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 about 1 year ago
Natural Language Processing Projects Heshaam Feili
(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 PoS-Tagging theory and terminology COMP3310 Natural Language Processing.
School of something FACULTY OF OTHER School of Computing FACULTY OF ENGINEERING Chunking: Shallow Parsing Eric Atwell, Language Research Group.
Special Topics in Computer Science Advanced Topics in Information Retrieval Lecture 10: Natural Language Processing and IR. Syntax and structural disambiguation.
Machine Learning Approaches to the Analysis of Large Corpora : A Survey Xunlei Rose Hu and Eric Atwell University of Leeds.
Introduction to Syntax and Context-Free Grammars Owen Rambow
Introduction to Computational Linguistics Dr. Radhika Mamidi ENG 270 Lecture 2.
Special Topics in Computer Science Advanced Topics in Information Retrieval Lecture 9: Natural Language Processing and IR. Tagging, WSD, and Anaphora Resolution.
Statistical NLP Winter 2009 Lecture 11: Parsing II Roger Levy Thanks to Jason Eisner & Dan Klein for slides.
Cluster Computing for Statistical Machine Translation Qin Gao, Kevin Gimpel, Alok Palikar, Andreas Zollmann Stephan Vogel, Noah Smith.
University of Sheffield NLP Machine Learning in GATE Angus Roberts, Horacio Saggion, Genevieve Gorrell.
School of something FACULTY OF OTHER School of Computing FACULTY OF ENGINEERING Machine Learning PoS-Taggers COMP3310 Natural Language Processing Eric.
SWG Strategy (C) Copyright IBM Corp. 2006, All Rights Reserved. International Technology Alliance Programme: Fact Extraction using a Controlled Natural.
The Learning Non-Isomorphic Tree Mappings for Machine Translation Jason Eisner - Johns Hopkins Univ. a b A B events of misinform wrongly report to-John.
Fredrik Olsson 1 Licentiate-thesis proposal, Software Architectures for Language Engineering: Designing for Information Refinement Fredrik Olsson.
SWG Strategy (C) Copyright IBM Corp. 2006, All Rights Reserved. v1 ACITA 2011 demonstration of ongoing NLP work Dave Braines, David Mott, ETS, Hursley,
Lernverfahren auf Basis von Parallelkorpora Learning Techniques based on Parallel Corpora Jonas Kuhn Universität des Saarlandes, Saarbrücken Heidelberg,
School of something FACULTY OF OTHER School of Computing FACULTY OF ENGINEERING Word Sense Disambiguation semantic tagging of text, for Confusion Set Disambiguation.
1() Information Extraction – why Google doesnt even come close Diana Maynard Natural Language Processing Group University of Sheffield, UK.
School of something FACULTY OF OTHER School of Computing FACULTY OF ENGINEERING Google Research: Theorizing from Data COMP3310 AI32 Natural Language Processing.
Hans Uszkoreit German Research Center for Artificial Intelligence and Saarland University at Saarbruecken Hans Uszkoreit German Research Center for Artificial.
School of something FACULTY OF OTHER School of Computing FACULTY OF ENGINEERING Parsing: computing the grammatical structure of English sentences COMP3310.
Supervised and Unsupervised learning for Natural language processing Manaal Faruqui Language Technologies Institute SCS, CMU.
1 Computational Tools for Linguists Inderjeet Mani Georgetown University
A Word-Class Approach to Labeling PSCFG Rules for Machine Translation (ACL 2011) Andreas Zollmann and Stephan Vogel Presented by Yun Huang 01/07/2011.
Three Basic Problems Compute the probability of a text: P m (W 1,N ) Compute maximum probability tag sequence: arg max T 1,N P m (T 1,N | W 1,N ) Compute.
Albert Gatt LIN3022 Natural Language Processing Lecture 10.
Source: Feiyu Xu, Jakub Piskorski 2002 Language Technology Information Extraction Feiyu Xu Jakub Piskorski DFKI LT-Lab.
Experiments in German Noun Chunking Michael Schiehlen Institut für Maschinelle Sprachverarbeitung Universität Stuttgart COLING.
News And the automated generation thereof the recent earthquake in
© 2016 SlidePlayer.com Inc. All rights reserved.