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This means if you try to use it as a normal dictionary the glosses tend to not contain the word for which you are listing the senses.
The arrival of WordNet on the scene, when it happened, was a big deal, since there weren't many ways to perform knowledge-rich NLP back then. The common ones were using a dictionary or a thesaurus. There was some effort to tie topic models with WordNet too, like LDAWN . And extending it, based on collocation information you could glean from the gloss - "eXtended WordNet" .
You still (occasionally) see its uses where you need some kind of rich prior knowledge. For ex, the "Hierarchical Probabilistic Neural Network Language Model" by Morin and Bengio , or cluster labeling (which uses embeddings with WordNet) . To quote an example from the latter, 'a word cluster containing words "dog" and "wolf" should not be labeled with either word, but as "canids"'. And you know "canids" is a super-category here, by looking up the precise relationships in WordNet.
My own Master's research looked at combining WordNet based lexical chaining with more "ML"-ish techniques like Hidden Markov Models . Which is why I know, or rather, vaguely remember, some of the stuff that was happening back then :-)
I think the primary reason why WordNet did not retain its popularity was it was a good "one off" solution. Worked well with "correct" English. You want to adapt it to your domain vocabulary? Heuristics. You want to use WordNet in another language? Well, someone needs to build one first. You want to use it to process text in internet lingo? Nope, hybrid models and heuristics. Also, at this time the amount of text available to train on was increasing by leaps and bounds, so the field moving toward ML heavy techniques made sense.
nc dict.org 2628
DEFINE wn hacker
Arch has an AUR dict-wn that does this. For those not using arch you can still clone the PKGBUILD and see how it grabs and compiles and installs.
git clone https://aur.archlinux.org/dict-wn
Cool that is was forked and effort put into the new version.
WordNet has a rich set of libraries in many languages to use the data. I didn’t see anything similar on their github repo.