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CMSC 473/673Natural Language Processing
Fall 2018
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Frank Ferraro
ITE 358
Monday: 2:15-3
Tuesday: 11:00-11:30
by appointment
Natural language processing
Semantics
Vision & language processing
Learning with low-to-no supervision
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Caroline Kery
Location TBD
Tuesday: 2-3:30pm
Thursday: 1-2:30pm
by appointment
Semantic parsing
Active learning
Data visualization
Analysis of educational data
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December 2016
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August 2018
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Potential Applications
ASR (automatic speech recognition)
Machine translation
Natural language generation
Document labeling/classification
Document summarization
Corpus exploration
Relation/information extraction
Entity identification
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Automatic speech recognition
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SPORTSDocument classification
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Machine translation
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https://cdn.arstechnica.net/wp-content/uploads/2015/11/Screen-Shot-2015-11-02-at-9.11.40-PM-640x543.png
Natural language generation
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Document summarization
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Corpus exploration
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Pat and Chandler agreed on a plan.
He said Pat would try the same tactic again.
Relation extraction
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Pat and Chandler agreed on a plan.
He said Pat would try the same tactic again.
Entity identification
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Pat and Chandler agreed on a plan.
He said Pat would try the same tactic again.
is “he” the same person as “Chandler?”
?
Entity identification
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Course Goals
Be introduced to some of the core problems and solutions of NLP (big picture)
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Course Goals
Be introduced to some of the core problems and solutions of NLP (big picture)
Learn different ways that success and progress can be measured in NLP
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Natural Language Processing
tensorflow
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Course Goals
Be introduced to some of the core problems and solutions of NLP (big picture)
Learn different ways that success and progress can be measured in NLP
Relate to statistics, machine learning, and linguistics
Implement NLP programs
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Course Goals
Be introduced to some of the core problems and solutions of NLP (big picture)
Learn different ways that success and progress can be measured in NLP
Relate to statistics, machine learning, and linguistics
Implement NLP programs
Read and analyze research papers
Practice your (written) communication skills
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http://www.qwantz.com/index.php?comic=170
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http://www.qwantz.com/index.php?comic=170
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Natural Language Processing≈
Computational Linguistics
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Natural Language Processing≈
Computational Linguisticsscience focus
computational biocomputational chemistry
computational X
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Natural Language Processing≈
Computational Linguisticsscience focus
computational biocomputational chemistry
computational X
build a system to translatecreate a QA system
engineering focus
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Natural Language Processing ≈ Computational Linguistics
Machine learning
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Natural Language Processing ≈ Computational Linguistics
Machine learning
Information Theory
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Natural Language Processing ≈ Computational Linguistics
Machine learning
Information Theory
Data Science
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Natural Language Processing ≈ Computational Linguistics
Machine learning
Information Theory
Data Science
Systems Engineering
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Natural Language Processing ≈ Computational Linguistics
Machine learning
Information Theory
Data Science
Systems Engineering
Logic
Theory of Computation
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Natural Language Processing ≈ Computational Linguistics
Machine learning
Information Theory
Data Science
Systems Engineering
Logic
Theory of Computation
Linguistics
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Natural Language Processing ≈ Computational Linguistics
Machine learning
Information Theory
Data Science
Systems Engineering
Logic
Theory of Computation
Linguistics
Cognitive Science
Psychology
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Natural Language Processing ≈ Computational Linguistics
Machine learning
Information Theory
Data Science
Systems Engineering
Logic
Theory of Computation
Linguistics
Cognitive Science
Psychology
Political Science
Digital Humanities
Education
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Natural Language Processing≈
Computational Linguisticsscience focus
computational biocomputational chemistry
computational X
build a system to translatecreate a QA system
engineering focus
these views can co-exist peacefully
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What Are Words?
Linguists don’t agree
(Human) Language-dependent
White-space separation is a sometimes okay (for written English longform)
Social media? Spoken vs. written? Other languages?
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What Are Words? Tokens vs. Types
The film got a great opening and the film went on to become a hit .
Type: an element of the vocabulary.
Token: an instance of that type in running text.
How many of each?
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Terminology: Tokens vs. TypesThe film got a great opening and the film went on to become a hit .
Tokens• The• film• got• a• great• opening• and• the• film• went• on• to• become• a• hit• .
Types• The• film• got• a• great• opening• and• the• went• on• to• become• hit• .
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Terminology: Tokens vs. TypesThe film got a great opening and the film went on to become a hit .
Tokens• The• film• got• a• great• opening• and• the• film• went• on• to• become• a• hit• .
Types• The• film• got• a• great• opening• and• the• went• on• to• become• hit• .
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http://www.qwantz.com/index.php?comic=170
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Adapted from Jason Eisner, Noah Smith
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orthography
Adapted from Jason Eisner, Noah Smith
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orthography
morphology
Adapted from Jason Eisner, Noah Smith
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orthography
morphology
Adapted from Jason Eisner, Noah Smith
lexemes
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orthography
morphology
Adapted from Jason Eisner, Noah Smith
lexemes
syntax
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orthography
morphology
Adapted from Jason Eisner, Noah Smith
lexemes
syntax
semantics
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orthography
morphology
Adapted from Jason Eisner, Noah Smith
lexemes
syntax
semantics
pragmatics
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orthography
morphology
Adapted from Jason Eisner, Noah Smith
lexemes
syntax
semantics
pragmatics
discourse
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Adapted from Jason Eisner, Noah Smith
NLP + Latent Modeling
explain what you see/annotate
with things “of importance” you don’t
orthography
morphology
lexemes
syntax
semantics
pragmatics
discourse
observed text
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orthography
morphology
lexemes
syntax
semantics
pragmatics
discourse
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orthography
morphology
lexemes
syntax
semantics
pragmatics
discourse
VISION
AUDIO
prosody
intonation
color
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Language is Productive
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Watergate
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Troopergate
Watergate Bridgegate
Deflategate
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Language is Ambiguous
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Ambiguity
Kids Make Nutritious Snacks
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Ambiguity
Kids Make Nutritious Snacks
Kids Prepare Nutritious Snacks
Kids Are Nutritious Snacks
sense ambiguity
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Ambiguity
British Left Waffles on Falkland Islands
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Ambiguity
British Left Waffles on Falkland Islands
British Left Waffles on Falkland Islands
British Left Waffles on Falkland Islands
lexical ambiguity
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Part of SpeechTagging
British Left Waffles on Falkland Islands
British Left Waffles on Falkland Islands
British Left Waffles on Falkland Islands
Adjective Noun Verb
Noun Verb Noun
lexical ambiguity
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Parts of Speech
Classes of words that behave like one another in “similar” contexts
Pronunciation (stress) can differ: object (noun: OB-ject) vs. object (verb: ob-JECT)
It can help improve the inputs to other systems
(text-to-speech, syntactic parsing)
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Ambiguity
Pat saw Chris with the telescope on the hill.
I ate the meal with friends.
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Ambiguity
Pat saw Chris with the telescope on the hill.
I ate the meal with friends.
syntactic ambiguity
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Language Can Be Surprising
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Garden PathSentences
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Garden PathSentences
The
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Garden PathSentences
The old
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Garden PathSentences
The old man
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Garden PathSentences
The old man the
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Garden PathSentences
The old man the boat
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Garden PathSentences
The old man the boat .
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Garden PathSentences
The old man the boat .
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Garden PathSentences
The complex houses married and single soldiers and their families.
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Garden PathSentences
The complex houses married and single soldiers and their families.
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Garden PathSentences
The rat the cat the dog chased killed ate the malt.
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Garden PathSentences
The rat that the cat the dog chased killed ate the malt.
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Garden PathSentences
The rat that the cat that the dog chased killed ate the malt.
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Garden PathSentences
The rat that the cat that the dog chased killed ate the malt.
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Garden PathSentences
The rat that the cat that the dog chased killed ate the malt.
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Garden PathSentences
The rat that the cat that the dog chased killed ate the malt.
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Garden PathSentences
[The rat [the cat [the dog chased] killed] ate the malt].
Language can have recursive patterns
Syntactic parsing can help identify those
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Syntactic Parsing
I ate the meal with friends
NP VP
VP NP PP
S
Syntactic parsing: perform a “meaningful” structural analysis according to grammatical rules
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Syntactic Parsing Can Help Disambiguate
I ate the meal with friends
NP VP
VP NP PP
S
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Syntactic Parsing Can Help Disambiguate
I ate the meal with friends
NP VP
VP NP PP
S
NP VP
S
VP NP
PPNP
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Clearly Show Ambiguity…But Not Necessarily All Ambiguity
I ate the meal with friends
NP VP
VP NP PP
S
I ate the meal with gusto
I ate the meal with a fork
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Discourse Processing
John stopped at the donut store.
Courtesy Jason Eisner
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Discourse Processing
John stopped at the donut store.
Courtesy Jason Eisner
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Discourse Processing
John stopped at the donut store before work.
Courtesy Jason Eisner
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Discourse Processing
John stopped at the donut store on his way home.
Courtesy Jason Eisner
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Discourse Processing
John stopped at the donut shop.
John stopped at the trucker shop.
John stopped at the mom & pop shop.
John stopped at the red shop.
Courtesy Jason Eisner
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Discourse Processing through Coreference
I spread the cloth on the table to protect it.
I spread the cloth on the table to display it.
Courtesy Jason Eisner
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I spread the cloth on the table to protect it.
I spread the cloth on the table to display it.
Courtesy Jason Eisner
Discourse Processing through Coreference
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I spread the cloth on the table to protect it.
I spread the cloth on the table to display it.
Courtesy Jason Eisner
Discourse Processing through Coreference
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http://www.qwantz.com/index.php?comic=170
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Three people have been fatally shot, and five people, including a mayor, were seriously wounded as a result of a Shining Path attack today.
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Three people have been fatally shot, and five people, including a mayor, were seriously wounded as a result of a Shining Path attack today.
score( )
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Three people have been fatally shot, and five people, including a mayor, were seriously wounded as a result of a Shining Path attack today.
pθ( )
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pθ(X)probabilistic model
objective F(θ)
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Gradient Ascent
θ2
θ1
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Gradient Ascent
θ2
θ1
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Gradient Ascent
θ2
θ1
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Gradient Ascent
“gradient of F with respect to θ”
θ2
θ1
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Gradient Ascent
“gradient of F with respect to θ”
gradient: a vector of derivatives, each with respect to θk while
holding all other variables constant
θ2
θ1
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http://www.qwantz.com/index.php?comic=170
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http://universaldependencies.org/part-of-speech & syntax for > 120 languages
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From Syntax to Shallow Semantics
http://corenlp.run/ (constituency & dependency)
https://github.com/hltcoe/predpatt
http://openie.allenai.org/
http://www.cs.rochester.edu/research/knext/browse/ (constituency trees)
http://rtw.ml.cmu.edu/rtw/
Angeli et al. (2015)
“Open Information Extraction”
a sampling of efforts
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Semantic Projection
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Administrivia
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Grading
Component 473 673
FiveAssignments
45% 30%
Midterm 10% 10%
GraduatePaper
--- 30%
Course Project 45% 30%
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Final Grades≥ Letter
90 A
80 B
70 C
65 D
0 F
≥ Letter
90 A-
80 B-
70 C-
65 D
0 F
473
673
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https://www.csee.umbc.edu/courses/undergraduate/473/f18
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Online Discussions
https://piazza.com/umbc/fall2018/cmsc473673
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Important Dates
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Late Policy
Everyone has a budget of 10 late days
If you have them left: assignments turned in after the deadline will be graded and recorded, no questions asked
If you don’t have any left: still turn assignments in. They could count in your favor in borderline cases
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Late Policy
Everyone has a budget of 10 late days
Use them as needed throughout the course
They’re meant for personal reasons and emergencies
Do not procrastinate
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Late Policy
Everyone has a budget of 10 late days
Contact me privately if an extended absence will occur