A keynote talk given in the Arcada Analytics Workshop, Helsinki on 8th of June, 2016. The other keynote talks were given by Peter Sarlin (Machine Learning and Network Analytics for Measuring Systemic Risk) and Amaury Lendasse (ELMVIS+: Fast Nonlinear Visualization Technique based on Cosine Distance and Extreme Learning Machines).
Timo Honkela: Meaning negotiations as phenomenon and as languages technology...Timo Honkela
Abstract:
Models of linguistic semantics can be viewed through representation and reasoning. This distinction concerns questions on how do we represent the world that we refer to by linguistic expressions and what kind of reasoning do we apply based on these representations. It has been commonplace to assume that each word or expression has one or a limited number of different, distinct senses. The classification task of disambiguation has been devised to find the right reference in each case. It is also possible to represent the world using a high-dimensional continuous space. In that case, we do not need to assume that the world is represented as a network of nodes and their connections. These mathematical representations go beyond the capacities provided by symbolic logic. The word embeddings has history that stems from vector space representations in information retrieval. When a framework of multidimensional continuous spaces is available, it is possible to study nuances of meaning that go beyond conducting disambiguation or choosing between alternatives within a logical framework.
In the present work, it is postulated that semantic processes are essentially subjective and thus individual. When high-dimensional continuous spaces are used to represent meanings and defining contextual distributions, subjective aspects can be modelled. It is possible to measure subjectivity of meaning. This can be studied, for instance, in the framework of brain research (Saalasti et al. 2019) or motions tracking (Honkela & Förger 2013). The methodology or measuring subjective contextually grounded meaning has been been presented, for instance, in Raitio et al. 2014. Further methodological work and an empirical demonstration is presented in Sintonen et al. (2014). When it is possible to represent individually contextual meaning of expressions, it is consequently possible to analyse the differences of meaning between two individuals. A hypothesis is that suitable data for the purpose of meaning negotiation can be collected, computational algorithms devised and applied in real world contexts that helps in meaning negotiations. An alternative view is to aim at defining the meaning of words in a precise way and to teach all people to use these definition. In this present work, it is claimed that that objectivity can be reached only to a degree as it would require vast human cognitive and time resources and the mapping between words and the world is doomed to be partial. This concern has implications both in scientific and in real world communication and representation and has been applied in building the Peace Machine framework.
Meaning negotiations
as phenomena and
as LT challenges
Timo Honkela
University of Helsinki
with Iiro Jääskeläinen (Aalto University) on
the Study of Individualized Meanings
using Brain Research
University of Helsinki, Topelia, F211
4th of April, 2019
Timo Honkela: Meaning negotiations as phenomenon and as languages technology ...Timo Honkela
Abstract:
Models of linguistic semantics can be viewed through representation and reasoning. This distinction concerns questions on how do we represent the world that we refer to by linguistic expressions and what kind of reasoning do we apply based on these representations. It has been commonplace to assume that each word or expression has one or a limited number of different, distinct senses. The classification task of disambiguation has been devised to find the right reference in each case. It is also possible to represent the world using a high-dimensional continuous space. In that case, we do not need to assume that the world is represented as a network of nodes and their connections. These mathematical representations go beyond the capacities provided by symbolic logic. The word embeddings has history that stems from vector space representations in information retrieval. When a framework of multidimensional continuous spaces is available, it is possible to study nuances of meaning that go beyond conducting disambiguation or choosing between alternatives within a logical framework.
In the present work, it is postulated that semantic processes are essentially subjective and thus individual. When high-dimensional continuous spaces are used to represent meanings and defining contextual distributions, subjective aspects can be modelled. It is possible to measure subjectivity of meaning. This can be studied, for instance, in the framework of brain research (Saalasti et al. 2019) or motions tracking (Honkela & Förger 2013). The methodology or measuring subjective contextually grounded meaning has been been presented, for instance, in Raitio et al. 2014. Further methodological work and an empirical demonstration is presented in Sintonen et al. (2014). When it is possible to represent individually contextual meaning of expressions, it is consequently possible to analyse the differences of meaning between two individuals. A hypothesis is that suitable data for the purpose of meaning negotiation can be collected, computational algorithms devised and applied in real world contexts that helps in meaning negotiations. An alternative view is to aim at defining the meaning of words in a precise way and to teach all people to use these definition. In this present work, it is claimed that that objectivity can be reached only to a degree as it would require vast human cognitive and time resources and the mapping between words and the world is doomed to be partial. This concern has implications both in scientific and in real world communication and representation and has been applied in building the Peace Machine framework.
Meaning negotiations
as phenomena and
as LT challenges
Timo Honkela
University of Helsinki
with Iiro Jääskeläinen (Aalto University) on
the Study of Individualized Meanings
using Brain Research
University of Helsinki, Topelia, F211
4th of April, 2019
Timo Honkela: From early to later Wittgenstein and Artificial IntelligenceTimo Honkela
Professor Timo Honkela presented an argument that there is a analogy between the developments that took place in Ludwig Wittgenstein's philosophy and in the artificial intelligence when turned away from relying rule-based systems. Honkela also discusses more in general epistemological questions, the underlying questions regarding the objectives and motivations of formalisation. Formalization often relies on assumptions such as the basic or primary role of objects, relations and properties or truth values and propositions. Honkela proposed a pattern and distribution based epistemology as an alternative.
Timo Honkela: Peace Machine: Peace from a difference perspective - Dialogue o...Timo Honkela
A presentation given in the National Dialogues Conference in Helsinki, Finland. The theme was how to use artificial intelligence, machine learning and other similar technologies to promote peace in the world. The three ares considered were language and meaning, emotions and society. Computers can help humans, for instance, by improving mutual understanding through meaning negotiations.
Timo Honkela: Turning quantity into quality and making concepts visible using...Timo Honkela
Professor Timo Honkela gave an invited talk in the Göran Mickwitz seminar that took place in Helsinki, 9th of February 2017. The event was organized in the honor of Doc. Jessica Parland-von Essen.
Timo Honkela: Meaning negotiations as phenomenon and as languages technology...Timo Honkela
Abstract:
Models of linguistic semantics can be viewed through representation and reasoning. This distinction concerns questions on how do we represent the world that we refer to by linguistic expressions and what kind of reasoning do we apply based on these representations. It has been commonplace to assume that each word or expression has one or a limited number of different, distinct senses. The classification task of disambiguation has been devised to find the right reference in each case. It is also possible to represent the world using a high-dimensional continuous space. In that case, we do not need to assume that the world is represented as a network of nodes and their connections. These mathematical representations go beyond the capacities provided by symbolic logic. The word embeddings has history that stems from vector space representations in information retrieval. When a framework of multidimensional continuous spaces is available, it is possible to study nuances of meaning that go beyond conducting disambiguation or choosing between alternatives within a logical framework.
In the present work, it is postulated that semantic processes are essentially subjective and thus individual. When high-dimensional continuous spaces are used to represent meanings and defining contextual distributions, subjective aspects can be modelled. It is possible to measure subjectivity of meaning. This can be studied, for instance, in the framework of brain research (Saalasti et al. 2019) or motions tracking (Honkela & Förger 2013). The methodology or measuring subjective contextually grounded meaning has been been presented, for instance, in Raitio et al. 2014. Further methodological work and an empirical demonstration is presented in Sintonen et al. (2014). When it is possible to represent individually contextual meaning of expressions, it is consequently possible to analyse the differences of meaning between two individuals. A hypothesis is that suitable data for the purpose of meaning negotiation can be collected, computational algorithms devised and applied in real world contexts that helps in meaning negotiations. An alternative view is to aim at defining the meaning of words in a precise way and to teach all people to use these definition. In this present work, it is claimed that that objectivity can be reached only to a degree as it would require vast human cognitive and time resources and the mapping between words and the world is doomed to be partial. This concern has implications both in scientific and in real world communication and representation and has been applied in building the Peace Machine framework.
Meaning negotiations
as phenomena and
as LT challenges
Timo Honkela
University of Helsinki
with Iiro Jääskeläinen (Aalto University) on
the Study of Individualized Meanings
using Brain Research
University of Helsinki, Topelia, F211
4th of April, 2019
Timo Honkela: Meaning negotiations as phenomenon and as languages technology ...Timo Honkela
Abstract:
Models of linguistic semantics can be viewed through representation and reasoning. This distinction concerns questions on how do we represent the world that we refer to by linguistic expressions and what kind of reasoning do we apply based on these representations. It has been commonplace to assume that each word or expression has one or a limited number of different, distinct senses. The classification task of disambiguation has been devised to find the right reference in each case. It is also possible to represent the world using a high-dimensional continuous space. In that case, we do not need to assume that the world is represented as a network of nodes and their connections. These mathematical representations go beyond the capacities provided by symbolic logic. The word embeddings has history that stems from vector space representations in information retrieval. When a framework of multidimensional continuous spaces is available, it is possible to study nuances of meaning that go beyond conducting disambiguation or choosing between alternatives within a logical framework.
In the present work, it is postulated that semantic processes are essentially subjective and thus individual. When high-dimensional continuous spaces are used to represent meanings and defining contextual distributions, subjective aspects can be modelled. It is possible to measure subjectivity of meaning. This can be studied, for instance, in the framework of brain research (Saalasti et al. 2019) or motions tracking (Honkela & Förger 2013). The methodology or measuring subjective contextually grounded meaning has been been presented, for instance, in Raitio et al. 2014. Further methodological work and an empirical demonstration is presented in Sintonen et al. (2014). When it is possible to represent individually contextual meaning of expressions, it is consequently possible to analyse the differences of meaning between two individuals. A hypothesis is that suitable data for the purpose of meaning negotiation can be collected, computational algorithms devised and applied in real world contexts that helps in meaning negotiations. An alternative view is to aim at defining the meaning of words in a precise way and to teach all people to use these definition. In this present work, it is claimed that that objectivity can be reached only to a degree as it would require vast human cognitive and time resources and the mapping between words and the world is doomed to be partial. This concern has implications both in scientific and in real world communication and representation and has been applied in building the Peace Machine framework.
Meaning negotiations
as phenomena and
as LT challenges
Timo Honkela
University of Helsinki
with Iiro Jääskeläinen (Aalto University) on
the Study of Individualized Meanings
using Brain Research
University of Helsinki, Topelia, F211
4th of April, 2019
Timo Honkela: From early to later Wittgenstein and Artificial IntelligenceTimo Honkela
Professor Timo Honkela presented an argument that there is a analogy between the developments that took place in Ludwig Wittgenstein's philosophy and in the artificial intelligence when turned away from relying rule-based systems. Honkela also discusses more in general epistemological questions, the underlying questions regarding the objectives and motivations of formalisation. Formalization often relies on assumptions such as the basic or primary role of objects, relations and properties or truth values and propositions. Honkela proposed a pattern and distribution based epistemology as an alternative.
Timo Honkela: Peace Machine: Peace from a difference perspective - Dialogue o...Timo Honkela
A presentation given in the National Dialogues Conference in Helsinki, Finland. The theme was how to use artificial intelligence, machine learning and other similar technologies to promote peace in the world. The three ares considered were language and meaning, emotions and society. Computers can help humans, for instance, by improving mutual understanding through meaning negotiations.
Timo Honkela: Turning quantity into quality and making concepts visible using...Timo Honkela
Professor Timo Honkela gave an invited talk in the Göran Mickwitz seminar that took place in Helsinki, 9th of February 2017. The event was organized in the honor of Doc. Jessica Parland-von Essen.
Timo Honkela: Tietokone lukemassa yli 100 miljoonaa eri kirjaa: Kielitieteen ...Timo Honkela
Professori Timo Honkelan esitelmä luonnonfilosofian seurassa 24.1.2017 aiheesta "Tietokone lukemassa yli 100 miljoonaa eri kirjaa: Kielitieteen ja filosofian näkökulmia". Tilaisuus järjestettiin Tieteiden talossa huoneessa 505. Erityisen tarkastelun kohteina olivat erilaiset merkitysteoriat ja niiden suhde koneoppimisen tutkimukseen. Yksi keskeinen johtopäätös oli, että koneoppimis- ja neuroverkkotutkimus tarjoaa tietoteoreettisille tarkasteluille uutta pohjaa.
Title in English:
Computer reading over one hundred books: Linguistics and philosophical views
Timo Honkela: Introducing the book Encyclopedia of Artificial Intelligence (i...Timo Honkela
The book "Encyclopedia of Artificial Intelligence" (Tekoälyn ensyklopedia), edited by Eero Hyvönen, Ilkka Karanta and Markku Syrjänen (1993), was an important landmark in the Finnish AI research and development. Finland has been and remains an important country in this field. Many of the authors were already prominent figures at that time, especially Professor and later Academician Teuvo Kohonen. Since then many authors have become processors in different universities. One of the authors, Tuomas Sandholm received in 2003 the IJCAI Computers and Thought Award and serves as a professor at Carnegie Mellon University.
Timo Honkela: Tekoälyn ja koneoppimisen uhat ja mahdollisuudet, Turku, 27.10....Timo Honkela
Professor Timo Honkela's Studia Generalia presentation for the Society of Futures Studies at Turku University, Finland, on Thursday 27th of October, 2016. The title of the talk is "Threats and opportunities related to artificial intelligence and machine learning". The topics include 1) an introduction to AI and ML, 2) information on why AI and ML are societally relevant just in this moment of history, 3) natural language processing based on ML, 4) presentation of meta-analysis in humanities (cf. Helsinki Studia Generalia presentation a week earlier), 5) AI and intuition, and 6) discussion on the positive and negative scenarios related to AI and ML.
Timo Honkela: Kohonen's Self-Organizing Maps for Intelligent Systems Developm...Timo Honkela
An invited talk given in the FODO'98, Foundations of Data Organization conference. The conference took place in Kobe, Japan, November 12-13, 1998. Main themes of the talk included Self-Organizing Maps (SOMs), Fuzzy Sets, context analysis, and systems of SOMs.
Timo Honkela: Ihminen+ -esitelmä, Mikkeli, 22.9.2016Timo Honkela
Timo Honkela discusses "Wise Machines" that help people to acquire knowledge and to understand and solve problems related to economy/business, emotions and health.
Esitelmä Eduskunnan tulevaisuusvaliokunnalle 4.5.2016 "Tieteen uuden valtatiet: Tiede 2.0 ja digitaaliset ihmistieteet".
A talk to the Committee for the Future of Finnish Parliament with the topic "New highways of science: Science 2.0 and Digital Humanities".
Timo Honkela: Modeling evolution and dynamical systemsTimo Honkela
A short tutorial for Modeling Meaning and Knowledge, 18 April 2016, at University of Helsinki. Followed by presentations by Mika Pantzar and Sakari Virkki.
Timo Honkela: Kuhn’s Structure of Scientific Revolutions and Gärdenfors’ Conc...Timo Honkela
A tutorial talk by Timo Honkela in Modeling Meaning and Knowledge series of mini-symposia at University of Helsinki. The talk took place on Monday, 4st of April, 2016.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Timo Honkela: Tietokone lukemassa yli 100 miljoonaa eri kirjaa: Kielitieteen ...Timo Honkela
Professori Timo Honkelan esitelmä luonnonfilosofian seurassa 24.1.2017 aiheesta "Tietokone lukemassa yli 100 miljoonaa eri kirjaa: Kielitieteen ja filosofian näkökulmia". Tilaisuus järjestettiin Tieteiden talossa huoneessa 505. Erityisen tarkastelun kohteina olivat erilaiset merkitysteoriat ja niiden suhde koneoppimisen tutkimukseen. Yksi keskeinen johtopäätös oli, että koneoppimis- ja neuroverkkotutkimus tarjoaa tietoteoreettisille tarkasteluille uutta pohjaa.
Title in English:
Computer reading over one hundred books: Linguistics and philosophical views
Timo Honkela: Introducing the book Encyclopedia of Artificial Intelligence (i...Timo Honkela
The book "Encyclopedia of Artificial Intelligence" (Tekoälyn ensyklopedia), edited by Eero Hyvönen, Ilkka Karanta and Markku Syrjänen (1993), was an important landmark in the Finnish AI research and development. Finland has been and remains an important country in this field. Many of the authors were already prominent figures at that time, especially Professor and later Academician Teuvo Kohonen. Since then many authors have become processors in different universities. One of the authors, Tuomas Sandholm received in 2003 the IJCAI Computers and Thought Award and serves as a professor at Carnegie Mellon University.
Timo Honkela: Tekoälyn ja koneoppimisen uhat ja mahdollisuudet, Turku, 27.10....Timo Honkela
Professor Timo Honkela's Studia Generalia presentation for the Society of Futures Studies at Turku University, Finland, on Thursday 27th of October, 2016. The title of the talk is "Threats and opportunities related to artificial intelligence and machine learning". The topics include 1) an introduction to AI and ML, 2) information on why AI and ML are societally relevant just in this moment of history, 3) natural language processing based on ML, 4) presentation of meta-analysis in humanities (cf. Helsinki Studia Generalia presentation a week earlier), 5) AI and intuition, and 6) discussion on the positive and negative scenarios related to AI and ML.
Timo Honkela: Kohonen's Self-Organizing Maps for Intelligent Systems Developm...Timo Honkela
An invited talk given in the FODO'98, Foundations of Data Organization conference. The conference took place in Kobe, Japan, November 12-13, 1998. Main themes of the talk included Self-Organizing Maps (SOMs), Fuzzy Sets, context analysis, and systems of SOMs.
Timo Honkela: Ihminen+ -esitelmä, Mikkeli, 22.9.2016Timo Honkela
Timo Honkela discusses "Wise Machines" that help people to acquire knowledge and to understand and solve problems related to economy/business, emotions and health.
Esitelmä Eduskunnan tulevaisuusvaliokunnalle 4.5.2016 "Tieteen uuden valtatiet: Tiede 2.0 ja digitaaliset ihmistieteet".
A talk to the Committee for the Future of Finnish Parliament with the topic "New highways of science: Science 2.0 and Digital Humanities".
Timo Honkela: Modeling evolution and dynamical systemsTimo Honkela
A short tutorial for Modeling Meaning and Knowledge, 18 April 2016, at University of Helsinki. Followed by presentations by Mika Pantzar and Sakari Virkki.
Timo Honkela: Kuhn’s Structure of Scientific Revolutions and Gärdenfors’ Conc...Timo Honkela
A tutorial talk by Timo Honkela in Modeling Meaning and Knowledge series of mini-symposia at University of Helsinki. The talk took place on Monday, 4st of April, 2016.
A Strategic Approach: GenAI in EducationPeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Read| The latest issue of The Challenger is here! We are thrilled to announce that our school paper has qualified for the NATIONAL SCHOOLS PRESS CONFERENCE (NSPC) 2024. Thank you for your unwavering support and trust. Dive into the stories that made us stand out!
Synthetic Fiber Construction in lab .pptxPavel ( NSTU)
Synthetic fiber production is a fascinating and complex field that blends chemistry, engineering, and environmental science. By understanding these aspects, students can gain a comprehensive view of synthetic fiber production, its impact on society and the environment, and the potential for future innovations. Synthetic fibers play a crucial role in modern society, impacting various aspects of daily life, industry, and the environment. ynthetic fibers are integral to modern life, offering a range of benefits from cost-effectiveness and versatility to innovative applications and performance characteristics. While they pose environmental challenges, ongoing research and development aim to create more sustainable and eco-friendly alternatives. Understanding the importance of synthetic fibers helps in appreciating their role in the economy, industry, and daily life, while also emphasizing the need for sustainable practices and innovation.
Acetabularia Information For Class 9 .docxvaibhavrinwa19
Acetabularia acetabulum is a single-celled green alga that in its vegetative state is morphologically differentiated into a basal rhizoid and an axially elongated stalk, which bears whorls of branching hairs. The single diploid nucleus resides in the rhizoid.
Francesca Gottschalk - How can education support child empowerment.pptxEduSkills OECD
Francesca Gottschalk from the OECD’s Centre for Educational Research and Innovation presents at the Ask an Expert Webinar: How can education support child empowerment?
How to Make a Field invisible in Odoo 17Celine George
It is possible to hide or invisible some fields in odoo. Commonly using “invisible” attribute in the field definition to invisible the fields. This slide will show how to make a field invisible in odoo 17.
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
Azure Interview Questions and Answers PDF By ScholarHat
Timo Honkela: Analysis of Qualitative Data using Machine Learning Methods
1. 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
2. Timo Honkela, Arcada Analytics Workshop, 8.6.2016
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
3. Timo Honkela, Arcada Analytics Workshop, 8.6.2016
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
4. Timo Honkela, Arcada Analytics Workshop, 8.6.2016
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
5. Timo Honkela, Arcada Analytics Workshop, 8.6.2016
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
6. Timo Honkela, Arcada Analytics Workshop, 8.6.2016
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
7. Timo Honkela, Arcada Analytics Workshop, 8.6.2016
Classical example: Learning meaning from context:
Maps of words in Grimm fairy tales
Honkela, Pulkki & Kohonen 1995
8. Timo Honkela, Arcada Analytics Workshop, 8.6.2016
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
9. Timo Honkela, Arcada Analytics Workshop, 8.6.2016
Transformining texts
into numerical vectors
WORD → VECTOR TEXT → MATRIX
Word weighting using, e.g., TF/IDF
Words → N-grams
Additional categorical information
10. Timo Honkela, Arcada Analytics Workshop, 8.6.2016
A common division of machine
learning algorithms
… and its relation
to underlying assumptions
in text analytics
11. Timo Honkela, Arcada Analytics Workshop, 8.6.2016
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)
12. Timo Honkela, Arcada Analytics Workshop, 8.6.2016
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)
15. Timo Honkela, Arcada Analytics Workshop, 8.6.2016
Ambiguity (homography & polysemy)
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
16. Timo Honkela, Arcada Analytics Workshop, 8.6.2016
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
17. Timo Honkela, Arcada Analytics Workshop, 8.6.2016
Labeling movements: Associating
high-dim. kinesthetic time series
with linguistic labels
Förger & Honkela 2014
19. Timo Honkela, Arcada Analytics Workshop, 8.6.2016
Definition of /
meaning of
Systemic risk
Peter Sarlin
Differences
between
experts in
different disciplines
and laypeople
20. Timo Honkela, Arcada Analytics Workshop, 8.6.2016
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
21. Timo Honkela, Arcada Analytics Workshop, 8.6.2016
GICA: Grounded Intersubjective
Concept Analysis
Sanat,
fraasit,
tulkinnat tms.
Kontekstit
Yksilöt
22. Timo Honkela, Arcada Analytics Workshop, 8.6.2016
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.