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 / 4 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
That is a... Very interesting idea for a product. Possibly something that the loony linguists at Princeton might do... If they weren't already too busy to be bothered. Or the other nerds at the Univ. Of Rome. Source: over 1 year ago
Anything you do here too will help your brain: https://wordnet.princeton.edu/. Source: over 1 year ago
Pretty slick. Reminds me of wn (wordnet) which has a CLI interface and I was a bit surprised at the disk-space cost that wn entailed (something like 70MB), but it makes sense if you have the whole DB worth of stuff. Source: over 1 year ago
I can't speak to Google's approach, but Princeton has WordNet, which sounds exactly like what you're looking for. Source: over 1 year ago
Ok so here is what I could find Wordnet which says you can use the contents of it in whatever you like: https://wordnet.princeton.edu. Source: almost 2 years ago
WordNet is a corpus of words, their different senses and relations between them. They are forming a tree, so that you can extract hypernyms by going up one level and get hyponyms the other way around. You can access it using the nltk python library. Here are some examples on how to use it. Source: almost 2 years ago
I used Node.js, WordNet data source for the dictionary and Cloudflare open DNS which is capable of DoH. - Source: dev.to / almost 2 years ago
Yes there is a lot of work to be done. Dictionary is taken from WordNet project as is https://wordnet.princeton.edu/ See the disclaimer on the site (https://idearamen.com/) which mentions this specifically. WordNet has over 170k words, so not an easy feat to work it out. I guess I can add some filters to start with. - Source: Hacker News / almost 2 years ago
For an open content dictionary, I recommend WordNet, by Princeton University, which has an online edition as well as a downloadable version and various interfaces. The project isn't a dictionary per se, but a project to map words according to their conceptual relationships like hypernyms, synonyms, and antonyms. The quality will be more consistent than from crowdsourced projects like Wiktionary or FreeDict. It... Source: almost 2 years ago
Have you checked out wordnet: https://wordnet.princeton.edu/? It is a dataset where words are grouped into sets of synonyms where each set expresses a distinct concept. Source: about 2 years ago
I got my definition from Princeton University’s WordNet, which details the semantic relationship between “deflect” and “block.”. Source: about 2 years ago
Without knowing more about what you're looking for, it's hard to make any recommendations. But, you might have luck querying WordNet or using some of the databases that Goh et al. (2016) used. Source: about 2 years ago
WordNet is used to "teach" the system the relationships between various words, and it is the pool of words (mostly nouns) which were used to categorize and label all of the images in the database: Https://wordnet.princeton.edu/. Source: about 2 years ago
Depending on how crazy you want to go. If you need a proper pool of English words, try to explore the project WordNet. You will need to learn to import external modules. WordNet has a number of Java implementations. It may give you a set of words as an array, a list, a set of Strings etc. Source: over 2 years ago
ImageNet is an image database organized according to the WordNet hierarchy (currently only the nouns), in which each node of the hierarchy is depicted by hundreds and thousands of images. The project has been instrumental in advancing computer vision and deep learning research. The data is available for free to researchers for non-commercial use. Source: almost 3 years ago
If you want some semantic information that is more scriptable (parts of speech, narrower/broader terms, etc), there's wordnet which has an offline package available on most operating systems (my Debian, OpenBSD, and FreeBSD systems all offered it). Source: almost 3 years ago
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