Owen Rambow 6 Minutes.

Slides:



Advertisements
Similar presentations
Quranic Arabic Corpus Data Mining & Text Analytics By Ismail Teladia & Abdullah Alazwari.
Advertisements

Proceedings of the Conference on Intelligent Text Processing and Computational Linguistics (CICLing-2007) Learning for Semantic Parsing Advisor: Hsin-His.
Multilinugual PennTools that capture parses and predicate-argument structures, and their use in Applications Martha Palmer, Aravind Joshi, Mitch Marcus,
June 6, 20073rd PIRE Meeting1 Tectogrammatical Representation of English in Prague Czech-English Dependency Treebank Lucie Mladová Silvie Cinková, Kristýna.
Overview of the Hindi-Urdu Treebank Fei Xia University of Washington 7/23/2011.
For Friday No reading Homework –Chapter 23, exercises 1, 13, 14, 19 –Not as bad as it sounds –Do them IN ORDER – do not read ahead here.
Towards Parsing Unrestricted Text into PropBank Predicate- Argument Structures ACL4 Project NCLT Seminar Presentation, 7th June 2006 Conor Cafferkey.
LING NLP 1 Introduction to Computational Linguistics Martha Palmer April 19, 2006.
CSE111: Great Ideas in Computer Science Dr. Carl Alphonce 219 Bell Hall Office hours: M-F 11:00-11:
Introduction to treebanks Session 1: 7/08/
CS4705 Natural Language Processing.  Regular Expressions  Finite State Automata ◦ Determinism v. non-determinism ◦ (Weighted) Finite State Transducers.
C. Varela; Adapted w/permission from S. Haridi and P. Van Roy1 Declarative Computation Model Defining practical programming languages Carlos Varela RPI.
Center for Computational Learning Systems Independent research center within the Engineering School NLP people at CCLS: Mona Diab, Nizar Habash, Martin.
Are Linguists Dinosaurs? 1.Statistical language processors seem to be doing away with the need for linguists. –Why do we need linguists when a machine.
Center for Computational Learning Systems Independent research center within the Engineering School NLP people at CCLS: Mona Diab, Nizar Habash, Martin.
Introduction to Syntax, with Part-of-Speech Tagging Owen Rambow September 17 & 19.
تمرين شماره 1 درس NLP سيلابس درس NLP در دانشگاه هاي ديگر ___________________________ راحله مکي استاد درس: دکتر عبدالله زاده پاييز 85.
Probabilistic Parsing Ling 571 Fei Xia Week 5: 10/25-10/27/05.
Lecture 1, 7/21/2005Natural Language Processing1 CS60057 Speech &Natural Language Processing Autumn 2005 Lecture 1 21 July 2005.
Arabic TTS (status & problems) O. Al Dakkak & N. Ghneim.
Context Free Grammars Reading: Chap 12-13, Jurafsky & Martin This slide set was adapted from J. Martin, U. Colorado Instructor: Paul Tarau, based on Rada.
ELN – Natural Language Processing Giuseppe Attardi
9/8/20151 Natural Language Processing Lecture Notes 1.
Lecture 12: 22/6/1435 Natural language processing Lecturer/ Kawther Abas 363CS – Artificial Intelligence.
Experiments on Building Language Resources for Multi-Modal Dialogue Systems Goals identification of a methodology for adapting linguistic resources for.
THE BIG PICTURE Basic Assumptions Linguistics is the empirical science that studies language (or linguistic behavior) Linguistics proposes theories (models)
PETRA – the Personal Embedded Translation and Reading Assistant Werner Winiwarter University of Vienna InSTIL/ICALL Symposium 2004 June 17-19, 2004.
An Extended GHKM Algorithm for Inducing λ-SCFG Peng Li Tsinghua University.
1 CSI 5180: Topics in AI: Natural Language Processing, A Statistical Approach Instructor: Nathalie Japkowicz Objectives of.
Parsing Introduction Syntactic Analysis I. Parsing Introduction 2 The Role of the Parser The Syntactic Analyzer, or Parser, is the heart of the front.
1 Introduction to Computational Linguistics Eleni Miltsakaki AUTH Fall 2005-Lecture 4.
What you have learned and how you can use it : Grammars and Lexicons Parts I-III.
Programming Languages and Design Lecture 3 Semantic Specifications of Programming Languages Instructor: Li Ma Department of Computer Science Texas Southern.
For Friday Finish chapter 24 No written homework.
For Monday Read chapter 26 Last Homework –Chapter 23, exercise 7.
LING 001 Introduction to Linguistics Spring 2010 Syntactic parsing Part-Of-Speech tagging Apr. 5 Computational linguistics.
For Friday Finish chapter 23 Homework –Chapter 23, exercise 15.
Supertagging CMSC Natural Language Processing January 31, 2006.
Syntactic Annotation of Slovene Corpora (SDT, JOS) Nina Ledinek ISJ ZRC SAZU
NLP. Introduction to NLP (U)nderstanding and (G)eneration Language Computer (U) Language (G)
FILTERED RANKING FOR BOOTSTRAPPING IN EVENT EXTRACTION Shasha Liao Ralph York University.
CSC 4181 Compiler Construction
For Monday Read chapter 26 Homework: –Chapter 23, exercises 8 and 9.
Overview of Statistical NLP IR Group Meeting March 7, 2006.
NATURAL LANGUAGE PROCESSING
By Kyle McCardle.  Issues with Natural Language  Basic Components  Syntax  The Earley Parser  Transition Network Parsers  Augmented Transition Networks.
Leonardo Zilio Supervisors: Prof. Dr. Maria José Bocorny Finatto
Grammar Grammar analysis.
Approaches to Machine Translation
Sentiment analysis algorithms and applications: A survey
PRESENTED BY: PEAR A BHUIYAN
CS 326 Programming Languages, Concepts and Implementation
Lecture – VIII Monojit Choudhury RS, CSE, IIT Kharagpur
[A Contrastive Study of Syntacto-Semantic Dependencies]
Basic Parsing with Context Free Grammars Chapter 13
Semantic Parsing for Question Answering
Intelligent Systems (AI-2) Computer Science cpsc422, Lecture 27
CPE 480 Natural Language Processing
--Mengxue Zhang, Qingyang Li
Tagging and Statistically Translating Latin Sentences
Intelligent Systems (AI-2) Computer Science cpsc422, Lecture 27
Lecture 9: Semantic Parsing
Intelligent Systems (AI-2) Computer Science cpsc422, Lecture 26
Approaches to Machine Translation
CS4705 Natural Language Processing
Computational Linguistics: New Vistas
Syntax vs Semantics Backus-Naur Form Extended BNF Derivations
Artificial Intelligence 2004 Speech & Natural Language Processing
Information Retrieval
Presentation transcript:

Owen Rambow 6 Minutes

Interests Theory Technologies/Resources Applications Syntax: its representation and computation (TAG), and its relation to discourse, prosody, semantics … Technologies/Resources Generation: sentence planning & realization Dependency parsing Dependency corpora Applications Generation applications (reports) Machine translation Dialog systems Summarization

Thesis (1994) Formal representation for German syntax Issue: combination of fixed and free word-order Solution: use description of trees, not trees (as in TAG) Current syntax interests: Tagalog, Arabic

Generation: Sentence Planning Work with Lyn Walker Issue: choosing syntactic constructions to achieve communicative goals in dialog systems Idea: use machine learning on preference-ranked options Ongoing: multimedia/speech output (with Noémie) Open issue: individual preferences Summarization?

Generation: Realization Joint work with Srinivas Bangalore, John Chen Use of declarative TAG grammar (hand-written or extracted) Approach: stochastic choice on trees using arborescent & linear language models Open issues: factoring of syntactic/lexical choice

Dependency Parsing Joint work with Alexis Nasr, Srinivas Bangalore, John Chen Idea: transform TAG trees into FSMs/FSTs Parse yields dependency tree Parse FSMs CKY-style Or, compose FSTs to a single large FST Open issues: supertagging, probabilistic model, domain adaptation

Corpora PropBank (Penn project): annotation of PTB with predicate-argument structures Verb-specific arg roles, lexicon Project: mapping PropBank to other, more “semantic” forms of annotation (VerbNet, LCS, Prague)