We think you have liked this presentation. If you wish to download it, please recommend it to your friends in any social system. Share buttons are a little bit lower. Thank you!
Presentation is loading. Please wait.
Published byAmbrose Sanders
Modified over 5 years ago
Natural Language Processing Projects Heshaam Feili firstname.lastname@example.org
(1)Persian Part-of-speech tagging –The large can can hold the water – D A N AUX V D N Using N-gram probabilities Hidden Markov Model Transformation model [Church 1988], [Charniak97], [Adwait96]
POS Taggers HMM-BasedCharniak model Statistical TrigramsTnT(trainable) Decision Tree- BasedTreeTagger(trainable) maximum entropy modelMx POST(trainable) )Tranformation- BasedEric brill tagger(trainable) HMM-BasedLT POS(trainable) HMM-BasedQtag(trainable) Fast Transformation-Based Learning tagger fnTBL (trainable)
Tagged persian data set –1000 sentence –May need some hand crafted actions ! Training method Evaluation method Needs some morphological smoothing (2 person) Project:
(2) Computational Grammars Seminar Unification grammar Augmented transition network Link grammar Tree adjoining grammar Categorical grammar Dependency grammar Head driven phrase structure grammar
Projects: Design & Implementation of Persian Computational grammar Parsing Algorithm Making a prototype ( 2 person ) Full grammar development (MS project)
(3) Statistical Parsing algorithms Probabilistic model –Probabilistic Context free grammar –N-Gram model Probabilistic Computational grammar Needs bracketed data set –(S (NP ((DET the)(N man)) ( VP (V killed) (NP ( (D the)(N dog)) ) )
Projects: Bracketing Persian Data Set –Use at least 1000 tagged sentence –Bracket the data set Implement an training model Evaluation phase –PARSEVAL metrics (2 Person)
(4) Machine Translation Architecture –Direct / Transfer / Interligua History Different Strategy Problems Current Status (1 person)
(5) Statistical MT Probabilistic model Training model Architecture Corpus Management EGYPT model … (2 person)
Project: English – Persian Statistical Translation system –Small data set exists … –Implement a statistical model –Needs Persian morphological analyzer Persian Pos tagger
(6) Persian morphology analyzer Inflection Verb Noun Auxiliary Adjective Adverb … Red House خانه ي قرمز Projects (1 Person)
School of something FACULTY OF OTHER School of Computing FACULTY OF ENGINEERING Chunking: Shallow Parsing Eric Atwell, Language Research Group.
Introduction to Syntax, with Part-of-Speech Tagging Owen Rambow September 17 & 19.
Fall 2008Programming Development Techniques 1 Topic 9 Symbol Manipulation Generating English Sentences Section This is an additional example to symbolic.
Part of Speech Tagging Importance Resolving ambiguities by assigning lower probabilities to words that don’t fit Applying to language grammatical rules.
LING NLP 1 Introduction to Computational Linguistics Martha Palmer April 19, 2006.
For Monday Read Chapter 23, sections 3-4 Homework –Chapter 23, exercises 1, 6, 14, 19 –Do them in order. Do NOT read ahead.
1 A Hidden Markov Model- Based POS Tagger for Arabic ICS 482 Presentation A Hidden Markov Model- Based POS Tagger for Arabic By Saleh Yousef Al-Hudail.
Tagging with Hidden Markov Models. Viterbi Algorithm. Forward-backward algorithm Reading: Chap 6, Jurafsky & Martin Instructor: Paul Tarau, based on Rada.
NLP and Speech Course Review. Morphological Analyzer Lexicon Part-of-Speech (POS) Tagging Grammar Rules Parser thethe – determiner Det NP → Det.
Part II. Statistical NLP Advanced Artificial Intelligence Part of Speech Tagging Wolfram Burgard, Luc De Raedt, Bernhard Nebel, Lars Schmidt-Thieme Most.
Probabilistic Parsing: Enhancements Ling 571 Deep Processing Techniques for NLP January 26, 2011.
Shallow Processing: Summary Shallow Processing Techniques for NLP Ling570 December 7, 2011.
PCFG Parsing, Evaluation, & Improvements Ling 571 Deep Processing Techniques for NLP January 24, 2011.
CS4705 Natural Language Processing. Regular Expressions Finite State Automata ◦ Determinism v. non-determinism ◦ (Weighted) Finite State Transducers.
Christel Kemke 2007/08 COMP 4060 Natural Language Processing Word Classes and English Grammar.
Midterm Review CS4705 Natural Language Processing.
Syntax Phrase and Clause in Present-Day English. The X’ phrase system Any X phrase in PDE consists of: – an optional specifier – X’ (X-bar) which is the.
Christel Kemke 2007/08 COMP 4060 Natural Language Processing Feature Structures and Unification.
Introduction to CL Session 1: 7/08/2011. What is computational linguistics? Processing natural language text by computers for practical applications.
NLP and Speech 2004 English Grammar
© 2020 SlidePlayer.com Inc. All rights reserved.