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Unfortunately, with the present database that runs this site, ns don"t have actually data around which senses the ~term~ room used most commonly. I"ve got ideas around how to fix this however will need to find a resource of "sense" frequencies. Hope there"s sufficient info above to help you understand the part of decided of ~term~, and guess at its most usual usage.

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Word Type

For those interested in a little info around this site: it"s a side job that I occurred while functioning on explicate Words and Related Words. Both that those jobs are based roughly words, however have lot grander goals. I had an idea for a website the simply explains the word species of the words the you find for - just like a dictionary, but focussed ~ above the component of decided of the words. And also since I already had a lot of the framework in location from the various other two sites, i figured that wouldn"t be too much an ext work to gain this up and running.

The thesaurus is based upon the exceptional Wiktionary job by wikimedia. I originally started with WordNet, but then realised that it was missing many species of words/lemma (determiners, pronouns, abbreviations, and also many more). This led to me to investigate the 1913 edition of Websters dictionary - which is now in the general public domain. However, after ~ a day"s job-related wrangling it right into a database i realised the there were much too numerous errors (especially with the part-of-speech tagging) for it to be viable because that Word Type.

Finally, i went ago to Wiktionary - i m sorry I already knew about, yet had been preventing because it"s not appropriately structured for parsing. That"s when I stumbled across the UBY task - an amazing task which needs much more recognition. The researchers have parsed the entirety of Wiktionary and also other sources, and compiled every little thing into a single unified resource. I merely extracted the Wiktionary entries and threw them right into this interface! so it took a little much more work 보다 expected, but I"m happy I kept at that after the first couple of blunders.

Special many thanks to the contributors of the open-source password that was used in this project: the UBY task (mentioned above),
mongodb and also express.js.

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Currently, this is based upon a variation of wiktionary i m sorry is a couple of years old. I setup to upgrade it come a newer version soon and also that update should lug in a bunch of brand-new word senses for countless words (or more accurately, lemma).