Biomedical Text Analytics for Characterizing Climate-Sensitive Disease

Parwez, Md. Aslam; Abulaish, Muhammad; Jahiruddin,

doi:10.1016/j.procs.2018.05.016

Cited by 3 publications

(3 citation statements)

References 14 publications

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“…Te crawled documents constitute a corpus C, which we use to evaluate the proposed approach. Te same corpus is exploited to extract the relational semantic information as discussed in [10,54] and utilized to construct a relational semantic repository, R l . Te corpus and the relational semantic repository are employed to generate the initial word representation by applying SVD on their underlying PPMI matrices.…”

Section: Proposed Approachmentioning

confidence: 99%

“…We retrieved 67516 abstracts, called corpus C, related to cholera, dengue, diarrhoea, infuenza, leishmaniasis, malaria, and meningitis diseases by querying the PubMed database. Te document retrieval process is discussed in detail in [10,54]. Moreover, we created the relational semantic repositoryR l from the relation triples ( < entity i , relation, entity j > ) extracted from the corpus.…”

Section: Corpus and The Relational Semantic Repositorymentioning

confidence: 99%

“…R l consists of disease symptom and their associations in the form of semantic triples, which are extracted using typed dependencies generated by Stanford parser (https://nlp.stanford.edu/software/lexparser.shtm) and fltered by employing MetaMap (https:// metamap.nlm.nih.gov/). Te process of extraction of relation triples is discussed in [10,54].…”

Section: Corpus and The Relational Semantic Repositorymentioning

confidence: 99%

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Biomedical Text Classification Using Augmented Word Representation Based on Distributional and Relational Contexts

Parwez

Fazil

Arif

et al. 2023

Computational Intelligence and Neuroscience

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Due to the increasing use of information technologies by biomedical experts, researchers, public health agencies, and healthcare professionals, a large number of scientific literatures, clinical notes, and other structured and unstructured text resources are rapidly increasing and being stored in various data sources like PubMed. These massive text resources can be leveraged to extract valuable knowledge and insights using machine learning techniques. Recent advancement in neural network-based classification models has gained popularity which takes numeric vectors (aka word representation) of training data as the input to train classification models. Better the input vectors, more accurate would be the classification. Word representations are learned as the distribution of words in an embedding space, wherein each word has its vector and the semantically similar words based on the contexts appear nearby each other. However, such distributional word representations are incapable of encapsulating relational semantics between distant words. In the biomedical domain, relation mining is a well-studied problem which aims to extract relational words, which associates distant entities generally representing the subject and object of a sentence. Our goal is to capture the relational semantics information between distant words from a large corpus to learn enhanced word representation and employ the learned word representation for various natural language processing tasks such as text classification. In this article, we have proposed an application of biomedical relation triplets to learn word representation through incorporating relational semantic information within the distributional representation of words. In other words, the proposed approach aims to capture both distributional and relational contexts of the words to learn their numeric vectors from text corpus. We have also proposed an application of the learned word representations for text classification. The proposed approach is evaluated over multiple benchmark datasets, and the efficacy of the learned word representations is tested in terms of word similarity and concept categorization tasks. Our proposed approach provides better performance in comparison to the state-of-the-art GloVe model. Furthermore, we have applied the learned word representations to classify biomedical texts using four neural network-based classification models, and the classification accuracy further confirms the effectiveness of the learned word representations by our proposed approach.

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Section: Proposed Approachmentioning

confidence: 99%

Section: Corpus and The Relational Semantic Repositorymentioning

confidence: 99%

Section: Corpus and The Relational Semantic Repositorymentioning

confidence: 99%

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