What are some ethical considerations when using word embeddings to analyze social media data? How can word embeddings be used in multimodal NLP tasks?

Comparing frozen versus trainable word embeddings in NLP. Explore the impact of using frozen versus trainable GloVe embeddings on natural language processing ... May 18, 2023 ... In summary, word embeddings are considered a powerful tool in NLP that can help algorithms understand language in a more nuanced and human-like ... Oct 30, 2023 ... These tokens are usually words that can also be phrases, punctuation marks, or even individual characters. Tokenization is the first step in NLP ... Nov 9, 2023 ... A text embedding is a piece of text projected into a high-dimensional latent space. The position of our text in this space is a vector, a long sequence of ... We can also apply pre-trained word embeddings to the word analogy problem. For example, “man : woman :: son : daughter” is an analogy. This sentence can also be ... The quintessential task of natural language processing (NLP) is to understand human language. ... Could we build a word embedding that captures relations between ... Mar 29, 2021 ... Recent studies have revealed a security threat to natural language processing (NLP) models, called the Backdoor Attack. Victim models can ... There are a wide variety of such methods; for example Word2Vec is actually not one but two separate methods (CBOW and skip-gram). The word embeddings can either ... Nov 18, 2020 ... This paper can be considered one of the first works to introduce an efficient distributed word representation model for different NLP tasks in the islamic ... Dec 6, 2021 ... A hidden problem in vocabulary expansion. Pretrained language models increasingly form the foundation of modern natural language processing.