'Penn treebank' presentation slideshows

CROWDSOURCING (ANAPHORIC) ANNOTATION

CROWDSOURCING (ANAPHORIC) ANNOTATION. Massimo Poesio University of Essex Part 1: Intro, Microtask crowdsourcing. ANNOTATED CORPORA: AN ERA OF PLENTY?.

1.04k views • 87 slides

Tree Kernels for Parsing: (Collins & Duffy, 2001)

Tree Kernels for Parsing: (Collins & Duffy, 2001). Advanced Statistical Methods in NLP Ling 572 February 28, 2012. Roadmap. Collins & Duffy, 2001 Tree Kernels for Parsing: Motivation Parsing as r eranking Tree kernels for similarity Case study: Penn Treebank parsing.

942 views • 83 slides

Exploring PropBanks for English and Hindi

Exploring PropBanks for English and Hindi. Ashwini Vaidya Dept of Linguistics University of Colorado, Boulder. Why is semantic information important?. Imagine an automatic question answering system Who created the first effective polio vaccine? Two possible choices:

861 views • 72 slides

Computational Models of Discourse and Dialogue 2011: Conversation in Social Media

Computational Models of Discourse and Dialogue 2011: Conversation in Social Media. Persuasion in Social Media. P ersuasion and argumentation in social media websites and forums. NLDS Social Media Dialogue Data.

871 views • 71 slides

CORpus analysis

724 views • 61 slides

Hand video

572 views • 46 slides

Pushpak Bhattacharyya CSE Dept., IIT Bombay 22 nd March, 2011

544 views • 44 slides

LIN3022 Natural Language Processing Lecture 8

LIN3022 Natural Language Processing Lecture 8. Albert Gatt. In this lecture. We begin to look at the task of syntactic parsing Overview of grammar formalisms (especially CFGs) Overview of parsing algorithms. The task. Example input sentence: Book that flight.

551 views • 41 slides

Language Generation

Language Generation. Some slides adapted from Michael Elhadad, David De Vault . Announcements. HW 4 can be turned in up to Monday, Dec. 8 th midnight without late penalties Your grades are now posted on courseworks although late days have not yet been taken into account.

460 views • 40 slides

Language Generation

Language Generation. Some slides adapted from . Today. Linguistic Generation Statistical Generation. Two Types of Problems. Conceptual: What to say How to organize Linguistic How to say it Words? Syntactic structure. A Language Generator. Content Planner. Presentation Plan. Data.

526 views • 39 slides

LING 581: Advanced Computational Linguistics

515 views • 39 slides

CSA2050: Natural Language Processing

603 views • 37 slides

Computational Morphology and Syntax of Natural Languages

Computational Morphology and Syntax of Natural Languages. Daniel Zeman http://ufal.mff.cuni.cz/course/npfl094 zeman@ufal.mff.cuni.cz. NPFL094. Presentations and talks will be in English Unless all students understand Czech Questions welcome in both Czech and English

553 views • 36 slides

Introduction to Natural Language Processing (NLP)

Introduction to Natural Language Processing (NLP). Dekang Lin Department of Computing Science University of Alberta lindek@cs.ualberta.ca. Outline. What is NLP Applications Challenges Linguistics Issues Part of Speech Tagging. What is Natural Language Processing?.

896 views • 26 slides

Question Answering Based on Semantic Graphs

432 views • 19 slides

ECMT- Oriented Generation T reebank

ECMT- Oriented Generation T reebank. Beijing Language and Culture University Oct. 2006. R ationalism and E mpiricism MT Approaches. rules. Shortcoming. R ationalism. MT. Advantage. E mpiricism. statistics. Advantages Human knowledge applied Shortcomings

965 views • 18 slides

Ch-1: Introduction (1.3 & 1.4 & 1.5) Prepared by Qaiser Abbas (07-0906)

234 views • 11 slides

Statistical Natural Language Parsing

165 views • 6 slides

Results: Word prominence detection models

Word Prominence Detection using Robust yet Simple Prosodic Features. Intonational Prominence in Spoken Communication Word prominence is acoustically realized by increased pitch , greater energy and longer duration than the neighboring words.

96 views • 1 slides

Table 1. Syntactic rules and their probabilities.

A VISUAL REPRESENTATION OF THE ENGLISH LANGUAGE. By David Temperley. Table 1. Syntactic rules and their probabilities.

89 views • 1 slides