| Challenge: | Existing systems for extracting relations expressed using nouns do not exist for relational noun. |
| Approach: | They contribute a lexicon of 6,224 labeled nouns which includes 1,446 relational noun. |
| Outcome: | The proposed classifier achieves 70.4% F1 on held out nouns among the most common 2,500 word types in Gigaword. |
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| Challenge: | Existing approaches to relation extraction use concatenating embeddings of head and tail entities . however, such representations capture the types of the entities involved, leading to false positives and confusion between relations involving entities of the same type. |
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| Challenge: | Concept and Named Entity Recognition (CNER) is a new unified task that handles concepts and entities mentioned in unstructured texts seamlessly. |
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| Challenge: | HERBERTa is a pipeline for a multilingual task involving two separate BERT models. |
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