https://scholar.dlu.edu.vn/handle/123456789/39 Fri, 15 May 2026 10:07:32 GMT 2026-05-15T10:07:32Z https://scholar.dlu.edu.vn/handle/123456789/4348 Title: Sách hướng dẫn thực hành phát triển ứng dụng Desktop Authors: Nguyễn, Thị Lương; Phan, Thị Thanh Nga; Thai, Duy Quy; Linh, Trần Thị Phương; Hoàng, Minh Tiến; Tạ, Hoàng Thắng Tue, 19 Nov 2024 00:00:00 GMT https://scholar.dlu.edu.vn/handle/123456789/4348 2024-11-19T00:00:00Z https://scholar.dlu.edu.vn/handle/123456789/4177 Title: Hướng dẫn thực hành phát triển ứng dụng Desktop Authors: Nguyễn, Thị Lương; Phan, Thị Thanh Nga; Linh, Trần Thị Phương; Thai, Duy Quy; Tạ, Hoàng Thắng; Hoàng, Minh Tiến Tue, 19 Nov 2024 00:00:00 GMT https://scholar.dlu.edu.vn/handle/123456789/4177 2024-11-19T00:00:00Z https://scholar.dlu.edu.vn/handle/123456789/2179 Title: The Combination of BERT and Data Oversampling for Answer Type Prediction Authors: Tạ, Hoàng Thắng Abstract: In this paper, we address the Task 1 (of the SMART Task 2021) of predicting the answer categories and types based on target ontologies, which could be useful in knowledge-based Question Answering (QA) systems. We introduced our method by combining the power of BERT architectures with data oversampling via replacements of linked terms to Wikidata and dependent noun phrases to attain the state-ofthe-art performance. The accuracy on the DBpedia dataset is 98.5%, whereas NDCG@5 and NDCG@10 are 72.7% and 66.4% respectively. Our model has the best performance compared to other teams, with the accuracy score of 98% and Mean Reciprocal Rank (MRR) of 70% on the Wikidata dataset. Fri, 01 Jan 2021 00:00:00 GMT https://scholar.dlu.edu.vn/handle/123456789/2179 2021-01-01T00:00:00Z https://scholar.dlu.edu.vn/handle/123456789/2178 Title: Mapping Process for the Task: Wikidata Statements to Text as Wikipedia Sentences Authors: Tạ, Hoàng Thắng; Alexander Gelbukha; Grigori Sidorov Abstract: Acknowledged as one of the most successful online cooperative projects in human society, Wikipedia has obtained rapid growth in recent years and desires continuously to expand content and disseminate knowledge values for everyone globally. The shortage of volunteers brings to Wikipedia many issues, including developing content for over 300 languages at the present. Therefore, the benefit that machines can automatically generate content to reduce human efforts on Wikipedia language projects could be considerable. In this paper, we propose our mapping process for the task of converting Wikidata statements to natural language text (WS2T) for Wikipedia projects at the sentence level. The main step is to organize statements, represented as a group of quadruples and triples, and then to map them to corresponding sentences in English Wikipedia. We evaluate the output corpus in various aspects: sentence structure analysis, noise filtering, and relationships between sentence components based on word embedding models. The results are helpful not only for the data-to-text generation task but also for other relevant works in the field. Sun, 23 Oct 2022 00:00:00 GMT https://scholar.dlu.edu.vn/handle/123456789/2178 2022-10-23T00:00:00Z