Possessive Relation Disambiguation of Japanese Genitive Marker No

Size: px
Start display at page:

Download "Possessive Relation Disambiguation of Japanese Genitive Marker No"

Transcription

1 Possessive Relation Disambiguation of Japanese Genitive Marker No Sumiyo Nishiguchi Osaka University Study for the Language and Culture 33 Osaka University June 28, nishiguchi/ 1 / 36

2 2 / 36

3 Abstract Japanese genitive postposition -no is ambiguous and denotes wider range of relations between two entities than the English possessive marker s. It has been suggested that inherently relational possessee nominals as in John s father disambiguates the relation (Partee 1997, Barker 1995) and that even a non-relational possessee noun as in John s poem can be type-raised into a relational noun based on the Qualia Structure (Vikner & Jensen 2002, Pustejovsky 1995). Since possessive relations can be even reversed in Japanese as in bag-no lady the lady who has a bag, I will argue that the Qualia Structure of not only the possessee but also the possessor nominal disambiguates the possessive relations. Possessor nouns are coerced to type-shift into relational nouns. 3 / 36

4 Outline Various Relations and Argument Reversal Various Relations Argument Reversal English Possessive, Noun Compound or PP? Type-Shifting Possessor into a Relational Noun Accompaniment: Type-raising by Telic Role Quantity: Type-raising by Constitutive Role Property Location 4 / 36

5 Various Relations Japanese genitive marker expresses wider range of relations between two entities than the English possessive. NP 1 -gen NP 2 expresses not only possession as in John s pen and part-whole relation as in John s leg, but also location, accompaniment, property and quantity: (1) [[no]] = λx.λy.r(y)(x) I possession: R = {< x, y > x owns y } (2) Tanaka-no kaban Tanaka-gen bag Tanaka s bag 5 / 36

6 II part-whole: R={< x, y > y is part of x} (3) Tanaka-no te Tanaka-gen hand Tanaka s hand III location: R = {< x, y > y is in x } (4) Tokyo-no tomodachi Tokyo-gen friend a friend in Tokyo 6 / 36

7 IV accompaniment: R = {< x, y > y carries x } (5) akai kaban-no hito red bag-gen person a person who carries a red bag 7 / 36

8 V property: R = {< x, y > x is dominant characteristic of y } (6) a. maho-no kuni magic-gen country a magic country b. Kaban-no Sanpei bag-gen Sanpei Bags Sanpei (a bag shop) c. Supa-no Maruetsu supermarket-gen Maruetsu Maruetsu Supermarket 8 / 36

9 VI quantity: R = {< x, y > the quantity of y is x } (7) a. Takusan-no tokei many-gen watch many watches b. san-bon-no enpitsu three-cl-gen pencil three pencils 9 / 36

10 Argument Reversal Reversal of the possessor argument between (I) and (IV-V): Tanaka-no kaban: Tanaka is the possessor Akai-kaban-no hito: hito is the possessor Kaban-no Tanaka: Tanaka is the possessor (seller) Controller-Controllee: Tanaka-no kuruma: the car is at Tanaka s disposal (Vikner & Jensen (2002) for the girl s car) boshi-no hito (a bag lady): a hat is at the lady s disposal 10 / 36

11 Such non-canonical relations, i.e., relations other than possession or part-whole relation, are more likely expressed in noun compounds rather than in possessives in English: (8) Relation Relation JapanesePossessive EnglishPossessive EnglishCompound EnglishPP Intersective possession Tanaka-no pen Tanaka s pen *Tanaka pen a pen of Tana part-whole Tanaka-no kao Tanaka s face *Tanaka face the face of Tan location Tokyo-no shinseki *Tokyo s relative Tokyo relatives the relatives in T accompaniment boshi-no fujin *hat s lady a hat lady a lady with a property inu-no onna-no *dog s girl *dog girl a dog of ko (a girl dog) female kind osu-no tora *male s tiger a male tiger a tiger of male Kaban-no Sanpei *bags Sanpei Bags Sanpei Sanpei of Ba maaruboro-no *Marlboro s Marlboro the country kuni country country of Marlboro quantity 1-kiro-no pasokon *1kg s computer 1kg computer a computer with Nonintersective property nise-no fukahire *fake s shark fin fake shark fin nise-no *fake s police fake police keisatsukan officer officer 11 / 36

12 Problems with Deriving Various Possessive Relations from NP 2 Possessive relations are ambiguous in both English and Japanese. For example, there are more than one interpretation available for Tanaka-no hon Tanaka s book. Tanaka s book may refer to the book that Tanaka owns or the book that Tanaka wrote (Barker 1995:87). Langacker (1993): ownership to be the prototypical meaning of the possessive construction, and other relations to be the instantiations. 12 / 36

13 Partee (1997): two syntactic types for John s (9) a. Free R type Syntax: [John s] NP/CN Semantics: λqλp[np (λz[ x[ y[[q(y) R(y)(z)] y = x] P(x)]])] b. Inherent relation type: inherited from relational nouns, e.g., brother, employee, and enemy Syntax: [John s] NP/TCN (TCN: transitive common noun) Semantics: λrλp[np (λz[ x[ y[r(z)(y) y = x] P(x)]])] (10) Syntax: [[John s] NP/TCN [friend] TCN ] NP Semantics: λrλp[john (λz. x[ y[r(z)(y) y = x] P(x)]](friend of ) = λp[john s(λz. x[ y[friend of (z)(y) y = x] P(x)]] 13 / 36

14 In Japanese case: I. Possession relation: prototypical II. Part-whole relation: can be derived lexically from a possessive nominal te hand (Barker 1995). However, other possessee nominals are not relational necessarily. III. Location: (11) a. Tokyo-no tomodachi a friend in Tokyo b. Kyoto-no shinseki Tokyo relative Tomodachi friend and shinseki relative are relational,friend-of x / relative-of x, but the relation between NP 1 and NP 2 is not friend-of or relative of but of location, NP 2 is in NP / 36

15 As far as we only consider NP 2 and apply (9b), there is no way to derive location, carrying, property and quantity relations. VI. Quantity (12) 1-kiro-no pasokon 1 kg computer No way to type-shift non-inherently relational noun pasokon personal computer into relational noun. Since weight is not part of the qualia structure, we cannot apply the strategy in Vikner&Jensen (2002), namely, the application of Q α as a type-raising function from a word to a relational noun with an unsaturated argument slot. (13) pasokon personal computer 15 / 36

16 2 3 pasokon personal computer TYPESTR = ARG1 = x artifact machine 2 3 D-ARG1 = w human 6 7 ARGSTR = 4D-E1 = e1 transition5 D-E2 = e2 process 2 3 FORMAL = x ( ) system unit, monitor, CONSTITUTIVE = keyboard,... QUALIA = 6 6 TELIC = calculate act e2, x AGENTIVE = make act e1, z, x (14) Argument Structure: λx[computer (x)] Qualia Structure α role: *λxλy[weigh (x)(y)] Q α (pasokon)= *λxλy[weigh (x)(y) computer (x)] 16 / 36

17 IV. Accompaniment: does not derive from the qualia structure of the possessor noun, either. (15) boshi-no fujin the hat lady Fujin a lady does not inherently carry a hat, so carrying or wearing is hard to be considered to be part of the qualia structure. V. Property (16) Kaban-no Sanpei Bags Sanpei NP1 bags carries more information Therefore, I propose that Japanese possessives need to consider the qualia structure of the possessor noun as well. 17 / 36

18 Relation Disambiguation by NP 1 Besides part-whole relation, it is rather possessor nominals than the posssessee nominals that carry more information about relations between two arguments. For example, Tokyo is a location, a bag is something to carry with, and onna woman and 2-kiro 2kg are properties. Even though these nouns are not lexically relational as brother is, our world knowledge that Tokyo is a location, a hat is a thing to wear, female is a property and 2kg is weight assigns carrying, locative and property interpretations to the possessive construction. We need to consider the lexical interpretations of non-relational possessor nouns and apply the type-shifting operators to the possessor noun in Japanese. 18 / 36

19 Vikner&Jensen 2002 applies the Qualia Structure (Pustejovsky 1995) of the possessee noun and type-shifts the possessee noun into a relational noun. John s poem can receive the interpretation of the poem that John composed by means of the meaning shifting operator Q A that raises poem into a two-place holder (17). Then, the type-shifted NP 2 combines with the possessive NP and the authorship relation is derived. (17) a. [[poem]] = λx.[poem (x)] b. Q A (poem) = λxλy[poem (x) compose (x)(y)] Japanese possessives need to look at the Qualia Structure of the possessor noun. The functions Q F, Q T, and Q C type-shift the possessor noun and various relations are inherited from the lexical input of the possessors respectively. 19 / 36

20 Shimazu et al. (1986) proposes comparing semantic features of both NP1 and NP2 ( ignition by features ) kodomo-no asobi children s play > animate-action 20 / 36

21 English possessives have been considered as determiners as in Partee (1997). However, in addition to the fact that Japanese is a language without overt determiners, semantically speaking, Japanese NP1-no in NP1-no NP2 is either intersective or nonintersective adjective with enriched meaning. 21 / 36

22 (18) a. [[wanpisu]] = λx.dress (x) b. [[ao no]] = λx.blue (x) c. [[ao no wanpisu]] = λx.blue (x) dress (x) (19) a. [[Tokyo no]] = λx.in Tokyo (x) b. [[shinseki]] = λx.relative (x) c. [[Tokyo no shinseki]] = λx.in Tokyo (x) relative (x) Similar to English fake, neither nise-no nor magai-no (fake, false) is intersective in that nise-no keisatsukan (false police officer) is not a policeman at all. Nise-no does not compose with extensions of police officers but with intensions. 22 / 36

23 (20) a. boshi-no hito a person who wears a hat b. boshi-no with a hat = who wears a hat [[boshi no]] = λx.wear (ɛy.hat (y))(x) 23 / 36

24 Accompaniment: Type-raising by Telic Role ɛ operator (cf. Cann, Kempson&Marten 2005) (21) a. boshi hat : ɛx.hat (x) : some x satisfying hat (x), if there is one hito man : ιy.man (y): the unique x satisfying man (x), if there is such a thing b. no: λx.λy.ιy.[y (y) TELIC X (ɛx.x (x))(y)] c. boshi-no: λy.ιy.[y (y) wear (ɛx.hat (x))(y)] d. boshi-no hito: λy.ιy.[y (y) wear (ɛx.hat (x))(y)](hito ) = ιy.[man (y) wear (ɛx.hat (x))(u)] 24 / 36

25 boshi hat TYPESTR = ARG1 = x clothing artifact D-ARG1 = w human D-ARG2 = z human ARGSTR = D-E1 = e1 transition D-E2 = e2 state FORMAL = x CONSTITUTIVE = material QUALIA = ( ) TELIC = wear result e2, w, x ( ) AGENTIVE = make act e1, z, x 25 / 36

26 Quantity: Type-raising by Constitutive Role (22) a. 1-kiro-no pasokon (a personal computer of 1 kg) b. 1-kiro 1 kg : λx.1kg (x) pasokon personal computer : λy.computer (y) c. no: λx.λy.ιy.[y (y) CONST X (ɛx.x (x))(y)] d. 1-kiro-no: λy.ιy.[y (y) weigh (ɛx.1kg (x))(y)] e. 1-kiro-no pasokon: λy.ιy.[y (y) weigh (ɛx.1kg (x))(y)](computer ) = ιy.[computer (y) weigh (ɛx.1kg (x))(u)] 26 / 36

27 1-kiro 1 kg TYPESTR = ARG1 = x weight unit of measurement D-ARG1 = y physobj D-ARG2 = w human ARGSTR = D-E1 = e1 process D-E2 = e2 state FORMAL = x ( ) QUALIA = CONSTITUTIVE = weigh e2, y, x ( ) TELIC = measure act e1, w, y 27 / 36

28 Property (23) Osu-no tora a male tiger 28 / 36

29 osu male TYPESTR = ARG1 = x gender QUALIA = [ FORMAL = x male gender ] (24) osu-no male : λy.ιy.[y (y) gender of (ɛx.male (x))(y)] (25) Maaruboro-no kuni Marlboro Country 29 / 36

30 Marlboro TYPESTR = ARG1 = x tobacco D-ARG1 = w human ARGSTR = D-E1 = e1 transition D-E2 = e2 process FORMAL = x ( ) AGENTIVE = make act e1. w, x QUALIA = CONSTITUTIVE = x tobacco ( ) TELIC = smoke act e2, w, x 30 / 36

31 (26) Maaruboro-no Marlboro s : λy.ιy.[y (y) make act (ɛx.marlboro (x))(y)] While the metonymy between country and its people needs to be interpreted further, type-raising of Marlboro allows its telic role to account for the relation between Marlboro and country. 31 / 36

32 Location (27) Tokyo-no shinseki Tokyo relatives (location) 32 / 36

33 Tokyo TYPESTR = ARG1 = x location [ ] ARGSTR = D-ARG1 = w physobj ( ) FORMAL = in w, x QUALIA = ( CONSTITUTIVE = in-japan x ) (28) Tokyo-no: λy.ιy.[y (y) in (ɛx.tokyo (x))(y)] 33 / 36

34 Which Qualia Structure is to be chosen? Maaruboro-no: 1. no: λx.λy.ιy.[y (y) AGENTIVE X (ɛx.x (x))(y)] Maaruboro-no: λy.ιy.[y (y) make act (ɛx.marlboro (x))(y)] 2. no: λx.λy.ιy.[y (y) TELIC X (ɛx.x (x))(y)] Maaruboro-no: λy.ιy.[y (y) smoke act (ɛx.marlboro (x))(y)] (29) λy.ιy.[y (y) R X (ɛx.x (x))(y)] is the most salient property of y in the utterance context 34 / 36

35 (30) a. Tokyo-no shinseki: a relative in Tokyo > a relative who takes care of business with Tokyo b. kaban-no hito: a person who carries a bag > a person who sells bags c. Kaban-no Tanaka: bags shop > a shop that makes bags 35 / 36

36 Japanese genitive postposition -no expresses different kinds of relations between entities. Not only type-raising possessee nominals does not predict the relations rightly in many cases. I proposed that the Qualia Structure of the possessor nouns needs to be considered for disambiguation of the relations between two nominals and that the possessor nouns should be raised into relational nouns according to the Qualia Role. The most salient Qualia Role of NP1 is selected. 36 / 36

Extended GL. Sumiyo Nishiguchi School of Management, Tokyo University of Science University March 14, 2013

Extended GL. Sumiyo Nishiguchi School of Management, Tokyo University of Science  University March 14, 2013 Extended GL Sumiyo Nishiguchi School of Management, Tokyo University of Science nishiguchi@rs.tus.ac.jp NLP2013@Nagoya University March 14, 2013 1 / 35 Limit to Generative Lexicon 2 / 35 Semantic Classification

More information

SEMANTICS OF POSSESSIVE DETERMINERS STANLEY PETERS DAG WESTERSTÅHL

SEMANTICS OF POSSESSIVE DETERMINERS STANLEY PETERS DAG WESTERSTÅHL SEMANTICS OF POSSESSIVE DETERMINERS STANLEY PETERS DAG WESTERSTÅHL Linguistics Department, Stanford University Department of Philosophy, Göteborg University peters csli.stanford.edu, dag.westerstahl phil.gu.se

More information

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

Semantics and Generative Grammar. The Semantics of Adjectival Modification 1. (1) Our Current Assumptions Regarding Adjectives and Common Ns The Semantics of Adjectival Modification 1 (1) Our Current Assumptions Regarding Adjectives and Common Ns a. Both adjectives and common nouns denote functions of type (i) [[ male ]] = [ λx : x D

More information

The Semantics of Definite DPs 1. b. Argument Position: (i) [ A politician ] arrived from Washington. (ii) Joe likes [ the politician ].

The Semantics of Definite DPs 1. b. Argument Position: (i) [ A politician ] arrived from Washington. (ii) Joe likes [ the politician ]. The Semantics of Definite DPs 1 Thus far, our semantics is able to interpret common nouns that occupy predicate position (1a). However, the most common position for common nouns to occupy is internal to

More information

Bringing machine learning & compositional semantics together: central concepts

Bringing machine learning & compositional semantics together: central concepts Bringing machine learning & compositional semantics together: central concepts https://githubcom/cgpotts/annualreview-complearning Chris Potts Stanford Linguistics CS 244U: Natural language understanding

More information

Fertilization of Case Frame Dictionary for Robust Japanese Case Analysis

Fertilization of Case Frame Dictionary for Robust Japanese Case Analysis Fertilization of Case Frame Dictionary for Robust Japanese Case Analysis Daisuke Kawahara and Sadao Kurohashi Graduate School of Information Science and Technology, University of Tokyo PRESTO, Japan Science

More information

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

Semantics and Generative Grammar. Pronouns and Variable Assignments 1. We ve seen that implicatures are crucially related to context. Pronouns and Variable Assignments 1 1. Putting this Unit in Context (1) What We ve Done So Far This Unit Expanded our semantic theory so that it includes (the beginnings of) a theory of how the presuppositions

More information

Modal Logics. Most applications of modal logic require a refined version of basic modal logic.

Modal Logics. Most applications of modal logic require a refined version of basic modal logic. Modal Logics Most applications of modal logic require a refined version of basic modal logic. Definition. A set L of formulas of basic modal logic is called a (normal) modal logic if the following closure

More information

1 Write the subject pronoun for each picture. Use the words in the box.

1 Write the subject pronoun for each picture. Use the words in the box. 1 Write the subject pronoun for each picture. Use the words in the box. I he she it we they 1 2_ 3 4 5_ 6 2. Label the pictures. Use the words in the box. Add the. chairs blackboard book desks rubber ruler

More information

Introduction to Semantics. The Formalization of Meaning 1

Introduction to Semantics. The Formalization of Meaning 1 The Formalization of Meaning 1 1. Obtaining a System That Derives Truth Conditions (1) The Goal of Our Enterprise To develop a system that, for every sentence S of English, derives the truth-conditions

More information

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

Introduction to Semantics. Pronouns and Variable Assignments. We ve seen that implicatures are crucially related to context. Pronouns and Variable Assignments 1. Putting this Unit in Context (1) What We ve Done So Far This Unit Expanded our semantic theory so that it includes (the beginnings of) a theory of how the presuppositions

More information

Revisit summer... go to the Fitzwilliam Museum!

Revisit summer... go to the Fitzwilliam Museum! Revisit summer... go to the Fitzwilliam Museum! Faculty of Philosophy Formal Logic Lecture 5 Peter Smith Peter Smith: Formal Logic, Lecture 5 2 Outline Propositional connectives, and the assumption of

More information

Semantics and Generative Grammar. Formal Foundations: A Basic Review of Sets and Functions 1

Semantics and Generative Grammar. Formal Foundations: A Basic Review of Sets and Functions 1 Formal Foundations: A Basic Review of Sets and Functions 1 1. Naïve Set Theory 1.1 Basic Properties of Sets A set is a group of objects. Any group of objects a, b, c forms a set. (1) Representation of

More information

CAS LX 522 Syntax I Fall 2000 October 10, 2000 Week 5: Case Theory and θ Theory. θ-theory continued

CAS LX 522 Syntax I Fall 2000 October 10, 2000 Week 5: Case Theory and θ Theory. θ-theory continued CAS LX 522 Syntax I Fall 2000 October 0, 2000 Paul Hagstrom Week 5: Case Theory and θ Theory θ-theory continued From last time: verbs have θ-roles (e.g., Agent, Theme, ) to assign, specified in the lexicon

More information

Worksheets for GCSE Mathematics. Ratio & Proportion. Mr Black's Maths Resources for Teachers GCSE 1-9. Number

Worksheets for GCSE Mathematics. Ratio & Proportion. Mr Black's Maths Resources for Teachers GCSE 1-9. Number Worksheets for GCSE Mathematics Ratio & Proportion Mr Black's Maths Resources for Teachers GCSE 1-9 Number Ratio & Proportion Worksheets Contents Differentiated Independent Learning Worksheets Simplifying

More information

(BASED ON MEMORY) HELD ON: TEST - I: REASONING ABILITY

(BASED ON MEMORY) HELD ON: TEST - I: REASONING ABILITY RRB OFFICER (PT) (BASED ON MEMORY) HELD ON: 06-11-2016 TEST - I: REASONING ABILITY Directions (Q. 1-5): Study the given information carefully to answer the given questions. Eight people - E, F, G, H, Q,

More information

50 Must Solve Algebra Questions

50 Must Solve Algebra Questions 50 Must Solve Algebra Questions 50 Must Solve Algebra Questions 1. Classic furniture gallery employs male and female carpenters to create designer chairs for their stores. 5 males and 3 females can create

More information

Parasitic Scope (Barker 2007) Semantics Seminar 11/10/08

Parasitic Scope (Barker 2007) Semantics Seminar 11/10/08 Parasitic Scope (Barker 2007) Semantics Seminar 11/10/08 1. Overview Attempts to provide a compositional, fully semantic account of same. Elements other than NPs in particular, adjectives can be scope-taking

More information

Lecture 3. Detailed Derivations using GL s Type Composition Logic. Type Clashes Coercions Types for Change Predicates

Lecture 3. Detailed Derivations using GL s Type Composition Logic. Type Clashes Coercions Types for Change Predicates Lecture 3. Detailed Derivations using GL s Type Composition Logic Type Clashes Coercions Types for Change Predicates 99 Classic GL Treatment of Dot Objects (123)a. Mary believes that John is sick. b. Mary

More information

IBPS RRB Office Assistant Prelims A. 1 B. 2 C. 3. D. no one A. R B. S C.Q D. U

IBPS RRB Office Assistant Prelims A. 1 B. 2 C. 3. D. no one A. R B. S C.Q D. U Memory Based Questions 18 th August 2018 IBPS RRB Office Assistant Prelims Logical Reasoning: Direction (1-5): Study the following information carefully and answer the questions given below: Six persons

More information

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

First Order Logic (1A) Young W. Lim 11/18/13 Copyright (c) 2013. Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any later version published by the Free Software

More information

50 Must Solve Algebra Questions

50 Must Solve Algebra Questions 50 Must Solve Algebra Questions 50 Must Solve Algebra Questions 1. Classic furniture gallery employs male and female carpenters to create designer chairs for their stores. 5 males and 3 females can create

More information

Section 2.1: Introduction to the Logic of Quantified Statements

Section 2.1: Introduction to the Logic of Quantified Statements Section 2.1: Introduction to the Logic of Quantified Statements In the previous chapter, we studied a branch of logic called propositional logic or propositional calculus. Loosely speaking, propositional

More information

Section 3.1 Statements, Negations, and Quantified Statements

Section 3.1 Statements, Negations, and Quantified Statements Section 3.1 Statements, Negations, and Quantified Statements Objectives 1. Identify English sentences that are statements. 2. Express statements using symbols. 3. Form the negation of a statement 4. Express

More information

Semantics and Generative Grammar. A Little Bit on Adverbs and Events

Semantics and Generative Grammar. A Little Bit on Adverbs and Events A Little Bit on Adverbs and Events 1. From Adjectives to Adverbs to Events We ve just developed a theory of the semantics of adjectives, under which they denote either functions of type (intersective

More information

THE LOGIC OF COMPOUND STATEMENTS

THE LOGIC OF COMPOUND STATEMENTS CHAPTER 2 THE LOGIC OF COMPOUND STATEMENTS Copyright Cengage Learning. All rights reserved. SECTION 2.1 Logical Form and Logical Equivalence Copyright Cengage Learning. All rights reserved. Logical Form

More information

SPEAKING MATHEMATICALLY. Prepared by Engr. John Paul Timola

SPEAKING MATHEMATICALLY. Prepared by Engr. John Paul Timola SPEAKING MATHEMATICALLY Prepared by Engr. John Paul Timola VARIABLE used as a placeholder when you want to talk about something but either (1) has one or more values but you don t know what they are, or

More information

Theory of Computation

Theory of Computation Theory of Computation (Feodor F. Dragan) Department of Computer Science Kent State University Spring, 2018 Theory of Computation, Feodor F. Dragan, Kent State University 1 Before we go into details, what

More information

Introduction. Predicates and Quantifiers. Discrete Mathematics Andrei Bulatov

Introduction. Predicates and Quantifiers. Discrete Mathematics Andrei Bulatov Introduction Predicates and Quantifiers Discrete Mathematics Andrei Bulatov Discrete Mathematics Predicates and Quantifiers 7-2 What Propositional Logic Cannot Do We saw that some declarative sentences

More information

6.1 Logic. Statements or Propositions. Negation. The negation of a statement, p, is not p and is denoted by p Truth table: p p

6.1 Logic. Statements or Propositions. Negation. The negation of a statement, p, is not p and is denoted by p Truth table: p p 6.1 Logic Logic is not only the foundation of mathematics, but also is important in numerous fields including law, medicine, and science. Although the study of logic originated in antiquity, it was rebuilt

More information

Semantics and Generative Grammar. Expanding Our Formalism, Part 1 1

Semantics and Generative Grammar. Expanding Our Formalism, Part 1 1 Expanding Our Formalism, Part 1 1 1. Review of Our System Thus Far Thus far, we ve built a system that can interpret a very narrow range of English structures: sentences whose subjects are proper names,

More information

One hint from secondary predication (from Baker 1997 (8) A secondary predicate cannot take the goal argument as subject of predication, wheth

One hint from secondary predication (from Baker 1997 (8) A secondary predicate cannot take the goal argument as subject of predication, wheth MIT, Fall 2003 1 The Double Object Construction (Larson 1988, Aoun & Li 1989) MIT, 24.951, Fr 14 Nov 2003 A familiar puzzle The Dative Alternation (1) a. I gave the candy to the children b. I gave the

More information

3., Susan Woman : Help yourself. That 's too bad. I'm afraid, but I can 't. Thanks a lot. Glad to meet you. . 7

3., Susan Woman : Help yourself. That 's too bad. I'm afraid, but I can 't. Thanks a lot. Glad to meet you. . 7 2002 3.. ( ) ( ). 3., Susan. 4.,. 16... - - - - - 1.,. 5.,. 3 3 6 3 6 6.,. Woman : Help yourself. That 's too bad. I'm afraid, but I can 't. Thanks a lot. Glad to meet you. 2.,. 3 ( ) 15-1. 7. [ 78 ].

More information

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

Proseminar on Semantic Theory Fall 2010 Ling 720. Remko Scha (1981/1984): Distributive, Collective and Cumulative Quantification 1. Introduction Remko Scha (1981/1984): Distributive, Collective and Cumulative Quantification (1) The Importance of Scha (1981/1984) The first modern work on plurals (Landman 2000) There are many ideas

More information

Semantics and Generative Grammar. An Introduction to Intensional Semantics 1

Semantics and Generative Grammar. An Introduction to Intensional Semantics 1 An Introduction to Intensional Semantics 1 1. The Inadequacies of a Purely Extensional Semantics (1) Our Current System: A Purely Extensional Semantics The extension of a complex phrase is (always) derived

More information

Computational Models - Lecture 4 1

Computational Models - Lecture 4 1 Computational Models - Lecture 4 1 Handout Mode Iftach Haitner and Yishay Mansour. Tel Aviv University. April 3/8, 2013 1 Based on frames by Benny Chor, Tel Aviv University, modifying frames by Maurice

More information

Hardegree, Formal Semantics, Handout of 8

Hardegree, Formal Semantics, Handout of 8 Hardegree, Formal Semantics, Handout 2015-04-07 1 of 8 1. Bound Pronouns Consider the following example. every man's mother respects him In addition to the usual demonstrative reading of he, x { Mx R[m(x),

More information

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:

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: Logic: The Big Picture Logic is a tool for formalizing reasoning. There are lots of different logics: probabilistic logic: for reasoning about probability temporal logic: for reasoning about time (and

More information

Assignment 2 SOLUTIONS

Assignment 2 SOLUTIONS MATHEMATICS 01-10-LW Business Statistics Martin Huard Fall 00 Assignment SOLUTIONS This assignment is due on Friday September 6 at the beginning of the class. Question 1 ( points) In a marketing research,

More information

Chapter 1 :: Bird s-eye View Approach to Algebra CHAPTER. Bird s-eye View Approach to Algebra

Chapter 1 :: Bird s-eye View Approach to Algebra CHAPTER. Bird s-eye View Approach to Algebra Chapter 1 :: Bird s-eye View Approach to Algebra CHAPTER 1 Bird s-eye View Approach to Algebra 23 Kim :: Advanced Math Workbook for the SAT 1.1 :: Factor Out! try it yourself Try these four sample questions

More information

The same definition may henceforth be expressed as follows:

The same definition may henceforth be expressed as follows: 34 Executing the Fregean Program The extension of "lsit under this scheme of abbreviation is the following set X of ordered triples: X := { E D x D x D : x introduces y to z}. How is this extension

More information

Stats Review Chapter 6. Mary Stangler Center for Academic Success Revised 8/16

Stats Review Chapter 6. Mary Stangler Center for Academic Success Revised 8/16 Stats Review Chapter Revised 8/1 Note: This review is composed of questions similar to those found in the chapter review and/or chapter test. This review is meant to highlight basic concepts from the course.

More information

Symbolic Logic. Alice E. Fischer. CSCI 1166 Discrete Mathematics for Computing February 5 6,

Symbolic Logic. Alice E. Fischer. CSCI 1166 Discrete Mathematics for Computing February 5 6, Symbolic Logic Alice E. Fischer CSCI 1166 Discrete Mathematics for Computing February 5 6, 20182018 Alice E. Fischer Symbolic Logic... 1/19 1 2 3 Alice E. Fischer Symbolic Logic... 2/19 Symbolic Logic

More information

Predicate Calculus. Lila Kari. University of Waterloo. Predicate Calculus CS245, Logic and Computation 1 / 59

Predicate Calculus. Lila Kari. University of Waterloo. Predicate Calculus CS245, Logic and Computation 1 / 59 Predicate Calculus Lila Kari University of Waterloo Predicate Calculus CS245, Logic and Computation 1 / 59 Predicate Calculus Alternative names: predicate logic, first order logic, elementary logic, restricted

More information

INTRODUCTION TO LOGIC 8 Identity and Definite Descriptions

INTRODUCTION TO LOGIC 8 Identity and Definite Descriptions 8.1 Qualitative and Numerical Identity INTRODUCTION TO LOGIC 8 Identity and Definite Descriptions Volker Halbach Keith and Volker have the same car. Keith and Volker have identical cars. Keith and Volker

More information

Hardegree, Formal Semantics, Handout of 8

Hardegree, Formal Semantics, Handout of 8 Hardegree, Formal Semantics, Handout 2015-03-24 1 of 8 1. Number-Words By a number-word or numeral 1 we mean a word (or word-like compound 2 ) that denotes a number. 3 In English, number-words appear to

More information

Commonsense Knowledge, Ontology and Ordinary Language

Commonsense Knowledge, Ontology and Ordinary Language Int. J. Reasoning-based Intelligent Systems, Vol. n, No. m, 2008 43 Commonsense Knowledge, Ontology and Ordinary Language Walid S. Saba Ameri Institutes for Research, 000 Thomas Jefferson Street, NW, Washington,

More information

Logic and Propositional Calculus

Logic and Propositional Calculus CHAPTER 4 Logic and Propositional Calculus 4.1 INTRODUCTION Many algorithms and proofs use logical expressions such as: IF p THEN q or If p 1 AND p 2, THEN q 1 OR q 2 Therefore it is necessary to know

More information

Predicate Logic & Quantification

Predicate Logic & Quantification Predicate Logic & Quantification Things you should do Homework 1 due today at 3pm Via gradescope. Directions posted on the website. Group homework 1 posted, due Tuesday. Groups of 1-3. We suggest 3. In

More information

Logics for Data and Knowledge Representation

Logics for Data and Knowledge Representation Logics for Data and Knowledge Representation 4. Introduction to Description Logics - ALC Luciano Serafini FBK-irst, Trento, Italy October 9, 2012 Origins of Description Logics Description Logics stem from

More information

Syllogistic Logic and its Extensions

Syllogistic Logic and its Extensions 1/31 Syllogistic Logic and its Extensions Larry Moss, Indiana University NASSLLI 2014 2/31 Logic and Language: Traditional Syllogisms All men are mortal. Socrates is a man. Socrates is mortal. Some men

More information

PENGUIN READERS. Five Famous Fairy Tales

PENGUIN READERS. Five Famous Fairy Tales PENGUIN READERS Five Famous Fairy Tales Introduction Jacob and Wilhelm Grimm the Brothers Grimm were good friends. Jacob was a quiet man and sometimes sad. Wilhelm was often very ill but he was a happier

More information

Deriving Distributivity from Discourse. Grant Xiaoguang Li Marlboro College

Deriving Distributivity from Discourse. Grant Xiaoguang Li Marlboro College Deriving Distributivity from Discourse Grant Xiaoguang Li Marlboro College This paper discusses the structure that incorporates information from discourse to derive distributivity. Following a proposal

More information

LIN1032 Formal Foundations for Linguistics

LIN1032 Formal Foundations for Linguistics LIN1032 Formal Foundations for Lecture 5 Albert Gatt In this lecture We conclude our discussion of the logical connectives We begin our foray into predicate logic much more expressive than propositional

More information

Semantics 2 Part 1: Relative Clauses and Variables

Semantics 2 Part 1: Relative Clauses and Variables Semantics 2 Part 1: Relative Clauses and Variables Sam Alxatib EVELIN 2012 January 17, 2012 Reviewing Adjectives Adjectives are treated as predicates of individuals, i.e. as functions from individuals

More information

Extended Generative Lexicon

Extended Generative Lexicon Extended Generatve Lexcon Sumyo Nsguc Scool of Management, Tokyo Unversty of Scence 00 Smokyoku, Kuk-cty, Satama -81, Japan nsguc@rs.tus.ac.jp Abstract Ts paper proposes an elaboraton of te Generatve Lexcon

More information

Introduction to Semantics. Common Nouns and Adjectives in Predicate Position 1

Introduction to Semantics. Common Nouns and Adjectives in Predicate Position 1 Common Nouns and Adjectives in Predicate Position 1 (1) The Lexicon of Our System at Present a. Proper Names: [[ Barack ]] = Barack b. Intransitive Verbs: [[ smokes ]] = [ λx : x D e. IF x smokes THEN

More information

Reflexives and non-fregean quantifiers

Reflexives and non-fregean quantifiers UCLA Working Papers in Linguistics, Theories of Everything Volume 17, Article 49: 439-445, 2012 Reflexives and non-fregean quantifiers Richard Zuber It is shown that depending on the subject noun phrase

More information

ANSWERS AND EXPLANATIONS

ANSWERS AND EXPLANATIONS www.tarainstitute.in ANSWERS AND EXPLANATIONS EXERCISE. (a) Given,? x? x??. (c) Let the numbers be x and x. Now, difference of numbers 0 i.e. x x 0 x 0 Larger number 0 0. (d) Suppose the first number is

More information

Reasoning. Inference. Knowledge Representation 4/6/2018. User

Reasoning. Inference. Knowledge Representation 4/6/2018. User Reasoning Robotics First-order logic Chapter 8-Russel Representation and Reasoning In order to determine appropriate actions to take, an intelligent system needs to represent information about the world

More information

INTRODUCTION TO LOGIC 8 Identity and Definite Descriptions

INTRODUCTION TO LOGIC 8 Identity and Definite Descriptions INTRODUCTION TO LOGIC 8 Identity and Definite Descriptions Volker Halbach The analysis of the beginning would thus yield the notion of the unity of being and not-being or, in a more reflected form, the

More information

Truth-Functional Logic

Truth-Functional Logic Truth-Functional Logic Syntax Every atomic sentence (A, B, C, ) is a sentence and are sentences With ϕ a sentence, the negation ϕ is a sentence With ϕ and ψ sentences, the conjunction ϕ ψ is a sentence

More information

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

Parsing. Based on presentations from Chris Manning s course on Statistical Parsing (Stanford) Parsing Based on presentations from Chris Manning s course on Statistical Parsing (Stanford) S N VP V NP D N John hit the ball Levels of analysis Level Morphology/Lexical POS (morpho-synactic), WSD Elements

More information

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

Logic Background (1A) Young W. Lim 12/14/15 Young W. Lim 12/14/15 Copyright (c) 2014-2015 Young W. Lim. Permission is granted to copy, distribute and/or modify this document under the terms of the GNU Free Documentation License, Version 1.2 or any

More information

Modality: A Standard Analysis. Modality

Modality: A Standard Analysis. Modality Modality: A Standard Analysis 1 Ling 406/802 Read Meaning and Grammar, Ch. 5.3.2; Kratzer 1991, pp. 639-644 Modality 2 Modality has to do with necessity and possibility of situations. Grammatical means

More information

Semantics and Generative Grammar. Quantificational DPs, Part 3: Covert Movement vs. Type Shifting 1

Semantics and Generative Grammar. Quantificational DPs, Part 3: Covert Movement vs. Type Shifting 1 Quantificational DPs, Part 3: Covert Movement vs. Type Shifting 1 1. Introduction Thus far, we ve considered two competing analyses of sentences like those in (1). (1) Sentences Where a Quantificational

More information

Generalized Quantifiers Logical and Linguistic Aspects

Generalized Quantifiers Logical and Linguistic Aspects Generalized Quantifiers Logical and Linguistic Aspects Lecture 1: Formal Semantics and Generalized Quantifiers Dag Westerståhl University of Gothenburg SELLC 2010 Institute for Logic and Cognition, Sun

More information

Grundlagenmodul Semantik All Exercises

Grundlagenmodul Semantik All Exercises Grundlagenmodul Semantik All Exercises Sommersemester 2014 Exercise 1 Are the following statements correct? Justify your answers in a single short sentence. 1. 11 {x x is a square number} 2. 11 {x {y y

More information

Class Notes: Tsujimura (2007), Ch. 5. Syntax (1), pp (3) a. [[akai hon]-no hyooshi] b. [akai [hon-no hyooshi]]

Class Notes: Tsujimura (2007), Ch. 5. Syntax (1), pp (3) a. [[akai hon]-no hyooshi] b. [akai [hon-no hyooshi]] Class otes: Tsujimura (2007), Ch. 5. yntax (1), pp. 206-220 p. 206 What is!ytx"?! n area in linguistics that deals with the REGULRLITY of how words are put together to create grammatical sentences What

More information

Logical Translations Jean Mark Gawron San Diego State University. 1 Introduction 2

Logical Translations Jean Mark Gawron San Diego State University. 1 Introduction 2 Logical Translations Jean Mark Gawron San Diego State University Contents 1 Introduction 2 2 Truth-Functional Connectives 2 2.1 And................................ 2 2.2 Or.................................

More information

HPSG: Binding Theory

HPSG: Binding Theory HPSG: Binding Theory Doug Arnold doug@essexacuk Introduction Binding Theory is to do with the syntactic restrictions on the distribution of referentially dependent items and their antecedents: reflexives/reciprocals

More information

Against generalized quantifiers (and what to replace them with)

Against generalized quantifiers (and what to replace them with) Against generalized quantifiers (and what to replace them with) Udo Klein SFB 732 Stuttgart, 06.11.08 Generalized quantifier theory the basic idea The sentence Exactly three students smoke. is true if

More information

NATIONAL TALENT SEARCH EXAMINATION (NTSE-2017) STAGE -1 BIHAR STATE : MAT SOLUTIONS (1) M-14 (2) K-13 (3) L-12 (4) S-21 J-10 L-12

NATIONAL TALENT SEARCH EXAMINATION (NTSE-2017) STAGE -1 BIHAR STATE : MAT SOLUTIONS (1) M-14 (2) K-13 (3) L-12 (4) S-21 J-10 L-12 Path to success KOTA (RAJASTHAN) TM NATIONAL TALENT SEARCH EXAMINATION (NTSE-2017) STAGE -1 BIHAR STATE : MAT Date: 13/11/2016 Max. Marks: 50 Questions 1-5 : Complete the Series 1. FLP, INS, LPV,? (1)

More information

To every formula scheme there corresponds a property of R. This relationship helps one to understand the logic being studied.

To every formula scheme there corresponds a property of R. This relationship helps one to understand the logic being studied. Modal Logic (2) There appeared to be a correspondence between the validity of Φ Φ and the property that the accessibility relation R is reflexive. The connection between them is that both relied on the

More information

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

Semantics and Generative Grammar. Quantificational DPs, Part 2: Quantificational DPs in Non-Subject Position and Pronominal Binding 1 Quantificational DPs, Part 2: Quantificational DPs in Non-Subject Position and Pronominal Binding 1 1. Introduction (1) Our Current System a. The Ds no, some, and every are type (Quantificational

More information

A Review of the Essentials of Extensional Semantics 1

A Review of the Essentials of Extensional Semantics 1 A Review of the Essentials of Extensional Semantics 1 1. The Big Picture (1) Our Ultimate Goal A precise, formal theory of a particular sub-component the human language faculty: the ability to productively

More information

SL - Binomial Questions

SL - Binomial Questions IB Questionbank Maths SL SL - Binomial Questions 262 min 244 marks 1. A random variable X is distributed normally with mean 450 and standard deviation 20. Find P(X 475). Given that P(X > a) = 0.27, find

More information

It rains now. (true) The followings are not propositions.

It rains now. (true) The followings are not propositions. Chapter 8 Fuzzy Logic Formal language is a language in which the syntax is precisely given and thus is different from informal language like English and French. The study of the formal languages is the

More information

Propositional Logic 1

Propositional Logic 1 Propositional Logic 1 Joseph Spring Department of Computer Science L1 - ormal Languages and Deduction Areas for Discussion Propositions Language for Representing Propositions Simple Propositions Compound

More information

Gradable Adjectives, Compounded Scales, Conjunction and Structured Meanings

Gradable Adjectives, Compounded Scales, Conjunction and Structured Meanings Gradable Adjectives, Compounded Scales, Conjunction and Structured Meanings Alan Bale (alanbale@mit.edu) Winter, 2007 1 Introduction General Issues: 1. What are the semantic properties correlated with

More information

An Inferential Approach

An Inferential Approach Motivation An Inferential Approach for Natural Language Semantics Daniel Kayser L.I.P.N. - Institut Galilée Université Paris-Nord Natural Language is important for Computer Science For theoretical reasons

More information

Sentence Planning 2: Aggregation

Sentence Planning 2: Aggregation Pipelined Microplanning Sentence Planning 2: Aggregation Lecture 10 February 28, 2012 Reading: Chapter 5, Reiter and Dale Document Plan! Lexical choice! PPSs Aggregation PPSs Referring Expression Gen!

More information

Spring 2012 Ling 753 A Review of Some Key Ideas in the Semantics of Plurals. 1. Introduction: The Interpretations of Sentences Containing Plurals

Spring 2012 Ling 753 A Review of Some Key Ideas in the Semantics of Plurals. 1. Introduction: The Interpretations of Sentences Containing Plurals A Review of Some Key Ideas in the Semantics of Plurals 1. Introduction: The Interpretations of Sentences Containing Plurals (1) Overarching Questions What are the truth-conditions of sentences containing

More information

Logic Overview, I. and T T T T F F F T F F F F

Logic Overview, I. and T T T T F F F T F F F F Logic Overview, I DEFINITIONS A statement (proposition) is a declarative sentence that can be assigned a truth value T or F, but not both. Statements are denoted by letters p, q, r, s,... The 5 basic logical

More information

, x {1, 2, k}, where k > 0. Find E(X). (2) (Total 7 marks)

, x {1, 2, k}, where k > 0. Find E(X). (2) (Total 7 marks) 1.) The probability distribution of a discrete random variable X is given by 2 x P(X = x) = 14, x {1, 2, k}, where k > 0. Write down P(X = 2). Show that k = 3. (1) Find E(X). (Total 7 marks) 2.) In a group

More information

Section Summary. Section 1.5 9/9/2014

Section Summary. Section 1.5 9/9/2014 Section 1.5 Section Summary Nested Quantifiers Order of Quantifiers Translating from Nested Quantifiers into English Translating Mathematical Statements into Statements involving Nested Quantifiers Translated

More information

SYLLOGISM CHAPTER 13.1 INTRODUCTION

SYLLOGISM CHAPTER 13.1 INTRODUCTION CHAPTER 13 SYLLOGISM 13.1 INTRODUCTION Syllogism is a Greek word that means inference or deduction. As such inferences are based on logic, then these inferences are called logical deduction. These deductions

More information

Logic for Computer Science - Week 2 The Syntax of Propositional Logic

Logic for Computer Science - Week 2 The Syntax of Propositional Logic Logic for Computer Science - Week 2 The Syntax of Propositional Logic Ștefan Ciobâcă November 30, 2017 1 An Introduction to Logical Formulae In the previous lecture, we have seen what makes an argument

More information

1 Zimmermann, Formal Semantics

1 Zimmermann, Formal Semantics 1 Zimmermann, Formal Semantics 1. Compositionality 1.1Frege s Principle Any decent language, whether natural or artificial, contains more than just finitely many expressions. In order to learn and understand

More information

Here is a little background for the readings I have submitted.

Here is a little background for the readings I have submitted. ASDP, 2013 Medieval Japanese Literature On the Road: Travel and Literature in Medieval Japan Here is a little background for the readings I have submitted. 1. Seeing a dead body lying among the stones

More information

Artificial Intelligence

Artificial Intelligence CS344: Introduction to Artificial Intelligence Pushpak Bhattacharyya CSE Dept., IIT Bombay Lecture 20-21 Natural Language Parsing Parsing of Sentences Are sentences flat linear structures? Why tree? Is

More information

INTENSIONS MARCUS KRACHT

INTENSIONS MARCUS KRACHT INTENSIONS MARCUS KRACHT 1. The Way Things Are This note accompanies the introduction of Chapter 4 of the lecture notes. I shall provide some formal background and technology. Let a language L be given

More information

Binding Theory Different types of NPs, constraints on their distribution

Binding Theory Different types of NPs, constraints on their distribution Binding Theory Different types of Ps, constraints on their distribution Ling 322 Read Syntax, Ch. 5 (Lecture notes based on Andrew Carnie s notes) 1 Different Types of Ps R-expressions An P that gets its

More information

SYLLOGISM CHAPTER 13.1 INTRODUCTION

SYLLOGISM CHAPTER 13.1 INTRODUCTION CHAPTER 13 SYLLOGISM 13.1 INTRODUCTION Syllogism is a Greek word that means inference or deduction. As such inferences are based on logic, then these inferences are called logical deduction. These deductions

More information

Lecture 7. Logic. Section1: Statement Logic.

Lecture 7. Logic. Section1: Statement Logic. Ling 726: Mathematical Linguistics, Logic, Section : Statement Logic V. Borschev and B. Partee, October 5, 26 p. Lecture 7. Logic. Section: Statement Logic.. Statement Logic..... Goals..... Syntax of Statement

More information

(7) a. [ PP to John], Mary gave the book t [PP]. b. [ VP fix the car], I wonder whether she will t [VP].

(7) a. [ PP to John], Mary gave the book t [PP]. b. [ VP fix the car], I wonder whether she will t [VP]. CAS LX 522 Syntax I Fall 2000 September 18, 2000 Paul Hagstrom Week 2: Movement Movement Last time, we talked about subcategorization. (1) a. I can solve this problem. b. This problem, I can solve. (2)

More information

Midterm Review Honors ICM Name: Per: Remember to show work to receive credit! Circle your answers! Sets and Probability

Midterm Review Honors ICM Name: Per: Remember to show work to receive credit! Circle your answers! Sets and Probability Midterm Review Honors ICM Name: Per: Remember to show work to receive credit! Circle your answers! Unit 1 Sets and Probability 1. Let U denote the set of all the students at Green Hope High. Let D { x

More information

Code No. : Sub. Code : 2 EN 21

Code No. : Sub. Code : 2 EN 21 Reg. No. :... Sub. Code : 2 EN 21 U.G. DEGREE EXAMINATION, NOVEMBER 2014. Second Semester Part II English PROSE, SHAKESPEARE, GRAMMAR AND COMPOSITION (For those who joined in July 2006 2007) Time : Three

More information

Models of Adjunction in Minimalist Grammars

Models of Adjunction in Minimalist Grammars Models of Adjunction in Minimalist Grammars Thomas Graf mail@thomasgraf.net http://thomasgraf.net Stony Brook University FG 2014 August 17, 2014 The Theory-Neutral CliffsNotes Insights Several properties

More information

2 A not-quite-argument for X-bar structure in noun phrases

2 A not-quite-argument for X-bar structure in noun phrases CAS LX 321 / GRS LX 621 Syntax: Introduction to Sentential Structure ovember 16, 2017 1 and pronouns (1) he linguists yodel. (2) We linguists yodel. (3) hey looked at us linguists. (4) hey looked at linguists.

More information