'Dependency tree' presentation slideshows

Localizing Dependencies: Consequences in NLP

2.08k views • 194 slides

Non-projective Dependency Parsing using Spanning Tree Algorithms Ryan McDonald, Fernando Pereira

824 views • 82 slides

Prague Dependency Treebank: Introduction

817 views • 71 slides

Dependency Treelet Translation: Syntactically Informed Phrasal SMT (ACL 2005)

Dependency Treelet Translation: Syntactically Informed Phrasal SMT (ACL 2005). Chris Quirk, Arul Menezes and Colin Cherry. Outline. Limitations of SMT and previous work Modeling and training Decoding Experiments Conclusion. Limitations of string-based phrasal SMT.

628 views • 57 slides

Data-Driven Dependency Parsing

Data-Driven Dependency Parsing. Kenji Sagae CSCI-544. Background: Natural Language Parsing. Syntactic analysis String to (tree) structure. S. VP. NP. PARSER. NP. He likes fish. N. Prn. V. He. likes. fish. Input. Output. S. VP. NP. PARSER. NP. He likes fish. N. Prn. V.

577 views • 39 slides

History of Major NLP Products & Services in Toshiba

History of Major NLP Products & Services in Toshiba. ???? ? JW-10????Japanese Word Processor ???? ? ASTRANSAC EJ? : EtoJ MT System ???? ? ASTRANSAC JE? : JtoE MT System ???? ? The ??? ? PC MT System (Internet & Personal) ???? ? News Watch? : Information Filtering Service

543 views • 39 slides

A Maximum Entropy-based Model for Answer Extraction

A Maximum Entropy-based Model for Answer Extraction. Dan Shen IGK, Saarland University Supervisors: Prof. Dietrich Klakow Dr. ir. Geert-Jan M. Kruijff. Part I -- Introduction. Answer Extraction Module in QA Statistical Method for Answer Extraction Motivation Framework.

570 views • 38 slides

mining unstructured healthcare data deep dhillon

482 views • 36 slides

Dependency Syntax. An Introduction

Dependency Syntax. An Introduction. Leonid Iomdin Institute for Information Transmission Problems, Russian Academy of Sciences iomdin@iitp.ru, iomdin@gmail.com. Program Overview: p. 1.

636 views • 36 slides

Unsupervised Dependency Parsing

538 views • 35 slides

A Path-based Transfer Model for Machine Translation

A Path-based Transfer Model for Machine Translation. Dekang Lin presented by Joshua Johanson. Training. Get a parallel corpus Source language is in dependency trees The text is word-aligned Extract the paths from dependency trees Learn translation rules from the paths using word alignment.

430 views • 31 slides

Dependency Syntax. An Introduction

Dependency Syntax. An Introduction. Leonid Iomdin Institute for Information Transmission Problems, Russian Academy of Sciences iomdin@iitp.ru, iomdin@gmail.com. Program Overview: p. 1.

383 views • 30 slides

Comparing Information Extraction Pattern Models

Comparing Information Extraction Pattern Models. Mark Stevenson and Mark A. Greenwood Natural Language Processing Group University of Sheffield, UK. Information Extraction Patterns.

305 views • 20 slides

Constituent and Dependency Trees

196 views • 7 slides

Constituent and Dependency Trees

Constituent and Dependency Trees. notes for CSCI-GA. 2590 Prof. Grishman. Constituent Structure Tree. S NP VP N V NP He ate ART N PP a salad P NP

187 views • 6 slides