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- # Cross-Encoder for Word Sense Relationships Classification
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- This model was trained on word sense relationships extracted by WordNet for the [semantic change type classification](https://github.com/ChangeIsKey/change-type-classification).
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- The model can be used to detect which kind of relatioships (among homonymy, antonymy, hypernonym, hyponymy, and co-hypnomy) intercur between word senses: Given a pair of word sense definitions, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order.
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- The training code is available here: [SBERT.net Training MS Marco](https://github.com/UKPLab/sentence-transformers/tree/master/examples/training/ms_marco)
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  <b> Citation </b>
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+ # Cross-Encoder for Word-Sense Relationship Classification
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+ This model has been trained on word sense relations extracted from WordNet.
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+ The model can be used to detect what kind of relationships (among homonymy, antonymy, hypernonymy, hyponymy, and co-hyponymy) occur between word senses: Given a pair of word sense definitions, predict the sense relationship (homonymy, antonymy, hypernonymy, hyponymy, and co-hyponymy).
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+ The training code can be found here: [https://github.com/ChangeIsKey/change-type-classification](https://github.com/ChangeIsKey/change-type-classification)
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  <b> Citation </b>
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