Browsing by Author "Nnanna Agwu Nwojo"

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Advancing Preauthorization Task in Healthcare: An Application of Deep Active Incremental Learning for Medical Text Classification
(Engineering, Technology & Applied Science Research, 2023-09-29) Nnanna Agwu Nwojo; Moussa Mahamat Boukar
This study presents a novel approach to medical text classification using a deep active incremental learning model, aiming to improve the automation of the preauthorization process in medical health insurance. By automating decision-making for request approval or denial through text classification techniques, the primary focus is on real-time prediction, utilization of limited labeled data, and continuous model improvement. The proposed approach combines a Bidirectional Long Short-Term Memory (Bi-LSTM) neural network with active learning, using uncertainty sampling to facilitate expert-based sample selection and online learning for continuous updates. The proposed model demonstrates improved predictive accuracy over a baseline Long Short-Term Memory (LSTM) model. Through active learning iterations, the proposed model achieved a 4% improvement in balanced accuracy over 100 iterations, underscoring its efficiency in continuous refinement using limited labeled data.
BASIC DEPENDENCY PARSING IN NATURAL LANGUAGE INFERENCE
(IEEE, 2017-02-02) Aleshinloye Abass Yusuf; Nnanna Agwu Nwojo; Moussa Mahamat Boukar
Parsing is the process of analyzing a sentence for it structure, content and meaning, this process uncover the structure, articulate the constituents and the relation between the constituents of the input sentence. This paper described the importance of parsing strategy in achieving entailment in natural language inference. Parsing is the basic task in processing natural language and it is also the basis for all natural language applications such as machine learning, question answering and information retrieval. We have used the parsing strategy in natural language inference to achieve entailment through an approach called normalization approach where entailment is achieved by removing or replacing some nodes as well as relations in a tree. This process requires a detailed understanding of the dependency structure, in order to generate a tree that does not contain nodes and relations that are irrelevant to the inference procedure. In order to achieve this, the dependency trees are transformed by applying some rewrite rules to the dependency tree