Papers by Luís Morgado da Costa
Linking the TUFS Basic Vocabulary to the Open Multilingual Wordnet (2020.lrec-1)
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| Challenge: | The TUFS Basic Vocabulary Modules are hand created, using commonly occurring vocabulary. |
| Approach: | They propose to link the TUFS Basic Vocabulary Modules with the Open Multilingual Wordnet to create a multilingual lexicon. |
| Outcome: | The proposed lexicons can be used to evaluate existing wordnets, add data to wordnet synsets and create new open wordnet for Khmer, Korean, Lao, Mongolian, Russian, Tagalog, Urdua nd Vietnamese. |
The Tembusu Treebank: An English Learner Treebank (2022.lrec-1)
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| Challenge: | a new treebank is created to help diagnose ungrammatical sentences using mal-rules . the Tembusu Learner Treebank is an open treebank created from the corpus of Learner English . |
| Approach: | They propose to use the Tembusu Learner Treebank to train a new parse-ranking model for the English Resource Grammar . the model incorporates mal-rules in the annotation of ungrammatical sentences . |
| Outcome: | The Tembusu Learner Treebank is an open treebank created from the NTU Corpus of Learner English . the treebank is unique for incorporating mal-rules in the annotation of ungrammatical sentences . |
Automated Writing Support Using Deep Linguistic Parsers (2020.lrec-1)
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Luís Morgado da Costa, Roger V P Winder, Shu Yun Li, Benedict Christopher Lin Tzer Liang, Joseph Mackinnon, Francis Bond
| Challenge: | Automated Grammar Error Detection (GED) and Grammar Erreor Correction (GEC) are tasks that have attracted some attention within the NLP community. |
| Approach: | They propose a web-based system that integrates English Grammatical Error Detection (GED) and course-specific stylistic guidelines to automatically review and provide feedback on student assignments. |
| Outcome: | The system integrates both general NLP methods and high precision parsers to check student assignments before they are submitted for grading. |
Enriching Linguistic Representation in the Cantonese Wordnet and Building the New Cantonese Wordnet Corpus (2022.lrec-1)
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| Challenge: | Currently, our wordnet includes a little over 5,200 concepts and 16,300 senses . |
| Approach: | They propose to improve the Cantonese Wordnet by increasing the general coverage, adding functional categories, enriching verbal representations and creating the Cannese WordNet Corpus . |
| Outcome: | The new version includes a little over 5,200 concepts and 16,300 senses . |