In linguistics, regularization is a phenomenon in language acquisition and language development, whereby irregular forms in morphology, syntax, etc., are replaced by regular ones. Examples are "gooses" instead of "geese" in child speech and replacement of the Middle English plural form for "cow", "kine", with "cows".
Erroneous regularization is also called overregularization. In overregularization the regular ways of modifying or connecting words are mistakenly applied to words that require irregular modifications or connections. It is a normal effect observed in the language of beginner and intermediate language-learners, whether native-speaker children or foreign-speaker adults. Because most natural languages have some irregular forms, moving beyond overregularization is a part of mastering them. Usually learners' brains move beyond overregularization naturally, as a consequence of being immersed in the language.
The same person may sometimes overregularize and sometimes say the correct form. Native-speaker adults can overregularize, but this does not happen often.
A course in Cognitive Linguistics: Usage-based linguistics
This is episode number seven in a course in Cognitive Linguistics. This episode presents the usage-based model of language in a series of ten claims that discuss the respective roles of domain-general cognitive processes, diachrony, frequency, analogy, categorization, gradience, and universals.
published: 08 May 2015
Linguistic Regularization of Topic Models - Anna Potapenko
Yandex School of Data Analysis Conference
Machine Learning: Prospects and Applications
https://yandexdataschool.com/conference
Topic modeling is a powerful tool for revealing an underlying thematic structure in large text collections. A lot of work has been done in enriching probabilistic generative models of texts in order to find more precise and linguistically motivated topic models. However, avoiding the bag-ofwords assumption usually leads to complicated and memory-inefficient models that can’t be freely combined with other topic models extensions.
To address this issue we are developing a non-Bayesian approach of additive regularization for topic models (ARTM). It is based on inducing an additional penalty term for each requirement and then solving a multi-criteria optimization t...
published: 26 Oct 2015
First Language Acquisition
Commented lecture slides (Susanne Flach)
Introduction to English Linguistics
English Department, Universität Zürich
published: 27 Apr 2020
William Matchin
The Language Organ: Architecture and Development
Abstract: The concepts of “the language organ” and “the language acquisition device” advanced by Chomsky and others in the tradition of Generative Grammar are controversial in part because their neurobiological instantiation is unclear. Here I address this by reviewing recent evidence from brain imaging and lesion-symptom mapping in aphasia. I propose a model for how the language organ develops in the brain in the context of the recent thesis of the architecture of the human language faculty and its evolution by Berwick & Chomsky, 2016. In this proposal, an innate syntactic computational system combines with domain general sequencing systems, which become gradually specialized for speech externalization during language development.
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Abra...
published: 28 Jul 2020
[Text Classification - NLP] 13 Dropout and L2 Regularization
summarize material about "The Linguistics Of Second Language Acquisition"
published: 06 Oct 2021
Jennifer Culbertson
Languages are Shaped by How We Learn Them
Human languages are extraordinarily diverse, and yet there are also systematic commonalities among them. Some of these commonalities can be considered design features of language. These are definitional properties, like regularity and compositionality (the ability to re-use and recombine linguistic units to create new meanings), that all languages share. Other commonalities are perhaps more surprising. For example, almost all languages place adjectives closer to the nouns they modify than numerals (e.g., English `two purple chair’ or Thai, เก้าอี้ ม่วง สอง, literally `chairs purple two’). And when languages form questions by moving a question word, they almost always move it to the left (e.g., English `What did you eat?’ or the Greek equivalent τι...
This is episode number seven in a course in Cognitive Linguistics. This episode presents the usage-based model of language in a series of ten claims that discus...
This is episode number seven in a course in Cognitive Linguistics. This episode presents the usage-based model of language in a series of ten claims that discuss the respective roles of domain-general cognitive processes, diachrony, frequency, analogy, categorization, gradience, and universals.
This is episode number seven in a course in Cognitive Linguistics. This episode presents the usage-based model of language in a series of ten claims that discuss the respective roles of domain-general cognitive processes, diachrony, frequency, analogy, categorization, gradience, and universals.
Yandex School of Data Analysis Conference
Machine Learning: Prospects and Applications
https://yandexdataschool.com/conference
Topic modeling is a powerful to...
Yandex School of Data Analysis Conference
Machine Learning: Prospects and Applications
https://yandexdataschool.com/conference
Topic modeling is a powerful tool for revealing an underlying thematic structure in large text collections. A lot of work has been done in enriching probabilistic generative models of texts in order to find more precise and linguistically motivated topic models. However, avoiding the bag-ofwords assumption usually leads to complicated and memory-inefficient models that can’t be freely combined with other topic models extensions.
To address this issue we are developing a non-Bayesian approach of additive regularization for topic models (ARTM). It is based on inducing an additional penalty term for each requirement and then solving a multi-criteria optimization task. We propose a set of regularization criteria to separate topics into two subsets: domain-specific topics containing lexical kernels of particular domain area and background topics containing common lexis words.
Until this moment, the ARTM approach has also been restricted by the bag-of-words assumption. Our work is focused on overcoming this
limitation and utilizing the sequential order of tokens to further improve additively regularized topics models. The key idea is to model gradual topic transition in natural language discourse. To this end we consider topic probabilities of sequential tokens of a text as a time series and perform smoothing based on the terminology occurred in a sliding window. Preferring similar topic distributions for adjacent words corresponds to the notion of topic coherence – a common intepretability measure in topic modeling that is shown to correlate well with human judgment.
We contribute our linguistically motivated regularizers into BigARTM. org – an open source project for online parallel topic modeling of large text collections. Results are obtained using real collections of scientificpapers and social networks data.
Yandex School of Data Analysis Conference
Machine Learning: Prospects and Applications
https://yandexdataschool.com/conference
Topic modeling is a powerful tool for revealing an underlying thematic structure in large text collections. A lot of work has been done in enriching probabilistic generative models of texts in order to find more precise and linguistically motivated topic models. However, avoiding the bag-ofwords assumption usually leads to complicated and memory-inefficient models that can’t be freely combined with other topic models extensions.
To address this issue we are developing a non-Bayesian approach of additive regularization for topic models (ARTM). It is based on inducing an additional penalty term for each requirement and then solving a multi-criteria optimization task. We propose a set of regularization criteria to separate topics into two subsets: domain-specific topics containing lexical kernels of particular domain area and background topics containing common lexis words.
Until this moment, the ARTM approach has also been restricted by the bag-of-words assumption. Our work is focused on overcoming this
limitation and utilizing the sequential order of tokens to further improve additively regularized topics models. The key idea is to model gradual topic transition in natural language discourse. To this end we consider topic probabilities of sequential tokens of a text as a time series and perform smoothing based on the terminology occurred in a sliding window. Preferring similar topic distributions for adjacent words corresponds to the notion of topic coherence – a common intepretability measure in topic modeling that is shown to correlate well with human judgment.
We contribute our linguistically motivated regularizers into BigARTM. org – an open source project for online parallel topic modeling of large text collections. Results are obtained using real collections of scientificpapers and social networks data.
The Language Organ: Architecture and Development
Abstract: The concepts of “the language organ” and “the language acquisition device” advanced by Chomsky and o...
The Language Organ: Architecture and Development
Abstract: The concepts of “the language organ” and “the language acquisition device” advanced by Chomsky and others in the tradition of Generative Grammar are controversial in part because their neurobiological instantiation is unclear. Here I address this by reviewing recent evidence from brain imaging and lesion-symptom mapping in aphasia. I propose a model for how the language organ develops in the brain in the context of the recent thesis of the architecture of the human language faculty and its evolution by Berwick & Chomsky, 2016. In this proposal, an innate syntactic computational system combines with domain general sequencing systems, which become gradually specialized for speech externalization during language development.
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Abralin ao Vivo - Linguists Online is an initiative of Abralin - Associação Brasileira de Linguística in cooperation with several linguistics associations.
For more information about Abralin ao Vivo - Linguists Online, visit: aovivo.abralin.org.
Please consider donating to Abralin’s funding for the preservation of indigenous languages: http://abral.in/donate.
__
Abralin ao Vivo - Linguists Online é um projeto da Abralin - Associação Brasileira de Linguística, com a colaboração de várias associações de linguística.
Para mais informações sobre o projeto, acesse a página aovivo.abralin.org.
Por favor, considere a possibilidade de doar para o fundo da Abralin para a preservação de línguas indígenas: http://abral.in/doe.
The Language Organ: Architecture and Development
Abstract: The concepts of “the language organ” and “the language acquisition device” advanced by Chomsky and others in the tradition of Generative Grammar are controversial in part because their neurobiological instantiation is unclear. Here I address this by reviewing recent evidence from brain imaging and lesion-symptom mapping in aphasia. I propose a model for how the language organ develops in the brain in the context of the recent thesis of the architecture of the human language faculty and its evolution by Berwick & Chomsky, 2016. In this proposal, an innate syntactic computational system combines with domain general sequencing systems, which become gradually specialized for speech externalization during language development.
__
Abralin ao Vivo - Linguists Online is an initiative of Abralin - Associação Brasileira de Linguística in cooperation with several linguistics associations.
For more information about Abralin ao Vivo - Linguists Online, visit: aovivo.abralin.org.
Please consider donating to Abralin’s funding for the preservation of indigenous languages: http://abral.in/donate.
__
Abralin ao Vivo - Linguists Online é um projeto da Abralin - Associação Brasileira de Linguística, com a colaboração de várias associações de linguística.
Para mais informações sobre o projeto, acesse a página aovivo.abralin.org.
Por favor, considere a possibilidade de doar para o fundo da Abralin para a preservação de línguas indígenas: http://abral.in/doe.
Languages are Shaped by How We Learn Them
Human languages are extraordinarily diverse, and yet there are also systematic commonalities among them. Some of thes...
Languages are Shaped by How We Learn Them
Human languages are extraordinarily diverse, and yet there are also systematic commonalities among them. Some of these commonalities can be considered design features of language. These are definitional properties, like regularity and compositionality (the ability to re-use and recombine linguistic units to create new meanings), that all languages share. Other commonalities are perhaps more surprising. For example, almost all languages place adjectives closer to the nouns they modify than numerals (e.g., English `two purple chair’ or Thai, เก้าอี้ ม่วง สอง, literally `chairs purple two’). And when languages form questions by moving a question word, they almost always move it to the left (e.g., English `What did you eat?’ or the Greek equivalent τι έφαγες?). Why do languages share these common features? Where do they come from? In this talk I argue that many of these commonalities–from general design features to highly specific syntactic patterns–are connected to how we learn and create new languages. To support this claim, I highlight recent evidence from laboratory experiments involving miniature artificial spoken and sign languages. These experiments explore how people learn new languages, how they build new meanings in them, and how they create new language systems wholesale. What they show is evidence for the real time emergence of language universals.
Dr. Jennifer Culbertson is a Reader in the School of Philosophy, Psychology, and Language Science at the University of Edinburgh and Director of the Centre for Language Evolution. Her research uses novel experimental and computational methods to better understand language acquisition, and the role that human cognition plays in explaining core features of natural language syntax and morphology.
__
Abralin ao Vivo - Linguists Online is an initiative of Abralin - Associação Brasileira de Linguística in cooperation with several linguistics associations.
For more information about Abralin ao Vivo - Linguists Online, visit: aovivo.abralin.org.
Please consider donating to Abralin’s funding for the preservation of indigenous languages: http://abral.in/donate.
__
Abralin ao Vivo - Linguists Online é um projeto da Abralin - Associação Brasileira de Linguística, com a colaboração de várias associações de linguística.
Para mais informações sobre o projeto, acesse a página aovivo.abralin.org.
Por favor, considere a possibilidade de doar para o fundo da Abralin para a preservação de línguas indígenas: http://abral.in/doe.
Languages are Shaped by How We Learn Them
Human languages are extraordinarily diverse, and yet there are also systematic commonalities among them. Some of these commonalities can be considered design features of language. These are definitional properties, like regularity and compositionality (the ability to re-use and recombine linguistic units to create new meanings), that all languages share. Other commonalities are perhaps more surprising. For example, almost all languages place adjectives closer to the nouns they modify than numerals (e.g., English `two purple chair’ or Thai, เก้าอี้ ม่วง สอง, literally `chairs purple two’). And when languages form questions by moving a question word, they almost always move it to the left (e.g., English `What did you eat?’ or the Greek equivalent τι έφαγες?). Why do languages share these common features? Where do they come from? In this talk I argue that many of these commonalities–from general design features to highly specific syntactic patterns–are connected to how we learn and create new languages. To support this claim, I highlight recent evidence from laboratory experiments involving miniature artificial spoken and sign languages. These experiments explore how people learn new languages, how they build new meanings in them, and how they create new language systems wholesale. What they show is evidence for the real time emergence of language universals.
Dr. Jennifer Culbertson is a Reader in the School of Philosophy, Psychology, and Language Science at the University of Edinburgh and Director of the Centre for Language Evolution. Her research uses novel experimental and computational methods to better understand language acquisition, and the role that human cognition plays in explaining core features of natural language syntax and morphology.
__
Abralin ao Vivo - Linguists Online is an initiative of Abralin - Associação Brasileira de Linguística in cooperation with several linguistics associations.
For more information about Abralin ao Vivo - Linguists Online, visit: aovivo.abralin.org.
Please consider donating to Abralin’s funding for the preservation of indigenous languages: http://abral.in/donate.
__
Abralin ao Vivo - Linguists Online é um projeto da Abralin - Associação Brasileira de Linguística, com a colaboração de várias associações de linguística.
Para mais informações sobre o projeto, acesse a página aovivo.abralin.org.
Por favor, considere a possibilidade de doar para o fundo da Abralin para a preservação de línguas indígenas: http://abral.in/doe.
This is episode number seven in a course in Cognitive Linguistics. This episode presents the usage-based model of language in a series of ten claims that discuss the respective roles of domain-general cognitive processes, diachrony, frequency, analogy, categorization, gradience, and universals.
Yandex School of Data Analysis Conference
Machine Learning: Prospects and Applications
https://yandexdataschool.com/conference
Topic modeling is a powerful tool for revealing an underlying thematic structure in large text collections. A lot of work has been done in enriching probabilistic generative models of texts in order to find more precise and linguistically motivated topic models. However, avoiding the bag-ofwords assumption usually leads to complicated and memory-inefficient models that can’t be freely combined with other topic models extensions.
To address this issue we are developing a non-Bayesian approach of additive regularization for topic models (ARTM). It is based on inducing an additional penalty term for each requirement and then solving a multi-criteria optimization task. We propose a set of regularization criteria to separate topics into two subsets: domain-specific topics containing lexical kernels of particular domain area and background topics containing common lexis words.
Until this moment, the ARTM approach has also been restricted by the bag-of-words assumption. Our work is focused on overcoming this
limitation and utilizing the sequential order of tokens to further improve additively regularized topics models. The key idea is to model gradual topic transition in natural language discourse. To this end we consider topic probabilities of sequential tokens of a text as a time series and perform smoothing based on the terminology occurred in a sliding window. Preferring similar topic distributions for adjacent words corresponds to the notion of topic coherence – a common intepretability measure in topic modeling that is shown to correlate well with human judgment.
We contribute our linguistically motivated regularizers into BigARTM. org – an open source project for online parallel topic modeling of large text collections. Results are obtained using real collections of scientificpapers and social networks data.
The Language Organ: Architecture and Development
Abstract: The concepts of “the language organ” and “the language acquisition device” advanced by Chomsky and others in the tradition of Generative Grammar are controversial in part because their neurobiological instantiation is unclear. Here I address this by reviewing recent evidence from brain imaging and lesion-symptom mapping in aphasia. I propose a model for how the language organ develops in the brain in the context of the recent thesis of the architecture of the human language faculty and its evolution by Berwick & Chomsky, 2016. In this proposal, an innate syntactic computational system combines with domain general sequencing systems, which become gradually specialized for speech externalization during language development.
__
Abralin ao Vivo - Linguists Online is an initiative of Abralin - Associação Brasileira de Linguística in cooperation with several linguistics associations.
For more information about Abralin ao Vivo - Linguists Online, visit: aovivo.abralin.org.
Please consider donating to Abralin’s funding for the preservation of indigenous languages: http://abral.in/donate.
__
Abralin ao Vivo - Linguists Online é um projeto da Abralin - Associação Brasileira de Linguística, com a colaboração de várias associações de linguística.
Para mais informações sobre o projeto, acesse a página aovivo.abralin.org.
Por favor, considere a possibilidade de doar para o fundo da Abralin para a preservação de línguas indígenas: http://abral.in/doe.
Languages are Shaped by How We Learn Them
Human languages are extraordinarily diverse, and yet there are also systematic commonalities among them. Some of these commonalities can be considered design features of language. These are definitional properties, like regularity and compositionality (the ability to re-use and recombine linguistic units to create new meanings), that all languages share. Other commonalities are perhaps more surprising. For example, almost all languages place adjectives closer to the nouns they modify than numerals (e.g., English `two purple chair’ or Thai, เก้าอี้ ม่วง สอง, literally `chairs purple two’). And when languages form questions by moving a question word, they almost always move it to the left (e.g., English `What did you eat?’ or the Greek equivalent τι έφαγες?). Why do languages share these common features? Where do they come from? In this talk I argue that many of these commonalities–from general design features to highly specific syntactic patterns–are connected to how we learn and create new languages. To support this claim, I highlight recent evidence from laboratory experiments involving miniature artificial spoken and sign languages. These experiments explore how people learn new languages, how they build new meanings in them, and how they create new language systems wholesale. What they show is evidence for the real time emergence of language universals.
Dr. Jennifer Culbertson is a Reader in the School of Philosophy, Psychology, and Language Science at the University of Edinburgh and Director of the Centre for Language Evolution. Her research uses novel experimental and computational methods to better understand language acquisition, and the role that human cognition plays in explaining core features of natural language syntax and morphology.
__
Abralin ao Vivo - Linguists Online is an initiative of Abralin - Associação Brasileira de Linguística in cooperation with several linguistics associations.
For more information about Abralin ao Vivo - Linguists Online, visit: aovivo.abralin.org.
Please consider donating to Abralin’s funding for the preservation of indigenous languages: http://abral.in/donate.
__
Abralin ao Vivo - Linguists Online é um projeto da Abralin - Associação Brasileira de Linguística, com a colaboração de várias associações de linguística.
Para mais informações sobre o projeto, acesse a página aovivo.abralin.org.
Por favor, considere a possibilidade de doar para o fundo da Abralin para a preservação de línguas indígenas: http://abral.in/doe.
In linguistics, regularization is a phenomenon in language acquisition and language development, whereby irregular forms in morphology, syntax, etc., are replaced by regular ones. Examples are "gooses" instead of "geese" in child speech and replacement of the Middle English plural form for "cow", "kine", with "cows".
Erroneous regularization is also called overregularization. In overregularization the regular ways of modifying or connecting words are mistakenly applied to words that require irregular modifications or connections. It is a normal effect observed in the language of beginner and intermediate language-learners, whether native-speaker children or foreign-speaker adults. Because most natural languages have some irregular forms, moving beyond overregularization is a part of mastering them. Usually learners' brains move beyond overregularization naturally, as a consequence of being immersed in the language.
The same person may sometimes overregularize and sometimes say the correct form. Native-speaker adults can overregularize, but this does not happen often.
A lot of these linguistic quirks originated in written text from various online fandoms ... into its current state, I returned to the internet linguist Gretchen McCulloch’s 2019 book, Because Internet.
... and linguistics ... The visitors were told that the academy organized regular literary sessions, conferences, cultural festivals, and poetry recitals to promote linguistic and cultural heritage.
</p><p>The visitors were told that the academy organized regular literary sessions, conferences, cultural festivals, and poetry recitals to promote linguistic and cultural heritage.
While these schools conduct regular assessments, they avoid detention. Institutions serving religious and linguistic minorities, protected under Articles 29 and 30 of the Constitution, remain ...
By WilliamR. Jones... Its impressiveness and poignancy embraces texts from 1990 to the present, with updates continuing regularly with over 1 billion words ... All languages do not have corpora to guide linguists, teachers, translators and researchers ... .
Require primary contractors for PIF-funded projects to conduct regular and ongoing due diligence on subcontractors’ companies to have a better understanding of labor conditions ... regular work or pay.
Knowledge engineering is not a new idea — library scientists and linguists regularly practice it ... using the intelligent textbook were 10% higher than those of students using the regular textbook.
In Prince George’s County, one of our teachers regularly uses this approach to culturally affirm students, honor their multilingual wealth of knowledge and offer students the opportunity to ...
For decades, AscensionColumbia St ... "This is just one of many adjustments we are regularly making to continually provide quality health care to our patients that is free from linguistic, cultural, and economic barriers," she wrote ... Mary's ... "Columbia St.
His explanations for the linguistic origins of many words, including names like Aragorn and Galadriel, shifted regularly ... It belongs to everyone who reads The Lord of the Rings and is dazzled by its linguistic possibilities.
By studying bilinguals who regularly navigate between these two languages, we aimed to understand how living between two linguistic worlds might dynamically shape our sensory experiences.”.
The issue of mother tongue education in the Philippines remains a focal point for discussion among linguists and educators in the country. With this in mind, I continue sharing not only my own opinions but also those of other linguists and educators.
‘Jai Hind’ transcends regional, linguistic and cultural differences and promotes unity among students from diverse backgrounds, it said. Regular use of greetings like ‘Jai Hind’ ...
GAZA, (PIC). The great national leader Ismail Haneyya has passed away, but his legacy will continue to inspire future generations ... Known for his eloquence and linguistic strength, Haneyya regularly delivered Friday sermons wherever he could ... ....
President Volodymyr Zelensky said he was receiving regular reports on efforts to capture the gunman ... Farion, a linguist, became a member of the nationalist Svoboda (Freedom) party in 2005 and was ...
