Generating Adjectives to Express the Speaker’s Argumentative Intent
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Generating Adjectives to Express the Speaker’s Argumentative Intent
Author : Michael Elhadad
Presented By
Mithun Balakrishna
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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
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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
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Goal of the Paper
Input to a generator capable of producing argumentative usages to adjectives
Combining the many interacting factors constraining the adjective selection
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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………”
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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
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Data and Motivation
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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
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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
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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
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Formal Representation of Argumentative Intent
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Formal Representation of Argumentative Intent
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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
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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”
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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
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Lexical Representation of Adjectives
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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
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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
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Adjectival Modification in Text Meaning Representation
Authors : Victor Raskin and Sergei Nirenburg
Presented By
Mithun Balakrishna
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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
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Goal of the Paper
Detecting and recording adjectival meaning
Compare with knowledge on adjectives in literature
Acquisition of lexical entries for adjectives
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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
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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
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The Ontological Approach to the Meaning of a Typical Adjective
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The Ontological Approach to the Meaning of a Typical Adjective
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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
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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
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Semantic and Computational Treatment of Adjectives: Old and New Trends
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Non-Property Based Adjectival Modifications
Semantic treatment of adjectives which cannot be reduced to the standard property-based type of adjectival modification
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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
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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
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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
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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
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Non-Property Based Adjectival Modifications – Event Related Adjectives
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Non-Property Based Adjectival Modifications – Relative Adjectives
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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
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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
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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
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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
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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”
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Stratified Logic
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Stratified Logic
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Stratified Logic
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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
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Lexical Pragmatic Inferences
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Lexical Pragmatic Inferences
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Lexical Pragmatic Inferences
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Scalar Implicatures
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Scalar Implicatures
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Simple Cancellation
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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
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OR – non-cancellation/ cancellation
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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
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Pragmatic Inference in sequence of utterances
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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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