Uchenna
Akujuobi
Profile
Uchenna Akujuobi is currently a research scientist with the SonyAI gastronomy team based in Tokyo. His research interests include network embedding, graph mining, information retrieval,text mining, and deep neural networks. Born in Nigeria, he obtained his BS degree in Saint Petersburg Electrotechnical University and his MS and PhD degree in the MINE Laboratory at the King Abdullah University of Science and Technology. He is motivated by the use of AI to augment human abilities for more creativity and improved performance. He believes in pushing the boundary between human possibilities and impossibilities one AI step at a time.
Publications
In the ever-evolving domain of food computing, named entity recognition (NER) presents transformative potential that extends far beyond mere word tagging in recipes. Its implications encompass intelligent recipe recommendations, health analysis, and personalization. Neverthe…
Over the last few years Literature-based Discovery (LBD) has regained popularity as a means to enhance the scientific research process. The resurgent interest has spurred the development of supervised and semi-supervised machine learning models aimed at making previously imp…
Aspect-Based Sentiment Analysis (ABSA) involves extracting opinions from textual data about specific entities and their corresponding aspects through various complementary subtasks. Several prior research has focused on developing ad hoc designs of varying complexities for t…
Motivated by the abundance of biomedical publications and the need to better understand the relationship between food and health, we study a new sentiment analysis task based on literature- based discovery. Many attempts have been made to introduce health into recipe recomme…
In this paper, we study an automatic hypothesis generation (HG) problem, which refers to the discovery of meaningful implicit connections between scientific terms, including but not limited to diseases, chemicals, drugs, and genes extracted from databases of biomedical publi…
Understanding the relationships between biomedical terms like viruses, drugs, and symptoms is essential in the fight against diseases. Many attempts have been made to introduce the use of machine learning to the scientific process of hypothesis generation (HG), which refers …
In this paper, we study an automatic hypothesis generation (HG) problem, which refers to the discovery of meaningfulimplicit connections between scientific terms, including but not limited to diseases, chemicals, drugs, and genes extracted fromdatabases of biomedical publica…
Blog
July 9, 2021 | Life at Sony AI
Revolutionizing Hypothesis Generation
In part, scientific research is led by the hypothesis – the supposition or proposal that forms the basis for further investigation. Traditionally, such hypotheses are formed by the researcher and their team, taking into account v…
In part, scientific research is led by the hypothesis – the supposition or proposal that forms the basis for further investigatio…
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