Dear all, I thank you for your efforts. To know more about word embedding and semantic similarity, please refer to the survey of our research group about the issue available at https://www.sciencedirect.com/science/article/pii/S0952197619301745. If you would like that we work on using these techniques to enrich Lexicographical Data on Wikidata, we will be honoured to do this. However, we will face two main problems. The first one is absolutely funding and the second one is that we need people to validate the information returned by these two techniques and adjust it if needed. Yours Sincerely, Houcemeddine Turki (he/him) Medical Student, Faculty of Medicine of Sfax, University of Sfax, Tunisia Undergraduate Researcher, UR12SP36 GLAM, Research and Education Coordinator, Wikimedia TN User Group Member, Wiki Project Med Member, WikiIndaba Steering Committee Member, Wikimedia and Library User Group Steering Committee Co-Founder, WikiLingua Maghreb ____________________ +21629499418
-------- Message d'origine -------- De : Thomas Douillard <[email protected]> Date : 2019/09/20 12:08 (GMT+01:00) À : "Discussion list for the Wikidata project." <[email protected]> Objet : [Wikidata] Lexical datas and automated learning – where it is answered to « I don’t believe in Wikidata senses developpment » I recently read the french sentence « Je ne crois pas au développement des sens. » — translation : I don’t believes senses with develop much (following links in a Wikidata Weekly summary, the slides on a french meeting about Wikidata lexicographical datas). I believe in it, (regardless of the arguments exposed in the slides), and I write this email to try to explain why. I’m curious to know if there is already some work on the automated discovering of lexicographical datas / senses thanks to the help of Wikidata items. There is tools for automated tagging of terms with the corresponding Wikidata item, that appeared on this mailing list and/or on the wikidata weekly summaries. There is also methods that can discover senses into texts using only the terms with no reference to any external « sense » like https://towardsdatascience.com/word-embedding-with-word2vec-and-fasttext-a209c1d3e12c and can discriminate several usages of the same word according to the context. Wikidata lexicographical datas and Wikibase items could close the loop between the 2 methods and allow us to semi automatically build tools that annotate texts with Wikidata items it there is something relevant in Wikidata, but if there is nono try to suggest to add datas on Wikidata, wether it’s a missing item or a missing sense for the term. It may even be possible to store word embeddings generated by word2vec methods into Wikidata senses. In conclusion, I think Wikidata senses will be used because they allow to close a gap. It does not depends only on a strong involvement in a volunteer traditional lexicographic community. If reasearchers of the language community dives into this and develop algorithms and easy to use tools to share there lexicographical datas in Wikidata, there could be a very positive feedback loop where numerous data ends to be added on Wikidata, where the store datas helps the algorithm to enrich text annotations, for example, and missing datas are semi automatically added thanks to user feedback. This is all just wishful thinking, but I thought this deserved to be shared, hopefully this will launch at list a thread of ideas/comment in here :) Thomas
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