TAPON-MT: A versatile framework for semantic labelling

作者：

Highlights：

• We present TAPON-MT, a versatile, customisable semantic-labelling framework.

• TAPON-MT labels both textual and structural elements in arbitrary datasets.

• TAPON-MT’s modular architecture enables adding complex features to the model.

• TAPON-MT allows the visualisation and validation of models for diagnosis of errors.

• Our validation with the NSF awards dataset, shows an improvement in accuracy.

摘要

•We present TAPON-MT, a versatile, customisable semantic-labelling framework.•TAPON-MT labels both textual and structural elements in arbitrary datasets.•TAPON-MT’s modular architecture enables adding complex features to the model.•TAPON-MT allows the visualisation and validation of models for diagnosis of errors.•Our validation with the NSF awards dataset, shows an improvement in accuracy.

论文关键词：Semantic labelling,Information integration,Machine learning

论文评审过程：Received 30 June 2018, Accepted 28 December 2018, Available online 17 February 2019, Version of Record 22 February 2019.

论文官网地址：https://doi.org/10.1016/j.is.2018.12.006