See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/351972310
Natural Language Processing to Identify Trends and Gaps in the Published
Science of Behavior Analysis
Presentation · May 2021
DOI: 10.13140/RG.2.2.15624.34560
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2 authors:
Jacob Sosine
David J. Cox
Behavioral Health Center of Excellence
Behavioral Health Center of Excellence
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5/29/21
Natural Language Processing
to Identify Trends and Gaps
in the Published Science of Behavior Analysis
Jacob Sosine, M.A., M.B.A., BCBA
David J. Cox, Ph.D., M.S.B., BCBA-D
What is Natural Language Processing
Data Pipeline
How NLP can be used moving forward
What is Natural Language Processing
Intersection Between Artificial Intelligence and Linguistics
Historically
Apply Rules and Structure to Data
More Recently
2 0 1 5 N LP M oved A w ay From S tatistical M od els
Structural Linguistic Theories of VB
N LP S tar ted L ookin g at the M ean in g or Fun ction
Used Statistical Methods to Understand Language
Techn iques are m ore related to the A n alysis of
Ver bal B ehavior
(Nadkarni et al., 2011)
(Annamoradnejad et al., 2021)
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What is Natural Language Processing
Intersection Between Artificial Intelligence and Linguistics
Allows Behavior Analysts to Study Verbal Behavior at Scale
Every Day
2.5 quintillion bytes of data are uploaded
682 Million Tweets are Posted
4.3 Billion Facebook Messages are Sent
A Lot of textual
behavior!
h3ps://blog.microfocus.com/how-much-data-is-created-on-the-internet-each-day
What is Natural Language Processing
Intersection Between Artificial Intelligence and Linguistics
Allows Behavior Analysts to Study Verbal Behavior at Scale
NLP has been utilized to ….
Identify indicators of Child Abuse from Pediatric
Medical Records
Detect Humor in Sentences
Classify Hate Speech on platforms like Twitter
Summarize Text
Answer Questions
(Goa et al., 2019)
What is Natural Language Processing
NLP and Research Literature
Explore and Visualize Bibliographic Data
Predict Emerging Research Trends
Inform Choice in Optimizing Research Designs
(Rzhetsky et al., 2015)
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What is Natural Language Processing
Data Pipeline
How NLP Can be Leveraged
Data Pipeline
Data Gathering
Data Extraction
Data Preprocessing
Exploratory Data
Analysis
Topic Modeling
Data Pipeline
Data Gathering
Automated
Article Retrieval
Simulated web browsing
Gathered open access articles
3
5/29/21
Data Pipeline
Data Gathering
Automated
Article Retrieval
4269 PDF’s Gathered
1958 - 2020
Data Pipeline
Data Gathering
Automated
Article Retrieval
1152 PDF’s Gathered
1978 - 2020
Data Pipeline
Data Gathering
Automated
Article Retrieval
3389 PDF’s Gathered
1968 - 2020
4
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Data Pipeline
Data Gathering
Automated
Article Retrieval
405 PDF’s Gathered
1982 - 2020
Data Pipeline
Data Gathering
Automated
Article Retrieval
548 PDF’s Gathered
2008 - 2020
Data Pipeline
Data Gathering
Automated
Article Retrieval
9763
Journal Articles
5
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Data Pipeline
Data Gathering
Automated
Article Retrieval
644 PDF’s Gathered
2016 - 2021
Health Service Research
Data Pipeline
Data Gathering
Data Extraction
10,407
Data Pipeline
Data Gathering
Data Extraction
10,407
6
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Data Pipeline
Data Gathering
Data Extraction
Data Preprocessing
Removes Noise from Our Dataset
Allows a computer to interpret our inputs
Data Pipeline
Data Gathering
Data Extraction
“ The matching law predicts not whether you will eat,
drink, sleep, play, or work at this very moment but the
fraction of hours per day you will spend eating, drinking,
sleeping, playing, or working; not whether the pitcher
Data Preprocessing
1. Part of Speech Tagging
will throw a fastball, curve, slider, or changeup at this
moment but the propor>on of each type of pitch he
will throw to a given batter.”
(Herrnstein R. J. 1977)
Data Pipeline
Data Gathering
Data Extraction
“ The matching law predicts not whether you will eat,
drink, sleep, play, or work at this very moment but the
frac=on of hours per day you will spend ea=ng, drinking,
sleeping, playing, or working; not whether the pitcher
Data Preprocessing
1. Part of Speech Tagging
will throw a fastball, curve, slider, or changeup at this
moment but the proportion of each type of pitch he
will throw to a given ba?er.”
(Herrnstein R. J. 1977)
Proper Nouns
7
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Data Pipeline
Data Gathering
Data Extraction
“ The matching law predicts not whether you will eat,
drink, sleep, play, or work at this very moment but the
frac=on of hours per day you will spend ea=ng, drinking,
sleeping, playing, or working; not whether the pitcher
Data Preprocessing
1. Part of Speech Tagging
will throw a fastball, curve, slider, or changeup at this
moment but the proportion of each type of pitch he
will throw to a given ba?er.”
(Herrnstein R. J. 1977)
Data Pipeline
Data Gathering
Data Extraction
“The matching law predicts not whether you will eat,
drink, sleep, play, or work at this very moment but the
frac=on of hours per day you will spend ea=ng, drinking,
sleeping, playing, or working; not whether the pitcher
Data Preprocessing
1. Part of Speech Tagging
2. Lowercase
will throw a fastball, curve, slider, or changeup at this
moment but the propor=on of each type of pitch he
will throw to a given ba?er.”
(Herrnstein R. J. 1977)
Data Pipeline
Data Gathering
Data Extraction
“ The matching law predicts not whether you will eat,
^
drink, sleep, play, or work at this very moment but the
fraction of hours per day you will spend eating, drinking,
sleeping, playing, or working; not whether the pitcher
Data Preprocessing
1. Part of Speech Tagging
2. Lowercase
will throw a fastball, curve, slider, or changeup at this
moment but the propor>on of each type of pitch he
will throw to a given batter.”
(Herrnstein R. J. 1977)
8
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Data Pipeline
Data Gathering
Data Extraction
“the matching law predicts not whether you will eat,
drink, sleep, play, or work at this very moment but the
frac<on of hours per day you will spend ea<ng, drinking,
sleeping, playing, or working; not whether the pitcher
Data Preprocessing
1. Part of Speech Tagging
2. Lowercase
will throw a fastball, curve, slider, or changeup at this
moment but the proportion of each type of pitch he
will throw to a given ba>er.”
(Herrnstein R. J. 1977)
Data Pipeline
Data Gathering
Data Extraction
“the matching law predicts not whether you will eat,
drink, sleep, play, or work at this very moment but the
fraction of hours per day you will spend eating, drinking,
sleeping, playing, or working; not whether the pitcher
Data Preprocessing
1. Part of Speech Tagging
2. Lowercase
3. Remove Punctuation & Symbols
will throw a fastball, curve, slider, or changeup at this
moment but the propor=on of each type of pitch he
will throw to a given batter.”
(Herrnstein R. J. 1977)
Data Pipeline
Data Gathering
Data Extraction
Data Preprocessing
1. Part of Speech Tagging
2. Lowercase
3. Remove Punctuation & Symbols
“the matching law predicts not whether you will eat,
^
drink, sleep, play, or work at this very moment but the
^
^
^
frac<on of hours per day you will spend ea<ng, drinking,
^
sleeping, playing, or working; not whether the pitcher
^
^
will throw a fastball, curve, slider, or changeup at this
^
^
^
moment but the propor<on of each type of pitch he
will throw to a given ba>er.”
^
(Herrnstein R. J. 1977)
^
^ ^
^
9
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Data Pipeline
Data Gathering
Data Extraction
“the matching law predicts not whether you will eat,
drink, sleep, play, or work at this very moment but the
frac<on of hours per day you will spend ea<ng, drinking,
sleeping, playing, or working; not whether the pitcher
Data Preprocessing
1. Part of Speech Tagging
2. Lowercase
3. Remove Punctuation & Symbols
will throw a fastball, curve, slider, or changeup at this
moment but the propor<on of each type of pitch he
will throw to a given ba>er.”
(Herrnstein R. J. 1977)
Data Pipeline
Data Gathering
Data Extraction
“the matching law predicts not whether you will eat,
drink, sleep, play, or work at this very moment but the
frac<on of hours per day you will spend ea<ng, drinking,
sleeping, playing, or working; not whether the pitcher
Data Preprocessing
1. Part of Speech Tagging
2. Lowercase
3. Remove Punctuation & Symbols
will throw a fastball, curve, slider, or changeup at this
moment but the propor<on of each type of pitch he
will throw to a given ba>er.”
4. Removing Stop Words & Numbers
(Herrnstein R. J. 1977)
(Fernández, et al., 2014)
Data Pipeline
Data Gathering
Data Extraction
Data Preprocessing
1. Part of Speech Tagging
2. Lowercase
3. Remove Punctuation & Symbols
4. Removing Stop Words & Numbers
“the matching law predicts not whether you will eat,
^
^
^
drink, sleep, play, or work at this very moment but the
^
^ ^ ^
^
^
frac<on of hours per day you will spend ea<ng, drinking,
^
^ ^
sleeping, playing, or working; not whether the pitcher
^
^
^
will throw a fastball, curve, slider, or changeup at this
^
^ ^
^
^
moment but the propor<on of each type of pitch he
^ ^
^ ^
will throw to a given ba>er.”
^
^
^
^ ^
(Herrnstein R. J. 1977)
^
(Fernández, et al., 2014)
10
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Data Pipeline
Data Gathering
Data Extraction
“the matching law predicts not whether you will eat,
drink, sleep, play, or work at this very moment but the
fraction of hours per day you will spend eating, drinking,
sleeping, playing, or working; not whether the pitcher
Data Preprocessing
1. Part of Speech Tagging
2. Lowercase
3. Remove Punctuation & Symbols
will throw a fastball, curve, slider, or changeup at this
moment but the propor=on of each type of pitch he
will throw to a given batter.”
4. Removing Stop Words & Numbers
(Herrnstein R. J. 1977)
(Fernández, et al., 2014)
Data Pipeline
Data Gathering
Data Extraction
“the matching law predicts not whether you will eat,
drink, sleep, play, or work at this very moment but the
frac<on of hours per day you will spend ea<ng, drinking,
sleeping, playing, or working; not whether the pitcher
Data Preprocessing
1. Part of Speech Tagging
2. Lowercase
3. Remove Punctuation & Symbols
will throw a fastball, curve, slider, or changeup at this
moment but the proportion of each type of pitch he
will throw to a given ba>er.”
4. Removing Stop Words & Numbers
(Herrnstein R. J. 1977)
5. Word Tokenization
Data Pipeline
Data Gathering
“the matching law predicts not whether you will eat,
1
2
3
4
5 6
drink, sleep, play, or work at this very moment but the
Data Extraction
7
8
9
10
11
frac<on of hours per day you will spend ea<ng, drinking,
12
13 14 15
16
17
18
sleeping, playing, or working; not whether the pitcher
Data Preprocessing
19
20
22
23
24
will throw a fastball, curve, slider, or changeup at this
1. Part of Speech Tagging
2. Lowercase
3. Remove Punctuation & Symbols
4. Removing Stop Words & Numbers
5. Word Tokenization
25
26
27
28
29
moment but the propor<on of each type of pitch he
30
31
will throw to a given ba>er.”
34
35
32
33
36
(Herrnstein R. J. 1977)
11
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Data Pipeline
Data Gathering
Data Extraction
“the matching law predicts not whether you will eat,
drink, sleep, play, or work at this very moment but the
fraction of hours per day you will spend eating, drinking,
sleeping, playing, or working; not whether the pitcher
Data Preprocessing
1. Part of Speech Tagging
2. Lowercase
3. Remove Punctuation & Symbols
will throw a fastball, curve, slider, or changeup at this
moment but the propor=on of each type of pitch he
will throw to a given ba>er.”
4. Removing Stop Words & Numbers
(Herrnstein R. J. 1977)
5. Word Tokenization
6. Lemmatization
Data Pipeline
Data Gathering
Data Extraction
Data Preprocessing
1. Part of Speech Tagging
2. Lowercase
3. Remove Punctuation & Symbols
“the matching law predicts not whether you will eat,
drink, sleep, play, or work at this very moment but the
frac<on of hours per day you will spend ea<ng, drinking,
^
sleeping, playing, or working; not whether the pitcher
will throw a fastball, curve, slider, or changeup at this
moment but the propor<on of each type of pitch he
will throw to a given ba>er.”
4. Removing Stop Words & Numbers
(Herrnstein R. J. 1977)
5. Word Tokenization
6. Lemmatization
Data Pipeline
Data Gathering
Data Extraction
“the matching law predicts not whether you will eat,
drink, sleep, play, or work at this very moment but the
frac<on of hours per day you will spend ea<ng, drinking,
sleeping, playing, or working; not whether the pitcher
Data Preprocessing
1. Part of Speech Tagging
2. Lowercase
3. Remove Punctuation & Symbols
4. Removing Stop Words & Numbers
5. Word Tokenization
will throw a fastball, curve, slider, or changeup at this
moment but the proportion of each type of pitch he
will throw to a given ba>er.”
(Herrnstein R. J. 1977)
6. Lemmatization
12
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Data Pipeline
Data Gathering
Data Extraction
Data Preprocessing
Exploratory Data
Analysis
Data Pipeline
Data Gathering
Data Extraction
Data Preprocessing
Exploratory Data
Analysis
Data Pipeline
MEDIAN
Data Gathering
Data Extraction
Data Preprocessing
Exploratory Data
Analysis
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Data Pipeline
50% of Data on article count
Data Gathering
Data Extraction
Data Preprocessing
Exploratory Data
Analysis
Data Pipeline
Data Gathering
Data Extraction
Individual Article
Word Counts
Data Preprocessing
Exploratory Data
Analysis
Data Pipeline
Data Gathering
Data Extraction
Data Preprocessing
Compare
Distributions
Exploratory Data
Analysis
14
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Data Pipeline
Data Pipeline
15
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Data Pipeline
Data Pipeline
Data Gathering
Data Extraction
Scattertext Visualization
Uses 2 data Sources
higher up the w ord is on the y-axis, the m ore
it w as used by data source 1.
Data Preprocessing
Topic Modeling
Y
Exploratory Data
Analysis
X
Data Pipeline
Data Gathering
Data Extraction
Scattertext Visualization
Uses 2 data Sources
The further right on the X axis The more
it is used by data source 2
Data Preprocessing
Topic Modeling
Y
Exploratory Data
Analysis
X
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Data Pipeline
Data Gathering
Data Extraction
Scattertext Visualization
Uses 2 data Sources
Frequently used terms by both sources
appear in the top right of the graph
Data Preprocessing
Topic Modeling
Y
Exploratory Data
Analysis
X
Data Pipeline
Data Gathering
Data Extraction
Scattertext Visualization
Uses 2 data Sources
Infrequently used terms by both sources
appear in the bottom left of the graph
Data Preprocessing
Topic Modeling
Y
Exploratory Data
Analysis
X
Data Pipeline
Data Gathering
Data Extraction
Scattertext Visualization
Uses 2 data Sources
Each Data Point Represents a Word and
Includes a label
Data Preprocessing
Topic Modeling
Y
Exploratory Data
Analysis
X
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Top 500 Words
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Most Frequent JABA
Most Frequent JEAB
19
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20
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Data Pipeline
Data Gathering
Data Extraction
Data Preprocessing
Exploratory Data
Analysis
Topic Modeling
Data Pipeline
Data Gathering
Data Extraction
Data Preprocessing
Exploratory Data
Analysis
Topic Modeling
21
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Data Pipeline
Data Gathering
Data Extraction
Data Preprocessing
Exploratory Data
Analysis
Unsupervised Machine Learning
Hidden Semantic Structure
Matching Law
Choice
Rate of Reinforcement
Concurrent Schedules of Reinforcement
Topic Modeling
Data Pipeline
Data Gathering
Data Extraction
Data Preprocessing
Exploratory Data
Analysis
Topic Modeling
Topic Model Visualizations Include
Topic Bubble
Each Bubble Represents a Topic
The Further Away the Bubble the More
Different the topic is
Horizontal Stacked Bar Chart
Blue Bars = Overall Frequency of Each
Word in our Dataset
Red Bars = Number of times a given term
is generated in a topic
= rank terms according to term
relevance
λ slider
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What is Natural Language Processing
What is Natural Language Processing
Data Pipeline
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What is Natural Language Processing
Data Pipeline
Data Collection and Transformation
Visually Inspect Textual Data
Recognize Similarities and Differences between
our verbal communities
Quantify Topics in
Behavior Analytic Research
What is Natural Language Processing
Data Pipeline
Only Able to Review Published Journal Articles
Largely a Result of Complex Behavior Chains
U n able to brin g in A n teced en ts an d Con sequen ces in to
our an alysis
What is Natural Language Processing
Data Pipeline
… I have also read books, not for what they said about
verbal behavior, but as records of verbal behavior. I have
done my share of comma-counting. I have listened to
people speaking and jotted down slips, curious phrases, or
interesting intraverbal sequences…
(Skinner, 1957, p. 454 )
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What is Natural Language Processing
Data Pipeline
How NLP can be used moving forward
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Data Pipeline
How NLP can be used moving forward
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What is Natural Language Processing
Data Pipeline
How NLP can be used moving forward
Mands
Tacts
Autoclitics
!
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What is Natural Language Processing
Data Pipeline
How NLP can be used moving forward
Even in its simplest applications NLP can increase our
understanding of behavior
Leveraging this technology can allow researchers to ask and
answer different research questions
NLP is an accessible tool for Behavior Analysts
What is Natural Language Processing
Data Pipeline
How NLP can be used moving forward
Even in its simplest applications NLP can increase our
understanding of behavior
Leveraging this technology can allow researchers to ask and
answer different research questions
NLP is an accessible tool for Behavior Analysts
References
•
H e r r n s te in , R . J . ( 1 9 9 7 ) . T h e m a tc h in g la w : P a p e r s in p s y c h o lo g y a n d e c o n o m ic s . ( H . R a c h lin & D . I. L a ib s o n , E d s .) . R u s s e ll S a g e F o u n d a tio n ; H a r v a r d U n iv e r s ity P r e s s .
•
B le i, D . M . ( 2 0 1 2 ) . P r o b a b ilis tic to p ic m o d e ls . C o m m u n ic a tio n s o f th e A C M , 5 5 ( 4 ) , 7 7 - 8 4 .
•
S a i f , H a s s a n ; F e r n á n d e z , M i r i a m ; H e , Y u l a n a n d A l a n i , H a r i t h ( 2 0 1 4 ) . O n s t o p w o r d s , f i l t e r i n g a n d d a t a s p a r s i t y f o r s e n t i m e n t a n a l y s i s o f T w i t t e r. I n : L R E C 2 0 1 4 , N i n t h I n t e r n a t i o n a l C o n f e r e n c e o n L a n g u a g e R e s o u r c e s a n d E v a l u a t i o n . P r o c e e d i n g s . , p p . 8 1 0 – 8 1 7 .
•
S k i n n e r, B . F . ( 1 9 5 7 ) . V e r b a l b e h a v i o r . N e w Y o r k : A p p l e t o n - C e n t u r y - C r o f t s .
•
A n n a m o r a d n e ja d , I., Z o g h i, G . ( 2 0 2 1 ) . C o lB e r t: U s in g B E R T s e n te n c e e m b e d d in g fo r h u m o r d e te c tio n . C o m p u ta tio n a n d L a n g u a g e . 1 - 6 .
•
D a v i d s o n , T . , W a r m s l e y , D . , M a c y , M . , & W e b e r, I . , ( 2 0 1 7 ) . A u t o m a t e d h a t e s p e e c h d e t e c t i o n a n d t h e p r o b l e m o f o ff e n s i v e l a n g u a g e . I C W S M . 1 - 3
•
N a d k a r n i, P. M ., O h n o - M a c h a d o , L ., & C h a p m a n , W . W . ( 2 0 1 1 ) . N a tu r a l la n g u a g e p r o c e s s in g : a n in tr o d u c tio n . J o u r n a l o f th e A m e r ic a n M e d ic a l In fo r m a tic s A s s o c ia tio n , 1 8 ( 5 ) , 5 4 4 – 5 5 1 . h ttp s ://d o i.o r g /1 0 .1 1 3 6 /a m ia jn l- 2 0 1 1 - 0 0 0 4 6 4
•
A n n a p r a g a d a A V , D o n a r u m a - K w o h M M , A n n a p r a g a d a A V , S ta r o s o ls k i Z A ( 2 0 2 1 ) A n a tu r a l la n g u a g e p r o c e s s in g a n d d e e p le a r n in g a p p r o a c h to id e n tify c h ild a b u s e fr o m p e d ia tr ic e le c tr o n ic m e d ic a l r e c o r d s . P L o S O N E 1 6 ( 2 ) : e 0 2 4 7 4 0 4 . h ttp s ://d o i.o r g /1 0 .1 3 7 1 /jo u r n a l.p o n e .0 2 4 7 4 0 4
•
M u k ta fin , E ., K u s r in i, P. ( 2 0 2 0 ) . S e n tim e n ts a n a ly s is o f c u s to m e r s a tis fa c tio n in p u b lic s e r v ic e s u s in g K - n e a r e s t n e ig h b o r s a lg o r ith m a n d n a tu r a l la n g u a g e p r o c e s s in g a p p r o a c h . T E L K O M N IK A T e le c o m m u n ic a tio n , C o m p u tin g , E le c tr o n ic s a n d C o n tr o l. 1 9 ( 1 ) ,1 4 6 - 1 5 4 .
•
P e n g , J . , Z h a o , M . , H a v r i l l a , J . , L i u , C . , W e n g , C . , G u t h r i e , W . , S c h u l t z , R . , W a n g , K . , & Z h o u , Y . ( 2 0 2 0 ) . N a t u r a l l a n g u a g e p r o c e s s i n g ( N L P ) t o o l s i n e x t r a c t i n g b i o m e d i c a l c o n c e p t s f r o m r e s e a r c h a r t i c l e s : a c a s e s t u d y o n a u t i s m s p e c t r u m d i s o r d e r. B M C M e d i c a l I n f o r m a t i c s a n d D e c i s i o n M a k i n g .
2 0 (11 ), 3 -9 .
•
T o m a n , M . , T e s a r, R . , J e z e k , K . , ( 2 0 1 4 ) . I n f l u e n c e o f w o r d n o r m a l i z a t i o n o n t e x t c l a s s i f i c a t i o n .
•
K n ig h t, K ., M a r c u , D ., ( 2 0 0 2 ) . S u m m a r iz a tio n b e y o n d s e n te n c e e x tr a c tio n : a p r o b a b ilis tic a p p r o a c h to s e n te n c e c o m p r e s s io n . A r tific ia l In te llig e n c e . 1 3 9 .
•
B le i, D ., C a r in , L ., D u n s o n , D . ( 2 0 1 0 ) . P r o b a b ilis tic to p ic m o d e ls : A fo c u s o n g r a p h ic a l m o d e l d e s ig n a n d a p p lic a tio n s to d o c u m e n t a n d im a g e a n a ly s is . IE E E S ig n a l P r o c e s s M a g . 2 7 ( 6 ) : 5 5 - 6 5 . d o i: 1 0 .1 1 0 9 /M S P.2 0 1 0 .9 3 8 0 7 9
•
A s m u s s e n , C . B . , M o l l e r, C . ( 2 0 1 9 ) . S m a r t l i t e r a t u r e r e v i e w : a p r a c t i c a l t o p i c m o d e l i n g a p p r o a c h t o e x p l o r a t o r y l i t e r a t u r e r e v i e w . J o u r n a l o f B i g D a t a 9 3 ( 6 ) : 2 - 1 8 . d o i : h t t p s : / / d o i . o r g / 1 0 . 1 1 8 6 / s 4 0 5 3 7 - 0 1 9 - 0 2 5 5 - 7
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J o h n s o n , M ., ( 2 0 0 9 ) H o w th e s ta tis tic a l r e v o lu tio n c h a n g e s ( c o m p u ta tio n a l) lin g u is tic s . C o g n ifiv e a n d L in g u is tic S c ie n c e s a n d C o m p u te r S c ie n c e . 3 - 1 1 .
•
F r a n c o p o u lo G ., M a r ia n i, J ., P a r o u b e k , P. ( 2 0 1 6 ) . A s tu d y o f r e u s e a n d p la g ia r is m in L R E C p a p e r s . E u r o p e a n L a n g u a g e R e s o u r c e s A s s o c ia tio n ( E L R A ) . 1 8 9 0 - 1 8 9 7
•
F r a n c o p o u lo G ., M a r ia n i, J ., P a r o u b e k , P. ( 2 0 1 6 ) . P r e d ic tiv e m o d e lin g : g u e s s in g th e N L P te r m s o f to m o r r o w . E u r o p e a n L a n g u a g e R e s o u r c e s A s s o c ia tio n ( E L R A ) . 3 3 6 – 3 4 3
•
K r e n n . M . , & Z e i l i n g e r, A . ( 2 0 2 0 ) . P r e d i c t i n g R e s e a r c h T r e n d s w i t h S e m a n t i c a n d N e u r a l N e t w o r k s w i t h a n a p p l i c a t i o n i n Q u a n t u m P h y s i c s . P r o c e e d i n g s o f t h e N a t i o n a l A c a d e m y o f S c i e n c e s . 1 1 7 ( 4 ) . 1 - 1 5 .
•
Rzhetsky, A., Foster, J.G., Foster, I ., Evans, J. A. (2015). Choosing experiments to accelerate collecEve discovery . Proceedings of the Na1onal Academy of Sciences . 10.1073/pnas.1509757112
•
Cabanac, G., Frommholz, I., M ayr, P. (2018). Bibliometric-enhanced informaEon retrieval. Scientometrics 116:1225–1227
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