'Text generation' presentation slideshows

Measurement and Classification of Humans and Bots in Internet Chat

Measurement and Classification of Humans and Bots in Internet Chat. By Steven Gianvecchio, Mengjun Xie, Zhenyu Wu, and Haining Wang College of William and Mary. Outline. Background Measurement Classification System Experimental Evaluation Conclusion. Outline. Background Measurement

514 views • 41 slides

BoM / CAWCR. Text Generation in the Next-Gen Forecast System (GFE)

BoM / CAWCR. Text Generation in the Next-Gen Forecast System (GFE). J Bally & T Leeuwenburg. Background & Drivers.... Next-Gen Forecast System. Better use of NWP models Systematic forecast process Temporal and spatial detail Can verify everything Efficiency gains

521 views • 37 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.

638 views • 36 slides

MO Q A Meaning Oriented Question Answering

MO Q A Meaning Oriented Question Answering. An AQUAINT Project from. ILIT. CRL is a research department in the School of Arts and Sciences at NMSU Director: Jim Cowie Currently has a staff of 10 PhDs Mainly focuses on language engineering research

408 views • 31 slides

A Comparison of Consecutive and Concurrent Input Text entry Techniques for Mobile Phones

440 views • 30 slides

Text Generation for Instances in the Component Library

Text Generation for Instances in the Component Library. Ken Barker, Jarred McGinnis Monday afternoon. Outline. Motivation Why UT is doing text generation Interface What goes in and what comes out Customization How to adapt for new classes Future Work What to do next. Motivation.

376 views • 28 slides

Centrifuser Output Min Yen Kan, 2001

405 views • 25 slides

What is NLG?

Text Generation Lab July 3, 2003 Dragomir R. Radev radev@umich.edu Johns Hopkins University Summer School 2003. What is NLG?. Mapping meaning to text Stages: Content selection Lexical selection Sentence structure: aggregation, referring expressions Discourse structure. The system. will.

398 views • 23 slides

How Many Words is a Picture Worth? Automatic Caption Generation for News Images

How Many Words is a Picture Worth? Automatic Caption Generation for News Images. Yansong Feng and Mirella Lapata. Ashish Bagate. What this paper is about. Explore the feasibility of automatic caption generation for images in news domain

294 views • 17 slides

Reading into writing

Reading into writing. Shared reading into writing PGCE FT - Week 4. Objectives. To understand how talk and reading are integral to the writing process To appreciate the complexity of the writing process To consider the place of purposeful activities for writing. Talk to Text.

469 views • 17 slides

Reading into writing

Reading into writing. Shared reading into writing PGCE FT - Week 4. Objectives. To understand how talk and reading are integral to the writing process To appreciate the complexity of the writing process To consider the place of purposeful activities for writing. Talk to Text.

481 views • 17 slides

Natural Language and Speech (parts of Chapters 8 & 9)

Natural Language and Speech (parts of Chapters 8 & 9). Natural Language in Computing. Use of human languages Translation Commands to computer Queries Text Database Searching Text generation Games. Commands to Computer. From computer-oriented to domain oriented

236 views • 14 slides

Natural Language and Speech (parts of Chapters 8 & 9)

Natural Language and Speech (parts of Chapters 8 & 9). Natural Language in Computing. Use of human languages Translation Commands to computer Queries Text Database Searching Text generation Games. Commands to Computer. From computer-oriented to domain oriented

154 views • 14 slides

Natural Language and Speech

Natural Language and Speech. Natural Language in Computing. Use of human languages Translation Commands to computer Queries Text Database Searching Text generation Games. Commands to Computer. From computer-oriented to domain oriented May not be more efficient than selection

126 views • 12 slides

Natural Language and Speech

Natural Language and Speech. Natural Language in Computing. Use of human languages Translation Commands to computer Queries Text Database Searching Text generation Games. Commands to Computer. From computer-oriented to domain oriented May not be more efficient than selection

129 views • 12 slides

Text-to-Text Generation

Text-to-Text Generation. Graesser, Rus, Stent, Walker, White. Input. Text Attractive to the Question Answering Attractive to DUC community Attractive to the Coreference/Referring expression community Semantic Representation PropBank. Types of Tasks.

294 views • 7 slides