Wiktionary VS WordNet

When you use the dictionary in English, as I tell to anyone learning English, the best dictionary is not a traditional dictionary, but is wiktionary.org, which itself includes the same definitions as the ones you've linked, but additionally contains "usage notes" that point out how rare and niche the archaic use of "America" to refer to a geographical continent is. It only comes up when you are talking about... Source: 11 months ago

For single words just use a dictionary. wiktionary.org works most of the time and usually is very informative. Source: 11 months ago

Wiktionary.org is very good if you can read Finnish well enough. I don't expect English wiktionary to have a lot of content. Source: 11 months ago

Two good resources to check for accurate pronunciation are wiktionary.org and forvo.com for a variety of voice recordings for words. Source: 12 months ago

For words, it helps a lot to look them up on wiktionary.org or dictionary.com and read the IPA. For example, "complicated" is a word which stresses the 1st syllable, but you've put the stress on the 3rd. Generally, the stress stays on the same part of the word as the root word (COM-pli-cate), and adding -ed or -ing doesn't change it. e.g. MO-ti-vate, MO-tivating, MO-tivated. Source: 12 months ago

TL;DR: The authors pretrain the model to classify images into Wordnet synsets[a] that appear in the caption, using a standard Cross Entropy loss. They keep the number of classes relatively small by removing any synsets that don't show up in captions at least 500 times in the dataset. It seems to work well. My immediate question is: Why not classify among the entire hierarchy of all Wordnet synsets? --- [a]... - Source: Hacker News / 3 days ago

To operationalize this intuition, the Microsoft and UC Berkeley researchers use WordNet and Wiktionary to augment the text in image-text pairs. The concept itself is augmented for isolated concepts, such as the class labels in ImageNet, whereas for captions (such as from GCC), the least common noun phrase is augmented. Equipped with this additional structured knowledge, contrastively pretrained models exhibit... - Source: dev.to / about 2 months ago

If you like this, definitely check out WordNet (https://wordnet.princeton.edu/). - Source: Hacker News / 4 months ago

I didn't understand well what you meant, but maybe this site can help you: https://wordnet.princeton.edu/. Source: about 1 year ago

What I'd do is work with a huge database like WordNet and then try to "extrapolate" BIP39 to 4096 words by creating queries against WordNet to obtain words meeting the constraints you'd like to keep. Source: over 1 year ago