Timo Honkela: Analysis of Qualitative Data using Machine Learning Methods

Timo Honkela, Arcada Analytics Workshop, 8.6.2016
Timo Honkela
Arcada Analytics Workshop
8 Jun 2016
Analytics of Qualitative Data
using Machine Learning
Methods
timo.honkela@helsinki.fi

Quantitative versus qualitative
● Quantitative data: can be measured, e.g.
distance, area, time, speed, volume, weight,
temperature, cost, etc.
● Qualitative data: described in linguistic terms
– Data can be observed but not measured
– Description typically includes a clear subjective
and/or contextual aspect
– Long texts can also be considered to be qualitative
data

Quantitative versus qualitative
● Quantitative data: can be measured, e.g.
distance, area, time, speed, volume, weight,
temperature, cost, etc.
● Qualitative data: described in linguistic terms
– Data can be observed but not measured
– Description typically includes a clear subjective
and/or contextual aspect
– Long texts can also be considered to be qualitative
data
Numbers
Words

Qualitative in quantitative terms
● Qualities and linguistic data can be represented
in quantitative form, too
● Example 1: colors
– a) numerical coding of prototypical colors
– b) statistics of color naming
● Example 2: words in contexts
– The form of a word does not, usually, tell about its
meaning
– The contexts in which words appear provide
information on their meaning

Early personal experiences on
rule-based natural language processing
● H. Jäppinen, T. Honkela, H. Hyötyniemi & A. Lehtola (1988):
A Multilevel Natural Language Processing Model.
Nordic Journal of Linguistics 11:69-87.
What is the turnover of the ten largest stock exchange companies in forestry?
Morphological analysis
Dependency parsing
Logical analysis
Database query formation
Result from the SQL database

Early personal experiences on
rule-based natural language processing
● H. Jäppinen, T. Honkela, H. Hyötyniemi & A. Lehtola (1988):
A Multilevel Natural Language Processing Model.
Nordic Journal of Linguistics 11:69-87.
What is the turnover of the ten largest stock exchange companies in forestry?
Morphological analysis
Dependency parsing
Logical analysis
Database query formation
Result from the SQL database
Traditional coding of
morphological, syntactic
and semantic
knowledge
Qualitative knowledge
comes “from the head”
of the knowledge
engineer / computational
linguist

Classical example: Learning meaning from context:
Maps of words in Grimm fairy tales
Honkela, Pulkki & Kohonen 1995

Classical example: Learning meaning from context:
Maps of words in Grimm fairy tales
Honkela, Pulkki & Kohonen 1995
Relations of words
are extracted from
the data using a machine
learning algorithm
(neural network:
self-organizing map)Word relations
emerge in an
unsupervised
manner

Transformining texts
into numerical vectors
WORD → VECTOR TEXT → MATRIX
Word weighting using, e.g., TF/IDF
Words → N-grams
Additional categorical information

A common division of machine
learning algorithms
… and its relation
to underlying assumptions
in text analytics

A common division of machine
learning algorithms
● Supervised learning:
Categorical ideas or theories are given
to the system
● Unsupervised learning:
Conceptual systems emergence
based on the data
● Reinforcement learning:
Models emergence based on the success
of the behavior (not very commonly used
in natural language modeling)

Complexities of
linguistic phenomena and data
● Ambiguity, polysemy
● Vagueness
● Contextuality, multimodality
● Change
● History dependence
● Subjectivity of interpretation and expression
(due to the uniqueness of each person's
experience)

Ambiguity (homography & polysemy)
and contextuality: case “ALUSTA”
● “ALUSTA”
"alku" N ELA SG
"alusta" N NOM SG
"alustaa" V PRES ACT NEG
"alustaa" V IMPV ACT SG2
"alustaa" V IMPV ACT NEG SG
"alunen" N PTV SG
"alus" N PTV SG
FINTWOL: Finnish Morphological Analyser
Copyright © Kimmo Koskenniemi & Lingsoft Oy 1995 – 2012
http://www2.lingsoft.fi/cgi-bin/fintwol
Alusta
Monta alusta
Näin monta alusta
Näin monta alusta
satamassa

and contextuality: case “ALUSTA”
● “ALUSTA”
"alku" N ELA SG
"alusta" N NOM SG
"alustaa" V PRES ACT NEG
"alustaa" V IMPV ACT SG2
"alustaa" V IMPV ACT NEG SG
"alunen" N PTV SG
"alus" N PTV SG
FINTWOL: Finnish Morphological Analyser
Copyright © Kimmo Koskenniemi & Lingsoft Oy 1995 – 2012
http://www2.lingsoft.fi/cgi-bin/fintwol
Alusta
Monta alusta
Näin monta alusta
Näin monta alusta
satamassa alas
taivaalta
http://favim.com/image/92863/

and contextuality: case “GET”
●
“ S: (v) get, acquire (come into the possession of something concrete or abstract) "She got a lot of paintings from her uncle"; "They acquired a new pet"; "Get your results the next day"; "Get permission to take a few days off from work"
●
S: (v) become, go, get (enter or assume a certain state or condition) "He became annoyed when he heard the bad news"; "It must be getting more serious"; "her face went red with anger"; "She went into ecstasy"; "Get going!"
●
S: (v) get, let, have (cause to move; cause to be in a certain position or condition) "He got his squad on the ball"; "This let me in for a big surprise"; "He got a girl into trouble"
●
S: (v) receive, get, find, obtain, incur (receive a specified treatment (abstract)) "These aspects of civilization do not find expression or receive an interpretation"; "His movie received a good review"; "I got nothing but trouble for my good intentions"
●
S: (v) arrive, get, come (reach a destination; arrive by movement or progress) "She arrived home at 7 o'clock"; "She didn't get to Chicago until after midnight"
●
S: (v) bring, get, convey, fetch (go or come after and bring or take back) "Get me those books over there, please"; "Could you bring the wine?"; "The dog fetched the hat"
●
S: (v) experience, receive, have, get (go through (mental or physical states or experiences)) "get an idea"; "experience vertigo"; "get nauseous"; "receive injuries"; "have a feeling"
●
S: (v) pay back, pay off, get, fix (take vengeance on or get even) "We'll get them!"; "That'll fix him good!"; "This time I got him"
●
S: (v) have, get, make (achieve a point or goal) "Nicklaus had a 70"; "The Brazilian team got 4 goals"; "She made 29 points that day"
●
S: (v) induce, stimulate, cause, have, get, make (cause to do; cause to act in a specified manner) "The ads induced me to buy a VCR"; "My children finally got me to buy a computer"; "My wife made me buy a new sofa"
●
S: (v) get, catch, capture (succeed in catching or seizing, especially after a chase) "We finally got the suspect"; "Did you catch the thief?"
●
S: (v) grow, develop, produce, get, acquire (come to have or undergo a change of (physical features and attributes)) "He grew a beard"; "The patient developed abdominal pains"; "I got funny spots all over my body"; "Well-developed breasts"
●
S: (v) contract, take, get (be stricken by an illness, fall victim to an illness) "He got AIDS"; "She came down with pneumonia"; "She took a chill"
●
S: (v) get (communicate with a place or person; establish communication with, as if by telephone) "Bill called this number and he got Mary"; "The operator couldn't get Kobe because of the earthquake"
●
S: (v) make, get (give certain properties to something) "get someone mad"; "She made us look silly"; "He made a fool of himself at the meeting"; "Don't make this into a big deal"; "This invention will make you a millionaire"; "Make yourself clear"
●
S: (v) drive, get, aim (move into a desired direction of discourse) "What are you driving at?"
●
S: (v) catch, get (grasp with the mind or develop an understanding of) "did you catch that allusion?"; "We caught something of his theory in the lecture"; "don't catch your meaning"; "did you get it?"; "She didn't get the joke"; "I just don't get him"
●
S: (v) catch, arrest, get (attract and fix) "His look caught her"; "She caught his eye"; "Catch the attention of the waiter"
●
S: (v) get, catch (reach with a blow or hit in a particular spot) "the rock caught her in the back of the head"; "The blow got him in the back"; "The punch caught him in the stomach"
●
S: (v) get (reach by calculation) "What do you get when you add up these numbers?"
●
S: (v) get (acquire as a result of some effort or action) "You cannot get water out of a stone"; "Where did she get these news?"
●
S: (v) get (purchase) "What did you get at the toy store?"
●
S: (v) catch, get (perceive by hearing) "I didn't catch your name"; "She didn't get his name when they met the first time"
●
S: (v) catch, get (suffer from the receipt of) "She will catch hell for this behavior!"
●
S: (v) get, receive (receive as a retribution or punishment) "He got 5 years in prison"
●
S: (v) scram, buzz off, fuck off, get, bugger off (leave immediately; used usually in the imperative form) "Scram!"
●
S: (v) get (reach and board) "She got the bus just as it was leaving"
●
S: (v) get, get under one's skin (irritate) "Her childish behavior really get to me"; "His lying really gets me"
●
S: (v) get (evoke an emotional response) "Brahms's `Requiem' gets me every time"
●
S: (v) catch, get (apprehend and reproduce accurately) "She really caught the spirit of the place in her drawings"; "She got the mood just right in her photographs"
●
S: (v) draw, get (earn or achieve a base by being walked by the pitcher) "He drew a base on balls"
●
S: (v) get (overcome or destroy) "The ice storm got my hibiscus"; "the cat got the goldfish"
●
S: (v) perplex, vex, stick, get, puzzle, mystify, baffle, beat, pose, bewilder, flummox, stupefy, nonplus, gravel, amaze, dumbfound (be a mystery or bewildering to) "This beats me!"; "Got me--I don't know the answer!"; "a vexing problem"; "
This question really stuck me"
●
S: (v) get down, begin, get, start out, start, set about, set out, commence (take the first step or steps in carrying out an action) "We began working at dawn"; "Who will start?"; "Get working as soon as the sun rises!"; "The first tourists began to arrive
in Cambodia"; "He began early in the day"; "Let's get down to work now"
●
S: (v) suffer, sustain, have, get (undergo (as of injuries and illnesses)) "She suffered a fracture in the accident"; "He had an insulin shock after eating three candy bars"; "She got a bruise on her leg"; "He got his arm broken in the scuffle"
●
S: (v) beget, get, engender, father, mother, sire, generate, bring forth (make (offspring) by reproduction) "Abraham begot Isaac"; "John fathered four daughters"
W
ordN
et3.1

Labeling movements: Associating
high-dim. kinesthetic time series
with linguistic labels
Förger & Honkela 2014
For us humans
meanings are
grounded in our
multimodal experiences
Consider how
children learn language;
not reading word
definitions from books

Labeling movements: Associating
high-dim. kinesthetic time series
with linguistic labels

RUNNING
WALKING
LIMPING
JOGGING

Definition of /
meaning of
Systemic risk
Peter Sarlin
Differences
between
experts in
different disciplines
and laypeople

GICA: Grounded Intersubjective
Concept Analysis
Sanat,
fraasit,
tulkinnat tms.
Kontekstit
Yksilöt
How to extend
text mining
like topic modeling
to include
subjective understanding?
Let's extend term-document
matrices into
Subject-Object-Context
tensors

GICA: Grounded Intersubjective
Concept Analysis
Sanat,
fraasit,
tulkinnat tms.
Kontekstit
Yksilöt

The word “health” in
State of the Union Addresses
Subjects on objects in contexts:
Using GICA method to quantify
epistemological subjectivity.
Timo Honkela, Juha Raitio, Krista Lagus,
Ilari T. Nieminen, Nina Honkela, and Mika Pantzar.
Proc. of IJCNN 2012.

Thank you for
your attention!

Timo Honkela: Analysis of Qualitative Data using Machine Learning Methods

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