Biological question answering with syntactic and semantic feature matching and an improved mean reciprocal ranking measurement

Lin, Ryan T.K.; Chiu, Justin Liang-Te; Dai, Hong-Jei; Day, Min-Yuh; Tsai, Richard Tzong-Han; Hsu, Wen-Lian

doi:10.1109/iri.2008.4583027

Cited by 9 publications

(6 citation statements)

References 15 publications

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“…Clustering and representation techniques assist with adequately exploring data collection and identifying clusters of similar information that share similar properties [15]. For example, to cluster text, the following algorithms have been used: Support Vector Machine (SVM) [16], k-means [17], Principal Component Analysis (PCA) [18], and Kohonen Self-Organizing Map (SOM) [19]. A full review of different clustering algorithms can be found in [20].…”

Section: Data Representation and Clusteringmentioning

confidence: 99%

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The Use of Artificial Intelligence to Bridge Multiple Narratives in Disaster Response

Ochoa¹

2023

Avantgarde Reliability Implications in Civil Engineering [Working Title]

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Disaster response presents the current situation, creates a summary of available information on the disaster, and sets the path for recovery and reconstruction. During the last 10 years, various disciplines have investigated disaster response twofold. First, researchers published several studies using state-of-the-art technologies for disaster response. Second, humanitarian organizations have produced numerous mission statements on how to respond to natural disasters. The former suggests questioning: If we have developed a considerable number of studies to respond to a natural disaster, how can we cross-validate its results with humanitarian organizations’ mission statements to bring the knowledge of specific disciplines into disaster response? To address the above question, the research proposes an experiment that considers both: knowledge produced in the form of 8364 abstracts of academic writing on Disaster Response and 1930 humanitarian organizations’ mission statements indexed online. The experiment uses an Artificial Intelligence algorithm, Neural Network, to perform the task of word embedding––Word2Vec––and an unsupervised machine learning algorithm for clustering––Self-Organizing Maps. Finally, it employs Human Intelligence for the selection of information and decision-making. The result is a digital infrastructure that will suggest actions and tools relevant to a specific scenario, providing valuable information loaded with architectural knowledge to guide the decision-makers at the operational level in tasks dealing with spatial and constructive constraints.

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Section: Data Representation and Clusteringmentioning

confidence: 99%

“…A full review of different clustering algorithms can be found in [20]. As shown in the work of [19], the unsupervised ML algorithm SOM [21,22] has proven to have excellent performance when clustering text data and reducing its dimensionality. Additionally, as presented in the work of [23].…”

Section: Data Representation and Clusteringmentioning

confidence: 99%

The Use of Artificial Intelligence to Bridge Multiple Narratives in Disaster Response

Ochoa¹

2023

Avantgarde Reliability Implications in Civil Engineering [Working Title]

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“…Semantic enrichment for biomedical text is becoming significant because of the huge corpus of biomedical vocabulary. The use of dictionaries [71,72], has been found in highly cited literature work such as [35,64,139]. Application of ontologies [51, 76,77] has been cited in the literature work such as [72,75,78,158] employing multiple ontologies for creating a better knowledge corpus.…”

Section: Regular Expression Matching For Question Answer Pair Extract...mentioning

confidence: 99%

Survey on the Biomedical Text Summarization Techniques with an Emphasis on Databases, Techniques, Semantic Approaches, Classification Techniques, and Similarity Measures

et al. 2023

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Biomedical text summarization (BTS) is proving to be an emerging area of work and research with the need for sustainable healthcare applications such as evidence-based medicine practice (EBM) and telemedicine which help effectively support healthcare needs of the society. However, with the rapid growth in the biomedical literature and the diversities in its structure and resources, it is becoming challenging to carry out effective text summarization for better insights. The goal of this work is to conduct a comprehensive systematic literature review of significant and high-impact literary work in BTS with a deep understanding of its major artifacts such as databases, semantic similarity measures, and semantic enrichment approaches. In the systematic literature review conducted, we applied search filters to find high-impact literature in the biomedical text summarization domain from IEEE, SCOPUS, Elsevier, EBSCO, and PubMed databases. The systematic literature review (SLR) yielded 81 works; those were analyzed for qualitative study. The in-depth study of the literature shows the relevance and efficacy of the deep learning (DL) approach, context-aware feature extraction techniques, and their relevance in BTS. Biomedical question answering (BQA) system is one of the most popular applications of text summarizations for building self-sufficient healthcare systems and are pointing to future research directions. The review culminates in realization of a proposed framework for the BQA system MEDIQA with design of better heuristics for content screening, document screening, and relevance ranking. The presented framework provides an evidence-based biomedical question answering model and text summarizer that can lead to real-time evidence-based clinical support system to healthcare practitioners.

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“…In [47], authors describe a QA system for computing answers to questions related to protein-protein interactions. They used a pipeline composed of named-entity recognition, semantic role labeling, question classification and query expansion.…”

Section: Other Research Workmentioning

confidence: 99%

Question answering for Biology

Neves

Leser

2015

Methods

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Biological question answering with syntactic and semantic feature matching and an improved mean reciprocal ranking measurement

Abstract: Abstract

Cited by 9 publications

References 15 publications

The Use of Artificial Intelligence to Bridge Multiple Narratives in Disaster Response

The Use of Artificial Intelligence to Bridge Multiple Narratives in Disaster Response

Survey on the Biomedical Text Summarization Techniques with an Emphasis on Databases, Techniques, Semantic Approaches, Classification Techniques, and Similarity Measures

Question answering for Biology

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