Extracting Process Graphs from Texts via Multi-Granularity Text Classification.

@inproceedings{Qian2019ExtractingPG,
  title={Extracting Process Graphs from Texts via Multi-Granularity Text Classification.},
  author={Chen Qian and Lijie Wen and Mingsheng Long and Yanwei Li and Amrendra Kumar and Jianmin Wang},
  year={2019}
}
Process graph extraction (PGE) is a recently emerged interdiscipline between natural language processing and business process management, which aims to extract process graphs expressed in texts. Previous process extractors heavily depend on manual features and ignore the potential relations between clues of different text granularities. In this paper, we formalize the PGE task into the multi-granularity text classification problem, and propose a hierarchical model to effectively model and… CONTINUE READING

References

Publications referenced by this paper.
SHOWING 1-10 OF 48 REFERENCES

Bioinformatic Workflow Extraction from Scientific Texts based on Word Sense Disambiguation

  • IEEE/ACM Transactions on Computational Biology and Bioinformatics
  • 2018
VIEW 9 EXCERPTS
HIGHLY INFLUENTIAL

and amd L ́ea A

Z. Haj-Yahia
  • Deleris, A. S.
  • 2019

A

A. Halioui, P. Valtchev, Diallo
  • B.
  • 2018
VIEW 2 EXCERPTS

B

Dalvi
  • B.; Huang, L.; Tandon, N.; et al.
  • 2018

BuildingDynamic KnowledgeGraphs fromText using Machine Reading Comprehension

Das
  • 2018

H

W. Feng, Zhuo
  • H.; and Kambhampati, S.
  • 2018