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
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
We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. We observe large improvements in accuracy at much lower computational cost, i.e. it takes less than a day to learn high quality word vectors from a 1.6 billion words data set. Furthermore, we show that these vectors provide state-of-the-art performance on our test set for measuring syntactic and semantic word similarities.
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
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
We propose two novel model architectures for computing continuous vector representations of words from very large data sets. The quality of these representations is measured in a word similarity task, and the results are compared to the previously best performing techniques based on different types of neural networks. We observe large improvements in accuracy at much lower computational cost, i.e. it takes less than a day to learn high quality word vectors from a 1.6 billion words data set. Furthermore, we show that these vectors provide state-of-the-art performance on our test set for measuring syntactic and semantic word similarities.
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
Today's #ise2023 lecture was focusing on Naive Bayes Classification, POS Tagging, and distributional semantics with Word Embeddings
https://drive.google.com/drive/folders/11Z3_UGQjGONyHyZbj_kIdgT-LglZH4Ob
#nlp #lecture #classification #wordembeddings #languagemodels #word2vec #hiddenMarkovModel #distributionalsemantics @fizise @KIT_Karlsruhe #stablediffusionart #creativeAI
JCLS
IRRJ
Harald Sack
Thomas E. Gladwin
Joachim Scharloth
Fakespeak