Generating Adjectives to Express the Speaker’s Argumentative Intent

Author : Michael Elhadad

Presented By

Mithun Balakrishna

Introduction

Express speaker’s intentions or argumentative orientation

Explanation component of ADVISOR Example – “Course is very hard” –

from the academic advisor does not refer to the property of the course but expresses his evaluation of the course

Problems Information cannot be found

directly in the knowledge-base Decision must be based on the

speaker’s goals, a hearer model and the object being modified

Decisions interact with the lexical properties of adjectives, syntax of the clause and other factors like collocation

Goal of the Paper

Input to a generator capable of producing argumentative usages to adjectives

Combining the many interacting factors constraining the adjective selection

Previous Work

Referential Usage – speaker wants the hearer to identify some object

Attributive Usage – speaker wants to inform the hearer of object property

Satisfy pragmatic constraint – “Poor John was beaten………”

Data and Motivation

Corpus – 40,000 Words, 700 occurrences of 150 distinct adjectives

Predicative and Attributive – 69 occurrences of 26 distinct adjectives

Cannot be found in knowledge-base describing courses

Data and Motivation

Information Needed to Choose an Adjective Input cannot be attribute property P to a

course C Marked and Neutral –

Data Structure is probably the hardest course and you would want to make sure that you could handle it.I really can’t tell you how difficult or easy they are.

Need to convey speaker argumentative intent

Information Needed to Choose an Adjective The description of this intent needs to

be scalar and relative to a background Absolute and Relative adjectives –

a small elephant is a big animala red box is as red as a red book

Relative adjectives depend not only on the object being modified (a good course is not good in the same sense as a good meal) but also depend on a model of the hearer

Formal Representation of Argumentative Intent Represent the argumentative orientation

using scalar nature and relativity The notation used is that of functional

descriptions (FDs) used in Functional Unification Grammars (FUGs) (Kay, 1979, Elhadad, 1990a)

the {} notation indicates that focus is a pointer to the value of the attribute of the scope of the ao in the FD

Formal Representation of Argumentative Intent

Formal Representation of Argumentative Intent

Lexical Representation of Adjectives

Information that needs to be present in the lexicon to describe adjectives

Lexical properties that constrain how adjectives can be used to convey an argumentative meaning

Lexical Representation of Adjectives Attributive or Predicative

“An old friend”; “My friend is old” Degree or Non-Degree

“Hard Course”; “Required Course” Marked or Neutral

“Hard”; “Difficult” Absolute or Relative

“What is that course? It looked very interesting”;“If you’re good at math - that might be a good course to take”

Lexical Representation of Adjectives

Similar to these selection restrictions but at the lexical level, lexical affinities or collocations“strongly recommended”; “very important”

The choice of the intensifier is constrained by the adjective

Lexical Representation of Adjectives

Interaction with other Surface Decision Whether to use adjective at all to satisfy an

argumentative intent and what adjective can be used when necessary

Verb lexically carries an argumentative evaluation of its object“I struggled with AI” (I took AI + I found AI hard.)“I enjoyed AI” (I took AI + I found AI interesting.)

“Data Structures follows Intro, and it is a very difficult course” – No verb to express both the notion of succession and evaluation of the course

Interaction with other Surface Decision The decision of using an adjective also

interacts with the choice of the head of the noun phrase being modified. For example, proper nouns cannot be pre-modified by adjectives

The decision to explicitly express the relativity of the adjectival modification (does the generator produce AI is hard or AI is hard for an undergrad course) depends on what information is encoded in the reference variable and reference-set features

Comments

Adjectival Modification in Text Meaning Representation

Authors : Victor Raskin and Sergei Nirenburg

Presented By

Mithun Balakrishna

Introduction MikroKosmos semantic analyzer -

component of a knowledge-based machine translation system

The purpose and result of the MikroKosmos analysis process is the derivation of an interlingual representation for natural language inputs

The language in which these representations are expressed is called the "text meaning representation" (TMR) language

TMR is a frame-based language

Goal of the Paper

Detecting and recording adjectival meaning

Compare with knowledge on adjectives in literature

Acquisition of lexical entries for adjectives

The Ontological Approach to the Meaning of a Typical Adjective

A simple, prototypical case of adjectival modification is a scalar adjective, which modifies a noun both syntactically and semantically

Associates meaning with a region on a scale which is defined as the range of an ontological property

The Ontological Approach to the Meaning of a Typical Adjective

Contribution of adjective to construction of TMR typically consists of inserting its meaning (a property-value pair) as a slot filler in a frame representing the meaning of the noun which this adjective syntactically modifies

The Ontological Approach to the Meaning of a Typical Adjective

The Ontological Approach to the Meaning of a Typical Adjective

Semantic and Computational Treatment of Adjectives: Old and New Trends

The literature on adjectives shows a scarcity of systematic semantic analyses or lexicographic descriptions of adjectives

Focus on taxonomies of adjectives differences between attributive and predicative syntactic transformations associated with various

adjectival usages on the qualitative/relative distinctions among

adjectives gradability/comparability of qualitative adjectives

Large-scale systems require entries for all lexical categories

Semantic and Computational Treatment of Adjectives: Old and New Trends

Clear that the scalar/non-scalar dichotomy, and not the attributive~predicative distinction which dominates the literature, is the single most important distinction in semantic treatment of adjectives

The continuous numerical scales associated with the true scalars also render the issue of gradability and comparability rather trivial

Grain size of description Principle of practical effability - stipulates that, in MT,

the target language should be expected to have a corresponding adjective of a comparably large grain-size

If the context does not allow the analyzer to select a specific solution, a coarser-grain solution is preferred

Semantic and Computational Treatment of Adjectives: Old and New Trends

Non-Property Based Adjectival Modifications

Semantic treatment of adjectives which cannot be reduced to the standard property-based type of adjectival modification

Non-Property Based Adjectival Modifications - Attitudes Good is a scalar but unlike in the case of

big, the LEX-MAP for Good does not contain a property-value pair that can be attached to the frame of the modified noun like house in the TMR

Instead, the meaning representation of good introduces an attitude on the part of the speaker with regard to the modified noun

Non-Property Based Adjectival Modifications – Temporal Adjectives

The purely temporal knowledge in MikroKosmos is recorded with the meaning of the entire proposition, and adjective entries are not marked for it

Some temporal adjectives are presented as derived from adverbs rather than nouns

occasional visitor is analyzed as a rhetorical paraphrase of visit occasionally

Non-Property Based Adjectival Modifications – Membership Adjectives

The lexical entry for this subclass focuses on two major elements: whether the modified norm is a

member of a certain set whether the properties of this noun

intersect significantly with those of the set members

authentic ,fake , nominal

Non-Property Based Adjectival Modifications – Event Related Adjectives

To derive file semantic part of an adjectival entry from a verbal entry, first one must identify the case, or thematic role (such as agent, theme, beneficiary, etc.) filled by the noun modified by the adjective in question

Abuse – abusive speech; abusive man

Non-Property Based Adjectival Modifications – Event Related Adjectives

Non-Property Based Adjectival Modifications – Relative Adjectives

Comments

A Uniform Treatment of Pragmatic Inferences in Simple and Complex Utterances and Sequences of Utterances

Author : Daniel Marcu and Graeme Hirst

Presented By

Mithun Balakrishna

Full account of natural language utterances cannot be given in terms of only syntactic and semantic phenomena

Need for – Conversant’s belief and intentions –

Scalar Implicature Discourse expectations, Discourse plans

and Discourse relations – Conversational Implicature

Introduction

Introduction

Defeasibility tricky notion to deal with Difficult to formalize a cancellation of

the presuppositions Previous Work-

Analyze context Extend boundaries Assign status of de-feasible information

Goal of Paper

Theoretical Framework – Stratified Logic, that can accommodate defeasible pragmatic inferences

An algorithm that computes the conversational, conventional, scalar, clausal, and normal state implicatures and the presuppositions associated with utterances

Stratified Logic Supports one type of indefeasible

information And two types of defeasible information

Infelicitously defeasible –“John regrets that Mary came to the party but she did not come”

Felicitously defeasible –“John does not regret that Mary came to the party because she did not come”

Stratified Logic

Stratified Logic

Stratified Logic

Algorithm Input is a set of first-order stratified

formulas that represent an adequate knowledge base

Builds the set of all possible interpretations for a given utterance using a generalization of the semantic tableau technique

The model ordering relation filters the optimistic interpretations

Defeasible inferences are checked

Lexical Pragmatic Inferences

Lexical Pragmatic Inferences

Lexical Pragmatic Inferences

Scalar Implicatures

Scalar Implicatures

Simple Cancellation

Complex Utterances

(1) Either Chris is not a bachelor or he regrets that Mary came to the party

(2) Chris is a bachelor or spinster

Presupposition – Chris is a (male) adult

OR – non-cancellation/ cancellation

OR – non-cancellation/ cancellation

OR- non-cancellation Mary came to the party Chris is a male Chris is an adult

OR- cancellation Chris is an adult

Pragmatic Inference in sequence of utterances

Conversational Implicatures in Indirect Replies

Q: Did you go shopping?

A: a. My car’s not running b. The timing belt broke

c. (So) I had to take the bus

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