Brainscape's Knowledge GenomeTM

Browse over 1 million classes created by top students, professors, publishers, and experts.


See full index

IST 664 > Lecture 5 > Flashcards

Lecture 5 Flashcards

Semantic Analysis

1
Q

Syntactic analysis

A
  • determines the syntactic category of the words
  • decides phrase structure – how words are grouped
  • assigns structural analysis to a sentence
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

Semantic analysis

A
  • creates a representation of the meaning of a sentence
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

Clearly syntactic structure affects meaning (e.g. word order, phrase
attachment)

A
  • “The man with the telescope watched Mary.”
  • “Mary watched the man with the telescope.”
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

But meaning can determine syntactic structure

A

Recall that lexicalized statistical parsing used head word affinities (probabilities) to help determine parsing.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

Tasks for Semantic Processing - 1

A

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.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

Tasks for Semantic Processing - 2

A

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.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

A semantic system

A

consists of different types of building blocks: entities, concepts, relations, and
predicates.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

A semantic representation

A

shows how to put together blocks of a semantic system to describe a situation or
“semantic world”

Enables reasoning about that
semantic world

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

Semantic Representations

A

To link the surface, linguistic elements to
the non-linguistic knowledge of the world

Many words, few concepts

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

Semantic Representations

A

To represent the variety at the lexical
level at a unified conceptual level
* Unambiguous representations;
canonical forms

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

Semantic Representations

A

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

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

Semantics that words (or base noun
phrases) represent – the objects
Entities

A

– individuals such as a particular person, location or product

  • John F. Kennedy, Washington,
    D.C., Cocoa Puffs
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

Semantics that words (or base noun
phrases) represent – the objects
Concepts

A

– the general category of
individuals such as

  • person, city, breakfast cereal
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

Semantics indicated by verbs, prepositional phrases and other structures

A

Relations between entities and concepts
* John F. Kennedy “is-a” person

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

Semantics indicated by verbs, prepositional phrases and other structures

A

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)

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

Semantics indicated by verbs, prepositional phrases and other structures

A

Predicates representing verb structures,
sometimes called events
* Semantic roles, case grammar
* Can also be used for relations
between objects

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
17
Q

Semantic Representations

A

Some representation approaches:
* First Order Logic
* Semantic Nets
* Conceptual Dependency
* Frames
* Rule-Based
* Conceptual Graphs

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
18
Q

Semantics of events in sentences

A

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.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
19
Q

Syntactic structure is not the same as semantic structure

A

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

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
20
Q

Fillmore, Charles (1968) “The Case for Case.”
* A response to Chomskyʼs disregard for any semantics
* “A semantically justified syntactic theory”

A

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.

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
21
Q

Verb-specific Roles

A

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

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
22
Q

Automatic Semantic Role Labelling (SRL)

A

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

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
23
Q

Automatic Semantic Role Labelling (SRL)

A

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
24
Q

Parse Tree Constituents

A
  • 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
25
Q

Standard Features of an Argument Structure that Supports Role Labeling

A

PREDICATE: The predicate verb from the trainingdata. Usually stemmed or lemmatized
* “face” and “explore”

26
Q

Standard Features of an Argument Structure that Supports Role Labeling

A

PHRASE TYPE: The phrase label of the argument candidate, e.g., NP, POS tags for single words

27
Q

Standard Features of an Argument Structure that Supports Role Labeling

A

POSITION: Whether the argument candidate is before or after the predicate.

28
Q

Standard Features of an Argument Structure that Supports Role Labeling

A

VOICE: Whether the predicate is in active or passive voice (passive voice is recognized if a past participle verb is preceded nearby by a form of the verb “be”)

29
Q

Standard Features of an Argument Structure that Supports Role Labeling

A

SUBCATEGORY: The phrase labels of the children of the predicate’s parent in the syntax tree, subcat of “faces” is “VP -> VBZ NP”

30
Q

Standard Features of an Argument Structure that Supports Role Labeling

A

PATH: The syntactic path through the parse tree from the argument constituent to the predicate.
* Arg0 for “faces”: NP -> S -> VP -> VBZ

31
Q

Standard Features of an Argument Structure that Supports Role Labeling

A

HEAD WORD: The head word of the argument constituent
* Main noun of NP (noun phrase)
* Main preposition of PP (prepositional phrase)
* The part of speech tag of the head word of the argument constituent.

32
Q

Standard Features of an Argument Structure that Supports Role Labeling

A

There are additional features such as:
* Temporal Cue Words: Special words occurring in ArgM-TMP phrases.
* Governing Category: The phrase label of the parent of the argument candidate

33
Q

Automatic SRL – Constraints and Challenges

A

Results of the labeling classifier are
probabilities for each label for that
constituent

34
Q

Automatic SRL – Constraints and Challenges

A

Use these with constraints to
assign a label
* Two constituents cannot have the
same argument label,
* A constituent cannot have more than
one label
* If two constituents have (different)
labels, they cannot have any overlap,
* No argument can overlap the
predicate.

35
Q

Automatic SRL – Constraints and Challenges

A

For each verb in a sentence, the number
of constituents in the parse tree are
large compared to the number of
semantic roles
* Can be hundreds of constituents eligible to be labeled a role
* Leads to the problem of too many
“negative” examples

36
Q

Sentiment Analysis - Affective States

A

Emotion: brief organically synchronized … evaluation of a major event
* angry, sad, joyful, fearful, ashamed, proud, elated
Mood: diffuse non-caused low-intensity long-duration change in subjective feeling
* cheerful, gloomy, irritable, listless, depressed, buoyant

37
Q

Sentiment Analysis - Affective States

A

Interpersonal stances: affective stance toward another person in a specific interaction
* friendly, flirtatious, distant, cold, warm,
supportive, contemptuous
Attitudes: enduring, affectively colored beliefs, dispositions towards objects or persons
* liking, loving, hating, valuing, desiring
Personality traits: stable personality dispositions and typical behavior tendencies
* nervous, anxious, reckless, morose, hostile, jealous

38
Q

Sentiment Analysis

A

Sentiment analysis is the detection of
attitudes - “enduring, affectively colored
beliefs, dispositions towards objects or
persons

39
Q

Sentiment Analysis - Challenges

A

Word sense ambiguity - Words can carry
sentiments offering useful information to
sentiment analysis task. But they also have
different meanings in different contexts

40
Q

Sentiment Analysis - Challenges

A

Subtlety, sarcasm or metaphor

41
Q

Sentiment Analysis - Challenges

A

Thwarted expectations and ordering effects - a lot of good words set up an expectation that is then negated.

42
Q

Sentiment Analysis - Challenges

A

Domain adaptation - Certain sentiment-related indicators seem domain-dependent; sentiment classifiers (especially those created via supervised
learning) have been shown to often be domain dependent

43
Q

Sentiment Polarity Classification

A

Treat as a document classification task
* Positive, negative, and (possibly) neutral
* sentiment words are often more
important than topic words, e.g., great,
excellent, horrible, bad, worst, etc.

44
Q

Sentiment Polarity Classification - Steps

A

Step 1 – Cleaning and Tokenization
* For text from web, deal with HTML
and XML markup
* Or Twitter mark-up (names, hash
tags)
* Capitalization (preserve for words in
all caps)
* Emoticons/emojis
* Useful code for twitter and other
social media text:

45
Q

Sentiment Polarity Classification - Steps

A

Step 2 - Extracting Features
Which words to use? (adjectives, or All words)
* All words turns out to work better
in many cases

Good to have syntax too.
* Counts of POS tags to characterize
text
* Constituent or dependency parses
* Particularly at phrase level to find
dependencies of opinion words
* Also for finding the scope of
negation

Handling negation is important
* Typical approach
1. Look for “prototype” negation word
(negation cue words) like not, no and
never
2. Add a “negated context” to the
features

46
Q

Sentiment Lexicons

A

Sentiment lexicons are lists of words and phrases that are commonly used to express positive or negative sentiments

47
Q

MPQA Subjectivity Lexicon

A

Subjectivity Lexicon from the MPQA project with Jan Wiebe
* Gives a list of 8,000+ words that have been judged to be
weakly or strongly positive, negative or neutral in subjectivity

48
Q

LIWC – Linguistic Inquiry and Word Count

A

Text analysis software based on dictionaries of word dimensions
* Dimensions can be syntactic
* Pronouns, past-tense verbs

  • Dimensions can be semantic
  • Social words, affect, cognitive mechanisms
49
Q

ANEW

A

Affective Norms for English Words
* Provides a set of emotional ratings for a large number of words in the
English language

50
Q

ANEW

A

Participants gave graded reactions from 1-9 on three dimensions
* Good/bad, psychological valence
* Active/passive, arousal valence
* Strong/weak, dominance valence

51
Q

Lexical Semantics - Lexicons

A

– list of words (or lexemes or stems) with basic info

52
Q

Lexical Semantics - Dictionaries

A

– a lexicon with definitions for each word sense
* Most are now available online

53
Q

Lexical Semantics - Thesauruses

A

– add synonyms/ antonym for each word sense
* WordNet

54
Q

Lexical Semantics - Semantic networks – add more semantic relations, including semantic categories
* WordNet, EuroWordNet

A
55
Q

Lexical Semantics - Ontologies

A

– add rules about entities, concepts and relations, semantic categories
* UMLS

56
Q

Lexical Semantics - Semantic Lexicon

A

– Lexicon where each word is assigned to a semantic class
* LIWC, ANEW, Subjectivity Lexicon

IST 664 (13 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2025 Bold Learning Solutions. Terms and Conditions