Phonological constraint induction in a connnectionist network: Learning OCP-Place constraints from data

Size: px
Start display at page:

Download "Phonological constraint induction in a connnectionist network: Learning OCP-Place constraints from data"

Transcription

1 PDF of article: Academia Sinica, October 30, 2013 Phonological constraint induction in a connnectionist network: Learning OCP-Place constraints from data John Alderete, Paul Tupper Simon Fraser University Stefan A. Frisch University of South Florida

2 Universal constraints One of the primary goals of linguistic theory is to explain language particular facts with universal principles. Optimality Theory: constraints are universal and innate (not learned); learning involves ranking apriori constraints Harmonic and MaxEnt grammar: constraints also given in advance, but constraint weights learned 2

3 Problems Language particular crazy rules (Blevins, Hayes): to retain predictiveness of UG constraints, need mechanism for inducing constraints Language particular constraints in language acquisition (Goad, Fikkert & Levelt, Levelt & van Oostendorp): differences between child and adult phonological processes require constraint induction Conclusion: learning the constraints themselves is an important part of learning 3

4 The argument Standard learning architecture of connectionist networks (c-nets) provides attractive advantages for constraint induction. Reasons to be optimistic about c-nets: 1. Relates linguistic analysis to psycholinguistic research 2. Connectionist systems good at capturing linguistic facts: similarity, gradience; categorical effects too 4

5 The argument, cont d 3. C-nets provide a very natural analysis of constraint induction. Connectionist constraint induction: Constraints in c-nets are soft, cf. hard constraints of symbolic computation. Constraints in c-nets are sets of connections (Smolensky). Example: Onset in BrbrNet is encoded as inhibitory links between output nodes. Learning constraints can therefore be straightforwardly modeling as constraint weight adjustment. 5

6 Outline 1. Linguistic background on Arabic 2. Formal background for connectionist networks 3. A c-net for learning the OCP in Arabic 4. Discussion and conclusion 6

7 Linguistic background Arabic morphology: roots and patterns Roots: strings of consonants Patterns: Add roots to form stems/words k-t-b write + CaaCiC ð kaatib writer Consonants in four place series (labials, coronals, dorsals, pharyngeals) Phonotactics: same series consonants tend not to co-occur in a root (classical grammarians, Greenberg, McCarthy) 7

8 Linguistic background, cont d Statistical analysis: Observed/Expected number of observed consonant pairs/ number expected by chance Observation: consonant pairs that have the same place, and are similar on other features, have very low O/E values Dubbed OCP-Place But coronal split into sonorants versus obstruents so secondary feature effects 8

9 Arabic co-occurrence by class Labial Cor stop Cor fric Dors Uvular Guttural Cor son Labial Cor stop Cor fric Dors Uvular Guttural Cor son 0.06

10 Linguistic background, cont d Some other secondary feature effects (statistically) Uvulars span dorsal and pharyngeal series Coronal stops and coronal fricatives Dorsals with uvularized coronals Pierrehumbert: Similarity predicts cooccurrence within place series 10

11 Arabic OCP by similarity O/E Observed Stoch model Similarity 11

12 Psycholinguistic findings Wordlikeness study: Jordanian Arabic speakers rate written nonsense words Lexical and linguistic factors: OCP violations (~30% variance explained) Lexical statistics (~20% variance expl.) Similarity (~20% variance expl.) Found impact of OCP-Place, differentiation of accidental and systematic gaps, and similarity effect 12

13 Connectionist Architecture Relatively structure free architecture for pattern learning (for some types of patterns, like phonotactics) Simple units with an activation level Activation level depends on weight between interconnected units Some structure assumed in our architecture (features and segments) Weights to be learned (standard technique) 13

14 Illustration: activation spread Input-output mappings: weighted sum of input activations, scaled by an activation function. 14!

15 Constraints in c-nets Subsymbolic constraints in c-nets: connections between units (Smolensky 1988) Parameters: single connection or sets of connections, negative or positive connections, constraint weight. Illustration: if connection is positive, it tries to put the receiving unit in the same state as the sending unit; constraint is can be said to be satisfied if this occurs. Brbrnet: Onset is inhibitory links between output nodes: push system to a state in which output nodes are sequences of 0-1, 0-1, which map to onset-peak 15

16 C-net for Arabic phonotactics Objective: try to build a c-net that can capture the OCP-Place effects with numerical computation Desideratum I: no apriori constraints; constraint weights set to random numbers Desideratum II: c-net should mimic human intuitions about wordlikeness Empirical challenge: can c-net learn OCPconstraints by adjusting constraint weights in response to Arabic roots? 16

17 The Autoassociator Production system (but not a realistic one): local encoding of triliteral root, tries to duplicate input in output Mature network either reproduces correct root or makes error in one or more consonants noise noise noise CLS48 17

18 The Assessor Input is feature representation of root Output interpreted as acceptability Exists = 1, Doesn t exist = -1 CLS48 18

19 Training assessor Backpropagation using output from autoassociator, from 3439 actual roots of Arabic, Correct word target = 1 Incorrect word target = -1 Learns word/ nonword through errors Hidden layer forces generalization CLS48 19

20 Results: Lexicon (a) All attested roots (c) OCP compliant roots (b) All possible roots (d) OCP violating roots

21 Results: Experiments Compare Assessor node outputs to human subject wordlikeness responses. Qualitative agreement with results for human subjects OCP ~48% variance explained Lexical statistics ~31% variance expl. Similarity ~14% variance explained Performance stable with respect to number of hidden nodes (the biggest structural variable in model) Conclusion: a relatively simple set of model parameters reproduced all of the significant effects of the psycholinguistic study 21

22 Analysis of hidden layer To what extent does the hidden layer encode the symbolic phonological generalizations? Quite a bit. CART analysis Creates a decision tree for the entire dataset based on the behavior of one hidden node at a time Statistical analysis Correlation between activation and OCP violation for each node 22

23 CART analysis example CART: method of imposing categorical analysis on messy data. Input-outputs: CART analysis looks at inputs and outputs, and finds features that are the best predictors of the data; applied recursively to produce a tree. Objective: if there s something simple that the hidden layer is doing, should be able to spot it. Illustration: CART analysis of one hidden node, shows that the node implements OCP-Phar quite well.

24 Statistical analysis Correlation of hidden node activation with violation of OCP Some represent OCP-Place in one node, others in two Consistent with results from CART analysis Often, strength of correlations and overlaps are interesting (i.e. reflect a pattern in the data) 24

25 Correlation example Labial stands alone strongly, coronal & dorsal more overlapping, coronal weak Consistent with lexical data CLS48 25

26 Summary Novel findings Learning with actual roots of Arabic and noise provides feedback for a phonotactic pattern grammar Behavior is qualitatively parallel to human intuitions of the OCP Hidden nodes have a symbolic interpretation, roughly corresponding to feature-specific OCP constraints (robust across reasonable number of nodes) 26

27 Limitations Only works with a fixed root structure, analogous to limitations of TRACE (though recurrent network versions have also been implemented) Modeler sets limited number of hidden nodes to force generalization (too few can t learn, too many overfit to lexicon) Model doesn t actually relate directly to psycholinguistic processes: Assessor node not an input-output processor 27

have to be modeled) or isolated words. Output of the system is a grapheme-tophoneme conversion system which takes as its input the spelling of words,

have to be modeled) or isolated words. Output of the system is a grapheme-tophoneme conversion system which takes as its input the spelling of words, A Language-Independent, Data-Oriented Architecture for Grapheme-to-Phoneme Conversion Walter Daelemans and Antal van den Bosch Proceedings ESCA-IEEE speech synthesis conference, New York, September 1994

More information

Markedness and Complex Stops: Evidence from Simplification Processes 1. Nick Danis Rutgers University

Markedness and Complex Stops: Evidence from Simplification Processes 1. Nick Danis Rutgers University Markedness and Complex Stops: Evidence from Simplification Processes 1 Nick Danis Rutgers University nick.danis@rutgers.edu WOCAL 8 Kyoto, Japan August 21-24, 2015 1 Introduction (1) Complex segments:

More information

Similarity Avoidance in the Proto-Indo-European Root

Similarity Avoidance in the Proto-Indo-European Root Volume 15 Issue 1 Proceedings of the 32nd Annual Penn Linguistics Colloquium University of Pennsylvania Working Papers in Linguistics Article 8 3-23-2009 Similarity Avoidance in the Proto-Indo-European

More information

Language Acquisition Fall 2010/Winter Lexical Categories. Afra Alishahi, Heiner Drenhaus

Language Acquisition Fall 2010/Winter Lexical Categories. Afra Alishahi, Heiner Drenhaus Language Acquisition Fall 2010/Winter 2011 Lexical Categories Afra Alishahi, Heiner Drenhaus Computational Linguistics and Phonetics Saarland University Children s Sensitivity to Lexical Categories Look,

More information

Parallel Evaluation in Stratal OT * Adam Baker University of Arizona

Parallel Evaluation in Stratal OT * Adam Baker University of Arizona Parallel Evaluation in Stratal OT * Adam Baker University of Arizona tabaker@u.arizona.edu 1.0. Introduction The model of Stratal OT presented by Kiparsky (forthcoming), has not and will not prove uncontroversial

More information

Module 12. Machine Learning. Version 2 CSE IIT, Kharagpur

Module 12. Machine Learning. Version 2 CSE IIT, Kharagpur Module 12 Machine Learning 12.1 Instructional Objective The students should understand the concept of learning systems Students should learn about different aspects of a learning system Students should

More information

Python Machine Learning

Python Machine Learning Python Machine Learning Unlock deeper insights into machine learning with this vital guide to cuttingedge predictive analytics Sebastian Raschka [ PUBLISHING 1 open source I community experience distilled

More information

Evolution of Symbolisation in Chimpanzees and Neural Nets

Evolution of Symbolisation in Chimpanzees and Neural Nets Evolution of Symbolisation in Chimpanzees and Neural Nets Angelo Cangelosi Centre for Neural and Adaptive Systems University of Plymouth (UK) a.cangelosi@plymouth.ac.uk Introduction Animal communication

More information

A Neural Network GUI Tested on Text-To-Phoneme Mapping

A Neural Network GUI Tested on Text-To-Phoneme Mapping A Neural Network GUI Tested on Text-To-Phoneme Mapping MAARTEN TROMPPER Universiteit Utrecht m.f.a.trompper@students.uu.nl Abstract Text-to-phoneme (T2P) mapping is a necessary step in any speech synthesis

More information

SOUND STRUCTURE REPRESENTATION, REPAIR AND WELL-FORMEDNESS: GRAMMAR IN SPOKEN LANGUAGE PRODUCTION. Adam B. Buchwald

SOUND STRUCTURE REPRESENTATION, REPAIR AND WELL-FORMEDNESS: GRAMMAR IN SPOKEN LANGUAGE PRODUCTION. Adam B. Buchwald SOUND STRUCTURE REPRESENTATION, REPAIR AND WELL-FORMEDNESS: GRAMMAR IN SPOKEN LANGUAGE PRODUCTION by Adam B. Buchwald A dissertation submitted to The Johns Hopkins University in conformity with the requirements

More information

Word Segmentation of Off-line Handwritten Documents

Word Segmentation of Off-line Handwritten Documents Word Segmentation of Off-line Handwritten Documents Chen Huang and Sargur N. Srihari {chuang5, srihari}@cedar.buffalo.edu Center of Excellence for Document Analysis and Recognition (CEDAR), Department

More information

Mandarin Lexical Tone Recognition: The Gating Paradigm

Mandarin Lexical Tone Recognition: The Gating Paradigm Kansas Working Papers in Linguistics, Vol. 0 (008), p. 8 Abstract Mandarin Lexical Tone Recognition: The Gating Paradigm Yuwen Lai and Jie Zhang University of Kansas Research on spoken word recognition

More information

Seminar - Organic Computing

Seminar - Organic Computing Seminar - Organic Computing Self-Organisation of OC-Systems Markus Franke 25.01.2006 Typeset by FoilTEX Timetable 1. Overview 2. Characteristics of SO-Systems 3. Concern with Nature 4. Design-Concepts

More information

Linguistics 220 Phonology: distributions and the concept of the phoneme. John Alderete, Simon Fraser University

Linguistics 220 Phonology: distributions and the concept of the phoneme. John Alderete, Simon Fraser University Linguistics 220 Phonology: distributions and the concept of the phoneme John Alderete, Simon Fraser University Foundations in phonology Outline 1. Intuitions about phonological structure 2. Contrastive

More information

UC Merced Proceedings of the Annual Meeting of the Cognitive Science Society

UC Merced Proceedings of the Annual Meeting of the Cognitive Science Society UC Merced Proceedings of the Annual Meeting of the Cognitive Science Society Title Investigating phonotactics using xenolinguistics: A novel word-picture matching paradigm Permalink https://escholarship.org/uc/item/8bx6s7vp

More information

Lexical phonology. Marc van Oostendorp. December 6, Until now, we have presented phonological theory as if it is a monolithic

Lexical phonology. Marc van Oostendorp. December 6, Until now, we have presented phonological theory as if it is a monolithic Lexical phonology Marc van Oostendorp December 6, 2005 Background Until now, we have presented phonological theory as if it is a monolithic unit. However, there is evidence that phonology consists of at

More information

CS Machine Learning

CS Machine Learning CS 478 - Machine Learning Projects Data Representation Basic testing and evaluation schemes CS 478 Data and Testing 1 Programming Issues l Program in any platform you want l Realize that you will be doing

More information

CS 598 Natural Language Processing

CS 598 Natural Language Processing CS 598 Natural Language Processing Natural language is everywhere Natural language is everywhere Natural language is everywhere Natural language is everywhere!"#$%&'&()*+,-./012 34*5665756638/9:;< =>?@ABCDEFGHIJ5KL@

More information

Introduction to Ensemble Learning Featuring Successes in the Netflix Prize Competition

Introduction to Ensemble Learning Featuring Successes in the Netflix Prize Competition Introduction to Ensemble Learning Featuring Successes in the Netflix Prize Competition Todd Holloway Two Lecture Series for B551 November 20 & 27, 2007 Indiana University Outline Introduction Bias and

More information

Knowledge Transfer in Deep Convolutional Neural Nets

Knowledge Transfer in Deep Convolutional Neural Nets Knowledge Transfer in Deep Convolutional Neural Nets Steven Gutstein, Olac Fuentes and Eric Freudenthal Computer Science Department University of Texas at El Paso El Paso, Texas, 79968, U.S.A. Abstract

More information

Dyslexia/dyslexic, 3, 9, 24, 97, 187, 189, 206, 217, , , 367, , , 397,

Dyslexia/dyslexic, 3, 9, 24, 97, 187, 189, 206, 217, , , 367, , , 397, Adoption studies, 274 275 Alliteration skill, 113, 115, 117 118, 122 123, 128, 136, 138 Alphabetic writing system, 5, 40, 127, 136, 410, 415 Alphabets (types of ) artificial transparent alphabet, 5 German

More information

Speech Recognition at ICSI: Broadcast News and beyond

Speech Recognition at ICSI: Broadcast News and beyond Speech Recognition at ICSI: Broadcast News and beyond Dan Ellis International Computer Science Institute, Berkeley CA Outline 1 2 3 The DARPA Broadcast News task Aspects of ICSI

More information

An Empirical and Computational Test of Linguistic Relativity

An Empirical and Computational Test of Linguistic Relativity An Empirical and Computational Test of Linguistic Relativity Kathleen M. Eberhard* (eberhard.1@nd.edu) Matthias Scheutz** (mscheutz@cse.nd.edu) Michael Heilman** (mheilman@nd.edu) *Department of Psychology,

More information

Specification and Evaluation of Machine Translation Toy Systems - Criteria for laboratory assignments

Specification and Evaluation of Machine Translation Toy Systems - Criteria for laboratory assignments Specification and Evaluation of Machine Translation Toy Systems - Criteria for laboratory assignments Cristina Vertan, Walther v. Hahn University of Hamburg, Natural Language Systems Division Hamburg,

More information

Consonant-Vowel Unity in Element Theory*

Consonant-Vowel Unity in Element Theory* Consonant-Vowel Unity in Element Theory* Phillip Backley Tohoku Gakuin University Kuniya Nasukawa Tohoku Gakuin University ABSTRACT. This paper motivates the Element Theory view that vowels and consonants

More information

Phonological encoding in speech production

Phonological encoding in speech production Phonological encoding in speech production Niels O. Schiller Department of Cognitive Neuroscience, Maastricht University, The Netherlands Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands

More information

Stochastic Phonology Janet B. Pierrehumbert Department of Linguistics Northwestern University Evanston, IL Introduction

Stochastic Phonology Janet B. Pierrehumbert Department of Linguistics Northwestern University Evanston, IL Introduction Stochastic Phonology Janet B. Pierrehumbert Department of Linguistics Northwestern University Evanston, IL 60208 1.0 Introduction In classic generative phonology, linguistic competence in the area of sound

More information

The Strong Minimalist Thesis and Bounded Optimality

The Strong Minimalist Thesis and Bounded Optimality The Strong Minimalist Thesis and Bounded Optimality DRAFT-IN-PROGRESS; SEND COMMENTS TO RICKL@UMICH.EDU Richard L. Lewis Department of Psychology University of Michigan 27 March 2010 1 Purpose of this

More information

A Simple VQA Model with a Few Tricks and Image Features from Bottom-up Attention

A Simple VQA Model with a Few Tricks and Image Features from Bottom-up Attention A Simple VQA Model with a Few Tricks and Image Features from Bottom-up Attention Damien Teney 1, Peter Anderson 2*, David Golub 4*, Po-Sen Huang 3, Lei Zhang 3, Xiaodong He 3, Anton van den Hengel 1 1

More information

Corpus Linguistics (L615)

Corpus Linguistics (L615) (L615) Basics of Markus Dickinson Department of, Indiana University Spring 2013 1 / 23 : the extent to which a sample includes the full range of variability in a population distinguishes corpora from archives

More information

SARDNET: A Self-Organizing Feature Map for Sequences

SARDNET: A Self-Organizing Feature Map for Sequences SARDNET: A Self-Organizing Feature Map for Sequences Daniel L. James and Risto Miikkulainen Department of Computer Sciences The University of Texas at Austin Austin, TX 78712 dljames,risto~cs.utexas.edu

More information

LING 329 : MORPHOLOGY

LING 329 : MORPHOLOGY LING 329 : MORPHOLOGY TTh 10:30 11:50 AM, Physics 121 Course Syllabus Spring 2013 Matt Pearson Office: Vollum 313 Email: pearsonm@reed.edu Phone: 7618 (off campus: 503-517-7618) Office hrs: Mon 1:30 2:30,

More information

Syntactic systematicity in sentence processing with a recurrent self-organizing network

Syntactic systematicity in sentence processing with a recurrent self-organizing network Syntactic systematicity in sentence processing with a recurrent self-organizing network Igor Farkaš,1 Department of Applied Informatics, Comenius University Mlynská dolina, 842 48 Bratislava, Slovak Republic

More information

Underlying Representations

Underlying Representations Underlying Representations The content of underlying representations. A basic issue regarding underlying forms is: what are they made of? We have so far treated them as segments represented as letters.

More information

Physics 270: Experimental Physics

Physics 270: Experimental Physics 2017 edition Lab Manual Physics 270 3 Physics 270: Experimental Physics Lecture: Lab: Instructor: Office: Email: Tuesdays, 2 3:50 PM Thursdays, 2 4:50 PM Dr. Uttam Manna 313C Moulton Hall umanna@ilstu.edu

More information

The presence of interpretable but ungrammatical sentences corresponds to mismatches between interpretive and productive parsing.

The presence of interpretable but ungrammatical sentences corresponds to mismatches between interpretive and productive parsing. Lecture 4: OT Syntax Sources: Kager 1999, Section 8; Legendre et al. 1998; Grimshaw 1997; Barbosa et al. 1998, Introduction; Bresnan 1998; Fanselow et al. 1999; Gibson & Broihier 1998. OT is not a theory

More information

A Minimalist Approach to Code-Switching. In the field of linguistics, the topic of bilingualism is a broad one. There are many

A Minimalist Approach to Code-Switching. In the field of linguistics, the topic of bilingualism is a broad one. There are many Schmidt 1 Eric Schmidt Prof. Suzanne Flynn Linguistic Study of Bilingualism December 13, 2013 A Minimalist Approach to Code-Switching In the field of linguistics, the topic of bilingualism is a broad one.

More information

Partial Class Behavior and Nasal Place Assimilation*

Partial Class Behavior and Nasal Place Assimilation* Partial Class Behavior and Nasal Place Assimilation* Jaye Padgett University of California, Santa Cruz 1. Introduction This paper has two goals. The first is to pursue and further motivate some ideas developed

More information

Proof Theory for Syntacticians

Proof Theory for Syntacticians Department of Linguistics Ohio State University Syntax 2 (Linguistics 602.02) January 5, 2012 Logics for Linguistics Many different kinds of logic are directly applicable to formalizing theories in syntax

More information

Artificial Neural Networks

Artificial Neural Networks Artificial Neural Networks Andres Chavez Math 382/L T/Th 2:00-3:40 April 13, 2010 Chavez2 Abstract The main interest of this paper is Artificial Neural Networks (ANNs). A brief history of the development

More information

Artificial Neural Networks written examination

Artificial Neural Networks written examination 1 (8) Institutionen för informationsteknologi Olle Gällmo Universitetsadjunkt Adress: Lägerhyddsvägen 2 Box 337 751 05 Uppsala Artificial Neural Networks written examination Monday, May 15, 2006 9 00-14

More information

Phonological Processing for Urdu Text to Speech System

Phonological Processing for Urdu Text to Speech System Phonological Processing for Urdu Text to Speech System Sarmad Hussain Center for Research in Urdu Language Processing, National University of Computer and Emerging Sciences, B Block, Faisal Town, Lahore,

More information

Lecture 1: Machine Learning Basics

Lecture 1: Machine Learning Basics 1/69 Lecture 1: Machine Learning Basics Ali Harakeh University of Waterloo WAVE Lab ali.harakeh@uwaterloo.ca May 1, 2017 2/69 Overview 1 Learning Algorithms 2 Capacity, Overfitting, and Underfitting 3

More information

Joan Bybee, Phonology and Language Use. Cambridge: Cambridge University Press, 2001,

Joan Bybee, Phonology and Language Use. Cambridge: Cambridge University Press, 2001, Reflections on usage-based phonology Review article of Joan Bybee, Phonology and Language Use. Cambridge: Cambridge University Press, 2001, xviii + 238 p. Geert Booij (Vrije Universiteit Amsterdam) The

More information

English Language and Applied Linguistics. Module Descriptions 2017/18

English Language and Applied Linguistics. Module Descriptions 2017/18 English Language and Applied Linguistics Module Descriptions 2017/18 Level I (i.e. 2 nd Yr.) Modules Please be aware that all modules are subject to availability. If you have any questions about the modules,

More information

A Stochastic Model for the Vocabulary Explosion

A Stochastic Model for the Vocabulary Explosion Words Known A Stochastic Model for the Vocabulary Explosion Colleen C. Mitchell (colleen-mitchell@uiowa.edu) Department of Mathematics, 225E MLH Iowa City, IA 52242 USA Bob McMurray (bob-mcmurray@uiowa.edu)

More information

Radical CV Phonology: the locational gesture *

Radical CV Phonology: the locational gesture * Radical CV Phonology: the locational gesture * HARRY VAN DER HULST 1 Goals 'Radical CV Phonology' is a variant of Dependency Phonology (Anderson and Jones 1974, Anderson & Ewen 1980, Ewen 1980, Lass 1984,

More information

Acoustic correlates of stress and their use in diagnosing syllable fusion in Tongan. James White & Marc Garellek UCLA

Acoustic correlates of stress and their use in diagnosing syllable fusion in Tongan. James White & Marc Garellek UCLA Acoustic correlates of stress and their use in diagnosing syllable fusion in Tongan James White & Marc Garellek UCLA 1 Introduction Goals: To determine the acoustic correlates of primary and secondary

More information

Chinese Language Parsing with Maximum-Entropy-Inspired Parser

Chinese Language Parsing with Maximum-Entropy-Inspired Parser Chinese Language Parsing with Maximum-Entropy-Inspired Parser Heng Lian Brown University Abstract The Chinese language has many special characteristics that make parsing difficult. The performance of state-of-the-art

More information

QuickStroke: An Incremental On-line Chinese Handwriting Recognition System

QuickStroke: An Incremental On-line Chinese Handwriting Recognition System QuickStroke: An Incremental On-line Chinese Handwriting Recognition System Nada P. Matić John C. Platt Λ Tony Wang y Synaptics, Inc. 2381 Bering Drive San Jose, CA 95131, USA Abstract This paper presents

More information

Learning Methods for Fuzzy Systems

Learning Methods for Fuzzy Systems Learning Methods for Fuzzy Systems Rudolf Kruse and Andreas Nürnberger Department of Computer Science, University of Magdeburg Universitätsplatz, D-396 Magdeburg, Germany Phone : +49.39.67.876, Fax : +49.39.67.8

More information

A Bayesian Model of Stress Assignment in Reading

A Bayesian Model of Stress Assignment in Reading Western University Scholarship@Western Electronic Thesis and Dissertation Repository March 2014 A Bayesian Model of Stress Assignment in Reading Olessia Jouravlev The University of Western Ontario Supervisor

More information

Linking Task: Identifying authors and book titles in verbose queries

Linking Task: Identifying authors and book titles in verbose queries Linking Task: Identifying authors and book titles in verbose queries Anaïs Ollagnier, Sébastien Fournier, and Patrice Bellot Aix-Marseille University, CNRS, ENSAM, University of Toulon, LSIS UMR 7296,

More information

Learning Structural Correspondences Across Different Linguistic Domains with Synchronous Neural Language Models

Learning Structural Correspondences Across Different Linguistic Domains with Synchronous Neural Language Models Learning Structural Correspondences Across Different Linguistic Domains with Synchronous Neural Language Models Stephan Gouws and GJ van Rooyen MIH Medialab, Stellenbosch University SOUTH AFRICA {stephan,gvrooyen}@ml.sun.ac.za

More information

Assignment 1: Predicting Amazon Review Ratings

Assignment 1: Predicting Amazon Review Ratings Assignment 1: Predicting Amazon Review Ratings 1 Dataset Analysis Richard Park r2park@acsmail.ucsd.edu February 23, 2015 The dataset selected for this assignment comes from the set of Amazon reviews for

More information

System Implementation for SemEval-2017 Task 4 Subtask A Based on Interpolated Deep Neural Networks

System Implementation for SemEval-2017 Task 4 Subtask A Based on Interpolated Deep Neural Networks System Implementation for SemEval-2017 Task 4 Subtask A Based on Interpolated Deep Neural Networks 1 Tzu-Hsuan Yang, 2 Tzu-Hsuan Tseng, and 3 Chia-Ping Chen Department of Computer Science and Engineering

More information

An Evaluation of the Interactive-Activation Model Using Masked Partial-Word Priming. Jason R. Perry. University of Western Ontario. Stephen J.

An Evaluation of the Interactive-Activation Model Using Masked Partial-Word Priming. Jason R. Perry. University of Western Ontario. Stephen J. An Evaluation of the Interactive-Activation Model Using Masked Partial-Word Priming Jason R. Perry University of Western Ontario Stephen J. Lupker University of Western Ontario Colin J. Davis Royal Holloway

More information

Evolutive Neural Net Fuzzy Filtering: Basic Description

Evolutive Neural Net Fuzzy Filtering: Basic Description Journal of Intelligent Learning Systems and Applications, 2010, 2: 12-18 doi:10.4236/jilsa.2010.21002 Published Online February 2010 (http://www.scirp.org/journal/jilsa) Evolutive Neural Net Fuzzy Filtering:

More information

Improved Effects of Word-Retrieval Treatments Subsequent to Addition of the Orthographic Form

Improved Effects of Word-Retrieval Treatments Subsequent to Addition of the Orthographic Form Orthographic Form 1 Improved Effects of Word-Retrieval Treatments Subsequent to Addition of the Orthographic Form The development and testing of word-retrieval treatments for aphasia has generally focused

More information

Analysis of Hybrid Soft and Hard Computing Techniques for Forex Monitoring Systems

Analysis of Hybrid Soft and Hard Computing Techniques for Forex Monitoring Systems Analysis of Hybrid Soft and Hard Computing Techniques for Forex Monitoring Systems Ajith Abraham School of Business Systems, Monash University, Clayton, Victoria 3800, Australia. Email: ajith.abraham@ieee.org

More information

Degeneracy results in canalisation of language structure: A computational model of word learning

Degeneracy results in canalisation of language structure: A computational model of word learning Degeneracy results in canalisation of language structure: A computational model of word learning Padraic Monaghan (p.monaghan@lancaster.ac.uk) Department of Psychology, Lancaster University Lancaster LA1

More information

An empirical study of learning speed in backpropagation

An empirical study of learning speed in backpropagation Carnegie Mellon University Research Showcase @ CMU Computer Science Department School of Computer Science 1988 An empirical study of learning speed in backpropagation networks Scott E. Fahlman Carnegie

More information

Consonants: articulation and transcription

Consonants: articulation and transcription Phonology 1: Handout January 20, 2005 Consonants: articulation and transcription 1 Orientation phonetics [G. Phonetik]: the study of the physical and physiological aspects of human sound production and

More information

Psychology and Language

Psychology and Language Psychology and Language Psycholinguistics is the study about the casual connection within human being linking experience with speaking and writing, and hearing and reading with further behavior (Robins,

More information

Phonological and Phonetic Representations: The Case of Neutralization

Phonological and Phonetic Representations: The Case of Neutralization Phonological and Phonetic Representations: The Case of Neutralization Allard Jongman University of Kansas 1. Introduction The present paper focuses on the phenomenon of phonological neutralization to consider

More information

Machine Learning and Data Mining. Ensembles of Learners. Prof. Alexander Ihler

Machine Learning and Data Mining. Ensembles of Learners. Prof. Alexander Ihler Machine Learning and Data Mining Ensembles of Learners Prof. Alexander Ihler Ensemble methods Why learn one classifier when you can learn many? Ensemble: combine many predictors (Weighted) combina

More information

9.85 Cognition in Infancy and Early Childhood. Lecture 7: Number

9.85 Cognition in Infancy and Early Childhood. Lecture 7: Number 9.85 Cognition in Infancy and Early Childhood Lecture 7: Number What else might you know about objects? Spelke Objects i. Continuity. Objects exist continuously and move on paths that are connected over

More information

How People Learn Physics

How People Learn Physics How People Learn Physics Edward F. (Joe) Redish Dept. Of Physics University Of Maryland AAPM, Houston TX, Work supported in part by NSF grants DUE #04-4-0113 and #05-2-4987 Teaching complex subjects 2

More information

On the Formation of Phoneme Categories in DNN Acoustic Models

On the Formation of Phoneme Categories in DNN Acoustic Models On the Formation of Phoneme Categories in DNN Acoustic Models Tasha Nagamine Department of Electrical Engineering, Columbia University T. Nagamine Motivation Large performance gap between humans and state-

More information

An Introduction to the Minimalist Program

An Introduction to the Minimalist Program An Introduction to the Minimalist Program Luke Smith University of Arizona Summer 2016 Some findings of traditional syntax Human languages vary greatly, but digging deeper, they all have distinct commonalities:

More information

learning collegiate assessment]

learning collegiate assessment] [ collegiate learning assessment] INSTITUTIONAL REPORT 2005 2006 Kalamazoo College council for aid to education 215 lexington avenue floor 21 new york new york 10016-6023 p 212.217.0700 f 212.661.9766

More information

The analysis starts with the phonetic vowel and consonant charts based on the dataset:

The analysis starts with the phonetic vowel and consonant charts based on the dataset: Ling 113 Homework 5: Hebrew Kelli Wiseth February 13, 2014 The analysis starts with the phonetic vowel and consonant charts based on the dataset: a) Given that the underlying representation for all verb

More information

arxiv: v1 [cs.cl] 2 Apr 2017

arxiv: v1 [cs.cl] 2 Apr 2017 Word-Alignment-Based Segment-Level Machine Translation Evaluation using Word Embeddings Junki Matsuo and Mamoru Komachi Graduate School of System Design, Tokyo Metropolitan University, Japan matsuo-junki@ed.tmu.ac.jp,

More information

FUZZY EXPERT. Dr. Kasim M. Al-Aubidy. Philadelphia University. Computer Eng. Dept February 2002 University of Damascus-Syria

FUZZY EXPERT. Dr. Kasim M. Al-Aubidy. Philadelphia University. Computer Eng. Dept February 2002 University of Damascus-Syria FUZZY EXPERT SYSTEMS 16-18 18 February 2002 University of Damascus-Syria Dr. Kasim M. Al-Aubidy Computer Eng. Dept. Philadelphia University What is Expert Systems? ES are computer programs that emulate

More information

On Human Computer Interaction, HCI. Dr. Saif al Zahir Electrical and Computer Engineering Department UBC

On Human Computer Interaction, HCI. Dr. Saif al Zahir Electrical and Computer Engineering Department UBC On Human Computer Interaction, HCI Dr. Saif al Zahir Electrical and Computer Engineering Department UBC Human Computer Interaction HCI HCI is the study of people, computer technology, and the ways these

More information

Florida Reading Endorsement Alignment Matrix Competency 1

Florida Reading Endorsement Alignment Matrix Competency 1 Florida Reading Endorsement Alignment Matrix Competency 1 Reading Endorsement Guiding Principle: Teachers will understand and teach reading as an ongoing strategic process resulting in students comprehending

More information

Cognitive Thinking Style Sample Report

Cognitive Thinking Style Sample Report Cognitive Thinking Style Sample Report Goldisc Limited Authorised Agent for IML, PeopleKeys & StudentKeys DISC Profiles Online Reports Training Courses Consultations sales@goldisc.co.uk Telephone: +44

More information

Manner assimilation in Uyghur

Manner assimilation in Uyghur Manner assimilation in Uyghur Suyeon Yun (suyeon@mit.edu) 10th Workshop on Altaic Formal Linguistics (1) Possible patterns of manner assimilation in nasal-liquid sequences (a) Regressive assimilation lateralization:

More information

Effects of Vocabulary and Phonotactic Probability on 2-Year-Olds Nonword Repetition

Effects of Vocabulary and Phonotactic Probability on 2-Year-Olds Nonword Repetition J Psycholinguist Res (2017) 46:507 524 DOI 10.1007/s10936-016-9448-9 Effects of Vocabulary and Phonotactic Probability on 2-Year-Olds Nonword Repetition Josje Verhagen 1 Elise de Bree 2 Hanna Mulder 1

More information

Individual Differences & Item Effects: How to test them, & how to test them well

Individual Differences & Item Effects: How to test them, & how to test them well Individual Differences & Item Effects: How to test them, & how to test them well Individual Differences & Item Effects Properties of subjects Cognitive abilities (WM task scores, inhibition) Gender Age

More information

AUTOMATIC DETECTION OF PROLONGED FRICATIVE PHONEMES WITH THE HIDDEN MARKOV MODELS APPROACH 1. INTRODUCTION

AUTOMATIC DETECTION OF PROLONGED FRICATIVE PHONEMES WITH THE HIDDEN MARKOV MODELS APPROACH 1. INTRODUCTION JOURNAL OF MEDICAL INFORMATICS & TECHNOLOGIES Vol. 11/2007, ISSN 1642-6037 Marek WIŚNIEWSKI *, Wiesława KUNISZYK-JÓŹKOWIAK *, Elżbieta SMOŁKA *, Waldemar SUSZYŃSKI * HMM, recognition, speech, disorders

More information

Parsing of part-of-speech tagged Assamese Texts

Parsing of part-of-speech tagged Assamese Texts IJCSI International Journal of Computer Science Issues, Vol. 6, No. 1, 2009 ISSN (Online): 1694-0784 ISSN (Print): 1694-0814 28 Parsing of part-of-speech tagged Assamese Texts Mirzanur Rahman 1, Sufal

More information

AGENDA LEARNING THEORIES LEARNING THEORIES. Advanced Learning Theories 2/22/2016

AGENDA LEARNING THEORIES LEARNING THEORIES. Advanced Learning Theories 2/22/2016 AGENDA Advanced Learning Theories Alejandra J. Magana, Ph.D. admagana@purdue.edu Introduction to Learning Theories Role of Learning Theories and Frameworks Learning Design Research Design Dual Coding Theory

More information

phone hidden time phone

phone hidden time phone MODULARITY IN A CONNECTIONIST MODEL OF MORPHOLOGY ACQUISITION Michael Gasser Departments of Computer Science and Linguistics Indiana University Abstract This paper describes a modular connectionist model

More information

Constraining X-Bar: Theta Theory

Constraining X-Bar: Theta Theory Constraining X-Bar: Theta Theory Carnie, 2013, chapter 8 Kofi K. Saah 1 Learning objectives Distinguish between thematic relation and theta role. Identify the thematic relations agent, theme, goal, source,

More information

Intervening to alleviate word-finding difficulties in children: case series data and a computational modelling foundation

Intervening to alleviate word-finding difficulties in children: case series data and a computational modelling foundation PCGN1003204 Techset Composition India (P) Ltd., Bangalore and Chennai, India 1/20/2015 Cognitive Neuropsychology, 2015 http://dx.doi.org/10.1080/02643294.2014.1003204 5 Intervening to alleviate word-finding

More information

Enhancing Unlexicalized Parsing Performance using a Wide Coverage Lexicon, Fuzzy Tag-set Mapping, and EM-HMM-based Lexical Probabilities

Enhancing Unlexicalized Parsing Performance using a Wide Coverage Lexicon, Fuzzy Tag-set Mapping, and EM-HMM-based Lexical Probabilities Enhancing Unlexicalized Parsing Performance using a Wide Coverage Lexicon, Fuzzy Tag-set Mapping, and EM-HMM-based Lexical Probabilities Yoav Goldberg Reut Tsarfaty Meni Adler Michael Elhadad Ben Gurion

More information

On-the-Fly Customization of Automated Essay Scoring

On-the-Fly Customization of Automated Essay Scoring Research Report On-the-Fly Customization of Automated Essay Scoring Yigal Attali Research & Development December 2007 RR-07-42 On-the-Fly Customization of Automated Essay Scoring Yigal Attali ETS, Princeton,

More information

The phonological grammar is probabilistic: New evidence pitting abstract representation against analogy

The phonological grammar is probabilistic: New evidence pitting abstract representation against analogy The phonological grammar is probabilistic: New evidence pitting abstract representation against analogy university October 9, 2015 1/34 Introduction Speakers extend probabilistic trends in their lexicons

More information

Pobrane z czasopisma New Horizons in English Studies Data: 18/11/ :52:20. New Horizons in English Studies 1/2016

Pobrane z czasopisma New Horizons in English Studies  Data: 18/11/ :52:20. New Horizons in English Studies 1/2016 LANGUAGE Maria Curie-Skłodowska University () in Lublin k.laidler.umcs@gmail.com Online Adaptation of Word-initial Ukrainian CC Consonant Clusters by Native Speakers of English Abstract. The phenomenon

More information

Phonological Encoding in Sentence Production

Phonological Encoding in Sentence Production Phonological Encoding in Sentence Production Caitlin Hilliard (chillia2@u.rochester.edu), Katrina Furth (kfurth@bcs.rochester.edu), T. Florian Jaeger (fjaeger@bcs.rochester.edu) Department of Brain and

More information

Neuro-Symbolic Approaches for Knowledge Representation in Expert Systems

Neuro-Symbolic Approaches for Knowledge Representation in Expert Systems Published in the International Journal of Hybrid Intelligent Systems 1(3-4) (2004) 111-126 Neuro-Symbolic Approaches for Knowledge Representation in Expert Systems Ioannis Hatzilygeroudis and Jim Prentzas

More information

A Pipelined Approach for Iterative Software Process Model

A Pipelined Approach for Iterative Software Process Model A Pipelined Approach for Iterative Software Process Model Ms.Prasanthi E R, Ms.Aparna Rathi, Ms.Vardhani J P, Mr.Vivek Krishna Electronics and Radar Development Establishment C V Raman Nagar, Bangalore-560093,

More information

1 st Quarter (September, October, November) August/September Strand Topic Standard Notes Reading for Literature

1 st Quarter (September, October, November) August/September Strand Topic Standard Notes Reading for Literature 1 st Grade Curriculum Map Common Core Standards Language Arts 2013 2014 1 st Quarter (September, October, November) August/September Strand Topic Standard Notes Reading for Literature Key Ideas and Details

More information

CS 1103 Computer Science I Honors. Fall Instructor Muller. Syllabus

CS 1103 Computer Science I Honors. Fall Instructor Muller. Syllabus CS 1103 Computer Science I Honors Fall 2016 Instructor Muller Syllabus Welcome to CS1103. This course is an introduction to the art and science of computer programming and to some of the fundamental concepts

More information

Grammars & Parsing, Part 1:

Grammars & Parsing, Part 1: Grammars & Parsing, Part 1: Rules, representations, and transformations- oh my! Sentence VP The teacher Verb gave the lecture 2015-02-12 CS 562/662: Natural Language Processing Game plan for today: Review

More information

Probability and Statistics Curriculum Pacing Guide

Probability and Statistics Curriculum Pacing Guide Unit 1 Terms PS.SPMJ.3 PS.SPMJ.5 Plan and conduct a survey to answer a statistical question. Recognize how the plan addresses sampling technique, randomization, measurement of experimental error and methods

More information

1/20 idea. We ll spend an extra hour on 1/21. based on assigned readings. so you ll be ready to discuss them in class

1/20 idea. We ll spend an extra hour on 1/21. based on assigned readings. so you ll be ready to discuss them in class If we cancel class 1/20 idea We ll spend an extra hour on 1/21 I ll give you a brief writing problem for 1/21 based on assigned readings Jot down your thoughts based on your reading so you ll be ready

More information

Developing a TT-MCTAG for German with an RCG-based Parser

Developing a TT-MCTAG for German with an RCG-based Parser Developing a TT-MCTAG for German with an RCG-based Parser Laura Kallmeyer, Timm Lichte, Wolfgang Maier, Yannick Parmentier, Johannes Dellert University of Tübingen, Germany CNRS-LORIA, France LREC 2008,

More information