3 data structure and algorithm
play

3. Data Structure and Algorithm 3.1 Proplets for Coding - PowerPoint PPT Presentation

Dec 21, 2023 •567 likes •780 views

A Computational Model of Natural Language Communication 31 3. Data Structure and Algorithm 3.1 Proplets for Coding Propositional Content 3.1.1 C ONTEXT PROPLETS REPRESENTING dog barks. (I) run. 2 3 2 3 sur: sur: 2 3 sur: verb: bark verb:

  1. A Computational Model of Natural Language Communication 31 3. Data Structure and Algorithm 3.1 Proplets for Coding Propositional Content 3.1.1 C ONTEXT PROPLETS REPRESENTING dog barks. (I) run. 2 3 2 3 sur: sur: 2 3 sur: verb: bark verb: run noun: dog 6 7 6 7 6 7 6 7 6 7 arg: dog arg: moi 6 7 6 7 6 7 fnc: bark 6 7 6 7 4 5 nc: 23 run pc: 22 bark 4 5 4 5 prn: 22 prn: 22 prn: 23 3.1.2 C ODING OF RELATIONS BETWEEN CONCEPTS VIA PROPLETS sur: sur: sur: dog bark run verb: context level: noun: verb: arg: moi fnc: bark arg: dog pc: 22 bark prn: 22 nc: 23 run prn: 22 prn: 23 � 2006 Roland Hausser c

  2. A Computational Model of Natural Language Communication 32 3.2 Internal Matching between Language and Context Proplets 3.2.1 L ANGUAGE PROPLETS REPRESENTING dog barks. (I) run. 2 sur: bellt 3 2 sur: fliehe 3 2 sur: Hund 3 verb: bark verb: run noun: dog 6 7 6 7 6 7 6 7 6 7 arg: dog arg: moi 6 7 6 7 6 7 fnc: bark 6 7 6 7 4 5 nc: 123 run pc: 122 bark 4 5 4 5 prn: 122 prn: 122 prn: 123 3.2.2 K EYS FOR LEXICAL LOOKUP IN THE SPEAKER - AND THE HEARER - MODE ← key for lexical lookup in the hearer-mode 2 3 sur: Hund ← key for lexical lookup in the speaker-mode noun: dog 6 7 6 7 fnc: 4 5 prn � 2006 Roland Hausser c

  3. A Computational Model of Natural Language Communication 33 3.2.3 Conditions on successful matching 1. Attribute condition The matching between two proplets A and B requires that the intersection of their attributes contains a predefined list of attributes regarded as relevant: {list} ⊆ {{proplet-A-attributes} ∩ {proplet-B-attributes}} 2. Value condition The matching between two proplets requires that the variables (and a fortiori the constants) of their common attributes are compatible. � 2006 Roland Hausser c

  4. A Computational Model of Natural Language Communication 34 3.2.4 I MPACT OF INTER - PROPLET RELATIONS ON MATCHING sur: bellt sur: fliehe bark run sur: Hund verb: verb: dog language level: noun: arg: dog arg: moi (horizontal relations) fnc: bark nc: 123 run pc: 122 bark prn: 122 prn: 122 prn: 123 internal matching (vertical relations) sur: sur: sur: dog bark run context level: noun: verb: verb: (horizontal relations) fnc: bark arg: dog arg: moi prn: 22 nc: 23 run pc: 22 bark prn: 22 prn: 23 � 2006 Roland Hausser c

  5. A Computational Model of Natural Language Communication 35 3.3 Storage of Proplets in a Word Bank 3.3.1 D ATA STRUCTURE OF A WORD BANK context language recognition recognition bellt sur: sur: bellt bark verb: bark verb: bark arg: dog arg: dog prn: 22 prn: 122 frontier Hund sur: sur: Hund noun: dog noun: dog dog fnc: bark fnc: bark internal matching nc: 23 run nc: 123 bark prn: 22 prn: 122 sur: sur: fliehe fliehe verb: run verb: run run arg: moi arg: moi pc: 22 bark pc: 122 bark prn: 23 prn: 123 context language action action � 2006 Roland Hausser c

  6. A Computational Model of Natural Language Communication 36 3.4 Time-linear Algorithm of LA-Grammar 3.4.1 Applying the Input/Output Equivalence Principle to language 1. Input and output at the language level are signs of natural language, such as phrases, sen- tences, or texts. 2. The parts into which the signs of natural language disassemble during intake and discharge are word forms. 3. The order of the parts during intake and discharge is time-linear. � 2006 Roland Hausser c

  7. A Computational Model of Natural Language Communication 37 3.4.2 T IME - LINEAR D ERIVATION (P RINCIPLE OF P OSSIBLE C ONTINUATIONS ) Julia John knows lexical lookup verb: know noun: Julia noun: John arg: fnc: fnc: mdr: mdr: mdr: prn: prn: prn: syntactic−semantic parsing: verb: know noun: Julia arg: fnc: 1 mdr: mdr: prn: prn: 22 verb: know noun: John noun: Julia arg: Julia fnc: know fnc: 2 mdr: mdr: mdr: prn: 22 prn: 22 prn: result of syntactic−semantic parsing: verb: know noun: John noun: Julia arg: Julia John fnc: know fnc: know mdr: mdr: mdr: prn: 22 prn: 22 prn: 22 � 2006 Roland Hausser c

  8. A Computational Model of Natural Language Communication 38 3.4.3 E XAMPLE OF AN LA-hear RULE APPLICATION rule name ss pattern nw pattern operations rule package » noun: α – » verb: β – copy α nw.arg rule level NOM+FV: {FV+OBJ, ...} fnc: arg: copy β ss.fnc 2 3 2 3 noun: Julia verb: know fnc: arg: proplet level 6 7 6 7 6 7 6 7 mdr: mdr: 4 5 4 5 prn: 22 prn: 3.4.4 R ESULT OF THE LA-hear RULE APPLICATION 2 3 2 3 noun: Julia verb: know fnc: know arg: Julia 6 7 6 7 6 7 6 7 mdr: mdr: 4 5 4 5 prn: 22 prn: 22 � 2006 Roland Hausser c

  9. A Computational Model of Natural Language Communication 39 3.4.5 N ON - TIME - LINEAR D ERIVATION (P RINCIPLE OF P OSS . S UBSTITUTIONS ) S NP VP phrase structure derivation V NP Julia knows John lexical lookup verb: know noun: Julia noun: John num: sg tense: pres num: sg gen: fem subj: gen: masc obj: verb: know tense: pres subj: unification obj: noun: John num: sg gen: masc result verb: know tense: pres subj: noun: Julia num: sg gen: fem obj: noun: John num: sg gen: masc 3.5 Cycle of Natural Language Communication � 2006 Roland Hausser c

  10. A Computational Model of Natural Language Communication 40 3.5.1 E XAMPLE OF AN LA-think RULE APPLICATION rule name ss pattern nw pattern operations rule package 2 3 2 3 verb: β noun: α output position ss rule level V N V: arg: X α Y fnc: β {V N V, ...} 4 5 4 5 mark α ss prn: k prn: k 2 3 verb: know arg: Julia John proplet level 6 7 6 7 mdr: 4 5 prn: 22 3.5.2 R ESULT OF THE LA-think RULE APPLICATION 2 3 2 3 verb: know noun: Julia arg: !Julia John fnc: know 6 7 6 7 6 7 6 7 mdr: mdr: 4 5 4 5 prn: 22 prn: 22 � 2006 Roland Hausser c

  11. A Computational Model of Natural Language Communication 41 3.5.3 S CHEMATIC PRODUCTION OF Julia knows John. activated sequence realization i V i.1 n n V N i.2 fv n n fv V N i.3 fv n n n fv n V N N i.4 fv p n n n fv n p V N N � 2006 Roland Hausser c

  12. A Computational Model of Natural Language Communication 42 3.5.4 T HE CYCLE OF NATURAL LANGUAGE COMMUNICATION hearer−mode speaker−mode recognition LA−hear LA−think LA−think sign context language context language proplets proplets proplets proplets action LA−speak � 2006 Roland Hausser c

  13. A Computational Model of Natural Language Communication 43 3.6 A Bare Bone Example of Database Semantics: DBS-letter 3.6.1 Isolated proplets representing the letters A, E, L, O, S, V 2 3 2 3 2 3 2 3 2 3 2 3 lett: A lett: E lett: L lett: O lett: S lett: V prev: prev: prev: prev: prev: prev: 6 7 6 7 6 7 6 7 6 7 6 7 6 7 6 7 6 7 6 7 6 7 6 7 next: next: next: next: next: next: 4 5 4 5 4 5 4 5 4 5 4 5 wrd: wrd: wrd: wrd: wrd: wrd: 3.6.2 D EFINITION OF LA-letter-IN FOR CONNECTING ISOLATED PROPLETS ST S = {(lett: α , {r-in})} 2 3 2 3 lett: α lett: β 5 copy α nw.prev r-in: prev: prev: {r-in} 4 5 4 copy β ss.next next: next: ST F = {(lett: β , rp r − in ) } � 2006 Roland Hausser c

  14. A Computational Model of Natural Language Communication 44 3.6.3 Example of an LA-letter-IN rule application rule name ss pattern nw pattern operations rule package 2 3 2 3 lett: α lett: β copy α nw.prev rule level r-in: prev: prev: {r-in} 4 5 4 5 copy β ss.next next: next: 2 3 2 3 lett: L lett: O prev: prev: proplet level 6 7 6 7 6 7 6 7 next: next: 4 5 4 5 wrd: wrd: 3.6.4 Result of the rule application 3.6.3 2 3 2 3 lett: L lett: O prev: prev: L 6 7 6 7 6 7 6 7 next: O next: 4 5 4 5 wrd: 1 wrd: 1 � 2006 Roland Hausser c

  15. A Computational Model of Natural Language Communication 45 3.6.5 Time-linear derivation connecting the letters of love L O V E input lexical lookup lett: L lett: O lett: V lett: E prev: prev: prev: prev: sequence of isolated proplets next: next: next: next: wrd: wrd: wrd: wrd: syntactic−semantic parsing: lett: L lett: O connecting prev: prev: 1 proplets next: next: wrd: 1 wrd: lett: L lett: O lett: V connecting prev: prev: l prev: proplets 2 next:O next: next: wrd: 1 wrd: 1 wrd: connecting lett: L lett: O lett: V lett: E 3 prev: prev: L prev:O prev: proplets next:O next:V next: next: wrd: 1 wrd: 1 wrd: 1 wrd: result of syntactic−semantic parsing: lett: L lett: O lett: V lett: E prev: prev:L prev:O prev:V next:O next:V next: E next: wrd: 1 wrd: 1 wrd: 1 wrd: 1 � 2006 Roland Hausser c

  16. A Computational Model of Natural Language Communication 46 3.6.6 Proplets for LOVE, LOSS, and ALSO in a Word Bank owner records member records 2 3 lett: A prev: 6 7 ˆ ˜ lett: A 6 7 next: L 4 5 wrd: 3 2 3 lett: E prev: V 6 7 ˆ ˜ lett:E 6 7 next: 4 5 wrd: 1 2 3 2 3 2 3 lett: L lett: L lett: L prev: prev: prev: A ˆ ˜ 6 7 6 7 6 7 lett:L 6 7 6 7 6 7 next: O next: O next: S 4 5 4 5 4 5 wrd: 1 wrd: 2 wrd: 3 � 2006 Roland Hausser c

Download Presentation
Download Policy: The content available on the website is offered to you 'AS IS' for your personal information and use only. It cannot be commercialized, licensed, or distributed on other websites without prior consent from the author. To download a presentation, simply click this link. If you encounter any difficulties during the download process, it's possible that the publisher has removed the file from their server.

Recommend

Data Structures Union Find Algorithm Theory WS 2012/13 Fabian Kuhn Union Find Data Structure

Data Structures Union Find Algorithm Theory WS 2012/13 Fabian Kuhn Union Find Data Structure

Chapter 4 Data Structures Union Find Algorithm Theory WS 2012/13 Fabian Kuhn Union Find Data Structure Also known as Disjoint Set Data Structure Manages partition of a set of elements set of disjoint sets Operations:

401 views • 11 slides
The EM Algorithm The EM algorithm Mixture models Why EM works EM variants Learning

The EM Algorithm The EM algorithm Mixture models Why EM works EM variants Learning

Preview The EM Algorithm The EM algorithm Mixture models Why EM works EM variants Learning with Missing Data The EM Algorithm Goal: Learn parameters of Bayes net with known structure Initialize parameters ignoring missing

363 views • 3 slides
CSE 421 Union Find DS Dijkstras Algorithm , Shayan Oveis Gharan 1 Union Find Data Structure

CSE 421 Union Find DS Dijkstras Algorithm , Shayan Oveis Gharan 1 Union Find Data Structure

CSE 421 Union Find DS Dijkstras Algorithm , Shayan Oveis Gharan 1 Union Find Data Structure Each set is represented as a tree of pointers, where every vertex is labeled with longest path ending at the vertex To check whether A,Q are in

429 views • 31 slides
CSE 101 DISJOINT SET DATA STRUCTURE OPERATIONS WHEN IS WORST-CASE PESSIMISTIC If we are using a

CSE 101 DISJOINT SET DATA STRUCTURE OPERATIONS WHEN IS WORST-CASE PESSIMISTIC If we are using a

Algorithm Design and Analysis Sanjoy Dasgupta Russell Impagliazzo and Ragesh Jaiswal russell@cs.ucsd.edu Lecture 10: Amortized analysis of Data Structures Thanks, Miles Jones CSE 101 DISJOINT SET DATA STRUCTURE OPERATIONS WHEN IS WORST-CASE

358 views • 32 slides
Week 7 Video 1 Clustering Clustering A type of Structure Discovery algorithm This type of

Week 7 Video 1 Clustering Clustering A type of Structure Discovery algorithm This type of

Week 7 Video 1 Clustering Clustering A type of Structure Discovery algorithm This type of method is also referred to as Dimensionality Reduction , based on a common application Clustering You have a large number of data points You

974 views • 63 slides
Algorithms and Data Structures IS311 Algorithm Sequence of steps used to solve a problem

Algorithms and Data Structures IS311 Algorithm Sequence of steps used to solve a problem

Algorithms and Data Structures IS311 Algorithm Sequence of steps used to solve a problem Data Structures and Operates on collection of data Each element of collection - > data structure Java Collections Framework

421 views • 14 slides
Online Algorithms Algorithm Theory WS 2012/13 Fabian Kuhn Online Computations Sometimes, an

Online Algorithms Algorithm Theory WS 2012/13 Fabian Kuhn Online Computations Sometimes, an

Chapter 8 Online Algorithms Algorithm Theory WS 2012/13 Fabian Kuhn Online Computations Sometimes, an algorithm has to start processing the input before the complete input is known For example, when storing data in a data structure, the

385 views • 16 slides
Online Algorithms Algorithm Theory WS 2012/13 Fabian Kuhn Online Computations Sometimes, an

Online Algorithms Algorithm Theory WS 2012/13 Fabian Kuhn Online Computations Sometimes, an

Chapter 8 Online Algorithms Algorithm Theory WS 2012/13 Fabian Kuhn Online Computations Sometimes, an algorithm has to start processing the input before the complete input is known For example, when storing data in a data structure, the

698 views • 33 slides
ECE 242 Data Structures Lecture 2 Algorithm Analysis September 11, 2009 ECE242 L2: Algorithm

ECE 242 Data Structures Lecture 2 Algorithm Analysis September 11, 2009 ECE242 L2: Algorithm

ECE 242 Data Structures Lecture 2 Algorithm Analysis September 11, 2009 ECE242 L2: Algorithm Analysis Overview Problem: How can we quantify if one algorithm is better than another? Primarily concerned about growth of runtime as input

358 views • 15 slides
The interval branch-and-prune algorithm for the protein structure determination by NMR Th er`

The interval branch-and-prune algorithm for the protein structure determination by NMR Th er`

Introduction Global optimisation The interval branch-and-prune algorithm for the protein structure determination by NMR Th er` ese E Malliavin Unit e de Bioinformatique Structurale Institut Pasteur and UMR CNRS 3528 Paris, France

603 views • 38 slides
Objec&ves Data structure: Heaps Data structure: Graphs Submit problem set 2 Jan 26,

Objec&ves Data structure: Heaps Data structure: Graphs Submit problem set 2 Jan 26,

1/26/18 Objec&ves Data structure: Heaps Data structure: Graphs Submit problem set 2 Jan 26, 2018 CSCI211 - Sprenkle 1 Review What is a priority queue? What is a heap? Proper&es Implementa&on What is the

342 views • 17 slides
Bundle Adjustment and SLAM 31 March 2014 1 Structure-From-Motion Two views initialization:

Bundle Adjustment and SLAM 31 March 2014 1 Structure-From-Motion Two views initialization:

3D Photography: Bundle Adjustment and SLAM 31 March 2014 1 Structure-From-Motion Two views initialization: 5-Point algorithm (Minimal Solver) 8-Point linear algorithm 7-Point algorithm E ( R,t) 2 Structure-From-Motion

987 views • 41 slides
Data Structures and Java Collections Framework 1 Algorithms and Data Structures

Data Structures and Java Collections Framework 1 Algorithms and Data Structures

IS311 Data Structures and Java Collections Framework 1 Algorithms and Data Structures Algorithm Sequence of steps used to solve a problem Operates on collection of data Each element of collection - > data structure

793 views • 54 slides
Data Structure Web Application PA3 Teaching Data Structure Ms.Ailada Bumroong 5522800390

Data Structure Web Application PA3 Teaching Data Structure Ms.Ailada Bumroong 5522800390

Data Structure Web Application PA3 Teaching Data Structure Ms.Ailada Bumroong 5522800390 Ms.Putchamon Puttamilinprateep 5522800408 Outline Introduction Implementation Prototype Web Application ADDITION DELETION The existing

119 views • 10 slides
Objectives Data structure: Heaps Data structure: Graphs Submit problem set 2 Jan 25, 2019

Objectives Data structure: Heaps Data structure: Graphs Submit problem set 2 Jan 25, 2019

1/25/19 Objectives Data structure: Heaps Data structure: Graphs Submit problem set 2 Jan 25, 2019 CSCI211 - Sprenkle 1 Review What is a priority queue? What is a heap? Properties Implementation What is the process for

215 views • 19 slides
Combinatorial RNA Design: Designability and Structure-Approximating Algorithm s 1 nuch 1 , 3 Yann

Combinatorial RNA Design: Designability and Structure-Approximating Algorithm s 1 nuch 1 , 3 Yann

RNA Secondary Structures Our Results Open Problems Combinatorial RNA Design: Designability and Structure-Approximating Algorithm s 1 nuch 1 , 3 Yann Ponty 1 , 2 Jozef Hale J an Ma Ladislav Stacho 1 1 Simon Fraser University, Canada 2

1.04k views • 66 slides
A Low Power Asynchronous GPS Baseband Processor Benjamin Z. Tang, Stephen Longfield, Jr., Sunil

A Low Power Asynchronous GPS Baseband Processor Benjamin Z. Tang, Stephen Longfield, Jr., Sunil

A Low Power Asynchronous GPS Baseband Processor Benjamin Z. Tang, Stephen Longfield, Jr., Sunil A. Bhave, Rajit Manohar Cornell University Benjamin Tang 05/07/2012 - 1/18 Motivation Augmented reality Micro robotics navigation

227 views • 19 slides
Warming up Storage-level Caches with Bonfire Yiying Zhang Gokul Soundararajan Mark W. Storer

Warming up Storage-level Caches with Bonfire Yiying Zhang Gokul Soundararajan Mark W. Storer

Warming up Storage-level Caches with Bonfire Yiying Zhang Gokul Soundararajan Mark W. Storer Lakshmi N. Bairavasundaram Sethuraman Subbiah Andrea C. Arpaci-Dusseau Remzi H. Arpaci-Dusseau 2 Does on-demand cache warmup still work ? 3 10s

661 views • 34 slides
Inducing a Discriminative Parser to Optimize Machine Translation Reordering Graham Neubig 1,2,3 ,

Inducing a Discriminative Parser to Optimize Machine Translation Reordering Graham Neubig 1,2,3 ,

Inducing a Discriminative Parser to Optimize Machine Translation Reordering Inducing a Discriminative Parser to Optimize Machine Translation Reordering Graham Neubig 1,2,3 , Taro Watanabe 2 , Shinsuke Mori 1 1 2 3 now at 1 Inducing a

980 views • 34 slides
We Crashed, Now What? Lorenzo Cavallaro Cristiano Giuffrida Andrew S. Tanenbaum Vrije

We Crashed, Now What? Lorenzo Cavallaro Cristiano Giuffrida Andrew S. Tanenbaum Vrije

We Crashed, Now What? Lorenzo Cavallaro Cristiano Giuffrida Andrew S. Tanenbaum Vrije Universiteit Amsterdam 6 th Usenix Workshop on Hot Topics in System Dependability October 3, 2010, Vancouver, BC, Canada We Crashed, Now What? Cristiano

772 views • 59 slides
ADVENTURES IN TIME & SPACE Jim Royer Syracuse University Joint work with Norman Danner

ADVENTURES IN TIME & SPACE Jim Royer Syracuse University Joint work with Norman Danner

ADVENTURES IN TIME & SPACE Jim Royer Syracuse University Joint work with Norman Danner Wesleyan University GeoCal06 Based on: Adventures in Time and Space, by N. Danner and J.S. Royer, Proceedings of the 2006 ACM Principles of

445 views • 31 slides
Hospice Item Set Presented By: CMS and RTI International February 4 5, 2014 Welcome Welcome

Hospice Item Set Presented By: CMS and RTI International February 4 5, 2014 Welcome Welcome

Hospice Item Set Presented By: CMS and RTI International February 4 5, 2014 Welcome Welcome Introductions General Housekeeping Slide 2 Hospice Item Set Training February 4-5, 2014 1 Day 1 Overview Day 1 Overview HQRP

1.22k views • 48 slides
Statistical Parsing Parsing context-free languages ar ltekin University of Tbingen

Statistical Parsing Parsing context-free languages ar ltekin University of Tbingen

Statistical Parsing Parsing context-free languages ar ltekin University of Tbingen Seminar fr Sprachwissenschaft November 8, 2016 Recap Parsing basics CKY Earley Closing Ingredients of a (natural language) parser .

1.17k views • 51 slides
Qualms About QAM: An Exploratory Study BY: EVGUENIA IGNATOVA PRINCIPAL INVESTIGATOR/SUPERVISOR:

Qualms About QAM: An Exploratory Study BY: EVGUENIA IGNATOVA PRINCIPAL INVESTIGATOR/SUPERVISOR:

10/2/17 Consider Qualms About QAM: An Exploratory Study BY: EVGUENIA IGNATOVA PRINCIPAL INVESTIGATOR/SUPERVISOR: DR. MAURICE FELDMAN CO-INVESTIGATOR: DR. ROSEMARY CONDILLAC Pre-QAM Findings Pre-QAM Findings u Feldman, Atkinson,

436 views • 4 slides

More recommend