[Corpora-List] CFP: Shared task on Taxonomy Enrichment for Russian

Varvara Logacheva varvara.logacheva at gmail.com
Mon Dec 16 10:42:49 CET 2019

We invite you to participate in the *Dialogue 2020 shared task on Taxonomy Enrichment *for the Russian Language: https://competitions.codalab.org/competitions/22168.

*Important dates*


First Call for Participation: December 16, 2019.


Release of the Training Data: December 16, 2019.


Release of the Test Data: January 31, 2020.


Submission of the Results: February 14, 2020.


Results of the Shared Task: February 28, 2020.


Taxonomies are tree structures which organize terms into a semantic hierarchy. Taxonomic relations (or hypernyms) are “is-a” relations: cat is-a animal, banana is-a fruit, Microsoft is-a company, etc. This type of relations is useful in a wide range of natural language processing tasks for performing semantic analysis. The goal of this semantic task is to extend an existing taxonomy with relations of previously unseen words.

Multiple evaluation campaigns for hypernym extraction (SemEval-2018 task 9 <https://repositori.upf.edu/handle/10230/35249>), taxonomy induction (Semeval-2016 task 13 <https://www.aclweb.org/anthology/S16-1168>, SemEval 2015 task 17 <https://www.aclweb.org/anthology/S15-2151/>), and most notably for taxonomy enrichment (SemEval-2016 task 14 <https://www.aclweb.org/anthology/S16-1169/>) were organized for English and other western European languages in the past. However, this is the first evaluation campaign of this kind for Russian and any Slavic language. Moreover, this task has a more realistic setting compared to the corresponding SemEval tasks as the participants are not given the definitions of words but only new unseen words in context.

*Task description*

Given words that are not yet included in the taxonomy, we need to associate each word with the appropriate hypernyms from an existing taxonomy. For example, given the input word “утка” (duck) we expect you to provide a list of its most probable 10 candidate hypernym synsets the word could be attached to, e.g. “animal”, “bird”, and so on. Here a word may refer to one, two or more “ancestors” (hypernym synsets) at the same time.

We believe that popular neural context-aware models (like ELMo and BERT) will be of particular use for this task as they can represent out-of-vocabulary words on the basis of their context. Therefore, everyone interested in testing these and other distributional semantic models are welcome to participate. We will also provide several baseline models based on distributional and neural language models.

*Further details and submission: https://competitions.codalab.org/competitions/22168 <https://competitions.codalab.org/competitions/22168> *


Irina.Nikishina at skoltech.ru

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