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Global trends and future directions in COVID-19 and leukemia research from 2020 to 2024. [PDF]
Discov OncolYang Z, Zhao H, Tian J.
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Bridging the gap in author names: building an enhanced author name dataset for biomedical literature system. [PDF]
J Am Med Inform AssocZhang L, Song N, Gui S, Wu K, Lu W.
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TurkMedNLI: a Turkish medical natural language inference dataset through large language model based translation. [PDF]
PeerJ Comput SciOğul İÜ, Soygazi F, Bostanoğlu BE.
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Named Entity Disambiguation for Resource-Poor Languages
Proceedings of the Eighth Workshop on Exploiting Semantic Annotations in Information Retrieval, 2015Named entity disambiguation (NED) is the task of linking ambiguous names in natural language text to canonical entities like people, organizations or places, registered in a knowledge base. The problem is well-studied for English text, but few systems have considered resource-poor languages that lack comprehensive name-entity dictionaries, entity ...
Gad-Elrab, M., Yosef, M., Weikum, G.
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Resources for Nepali Word Sense Disambiguation
2008 International Conference on Natural Language Processing and Knowledge Engineering, 2008Word sense disambiguation (WSD) is a process of identifying proper meaning of words that may have multiple meanings. It is regarded as one of the most challenging problems in the field of natural language processing (NLP). Nepali Language also has words that have multiple meanings, thus giving rise to the problem of WSD in it.
Niraj Shrestha +2 more
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Minimalist Entity Disambiguation for Mid-Resource Languages
Proceedings of The Fourth Workshop on Simple and Efficient Natural Language Processing (SustaiNLP), 2023For many languages and applications, even though enough data is available for training Named Entity Disambiguation (NED) systems, few off-the-shelf models are available for use in practice. This is due to both the large size of state-of-the-art models, and to the computational requirements for recreating them from scratch.
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Keyword-Driven Resource Disambiguation over RDF Knowledge Bases
2013Keyword search is the most popular way to access information. In this paper we introduce a novel approach for determining the correct resources for user-supplied queries based on a hidden Markov model. In our approach the user-supplied query is modeled as the observed data and the background knowledge is used for parameter estimation.
Shekarpour, Saeedeh +2 more
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