'Dependency relations' presentation slideshows

CPSC 503 Computational Linguistics

CPSC 503 Computational Linguistics. Lecture 9 Giuseppe Carenini. Slide Source for Dependency Parsing : Joakim Nivre , Uppsala Universitet. Big Picture: Syntax & Parsing. Constituency vs. Dependency structures. n e ws h a d li tt l e. Eco n om i c.

973 views • 73 slides

NL search: hype or reality?

884 views • 72 slides

F-cell

TEMPLATE:. disk LHKF. F-cell. disk LHKF. Q. E. K. F-cell. circle LHKF. F. H. L. F-cell. S. D. Q. E. K. F. H. L. rectangle DEQS. polyline DEQS. rectangle DEQS. polyline DEQS. rectangle DEQS. S. D. B-cell. B-cell. B-cell. B-cell. B-cell. disk LHKF. circle

735 views • 62 slides

Constraint Based Hindi Parser

Constraint Based Hindi Parser. LTRC, IIIT Hyderabad. Introduction. Broad coverage parser Very crucial IL-IL MT systems, IE, co-reference resolution, etc. Why Dependency ?. Phrase Structures Intrinsically presumes order

610 views • 45 slides

Using Wikipedia for Hierarchical Finer Categorization of Named Entities

Using Wikipedia for Hierarchical Finer Categorization of Named Entities. Aasish Pappu Language Technologies Institute Carnegie Mellon University. PACLIC 2009. outline. 1 Introduction 2 Related Work 3 Corpus Creation 4 Features 5 Experiments 6 Conclusion.

539 views • 43 slides

Experiments with a Multilanguage Non-Projective Dependency Parser

534 views • 42 slides

Bayesian Subtree Alignment Model based on Dependency Trees

Bayesian Subtree Alignment Model based on Dependency Trees. Toshiaki Nakazawa Sadao Kurohashi Kyoto University. 2011/11/ 11 @ IJCNLP2011. Outline. Background Related Work Bayesian Subtree Alignment Model Model Training Experiments Conclusion. Background.

438 views • 33 slides

CPSC 503 Computational Linguistics

CPSC 503 Computational Linguistics. Lecture 10 Giuseppe Carenini. Knowledge-Formalisms Map. State Machines (and prob. versions) (Finite State Automata,Finite State Transducers, Markov Models ). Morphology. Logical formalisms (First-Order Logics). Syntax.

454 views • 33 slides

F-cell

TEMPLATE:. disk LHKF. F-cell. disk LHKF. Q. E. K. F-cell. circle LHKF. F. H. L. F-cell. S. D. Q. E. K. F. H. L. rectangle DEQS. polyline DEQS. rectangle DEQS. polyline DEQS. rectangle DEQS. S. D. B-cell. B-cell. B-cell. B-cell. B-cell. disk LHKF. circle

389 views • 33 slides

CPSC 503 Computational Linguistics

CPSC 503 Computational Linguistics. Lecture 10 Giuseppe Carenini. Knowledge-Formalisms Map. State Machines (and prob. versions) (Finite State Automata,Finite State Transducers, Markov Models ). Morphology. Logical formalisms (First-Order Logics). Syntax.

344 views • 33 slides

F-cell

TEMPLATE:. disk LHKF. F-cell. disk LHKF. Q. E. K. F-cell. circle LHKF. F. H. L. F-cell. S. D. Q. E. K. F. H. L. rectangle DEQS. polyline DEQS. rectangle DEQS. polyline DEQS. rectangle DEQS. S. D. B-cell. B-cell. B-cell. B-cell. B-cell. disk LHKF. circle

368 views • 31 slides

Annotation of Grammatemes in the Prague Dependency Treebank 2.0

407 views • 30 slides

Statistical Phrase Alignment Model Using Dependency Relation Probability

Statistical Phrase Alignment Model Using Dependency Relation Probability. Toshiaki Nakazawa and Sadao Kurohashi Kyoto University. Outline. Background Tree-based Statistical Phrase Alignment Model Model Training Experiments Conclusions. Conventional Word Sequence Alignment.

413 views • 28 slides

SI485i : NLP

349 views • 19 slides

QGSTEC System Description JUQGG: A Rule based approach

QGSTEC System Description JUQGG: A Rule based approach. Santanu Pal, Tapabrata Mondal, Partha Pakray, Dipankar Das and Sivaji Bandyopadhyay Department of Computer Science and Engineering Jadavpur University Kolkata-700032, India. Problem Description.

303 views • 16 slides

Dependency Matrix and Duration in Time Based Composition of Learning Paths

Dependency Matrix and Duration in Time Based Composition of Learning Paths. Ayman Moghnieh, Fabien Girardin, Joseph Blat Department of Technologies, Pompeu Fabra University, Barcelona, Spain. Planning Learning Paths for L.L.L. Environment

190 views • 9 slides

To increase concurrency Commit soon To protect against failures Commit as late as possible

To increase concurrency Commit soon To protect against failures Commit as late as possible Analogy Real life actions | system transactions Goal is to: Maximize commitment of actions/transactions Minimize rollback Maximize concurrency Minimize blocking. Obstables

192 views • 6 slides