, 21 January 2004. Gartner, December 2002:

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1.28k views • 117 slides
Inductive Approaches to the Detection and Classification of Semantic Relation Mentions

Inductive Approaches to the Detection and Classification of Semantic Relation Mentions

Inductive Approaches to the Detection and Classification of Semantic Relation Mentions. Depth Report Examination Presentation Gabor Melli August 27, 2007. http://www.gabormelli.com/2007/2007_DepthReport_Melli_Presentation.ppt. Overview. Introduction ( ~ 5 mins .)

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936 views • 82 slides
Improved Relation Extraction with Feature-Rich Compositional Embedding Models

Improved Relation Extraction with Feature-Rich Compositional Embedding Models

Improved Relation Extraction with Feature-Rich Compositional Embedding Models. Mo Yu *. Matt Gormley *. September 21, 2015 EMNLP. Mark Dredze. *Co-first authors. FCM or: How I Learned to Stop Worrying (about Deep Learning) and Love Features. Mo Yu *. Matt Gormley *.

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311 views • 30 slides
KBP2014 Entity Linking Scorer

KBP2014 Entity Linking Scorer

KBP2014 Entity Linking Scorer. Xiaoman Pan, Qi Li, Heng Ji , Xiaoqiang Luo , Ralph Grishman jih @ rpi.edu. Overview. We will apply two steps to evaluate the KBP2014 Entity Discovery and Linking results. 1. Clustering score 2. Entity Linking ( Wikification ) F-score. Overview.

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317 views • 19 slides
GATE Evaluation Tools

GATE Evaluation Tools

GATE Evaluation Tools. GATE Training Course October 2006 Kalina Bontcheva. System development cycle. Collect corpus of texts Annotate manually gold standard Develop system Evaluate performance Go back to step 3, until desired performance is reached. Corpora and System Development.

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328 views • 19 slides
KBP2014 Entity Linking Scorer B-cubed++

KBP2014 Entity Linking Scorer B-cubed++

KBP2014 Entity Linking Scorer B-cubed++. Xiaoman Pan, Qi Li and Heng Ji {panx2,liq7,jih}rpi.edu. Overview. We augment the B-Cubed++ metric to evaluate the KBP2014 Entity Discovery and Linking results .

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215 views • 14 slides
Linking Entities in # Microposts

Linking Entities in # Microposts

Linking Entities in # Microposts. International Institute of information technology, hyderabad. Introduction.

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194 views • 13 slides

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