Improving Semantic Parsing Using Statistical Word Sense Disambiguation (Student Abstract)

Bose, Ritwik; Vashishtha, Siddharth; Allen, James F.

doi:10.1609/aaai.v34i10.7150

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…Allen et al (2018) studied how different extensions and customizations of the TRIPS parser affected performance [15]. Bose et al (2020) used decisions from a statistical word sense disambiguation system SupWSD to advise the logical semantic parser TRIPS. Significant improvement across all metrics was found using this approach, with roughly 14% improvement to raw accuracy, although the research was not conducted on biomedical literature specifically [56].…”

Section: Related Workmentioning

confidence: 99%

Humans and machines in biomedical knowledge curation: hypertrophic cardiomyopathy molecular mechanisms’ representation

Glavaški

Velicki

2021

BioData Mining

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Background Biomedical knowledge is dispersed in scientific literature and is growing constantly. Curation is the extraction of knowledge from unstructured data into a computable form and could be done manually or automatically. Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiac disease, with genotype–phenotype associations still incompletely understood. We compared human- and machine-curated HCM molecular mechanisms’ models and examined the performance of different machine approaches for that task. Results We created six models representing HCM molecular mechanisms using different approaches and made them publicly available, analyzed them as networks, and tried to explain the models’ differences by the analysis of factors that affect the quality of machine-curated models (query constraints and reading systems’ performance). A result of this work is also the Interactive HCM map, the only publicly available knowledge resource dedicated to HCM. Sizes and topological parameters of the networks differed notably, and a low consensus was found in terms of centrality measures between networks. Consensus about the most important nodes was achieved only with respect to one element (calcium). Models with a reduced level of noise were generated and cooperatively working elements were detected. REACH and TRIPS reading systems showed much higher accuracy than Sparser, but at the cost of extraction performance. TRIPS proved to be the best single reading system for text segments about HCM, in terms of the compromise between accuracy and extraction performance. Conclusions Different approaches in curation can produce models of the same disease with diverse characteristics, and they give rise to utterly different conclusions in subsequent analysis. The final purpose of the model should direct the choice of curation techniques. Manual curation represents the gold standard for information extraction in biomedical research and is most suitable when only high-quality elements for models are required. Automated curation provides more substance, but high level of noise is expected. Different curation strategies can reduce the level of human input needed. Biomedical knowledge would benefit overwhelmingly, especially as to its rapid growth, if computers were to be able to assist in analysis on a larger scale.

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Section: Related Workmentioning

confidence: 99%

Humans and machines in biomedical knowledge curation: hypertrophic cardiomyopathy molecular mechanisms’ representation

Glavaški

Velicki

2021

BioData Mining

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An ontology-driven approach to information alignment in estimating price elasticity of electricity demand

T.N.,

E.V.,

V.I.

2024

Energy Systems Research

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The significance of the work lies in the increasingly cross-disciplinary nature of emerging research problems, which necessitate the collaboration of experts from various domains to address them. To achieve this, it is essential for all research contributors to align their perspectives on the processes involved. The use of semantic technologies such as ontology-driven modeling ensures concept alignment and structuring of data and knowledge. The estimation of cross-sectoral component of price elasticity coefficients of electricity demand relies on an array of mathematical models, each built by different experts. To solve this problem, it is crucial to provide data exchange between models whose outputs complement each other. To this end, we perform an ontological analysis of information flows between models and provide examples of graphical ontologies thus designed. The semantic analysis of information flows and the system of ontologies allow the use of these models not only by their original creators but also by a broader community of researchers.

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Improving Semantic Parsing Using Statistical Word Sense Disambiguation (Student Abstract)

Cited by 2 publications

References 3 publications

Humans and machines in biomedical knowledge curation: hypertrophic cardiomyopathy molecular mechanisms’ representation

Humans and machines in biomedical knowledge curation: hypertrophic cardiomyopathy molecular mechanisms’ representation

An ontology-driven approach to information alignment in estimating price elasticity of electricity demand

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