Extraction of Relations Between Entities from Human-Generated Content on Social Networks

Adriani, Marco; Brambilla, Marco; Giovanni, Marco Di

doi:10.1007/978-3-030-51253-8_7

Cited by 1 publication

(1 citation statement)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the cross-validations, the entire data set was randomly split three times, each time into three sets (50% training, 25% validation, and 25% testing). Same as studies for similar applications (e.g., Dhillon et al 2012;Adriani et al 2020), the number of folds in this research was set to three to keep more data for testing in each fold. The confidence intervals of the mean values for the evaluation measures were also calculated to evaluate the sensitivity of the performance results.…”

Section: Discussionmentioning

confidence: 99%

Semantic Neural Network Ensemble for Automated Dependency Relation Extraction from Bridge Inspection Reports

Liu

El-Gohary

2021

J. Comput. Civ. Eng.

View full text Add to dashboard Cite

Bridge inspection reports are important sources of technically detailed data/information about bridge conditions and maintenance history, yet remain untapped for bridge deterioration prediction. To capitalize on these reports for improved bridge deterioration prediction, there is a need for dependency parsing methods, in order to extract dependency relations from the reports for linking the isolated words into concepts and representing the semantically low concepts in a semantically rich structured way. To address this need, this paper proposes a novel semantic neural network ensemble (NNE)-based dependency parsing methodology. It uses a similarity-based method to sample similarly distributed configurations into the same clusters, a set of constituent neural network (NN) classifiers to learn from both the syntactic and semantic text features of the similarly distributed and therefore more easily separable configurations, and a combiner classifier to capture the classification patterns of the NN classifiers to make final predictions on the transition types. The proposed dependency parsing methodology was evaluated in extracting dependency relations from bridge inspection reports for representing information-about bridge conditions and maintenance actions-in a semantically rich structured way. It achieved a precision, recall, and F-1 measure of 96.6%, 90.4%, and 93.3% with a margin of error of 3.8%, 4.4%, and 3.8% at the semantic information element level, and 88.2%, 81.5%, and 84.7% with a margin of error of 5.4%, 5.8%, and 5.4% at the semantic information set level, respectively.

show abstract

Section: Discussionmentioning

confidence: 99%