Papers with manual validation
Automatic Data Acquisition for Event Coreference Resolution (2021.eacl-main)
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| Challenge: | lexical paraphrases and high precision rules informed by news discourse structure can be used to collect coreferential and non-coreferential event pairs from unlabeled English news articles. |
| Approach: | They propose to use lexical paraphrases and news discourse structure to automatically collect coreferential and non-coreferential event pairs from unlabeled English news articles. |
| Outcome: | The proposed model performs better than the supervised model on evaluation datasets with different event domains and text genres. |
Resource of Wikipedias in 31 Languages Categorized into Fine-Grained Named Entities (2022.coling-1)
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| Challenge: | a resource of Wikipedias in 31 languages is categorized into Extended Named Entity (ENE) ENE version 8 has 219 fine-grained NE categories. |
| Approach: | They describe a resource of Wikipedias in 31 languages categorized into Extended Named Entity (ENE) they first categorized 920 K Japanese Wikipedia pages using machine learning, then shared a task of Wikipedia categorization into 30 languages . |
| Outcome: | The proposed system is based on a dataset of Japanese Wikipedia pages . the dataset shows the best performance among the 30 languages . |
The MWN.PT WordNet for Portuguese: Projection, Validation, Cross-lingual Alignment and Distribution (2020.lrec-1)
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António Branco, Sara Grilo, Márcia Bolrinha, Chakaveh Saedi, Ruben Branco, João Silva, Andreia Querido, Rita de Carvalho, Rosa Gaudio, Mariana Avelãs, Clara Pinto
| Challenge: | Lexical semantic networks are pervasive in natural language processing . Lexical ontologies play a key role in virtually all major applications . |
| Approach: | The present paper presents the MWN.PT WordNet for Portuguese . it is the largest high quality, manually validated and cross-lingually integrated wordnet of Portuguese based on the Princeton WordNet of English . |
| Outcome: | The MWN.PT WordNet for Portuguese includes 41,000 concepts expressed by 38,000 lexical units. |