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#distributionalsemantics — Public Fediverse posts

Live and recent posts from across the Fediverse tagged #distributionalsemantics, aggregated by home.social.

  1. Martin Schäfer @[email protected] ·

    My book review of "Lexical variation and change" by Geeraerts et al. is out on linguist list, check it out here:
    linguistlist.org/issues/35-340
    #linguistics #distributionalSemantics

    #linguistics #distributionalsemantics
  2. Martin Schäfer @[email protected] ·

    My book review of "Lexical variation and change" by Geeraerts et al. is out on linguist list, check it out here:
    linguistlist.org/issues/35-340
    #linguistics #distributionalSemantics

    #linguistics #distributionalsemantics
  3. Martin Schäfer @[email protected] ·

    My book review of "Lexical variation and change" by Geeraerts et al. is out on linguist list, check it out here:
    linguistlist.org/issues/35-340
    #linguistics #distributionalSemantics

    #linguistics #distributionalsemantics
  4. Martin Schäfer @[email protected] ·

    My book review of "Lexical variation and change" by Geeraerts et al. is out on linguist list, check it out here:
    linguistlist.org/issues/35-340
    #linguistics #distributionalSemantics

    #distributionalsemantics #linguistics
  5. Martin Schäfer @[email protected] ·

    My book review of "Lexical variation and change" by Geeraerts et al. is out on linguist list, check it out here:
    linguistlist.org/issues/35-340
    #linguistics #distributionalSemantics

    #linguistics #distributionalsemantics
  6. Harald Sack @[email protected] ·

    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
    arxiv.org/abs/1301.3781

    #HistoryOfAI #AI #ise2024 #lecture #distributionalsemantics #wordembeddings #embeddings @sourisnumerique @enorouzi @fizise

    #historyofai #ai #ise2024 #lecture #distributionalsemantics #wordembeddings
  7. Harald Sack @[email protected] ·

    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
    arxiv.org/abs/1301.3781

    #HistoryOfAI #AI #ise2024 #lecture #distributionalsemantics #wordembeddings #embeddings @sourisnumerique @enorouzi @fizise

    #historyofai #ai #ise2024 #lecture #distributionalsemantics #wordembeddings
  8. Harald Sack @[email protected] ·

    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
    arxiv.org/abs/1301.3781

    #HistoryOfAI #AI #ise2024 #lecture #distributionalsemantics #wordembeddings #embeddings @sourisnumerique @enorouzi @fizise

    #historyofai #ai #ise2024 #lecture #distributionalsemantics #wordembeddings
  9. Harald Sack @[email protected] ·

    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
    arxiv.org/abs/1301.3781

    #HistoryOfAI #AI #ise2024 #lecture #distributionalsemantics #wordembeddings #embeddings @sourisnumerique @enorouzi @fizise

    #embeddings #wordembeddings #distributionalsemantics #lecture #ise2024 #ai
  10. Harald Sack @[email protected] ·

    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
    arxiv.org/abs/1301.3781

    #HistoryOfAI #AI #ise2024 #lecture #distributionalsemantics #wordembeddings #embeddings @sourisnumerique @enorouzi @fizise

    #historyofai #ai #ise2024 #lecture #distributionalsemantics #wordembeddings
  11. Harald Sack @[email protected] ·

    Besides Wittgenstein, we also quote linguist John Rupert Firth (1890–1960) with "You shall know a word by the company it keeps!" when introducing the principles of distributional semantics as the foundation for word embeddings and large language models.

    J.R. Firth (1957), A synopsis of linguistic theory, Studies in linguistic analysis, Blackwell, Oxford: cs.brown.edu/courses/csci2952d

    #lecture #llm #nlp #distributionalsemantics @fizise @fiz_karlsruhe @enorouzi @sourisnumerique @shufan #wittgenstein

    #lecture #llm #nlp #distributionalsemantics #wittgenstein
  12. Harald Sack @[email protected] ·

    Besides Wittgenstein, we also quote linguist John Rupert Firth (1890–1960) with "You shall know a word by the company it keeps!" when introducing the principles of distributional semantics as the foundation for word embeddings and large language models.

    J.R. Firth (1957), A synopsis of linguistic theory, Studies in linguistic analysis, Blackwell, Oxford: cs.brown.edu/courses/csci2952d

    #lecture #llm #nlp #distributionalsemantics @fizise @fiz_karlsruhe @enorouzi @sourisnumerique @shufan #wittgenstein

    #lecture #llm #nlp #distributionalsemantics #wittgenstein
  13. Harald Sack @[email protected] ·

    Besides Wittgenstein, we also quote linguist John Rupert Firth (1890–1960) with "You shall know a word by the company it keeps!" when introducing the principles of distributional semantics as the foundation for word embeddings and large language models.

    J.R. Firth (1957), A synopsis of linguistic theory, Studies in linguistic analysis, Blackwell, Oxford: cs.brown.edu/courses/csci2952d

    #lecture #llm #nlp #distributionalsemantics @fizise @fiz_karlsruhe @enorouzi @sourisnumerique @shufan #wittgenstein

    #lecture #llm #nlp #distributionalsemantics #wittgenstein
  14. Harald Sack @[email protected] ·

    Besides Wittgenstein, we also quote linguist John Rupert Firth (1890–1960) with "You shall know a word by the company it keeps!" when introducing the principles of distributional semantics as the foundation for word embeddings and large language models.

    J.R. Firth (1957), A synopsis of linguistic theory, Studies in linguistic analysis, Blackwell, Oxford: cs.brown.edu/courses/csci2952d

    #lecture #llm #nlp #distributionalsemantics @fizise @fiz_karlsruhe @enorouzi @sourisnumerique @shufan #wittgenstein

    #wittgenstein #distributionalsemantics #nlp #llm #lecture
  15. Harald Sack @[email protected] ·

    Besides Wittgenstein, we also quote linguist John Rupert Firth (1890–1960) with "You shall know a word by the company it keeps!" when introducing the principles of distributional semantics as the foundation for word embeddings and large language models.

    J.R. Firth (1957), A synopsis of linguistic theory, Studies in linguistic analysis, Blackwell, Oxford: cs.brown.edu/courses/csci2952d

    #lecture #llm #nlp #distributionalsemantics @fizise @fiz_karlsruhe @enorouzi @sourisnumerique @shufan #wittgenstein

    #lecture #llm #nlp #distributionalsemantics #wittgenstein
  16. Harald Sack @[email protected] ·

    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: drive.google.com/file/d/1WcVlk

    #wittgenstein #nlp #wordembeddings #distributionalsemantics #lecture @fiz_karlsruhe @fizise @enorouzi @shufan @sourisnumerique #aiart #generativeai

    #ise2024 #wittgenstein #nlp #wordembeddings #distributionalsemantics #lecture
  17. Harald Sack @[email protected] ·

    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: drive.google.com/file/d/1WcVlk

    #wittgenstein #nlp #wordembeddings #distributionalsemantics #lecture @fiz_karlsruhe @fizise @enorouzi @shufan @sourisnumerique #aiart #generativeai

    #ise2024 #wittgenstein #nlp #wordembeddings #distributionalsemantics #lecture
  18. Harald Sack @[email protected] ·

    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: drive.google.com/file/d/1WcVlk

    #wittgenstein #nlp #wordembeddings #distributionalsemantics #lecture @fiz_karlsruhe @fizise @enorouzi @shufan @sourisnumerique #aiart #generativeai

    #ise2024 #wittgenstein #nlp #wordembeddings #distributionalsemantics #lecture
  19. Harald Sack @[email protected] ·

    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: drive.google.com/file/d/1WcVlk

    #wittgenstein #nlp #wordembeddings #distributionalsemantics #lecture @fiz_karlsruhe @fizise @enorouzi @shufan @sourisnumerique #aiart #generativeai

    #generativeai #aiart #lecture #distributionalsemantics #wordembeddings #nlp
  20. Harald Sack @[email protected] ·

    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: drive.google.com/file/d/1WcVlk

    #wittgenstein #nlp #wordembeddings #distributionalsemantics #lecture @fiz_karlsruhe @fizise @enorouzi @shufan @sourisnumerique #aiart #generativeai

    #ise2024 #wittgenstein #nlp #wordembeddings #distributionalsemantics #lecture
  21. Harald Sack @[email protected] ·

    "You shall know a word by the company it keeps." Distributional Semantics as the basis for self supervised learning as in today's large language models. This is what my colleague @MahsaVafaie and I are going to talk about in this #kg2023 lecture excursion.
    OpenHPI video: open.hpi.de/courses/knowledgeg
    youtube video: youtube.com/watch?v=EYAY8nmhpd
    slides: zenodo.org/records/10185262
    @fiz_karlsruhe @fizise @tabea @sashabruns @enorouzi #semantics #distributionalsemantics #llm #llms #cooccurrence #wittgenstein

    #kg2023 #semantics #distributionalsemantics #llm #llms #cooccurrence
  22. Harald Sack @[email protected] ·

    "You shall know a word by the company it keeps." Distributional Semantics as the basis for self supervised learning as in today's large language models. This is what my colleague @MahsaVafaie and I are going to talk about in this #kg2023 lecture excursion.
    OpenHPI video: open.hpi.de/courses/knowledgeg
    youtube video: youtube.com/watch?v=EYAY8nmhpd
    slides: zenodo.org/records/10185262
    @fiz_karlsruhe @fizise @tabea @sashabruns @enorouzi #semantics #distributionalsemantics #llm #llms #cooccurrence #wittgenstein

    #kg2023 #semantics #distributionalsemantics #llm #llms #cooccurrence
  23. Harald Sack @[email protected] ·

    "You shall know a word by the company it keeps." Distributional Semantics as the basis for self supervised learning as in today's large language models. This is what my colleague @MahsaVafaie and I are going to talk about in this #kg2023 lecture excursion.
    OpenHPI video: open.hpi.de/courses/knowledgeg
    youtube video: youtube.com/watch?v=EYAY8nmhpd
    slides: zenodo.org/records/10185262
    @fiz_karlsruhe @fizise @tabea @sashabruns @enorouzi #semantics #distributionalsemantics #llm #llms #cooccurrence #wittgenstein

    #kg2023 #semantics #distributionalsemantics #llm #llms #cooccurrence
  24. Harald Sack @[email protected] ·

    "You shall know a word by the company it keeps." Distributional Semantics as the basis for self supervised learning as in today's large language models. This is what my colleague @MahsaVafaie and I are going to talk about in this #kg2023 lecture excursion.
    OpenHPI video: open.hpi.de/courses/knowledgeg
    youtube video: youtube.com/watch?v=EYAY8nmhpd
    slides: zenodo.org/records/10185262
    @fiz_karlsruhe @fizise @tabea @sashabruns @enorouzi #semantics #distributionalsemantics #llm #llms #cooccurrence #wittgenstein

    #wittgenstein #cooccurrence #llms #llm #distributionalsemantics #semantics
  25. Harald Sack @[email protected] ·

    "You shall know a word by the company it keeps." Distributional Semantics as the basis for self supervised learning as in today's large language models. This is what my colleague @MahsaVafaie and I are going to talk about in this #kg2023 lecture excursion.
    OpenHPI video: open.hpi.de/courses/knowledgeg
    youtube video: youtube.com/watch?v=EYAY8nmhpd
    slides: zenodo.org/records/10185262
    @fiz_karlsruhe @fizise @tabea @sashabruns @enorouzi #semantics #distributionalsemantics #llm #llms #cooccurrence #wittgenstein

    #kg2023 #semantics #distributionalsemantics #llm #llms #cooccurrence
  26. Martin Schäfer @[email protected] ·

    Getting ready to leave for #iclc16 in Düsseldorf, here a small teaser for my talk on Tuesday 14:45 on the #affix rivalry between -ity and -ness in #English:
    Why to some adjectives take -ity (insular -> insularity), while others take -ness (red -> redness)? Many factors have been considered, I use #distributionalSemantics to explore the role of the adjective's meaning. Mapping the vectors on a two dimensional space with t-SNE, a dimensionality reduction technique, the resulting visualization shows that adjective meaning might indeed be a highly relevant factor. For example, even for adjectives with the same ending -ive, the bases of those taking -ity (e.g. narrativity) and those taking -ness (distinctiveness) fall into two clear clusters.
    Looking forward to seeing some of you there :)
    #wordformation

    #wordformation #distributionalsemantics #english #affix #iclc16
  27. Martin Schäfer @[email protected] ·

    Getting ready to leave for #iclc16 in Düsseldorf, here a small teaser for my talk on Tuesday 14:45 on the #affix rivalry between -ity and -ness in #English:
    Why to some adjectives take -ity (insular -> insularity), while others take -ness (red -> redness)? Many factors have been considered, I use #distributionalSemantics to explore the role of the adjective's meaning. Mapping the vectors on a two dimensional space with t-SNE, a dimensionality reduction technique, the resulting visualization shows that adjective meaning might indeed be a highly relevant factor. For example, even for adjectives with the same ending -ive, the bases of those taking -ity (e.g. narrativity) and those taking -ness (distinctiveness) fall into two clear clusters.
    Looking forward to seeing some of you there :)
    #wordformation

    #wordformation #distributionalsemantics #english #affix #iclc16
  28. Martin Schäfer @[email protected] ·

    Getting ready to leave for #iclc16 in Düsseldorf, here a small teaser for my talk on Tuesday 14:45 on the #affix rivalry between -ity and -ness in #English:
    Why to some adjectives take -ity (insular -> insularity), while others take -ness (red -> redness)? Many factors have been considered, I use #distributionalSemantics to explore the role of the adjective's meaning. Mapping the vectors on a two dimensional space with t-SNE, a dimensionality reduction technique, the resulting visualization shows that adjective meaning might indeed be a highly relevant factor. For example, even for adjectives with the same ending -ive, the bases of those taking -ity (e.g. narrativity) and those taking -ness (distinctiveness) fall into two clear clusters.
    Looking forward to seeing some of you there :)
    #wordformation

    #wordformation #distributionalsemantics #english #affix #iclc16
  29. Martin Schäfer @[email protected] ·

    Getting ready to leave for #iclc16 in Düsseldorf, here a small teaser for my talk on Tuesday 14:45 on the #affix rivalry between -ity and -ness in #English:
    Why to some adjectives take -ity (insular -> insularity), while others take -ness (red -> redness)? Many factors have been considered, I use #distributionalSemantics to explore the role of the adjective's meaning. Mapping the vectors on a two dimensional space with t-SNE, a dimensionality reduction technique, the resulting visualization shows that adjective meaning might indeed be a highly relevant factor. For example, even for adjectives with the same ending -ive, the bases of those taking -ity (e.g. narrativity) and those taking -ness (distinctiveness) fall into two clear clusters.
    Looking forward to seeing some of you there :)
    #wordformation

    #iclc16 #affix #english #distributionalsemantics #wordformation
  30. Harald Sack @[email protected] ·

    Last #NLP chapter of our #ISE2023 lecture last week was on distributional semantics and word embeddings. Of course, Wittgenstein had to be mentioned...

    #lecture #distributionalsemantics #wittgenstein #stablediffusionart #creativeAI

    @fizise @KIT_Karlsruhe

    #nlp #ise2023 #lecture #distributionalsemantics #wittgenstein #stablediffusionart
  31. Harald Sack @[email protected] ·

    Last #NLP chapter of our #ISE2023 lecture last week was on distributional semantics and word embeddings. Of course, Wittgenstein had to be mentioned...

    #lecture #distributionalsemantics #wittgenstein #stablediffusionart #creativeAI

    @fizise @KIT_Karlsruhe

    #nlp #ise2023 #lecture #distributionalsemantics #wittgenstein #stablediffusionart
  32. Harald Sack @[email protected] ·

    Last #NLP chapter of our #ISE2023 lecture last week was on distributional semantics and word embeddings. Of course, Wittgenstein had to be mentioned...

    #lecture #distributionalsemantics #wittgenstein #stablediffusionart #creativeAI

    @fizise @KIT_Karlsruhe

    #nlp #ise2023 #lecture #distributionalsemantics #wittgenstein #stablediffusionart
  33. Harald Sack @[email protected] ·

    Last #NLP chapter of our #ISE2023 lecture last week was on distributional semantics and word embeddings. Of course, Wittgenstein had to be mentioned...

    #lecture #distributionalsemantics #wittgenstein #stablediffusionart #creativeAI

    @fizise @KIT_Karlsruhe

    #creativeai #stablediffusionart #wittgenstein #distributionalsemantics #lecture #ise2023
  34. Harald Sack @[email protected] ·

    Last #NLP chapter of our #ISE2023 lecture last week was on distributional semantics and word embeddings. Of course, Wittgenstein had to be mentioned...

    #lecture #distributionalsemantics #wittgenstein #stablediffusionart #creativeAI

    @fizise @KIT_Karlsruhe

    #nlp #ise2023 #lecture #distributionalsemantics #wittgenstein #stablediffusionart
  35. Harald Sack @[email protected] ·

    Today's #ise2023 lecture was focusing on Naive Bayes Classification, POS Tagging, and distributional semantics with Word Embeddings

    drive.google.com/drive/folders

    #nlp #lecture #classification #wordembeddings #languagemodels #word2vec #hiddenMarkovModel #distributionalsemantics @fizise @KIT_Karlsruhe #stablediffusionart #creativeAI

    #ise2023 #nlp #lecture #classification #wordembeddings #languagemodels
  36. Harald Sack @[email protected] ·

    Today's #ise2023 lecture was focusing on Naive Bayes Classification, POS Tagging, and distributional semantics with Word Embeddings

    drive.google.com/drive/folders

    #nlp #lecture #classification #wordembeddings #languagemodels #word2vec #hiddenMarkovModel #distributionalsemantics @fizise @KIT_Karlsruhe #stablediffusionart #creativeAI

    #ise2023 #nlp #lecture #classification #wordembeddings #languagemodels
  37. Harald Sack @[email protected] ·

    Today's #ise2023 lecture was focusing on Naive Bayes Classification, POS Tagging, and distributional semantics with Word Embeddings

    drive.google.com/drive/folders

    #nlp #lecture #classification #wordembeddings #languagemodels #word2vec #hiddenMarkovModel #distributionalsemantics @fizise @KIT_Karlsruhe #stablediffusionart #creativeAI

    #ise2023 #nlp #lecture #classification #wordembeddings #languagemodels
  38. Harald Sack @[email protected] ·

    Today's #ise2023 lecture was focusing on Naive Bayes Classification, POS Tagging, and distributional semantics with Word Embeddings

    drive.google.com/drive/folders

    #nlp #lecture #classification #wordembeddings #languagemodels #word2vec #hiddenMarkovModel #distributionalsemantics @fizise @KIT_Karlsruhe #stablediffusionart #creativeAI

    #creativeai #stablediffusionart #distributionalsemantics #hiddenmarkovmodel #word2vec #languagemodels
  39. Harald Sack @[email protected] ·

    Today's #ise2023 lecture was focusing on Naive Bayes Classification, POS Tagging, and distributional semantics with Word Embeddings

    drive.google.com/drive/folders

    #nlp #lecture #classification #wordembeddings #languagemodels #word2vec #hiddenMarkovModel #distributionalsemantics @fizise @KIT_Karlsruhe #stablediffusionart #creativeAI

    #ise2023 #nlp #lecture #classification #wordembeddings #languagemodels
  40. 4sphere @[email protected] ·

    I somehow just learned about semantic folding
    Still trying to learn more about it, but what's really messing with my head is that word embeddings are matrices. Are there any interesting connections to be made between this approach and things like DisCoCat?

    #NLP #DistributionalSemantics #DisCoCat

    en.wikipedia.org/wiki/Semantic
    en.wikipedia.org/wiki/DisCoCat

    #discocat #distributionalsemantics #nlp
  41. 4sphere @[email protected] ·

    I somehow just learned about semantic folding
    Still trying to learn more about it, but what's really messing with my head is that word embeddings are matrices. Are there any interesting connections to be made between this approach and things like DisCoCat?

    #NLP #DistributionalSemantics #DisCoCat

    en.wikipedia.org/wiki/Semantic
    en.wikipedia.org/wiki/DisCoCat

    #discocat #distributionalsemantics #nlp
  42. 4sphere @[email protected] ·

    I somehow just learned about semantic folding
    Still trying to learn more about it, but what's really messing with my head is that word embeddings are matrices. Are there any interesting connections to be made between this approach and things like DisCoCat?

    #NLP #DistributionalSemantics #DisCoCat

    en.wikipedia.org/wiki/Semantic
    en.wikipedia.org/wiki/DisCoCat

    #discocat #distributionalsemantics #nlp
  43. 4sphere @[email protected] ·

    I somehow just learned about semantic folding
    Still trying to learn more about it, but what's really messing with my head is that word embeddings are matrices. Are there any interesting connections to be made between this approach and things like DisCoCat?

    #NLP #DistributionalSemantics #DisCoCat

    en.wikipedia.org/wiki/Semantic
    en.wikipedia.org/wiki/DisCoCat

    #nlp #distributionalsemantics #discocat
  44. 4sphere @[email protected] ·

    I somehow just learned about semantic folding
    Still trying to learn more about it, but what's really messing with my head is that word embeddings are matrices. Are there any interesting connections to be made between this approach and things like DisCoCat?

    #NLP #DistributionalSemantics #DisCoCat

    en.wikipedia.org/wiki/Semantic
    en.wikipedia.org/wiki/DisCoCat

    #discocat #distributionalsemantics #nlp