#wordembeddings — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #wordembeddings, aggregated by home.social.
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🧠📊 How can we measure imageability in literary texts?
The authors approach how words evoke sensory experience and test whether multimodal #WordEmbeddings can better capture #imageability, #visuality, and #concreteness than text-only models, from words to sentences to poems.
#CCLS2025 #JCLS #CLS -
🧠📊 How can we measure imageability in literary texts?
The authors approach how words evoke sensory experience and test whether multimodal #WordEmbeddings can better capture #imageability, #visuality, and #concreteness than text-only models, from words to sentences to poems.
#CCLS2025 #JCLS #CLS -
🧠📊 How can we measure imageability in literary texts?
The authors approach how words evoke sensory experience and test whether multimodal #WordEmbeddings can better capture #imageability, #visuality, and #concreteness than text-only models, from words to sentences to poems.
#CCLS2025 #JCLS #CLS -
🧠📊 How can we measure imageability in literary texts?
The authors approach how words evoke sensory experience and test whether multimodal #WordEmbeddings can better capture #imageability, #visuality, and #concreteness than text-only models, from words to sentences to poems.
#CCLS2025 #JCLS #CLS -
🧠📊 How can we measure imageability in literary texts?
The authors approach how words evoke sensory experience and test whether multimodal #WordEmbeddings can better capture #imageability, #visuality, and #concreteness than text-only models, from words to sentences to poems.
#CCLS2025 #JCLS #CLS -
It's already the last talk of #CCLS2025 😱
Yuri Bizzoni, Pascale Feldkamp, Kristoffer L. Nielbo: Encoding Imagism? Measuring Literary Imageability, Visuality and Concreteness via Multimodal Word Embeddings (https://doi.org/10.26083/tuprints-00030154)
#Measuring #LiteraryImageability #WordEmbeddings -
It's already the last talk of #CCLS2025 😱
Yuri Bizzoni, Pascale Feldkamp, Kristoffer L. Nielbo: Encoding Imagism? Measuring Literary Imageability, Visuality and Concreteness via Multimodal Word Embeddings (https://doi.org/10.26083/tuprints-00030154)
#Measuring #LiteraryImageability #WordEmbeddings -
It's already the last talk of #CCLS2025 😱
Yuri Bizzoni, Pascale Feldkamp, Kristoffer L. Nielbo: Encoding Imagism? Measuring Literary Imageability, Visuality and Concreteness via Multimodal Word Embeddings (https://doi.org/10.26083/tuprints-00030154)
#Measuring #LiteraryImageability #WordEmbeddings -
It's already the last talk of #CCLS2025 😱
Yuri Bizzoni, Pascale Feldkamp, Kristoffer L. Nielbo: Encoding Imagism? Measuring Literary Imageability, Visuality and Concreteness via Multimodal Word Embeddings (https://doi.org/10.26083/tuprints-00030154)
#Measuring #LiteraryImageability #WordEmbeddings -
It's already the last talk of #CCLS2025 😱
Yuri Bizzoni, Pascale Feldkamp, Kristoffer L. Nielbo: Encoding Imagism? Measuring Literary Imageability, Visuality and Concreteness via Multimodal Word Embeddings (https://doi.org/10.26083/tuprints-00030154)
#Measuring #LiteraryImageability #WordEmbeddings -
Published at #IRRJ: "Graph Embeddings to Empower Entity Retrieval" by Emma J. Gerritse, Faegheh Hasibi, and Arjen P. de Vries. #EntityRetrieval, #KnowledgeGraphEmbeddings, #WordEmbeddings
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Published at #IRRJ: "Graph Embeddings to Empower Entity Retrieval" by Emma J. Gerritse, Faegheh Hasibi, and Arjen P. de Vries. #EntityRetrieval, #KnowledgeGraphEmbeddings, #WordEmbeddings
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Published at #IRRJ: "Graph Embeddings to Empower Entity Retrieval" by Emma J. Gerritse, Faegheh Hasibi, and Arjen P. de Vries. #EntityRetrieval, #KnowledgeGraphEmbeddings, #WordEmbeddings
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Published at #IRRJ: "Graph Embeddings to Empower Entity Retrieval" by Emma J. Gerritse, Faegheh Hasibi, and Arjen P. de Vries. #EntityRetrieval, #KnowledgeGraphEmbeddings, #WordEmbeddings
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Published at #IRRJ: "Graph Embeddings to Empower Entity Retrieval" by Emma J. Gerritse, Faegheh Hasibi, and Arjen P. de Vries. #EntityRetrieval, #KnowledgeGraphEmbeddings, #WordEmbeddings
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Next stop in our NLP timeline is 2013, the introduction of low dimensional dense word vectors - so-called "word embeddings" - based on distributed semantics, as e.g. word2vec by Mikolov et al. from Google, which enabled representation learning on text.
T. Mikolov et al. (2013). Efficient Estimation of Word Representations in Vector Space.
https://arxiv.org/abs/1301.3781#NLP #AI #wordembeddings #word2vec #ise2025 #historyofscience @fiz_karlsruhe @fizise @tabea @sourisnumerique @enorouzi
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Next stop in our NLP timeline is 2013, the introduction of low dimensional dense word vectors - so-called "word embeddings" - based on distributed semantics, as e.g. word2vec by Mikolov et al. from Google, which enabled representation learning on text.
T. Mikolov et al. (2013). Efficient Estimation of Word Representations in Vector Space.
https://arxiv.org/abs/1301.3781#NLP #AI #wordembeddings #word2vec #ise2025 #historyofscience @fiz_karlsruhe @fizise @tabea @sourisnumerique @enorouzi
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Next stop in our NLP timeline is 2013, the introduction of low dimensional dense word vectors - so-called "word embeddings" - based on distributed semantics, as e.g. word2vec by Mikolov et al. from Google, which enabled representation learning on text.
T. Mikolov et al. (2013). Efficient Estimation of Word Representations in Vector Space.
https://arxiv.org/abs/1301.3781#NLP #AI #wordembeddings #word2vec #ise2025 #historyofscience @fiz_karlsruhe @fizise @tabea @sourisnumerique @enorouzi
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Next stop in our NLP timeline is 2013, the introduction of low dimensional dense word vectors - so-called "word embeddings" - based on distributed semantics, as e.g. word2vec by Mikolov et al. from Google, which enabled representation learning on text.
T. Mikolov et al. (2013). Efficient Estimation of Word Representations in Vector Space.
https://arxiv.org/abs/1301.3781#NLP #AI #wordembeddings #word2vec #ise2025 #historyofscience @fiz_karlsruhe @fizise @tabea @sourisnumerique @enorouzi
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Next stop in our NLP timeline is 2013, the introduction of low dimensional dense word vectors - so-called "word embeddings" - based on distributed semantics, as e.g. word2vec by Mikolov et al. from Google, which enabled representation learning on text.
T. Mikolov et al. (2013). Efficient Estimation of Word Representations in Vector Space.
https://arxiv.org/abs/1301.3781#NLP #AI #wordembeddings #word2vec #ise2025 #historyofscience @fiz_karlsruhe @fizise @tabea @sourisnumerique @enorouzi
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For the possibly vanishingly small number of people it might interest: Made a presentation on User Research and semantic vectors/word embeddings from AI, speculatively exploring possible applications: https://youtu.be/tPiv4LpZCvU?si=bSUDkOywd9GnXL2e
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For the possibly vanishingly small number of people it might interest: Made a presentation on User Research and semantic vectors/word embeddings from AI, speculatively exploring possible applications: https://youtu.be/tPiv4LpZCvU?si=bSUDkOywd9GnXL2e
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For the possibly vanishingly small number of people it might interest: Made a presentation on User Research and semantic vectors/word embeddings from AI, speculatively exploring possible applications: https://youtu.be/tPiv4LpZCvU?si=bSUDkOywd9GnXL2e
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For the possibly vanishingly small number of people it might interest: Made a presentation on User Research and semantic vectors/word embeddings from AI, speculatively exploring possible applications: https://youtu.be/tPiv4LpZCvU?si=bSUDkOywd9GnXL2e
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For the possibly vanishingly small number of people it might interest: Made a presentation on User Research and semantic vectors/word embeddings from AI, speculatively exploring possible applications: https://youtu.be/tPiv4LpZCvU?si=bSUDkOywd9GnXL2e
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In 2013, Mikolov et al. (from Google) published word2vec, a neural network based framework to learn distributed representations of words as dense vectors in continuous space, aka word embeddings.
T. Mikolov et al. (2013). Efficient Estimation of Word Representations in Vector Space. arXiv:1301.3781
https://arxiv.org/abs/1301.3781#HistoryOfAI #AI #ise2024 #lecture #distributionalsemantics #wordembeddings #embeddings @sourisnumerique @enorouzi @fizise
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In 2013, Mikolov et al. (from Google) published word2vec, a neural network based framework to learn distributed representations of words as dense vectors in continuous space, aka word embeddings.
T. Mikolov et al. (2013). Efficient Estimation of Word Representations in Vector Space. arXiv:1301.3781
https://arxiv.org/abs/1301.3781#HistoryOfAI #AI #ise2024 #lecture #distributionalsemantics #wordembeddings #embeddings @sourisnumerique @enorouzi @fizise
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In 2013, Mikolov et al. (from Google) published word2vec, a neural network based framework to learn distributed representations of words as dense vectors in continuous space, aka word embeddings.
T. Mikolov et al. (2013). Efficient Estimation of Word Representations in Vector Space. arXiv:1301.3781
https://arxiv.org/abs/1301.3781#HistoryOfAI #AI #ise2024 #lecture #distributionalsemantics #wordembeddings #embeddings @sourisnumerique @enorouzi @fizise
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In 2013, Mikolov et al. (from Google) published word2vec, a neural network based framework to learn distributed representations of words as dense vectors in continuous space, aka word embeddings.
T. Mikolov et al. (2013). Efficient Estimation of Word Representations in Vector Space. arXiv:1301.3781
https://arxiv.org/abs/1301.3781#HistoryOfAI #AI #ise2024 #lecture #distributionalsemantics #wordembeddings #embeddings @sourisnumerique @enorouzi @fizise
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In 2013, Mikolov et al. (from Google) published word2vec, a neural network based framework to learn distributed representations of words as dense vectors in continuous space, aka word embeddings.
T. Mikolov et al. (2013). Efficient Estimation of Word Representations in Vector Space. arXiv:1301.3781
https://arxiv.org/abs/1301.3781#HistoryOfAI #AI #ise2024 #lecture #distributionalsemantics #wordembeddings #embeddings @sourisnumerique @enorouzi @fizise
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In lecture 05 of our #ise2024 lecture series, we are introducing the concept of distributed semantics and are referring (amongst others) to Ludwig Wittgenstein and his approach to the philosophy of language, and combine it with the idea of word vectors and embeddings.
lecture slides: https://drive.google.com/file/d/1WcVlkcUr33u5JmFcadkwtePpXJrv03n2/view?usp=sharing
#wittgenstein #nlp #wordembeddings #distributionalsemantics #lecture @fiz_karlsruhe @fizise @enorouzi @shufan @sourisnumerique #aiart #generativeai
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In lecture 05 of our #ise2024 lecture series, we are introducing the concept of distributed semantics and are referring (amongst others) to Ludwig Wittgenstein and his approach to the philosophy of language, and combine it with the idea of word vectors and embeddings.
lecture slides: https://drive.google.com/file/d/1WcVlkcUr33u5JmFcadkwtePpXJrv03n2/view?usp=sharing
#wittgenstein #nlp #wordembeddings #distributionalsemantics #lecture @fiz_karlsruhe @fizise @enorouzi @shufan @sourisnumerique #aiart #generativeai
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In lecture 05 of our #ise2024 lecture series, we are introducing the concept of distributed semantics and are referring (amongst others) to Ludwig Wittgenstein and his approach to the philosophy of language, and combine it with the idea of word vectors and embeddings.
lecture slides: https://drive.google.com/file/d/1WcVlkcUr33u5JmFcadkwtePpXJrv03n2/view?usp=sharing
#wittgenstein #nlp #wordembeddings #distributionalsemantics #lecture @fiz_karlsruhe @fizise @enorouzi @shufan @sourisnumerique #aiart #generativeai
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In lecture 05 of our #ise2024 lecture series, we are introducing the concept of distributed semantics and are referring (amongst others) to Ludwig Wittgenstein and his approach to the philosophy of language, and combine it with the idea of word vectors and embeddings.
lecture slides: https://drive.google.com/file/d/1WcVlkcUr33u5JmFcadkwtePpXJrv03n2/view?usp=sharing
#wittgenstein #nlp #wordembeddings #distributionalsemantics #lecture @fiz_karlsruhe @fizise @enorouzi @shufan @sourisnumerique #aiart #generativeai
-
In lecture 05 of our #ise2024 lecture series, we are introducing the concept of distributed semantics and are referring (amongst others) to Ludwig Wittgenstein and his approach to the philosophy of language, and combine it with the idea of word vectors and embeddings.
lecture slides: https://drive.google.com/file/d/1WcVlkcUr33u5JmFcadkwtePpXJrv03n2/view?usp=sharing
#wittgenstein #nlp #wordembeddings #distributionalsemantics #lecture @fiz_karlsruhe @fizise @enorouzi @shufan @sourisnumerique #aiart #generativeai
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Hi there! 😊 I'm Aleena, a junior computer science researcher at SINTEF AS since May 2021. In #Fakespeak,
I primarily focus on collecting #Norwegian datasets and assisting with text analysis. My interests revolve
around #NLP applications and contextualized #wordembeddings. Outside of work, I like to unwind with
activities such as table tennis, cooking, enjoying music, and playing games. -
Hi there! 😊 I'm Aleena, a junior computer science researcher at SINTEF AS since May 2021. In #Fakespeak,
I primarily focus on collecting #Norwegian datasets and assisting with text analysis. My interests revolve
around #NLP applications and contextualized #wordembeddings. Outside of work, I like to unwind with
activities such as table tennis, cooking, enjoying music, and playing games. -
Hi there! 😊 I'm Aleena, a junior computer science researcher at SINTEF AS since May 2021. In #Fakespeak,
I primarily focus on collecting #Norwegian datasets and assisting with text analysis. My interests revolve
around #NLP applications and contextualized #wordembeddings. Outside of work, I like to unwind with
activities such as table tennis, cooking, enjoying music, and playing games. -
Hi there! 😊 I'm Aleena, a junior computer science researcher at SINTEF AS since May 2021. In #Fakespeak,
I primarily focus on collecting #Norwegian datasets and assisting with text analysis. My interests revolve
around #NLP applications and contextualized #wordembeddings. Outside of work, I like to unwind with
activities such as table tennis, cooking, enjoying music, and playing games. -
Hi there! 😊 I'm Aleena, a junior computer science researcher at SINTEF AS since May 2021. In #Fakespeak,
I primarily focus on collecting #Norwegian datasets and assisting with text analysis. My interests revolve
around #NLP applications and contextualized #wordembeddings. Outside of work, I like to unwind with
activities such as table tennis, cooking, enjoying music, and playing games. -
Next important step in our brief history of (large) #languagemodels is the use of word embeddings, i.e. mapping words onto dense vector spaces while preserving their semantics in terms of vector distances allowing for analogies via vector arithmetics.
In 2013 Word2Vec was introduced by Mikolov et al.
Slides: https://drive.google.com/file/d/1atNvMYNkeKDwXP3olHXzloa09S5pzjXb/view?usp=drive_link
@fizise #llm #ai #artificialintelligence #wordembeddings #machinelearning #lecture -
Next important step in our brief history of (large) #languagemodels is the use of word embeddings, i.e. mapping words onto dense vector spaces while preserving their semantics in terms of vector distances allowing for analogies via vector arithmetics.
In 2013 Word2Vec was introduced by Mikolov et al.
Slides: https://drive.google.com/file/d/1atNvMYNkeKDwXP3olHXzloa09S5pzjXb/view?usp=drive_link
@fizise #llm #ai #artificialintelligence #wordembeddings #machinelearning #lecture -
Next important step in our brief history of (large) #languagemodels is the use of word embeddings, i.e. mapping words onto dense vector spaces while preserving their semantics in terms of vector distances allowing for analogies via vector arithmetics.
In 2013 Word2Vec was introduced by Mikolov et al.
Slides: https://drive.google.com/file/d/1atNvMYNkeKDwXP3olHXzloa09S5pzjXb/view?usp=drive_link
@fizise #llm #ai #artificialintelligence #wordembeddings #machinelearning #lecture -
Next important step in our brief history of (large) #languagemodels is the use of word embeddings, i.e. mapping words onto dense vector spaces while preserving their semantics in terms of vector distances allowing for analogies via vector arithmetics.
In 2013 Word2Vec was introduced by Mikolov et al.
Slides: https://drive.google.com/file/d/1atNvMYNkeKDwXP3olHXzloa09S5pzjXb/view?usp=drive_link
@fizise #llm #ai #artificialintelligence #wordembeddings #machinelearning #lecture -
Next important step in our brief history of (large) #languagemodels is the use of word embeddings, i.e. mapping words onto dense vector spaces while preserving their semantics in terms of vector distances allowing for analogies via vector arithmetics.
In 2013 Word2Vec was introduced by Mikolov et al.
Slides: https://drive.google.com/file/d/1atNvMYNkeKDwXP3olHXzloa09S5pzjXb/view?usp=drive_link
@fizise #llm #ai #artificialintelligence #wordembeddings #machinelearning #lecture
