Results 41 to 50 of about 14,087 (197)
AIDArabic A Named-Entity Disambiguation Framework for Arabic Text [PDF]
Proceedings of the EMNLP 2014 Workshop on Arabic Natural Language Processing (ANLP), 2014 There has been recently a great progress in the field of automatically generated knowledge bases and corresponding disambiguation systems that are capable of mapping text mentions onto canonical entities. Efforts like the before mentioned have enabled researchers and analysts from various disciplines to semantically “understand” contents. However, most
Yosef, Mohamed Amir +2 more
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Computational Linguistics, 2021 Nowadays, most research conducted in the field of abstractive text summarization focuses on neural-based models alone, without considering their combination with knowledge-based approaches that could further enhance their efficiency.
Panagiotis Kouris +2 more
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Improving the Performance of Vietnamese–Korean Neural Machine Translation with Contextual Embedding
Applied Sciences, 2021 With the recent evolution of deep learning, machine translation (MT) models and systems are being steadily improved. However, research on MT in low-resource languages such as Vietnamese and Korean is still very limited.
Van-Hai Vu +3 more
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Disambiguating named entities by semantic web
Advances in Intelligent Systems Research, 2014 There are many words in natural language sentences which describe an object or event in real world. An important step to understand a sentence is finding the exact meaning of all words. In this paper we propose an approach to identify meaning of each named entity of a text by using web of data (LOD) as a large scale knowledge base.
Koohpeyma Fateme, Azari Ideh
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Name Disambiguation from link data in a collaboration graph using temporal and topological features [PDF]
, 2015 In a social community, multiple persons may share the same name, phone number or some other identifying attributes. This, along with other phenomena, such as name abbreviation, name misspelling, and human error leads to erroneous aggregation of records ...
Hasan, Mohammad Al +2 more
core +2 more sources
Analysis Name Entity Disambiguation Using Mining Evidence Method [PDF]
Paradigma - Jurnal Komputer dan Informatika, 2020 Hadith is the second guideline and source of Islamic teachings after the Qur'an. One of the most Saheeh hadith is the book of Saheeh al-Bukhaari. Hadith Sahih Bukhari has a chain of narrators, hadith numbers, and contents of different contents. This tradition also has science that discusses the history of the narrators of the hadith called the Science ...
Adelya Astari +2 more
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Reading bots: The implication of deep learning on guided reading
Frontiers in Psychology, 2023 This study introduces the application of deep-learning technologies in automatically generating guidance for independent reading. The study explores and demonstrates how to incorporate the latest advances in deep-learning-based natural language ...
Baorong Huang +3 more
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Graph Ranking for Collective Named Entity Disambiguation [PDF]
Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2014 Named Entity Disambiguation (NED) refers to the task of mapping different named entity mentions in running text to their correct interpretations in a specific knowledge base (KB). This paper presents a collective disambiguation approach using a graph model.
Ayman Alhelbawy, Robert Gaizauskas
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Advanced Intelligent Systems, EarlyView.OntoLogX is an autonomous AI agent that uses large language models to transform unstructured cyber security logs into ontology grounded knowledge graphs. By integrating retrieval augmented generation, iterative correction, and a light‐weight log ontology, OntoLogX produces semantically consistent intelligence that links raw log events to MITRE ATT & CK
Luca Cotti +4 more
wiley +1 more source
Classifying relations for biomedical named entity disambiguation [PDF]
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing Volume 3 - EMNLP '09, 2009 Named entity disambiguation concerns linking a potentially ambiguous mention of named entity in text to an unambiguous identifier in a standard database. One approach to this task is supervised classification. However, the availability of training data is often limited, and the available data sets tend to be imbalanced and, in some cases, heterogeneous.
Wang, Xinglong +2 more
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