🎓 Knowledge from Crowds 👪👪

Most 👅🔢 linguistic data is in the heads of the 👪 people who speak each language, not in the zeros and ones of 🔢 digitized data. We have been developing new methods for learning language information directly from the public, for languages around the 🌍 world. Our approach to the crowd is discussed in

Full article: Crowdsourcing Microdata for Cost-Effective and Reliable Lexicography

Benjamin, Martin (2015)
Editors: Li, Lan; Mckeown, Jamie; Liu, Liming
Published in: Proceedings of AsiaLex 2015 Hong Kong, p. 213-221

Abstract
Lexicography has long faced the challenge of having too few specialists to document too many words in too many languages with too many linguistic features. Great dictionaries are invariably the product of many person-years of labor, whether the lifetime work of an individual or the lengthy collaboration of a team. Is it possible to use public contributions to vastly reduce the time and cost of producing a dictionary while ensuring high quality? Crowdsourcing, often seen as the solution for large-scale data acquisition or analysis, is fraught with problems in the context of lexicography. Language is not binary, so there may be no one right answer to say that a word “means” a particular definition, or that a word in one language “is” the same as a particular translation term. People may misinterpret instructions or misread terms or make typographical or conceptual errors. Some crowd members intentionally add bad data. Without a payment system, incentives for participation are slim; micro-payments introduce the incentive to maximize income over quality. Our project introduces a public interface that breaks lexicographic data collection into targeted microtasks, within a stimulating game environment on Facebook, phones, and the web. Players earn points for answers that win consensus. Validation is achieved by redundancy, while malicious users are detected through persistent deviations. Data can be collected for any language, in an integrated multilingual framework focused on the serial production of monolingual dictionaries linked at the concept level. Questions are sequential, first eliciting a lemma, then a definition, then other information, according to a prioritized concept list. The method can also be used to merge existing data sets. Intensive trials are currently underway in Vietnamese, with the inclusion of additional Asian languages an explicit objective. /info/crowds

Kamusi GOLD

These are the languages for which we have datasets that we are actively working toward putting online. Languages that are Active for you to search are marked with "A" in the list below.

Key

•A = Active language, aligned and searchable
•c = Data 🔢 elicited through the Comparative African Word List
•d = Data from independent sources that Kamusi participants align playing 🐥📊 DUCKS
•e = Data from the 🎮 games you can play on 😂🌎🤖 EmojiWorldBot
•P = Pending language, data in queue for alignment
•w = Data from 🔠🕸 WordNet teams

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Our biggest struggle is keeping Kamusi online and keeping it free. We cannot charge money for our services because that would block access to the very people we most want to benefit, the students and speakers of languages around the world that are almost always excluded from information technology. So, we ask, request, beseech, beg you, to please support our work by donating as generously as you can to help build and maintain this unique public resource.

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