CS460/626 : Natural Language Processing/Speech, NLP and the Web

Similar documents
CS 712: Topics in NLP Linguistic Phrases and Statistical Phrases

CS626: NLP, Speech and the Web. Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture 14: Parsing Algorithms 30 th August, 2012

Binding Theory Different types of NPs, constraints on their distribution

Artificial Intelligence

CS460/626 : Natural Language Processing/Speech, NLP and the Web (Lecture 8 POS tagset) Pushpak Bhattacharyya CSE Dept., IIT Bombay 17 th Jan, 2012

CS460/626 : Natural Language

HPSG: Binding Theory

CS : Speech, NLP and the Web/Topics in AI

Andrew Carnie, Structural Relations. The mathematical properties of phrase structure trees

Processing/Speech, NLP and the Web

CS460/626 : Natural Language

Some binding facts. Binding in HPSG. The three basic principles. Binding theory of Chomsky

Introduction to Semantics. The Formalization of Meaning 1

Quantification in the predicate calculus

Semantics and Generative Grammar. The Semantics of Adjectival Modification 1. (1) Our Current Assumptions Regarding Adjectives and Common Ns

Natural Language Processing

Natural Language Processing : Probabilistic Context Free Grammars. Updated 5/09

Hardegree, Formal Semantics, Handout of 8

Control and Tough- Movement

Reflexives and non-fregean quantifiers

Natural Language Processing CS Lecture 06. Razvan C. Bunescu School of Electrical Engineering and Computer Science

Deductive Systems. Lecture - 3

Semantics and Pragmatics of NLP The Semantics of Discourse: Overview

Control and Tough- Movement

Propositional Language - Semantics

CS460/626 : Natural Language Processing/Speech, NLP and the Web (Lecture 18 Alignment in SMT and Tutorial on Giza++ and Moses)

CS206 Lecture 21. Modal Logic. Plan for Lecture 21. Possible World Semantics

Constituency. Doug Arnold

Suppose h maps number and variables to ɛ, and opening parenthesis to 0 and closing parenthesis

First Order Logic (1A) Young W. Lim 11/18/13

Statistical Methods for NLP

Semantics 2 Part 1: Relative Clauses and Variables

Lecture 13: Structured Prediction

Model-Theory of Property Grammars with Features

CS 512, Spring 2017, Handout 10 Propositional Logic: Conjunctive Normal Forms, Disjunctive Normal Forms, Horn Formulas, and other special forms

Semantics and Generative Grammar. Quantificational DPs, Part 2: Quantificational DPs in Non-Subject Position and Pronominal Binding 1

Lecture 7. Logic. Section1: Statement Logic.

Proseminar on Semantic Theory Fall 2010 Ling 720 The Basics of Plurals: Part 1 1 The Meaning of Plural NPs and the Nature of Predication Over Plurals

Introduction to Semantics. Pronouns and Variable Assignments. We ve seen that implicatures are crucially related to context.

The Importance of Being Formal. Martin Henz. February 5, Propositional Logic

Parsing with Context-Free Grammars

Propositional Logic: Methods of Proof (Part II)

Propositional Logic: Methods of Proof (Part II)

1 Rules in a Feature Grammar

564 Lecture 25 Nov. 23, Continuing note on presuppositional vs. nonpresuppositional dets.

Computational Models - Lecture 4

EXERCISE 10 SOLUTIONS

E-type interpretation without E-type pronoun: How Peirce s Graphs. capture the uniqueness implication of donkey sentences

Predicate Logic: Syntax

Semantics and Generative Grammar. Pronouns and Variable Assignments 1. We ve seen that implicatures are crucially related to context.

Propositional Logic: Models and Proofs

Proseminar on Semantic Theory Fall 2010 Ling 720. Remko Scha (1981/1984): Distributive, Collective and Cumulative Quantification

Give students a few minutes to reflect on Exercise 1. Then ask students to share their initial reactions and thoughts in answering the questions.

September 13, Cemela Summer School. Mathematics as language. Fact or Metaphor? John T. Baldwin. Framing the issues. structures and languages

DO NOT FORGET!!! WRITE YOUR NAME HERE. Philosophy 109 Final Exam, Spring 2007 (80 points total) ~ ( x)

Lecture 7: Relations

Interpreting quotations

UC Berkeley, Philosophy 142, Spring 2016 John MacFarlane Philosophy 142

Statistical Methods for NLP

CHAPTER THREE: RELATIONS AND FUNCTIONS

Unit 2: Tree Models. CS 562: Empirical Methods in Natural Language Processing. Lectures 19-23: Context-Free Grammars and Parsing

Preliminary Matters. Semantics vs syntax (informally):

First Order Logic (1A) Young W. Lim 11/5/13

TALP at GeoQuery 2007: Linguistic and Geographical Analysis for Query Parsing

Probabilistic Context Free Grammars

Logic Background (1A) Young W. Lim 12/14/15

Ling 130 Notes: Syntax and Semantics of Propositional Logic

Context Free Grammars

Ling 5801: Lecture Notes 7 From Programs to Context-Free Grammars

Generalized Quantifiers Logical and Linguistic Aspects

3 The Semantics of the Propositional Calculus

PL: Truth Trees. Handout Truth Trees: The Setup

Examples: P: it is not the case that P. P Q: P or Q P Q: P implies Q (if P then Q) Typical formula:

Parsing. Based on presentations from Chris Manning s course on Statistical Parsing (Stanford)

3.8.1 Sum Individuals and Discourse Referents for Sum Individuals

Continuations in Type Logical Grammar. Grafting Trees: (with Chris Barker, UCSD) NJPLS, 27 February Harvard University

Foundations of Mathematics MATH 220 FALL 2017 Lecture Notes

10/17/04. Today s Main Points

Parts 3-6 are EXAMPLES for cse634

Graphical models for part of speech tagging

2 Higginbotham & May s proposal

a. ~p : if p is T, then ~p is F, and vice versa

1. The Semantic Enterprise. 2. Semantic Values Intensions and Extensions. 3. Situations

UNIT 4 NOTES: PROPERTIES & EXPRESSIONS

Introduction to Computational Linguistics

POS-Tagging. Fabian M. Suchanek

Predicate Calculus - Syntax

Proofs. Chapter 2 P P Q Q

Proseminar on Semantic Theory Fall 2013 Ling 720 The Proper Treatment of Quantification in Ordinary English, Part 1: The Fragment of English

CS1021. Why logic? Logic about inference or argument. Start from assumptions or axioms. Make deductions according to rules of reasoning.

Alternative Notations and Connectives

More on HMMs and other sequence models. Intro to NLP - ETHZ - 18/03/2013

A brief introduction to Logic. (slides from

Propositional Logic: Logical Agents (Part I)

Einführung in die Computerlinguistik

1. Background. Task: Determine whether a given string of words is a grammatical (well-formed) sentence of language L i or not.

Empirical Methods in Natural Language Processing Lecture 11 Part-of-speech tagging and HMMs

Introduction to Sets and Logic (MATH 1190)

Parsing Beyond Context-Free Grammars: Tree Adjoining Grammars

2/2/2018. CS 103 Discrete Structures. Chapter 1. Propositional Logic. Chapter 1.1. Propositional Logic

Transcription:

CS460/626 : Natural Language Processing/Speech, NLP and the Web Lecture 23: Binding Theory Pushpak Bhattacharyya CSE Dept., IIT Bombay 8 th Oct, 2012

Parsing Problem Semantics Part of Speech Tagging NLP Trinity Morph Analysis Marathi French CRF HMM MEMM Hindi English Language Algorithm

Coindexing Ram p saw himself p in the mirror *Ram p saw himself q in the mirror Ram p saw him q in the mirror *Ram p saw him p in the mirror The grandmother p of Ram q s distant uncle r saw him s the mirror *The grandmother p of Ram q s distant uncle r saw him p the mirror The grandmother p of Ram q s distant uncle r saw herself p the mirror *The grandmother p of Ram q s distant uncle r saw himself p the mirror

Perspective Deep understanding level Interlingual level Conceptual transfer Logico-semantic level Semantic transfer Mixing levels Multilevel transfer Ascending transfer Ontological interlingua Semantico-linguistic interlingua SPA-structures (semantic & predicate-argument) Multilevel description CRF Algorithm Parsing Problem Semantics Part of Speech Tagging NLP Trinity Morph Analysis Marathi French HMM Hindi English MEMM Language Syntactico-functional level Syntactic transfer (deep) F-structures (functional) Syntagmatic level Syntactic transfer (surface) C-structures (constituent) Morpho-syntactic level Semi-direct translation Descending transfers Tagged text Graphemic level Direct translation Text 4

R-Expressions Consider the sentence: Ram found a blanket in the green bag Ram, a key in the green bag are called Referring Expressions or R-expressions Key Definition: An R-expression is an entity that gets its meaning by referring to an entity in the world

Not all NPs are R-Expressions Ram found himself a blanket in the green bag Himself must refer back to Ram and not to something in the outside world. Key Definition: An Anaphor is an NP that obligatorily gets its meaning from another NP in the sentence.

Types of Anaphors Reflexive pronouns Himself, herself, themselves Reciprocals Each other, one another Ram and Shyam saw each other

Pronouns Get the meaning not necessarily from the same sentence Ram told Shyam that he should collect the Ram told Shyam that he should collect the blanket

Pronouns with forward reference That he will succeed, was known a priori to Ram

Anaphors have definite syntactic position * himself Ram found a blanket

Coindex and Antecedent Definition: An NP that gives its meaning to an anaphor (or pronoun) is called an Antecedent Ram found himself a blanket Ram: antecedent Himself: anaphor Coindexing convention: [Ram] i found [himself] i a [blanket] j [Ram] i told [Shyam] j that [he] i should collect the [blanket] k [Ram] i told [Shyam] j that [he] j should collect the [blanket] k [Ram] i told [Shyam] j that [he] k should collect the [blanket] l Definition: NPs with the same index are said to be coindexed with each other Definition: NPs with the same index are said to corefer (i.e., refer to the same object in the outside world)

Binding Theory

Binding The relation between an antecedent and an anaphor/pronoun is a pretty rigid structural relation Ram i found himself i a blanket *Ram i found himself j a blanket *[The servant of Ram i ] j found himself i a blanket [The servant of Ram i ] j found himself j a blanket

Key Definitions

Domination Essentially the specification of a tree (very familiar to computer scientists!) Axioms of domination (x <= D y means x dominates y) (a) X <= D X (b) if X <= D Y <= D Z then X <= D Z (c) if X <= D Y <= D X then X =Y (d) if X <= D Z and Y <= D Z then either X <= D Y or Y <= D X (or both if X=Y=Z)

Immediate Domination and Exhaustive Domination Immediate Domination: Direct Parent Child relation Exhaustive Domination: Node A Exhaustive Domination: Node A exhaustively dominates a set of nodes {B, C,, D}, if it immediately dominates all the members of the set and and there is no node G immediately dominated by A that is not a member of this set.

Constituency Constituent: A set of nodes exhaustively dominated by a single node Constituent-of: B is a constituent of A iff A dominates B Immediate-constituent-of: B is an immediate-constituent-of A iff A immediately dominates B

Precedence ( said first relation) S NP VP S dominates NP and VP {NP VP} forms a constituent But NP precedes VP Definition: Node A precedes node B iff A is to the left of B and neither A dominates B nor B dominates A and every node dominating A either appears to the left of B or dominates B

No crossing branches constraint If one node X precedes another node Y then X and all nodes dominated by X must precede Y and all nodes dominated by Y

Axioms of Precedence Lets denote precedes by the symbol ~ (a) If X ~ Y then NOT(Y ~ X) (b) If X~Y~Z then X~Z (c) If X~Y or Y~X then NOT(X <= D Y) and NOT(Y <= D X) (d) X~Y iff for all terminals U, V, X <= D U and Y <= D V jointly imply U~V No crossing of branch; no discontinuous constituent

Fundamental concept: c- command (informal): A node c-commands its sisters and all the daughters (and granddaughters and greatgranddaughters etc.) of its sisters. (formal): Node A c-commands node B if every branching node dominating A also dominates B, and neither A nor B dominate each other.

Example M N O What does A c-command? What does G c-command? A B C D E M G H I J

Example M N O What does A c-command? What does G c-command? A C B Ans: A c-commands B and C,D,E,F,G,H,I,J G c-commands only H D E M G H I J

Symmetric C-command and Asymmetric c-command A symmetrically c-commands B, if A c- commands B and B c-commands A A asymmetrically c-commands B, if A c- A asymmetrically c-commands B, if A c- commands B and B does not c- command A

Exercise D 1 NP 1 O The N AP 1 boy little S T will V buy NP 2 N 2 apples VP PP P from 1.What nodes dominate grocer 2. What nodes immediately dominate D3 the 3. Do will and buy form a constituent? 4. What nodes does N 1 boy c-command? 5. What nodes does NP1 c-command? 6. What is V s mother? 7. What nodes does will precede? 8. List all the sets of sisters in the tree. 9. What is PP s mother? 10. Do NP1 and VP symmetrically or assymetrically c-command one another? 11. List all the nodes c-commanded by V NP 3 D 3 the N 3 grocer 12. What is the subject of the sentence? 13. What is the object of the sentence? 14. What is the object of the preposition? 15. Is NP 3 a constituent of VP? 16. What node(s) in NP 3 an immediate constituent of? 17. What node(s) does VP exhaustively dominate? 18. What is the root node? 19. List all the terminal nodes. 20. What immediately precedes grocer?

Correctness and incorrectness of binding Sita p saw herself p in the mirror. [The mother of Sita q ] p saw herself p in the mirror. *[The mother of Sita q ] p saw herself q in the mirror.

From the tree S NP 1 VP D 1 PP 1 V saw NP 3 PP 2 The saw NP 3 N 1 P N 1 2 Sita P 2 mother in of N 3 herself NP 4 D 2 the N 4 mirror

Case A S NP 1 VP D 1 PP 1 V saw NP 3 PP 2 The saw NP 3 N 1 P N 1 2 Sita P 2 mother in of N 3 (ANAphor) herself NP 4 D 2 the N 4 mirror

Case A Observations NP mother -> herself NP sita -> herself NP mother -> her (meaning Sita)

Case B S NP 1 VP N 1 V Sita saw NP 2 PP 1 N 2 (ANA) herself P 1 in NP 3 D 1 the N 3 mirror

Case B Observations NP sita -> herself NP sita -> her

Rules Positive Rule of Binding for Anaphor Anaphor can be bound only to its c- commanding and preceding NP Negative Rule of Binding for Pronoun Pronoun cannot be bound to a c- commanding NP

Definition of binding A binds B if A c-commands B, and A and B are coindexed Why is the following wrong? *herself saw Sita in the mirror

Binding domain The syntactic space in which the anaphor must find its antecedent is called a binding domain. Usually the binding domain is the clause.

Significance of binding domain Sita saw herself in the mirror Sita said that she saw the mirror *Sita said that herself saw the mirror Sita said that she saw herself in the mirror

From the tree S NP 1 VP V saw NP 3 PP 2 saw NP 3 N 1 P 2 NP 4 Sita N 3 herself in D 2 the N 4 mirror

From the tree S VP NP S V said VP N Sita NP V saw NP N 1 D 2 the N mirror she

From the tree S VP NP S V said VP N Sita NP V saw NP N 1 D 2 the N mirror herself

From the tree S VP NP S V said VP N Sita NP V saw NP PP N 1 she N herself P in NP D 2 the N mirror

Binding principle A An anaphor must be bound in its binding domain

From the tree S NP 1 VP V saw NP 3 PP 2 saw NP 3 N 1 P 2 NP 4 Sita p N 3 her p in D 2 the N 4 mirror

From the tree S VP NP S V said VP N Sita p NP V saw NP PP N 1 She p/q N her r P in NP D 2 the N mirror

Binding principle B Definition: Free- not bound A pronoun must be free in its binding domain.

Binding principle C A R-expression (referring expression) must be free.