Robuta

Sanghyuk Choi, Taeuk Kim, Jinseok Seol, Sang-goo Lee. Proceedings of the First Workshop on Subword and Character Level Models in NLP. 2017.

Have you ever wondered how a computer knows that a cat is more like a dog than a car? To a machine,... Tagged with nlp, pytorch, bert, ai.

02/13/23 - Word embeddings are an essential instrument in many NLP tasks. Most available resources are trained on general language from Web c...

Vaibhav Kumar, Tenzin Bhotia, Vaibhav Kumar. Proceedings of Second Workshop for NLP Open Source Software (NLP-OSS). 2020.

We propose new static word embeddings optimised for sentence semantic representation. We first extract word embeddings from a pre-trained Sentence Transformer,...

Aleksandra Piktus, Necati Bora Edizel, Piotr Bojanowski, Edouard Grave, Rui Ferreira, Fabrizio Silvestri. Proceedings of the 2019 Conference of the North...

Food ontologies require significant effort to create and maintain, as it involves manual and time-consuming tasks, often with limited alignment to the underl...

Ishrak Hayet, Zijun Yao, Bo Luo. Findings of the Association for Computational Linguistics: EMNLP 2022. 2022.

06/21/17 - Word embedding models such as Skip-gram learn a vector-space representation for each word, based on the local word collocation pat...

Abstract page for arXiv paper 1601.00893: The Role of Context Types and Dimensionality in Learning Word Embeddings

Sapan Shah, Sreedhar Reddy, Pushpak Bhattacharyya. Proceedings of the 29th International Conference on Computational Linguistics. 2022.

Define word embedding and explore NLP and technology use cases. Understand how word embedding and vectorization work and explore techniques like TF-IDF and...

Comparing word similarity from embeddings to human-rated word associations - sodascience/dutch_sound_symbolism

A deep-dive into how FastText enriches word vectors with subword information

Abstract page for arXiv paper 2407.00340: The Echoes of the 'I': Tracing Identity with Demographically Enhanced Word Embeddings