Lecture 5 Flashcards
Semantic Analysis
Syntactic analysis
- determines the syntactic category of the words
- decides phrase structure – how words are grouped
- assigns structural analysis to a sentence
Semantic analysis
- creates a representation of the meaning of a sentence
Clearly syntactic structure affects meaning (e.g. word order, phrase
attachment)
- “The man with the telescope watched Mary.”
- “Mary watched the man with the telescope.”
But meaning can determine syntactic structure
Recall that lexicalized statistical parsing used head word affinities (probabilities) to help determine parsing.
Tasks for Semantic Processing - 1
Decide if one sentence is a paraphrase of another (two way).
Your marks on the tests were excellent.
You scored very high on the exams.
Tasks for Semantic Processing - 2
Entailment: decide if the truth of one sentence implies the truth of
another (one way).
John lives in Toronto.
implies John’s residence is in Canada.
A semantic system
consists of different types of building blocks: entities, concepts, relations, and
predicates.
A semantic representation
shows how to put together blocks of a semantic system to describe a situation or
“semantic world”
Enables reasoning about that
semantic world
Semantic Representations
To link the surface, linguistic elements to
the non-linguistic knowledge of the world
Many words, few concepts
Semantic Representations
To represent the variety at the lexical
level at a unified conceptual level
* Unambiguous representations;
canonical forms
Semantic Representations
Structures composed from a set of
symbols
* All languages have a predicate-
argument structure
* Correspond to relationships that hold
among concepts underlying
constituent words and phrases of a
sentence, and then across sentences
Semantics that words (or base noun
phrases) represent – the objects
Entities
– individuals such as a particular person, location or product
- John F. Kennedy, Washington,
D.C., Cocoa Puffs
Semantics that words (or base noun
phrases) represent – the objects
Concepts
– the general category of
individuals such as
- person, city, breakfast cereal
Semantics indicated by verbs, prepositional phrases and other structures
Relations between entities and concepts
* John F. Kennedy “is-a” person
Semantics indicated by verbs, prepositional phrases and other structures
Relations between entities or between
concepts
* Hierarchy of specific to more general
concepts
* Wide variety of other relations (e.g.,
people are related to organizations,
locations are related to people, etc)
Semantics indicated by verbs, prepositional phrases and other structures
Predicates representing verb structures,
sometimes called events
* Semantic roles, case grammar
* Can also be used for relations
between objects
Semantic Representations
Some representation approaches:
* First Order Logic
* Semantic Nets
* Conceptual Dependency
* Frames
* Rule-Based
* Conceptual Graphs
Semantics of events in sentences
In a sentence, a verb and its semantic roles form a proposition; the verb can be called the predicate and the roles are known as arguments.
Syntactic structure is not the same as semantic structure
Syntactic similarities hide semantic dissimilarities
* We baked every Saturday morning.
* The pie baked to a golden brown.
* This oven bakes evenly.
3 subject NPs perform very different roles in regard to bake
Fillmore, Charles (1968) “The Case for Case.”
* A response to Chomskyʼs disregard for any semantics
* “A semantically justified syntactic theory”
Some of Fillmore’s original set of roles still in use as general descriptors of
roles
Agentive (A) - the instigator of the action, an animate being
* John opened the door.
* The door was opened by John.
Instrumental (I) - the thing used to perform the action, an inanimate object
* The key opened the door.
* John opened the door with the key.
Locative (L) - the location or spatial orientation of the state or action of the verb
* Itʼs windy in Chicago.
Verb-specific Roles
General thematic roles don’t work
for many verbs and roles
* Verb-specific roles are proposed in
treebanks
* PropBank annotates the verbs of
Penn Treebank
* FrameNet annotates the British
National Corpus
Automatic Semantic Role Labelling (SRL)
Define an algorithm that will process text and recognize roles for each
verb
* Task: given a verb in a sentence, find and label all arguments
Automatic Semantic Role Labelling (SRL)
A machine learning classification task: for each constituent in the
parse tree of the sentence, classify the argument role it has for the
verb
- For each constituent, define features of semantic roles
- Each feature describes some aspect of a text phrase that can help
determine its semantic role of a verb, e.g., the verb, POS tags, its position
in parse tree, etc. - Machine Learning process:
- Training a classifier on Treebank annotated with semantic roles (PropBank
or FrameNet) - Then classify syntactic phrases as to their roles
Parse Tree Constituents
- Each noun phrase is a candidate for role labeling based on its function relative to
its head verb (note explore has Arg0 at a distance.) - Define features from sentence processed into parse tree with Part-of-Speech tags
on words
