Research on Text Mining of Railway Safety Supervisors Performance Based on BiLSTM and CRF

Abstract

In the era of big data with unstructured data, the potential value of unstructured data mining is an effective way to solve the industry problem. Based on a large number of data recorded from the actual text of the performance plan of railway security supervisors, this paper proposes a text similarity calculation method based on text mining technology to calculate whether the personnel performance plan matches the reality. First, the text Named Entity Recognition(NER) is realized by using the combination of Bi-directional Long Short Term Memory (BiLSTM) and the Conditional Random Fields (CRF). Then based on the concept similarity calculation of HowNet, we designed the method of calculating the similarity between the same named entity. After that, different weights are given according to the importance of named entity, and a method of computing similarity between named entities is designed.. Finally, according to the given threshold, to obtain whether planning and realistic matching, and the actual personnel working situation, provide the basis for personnel assessment for the management personnel.

New Avenues for Automated Railway Safety Information Processing in Enterprise Architecture: An NLP Approach

“…To measure text similarity and identify names and entities from unstructured text, BiLSTM and Condition Random Field (CRF) models are used respectively. The models are combined in order to evaluate the performance and progress of railway personnel [37].…”