RESUME INFORMATION EXTRACTION WITH A NOVEL TEXT BLOCK SEGMENTATION ALGORITHM

In recent years, we have witnessed the rapid development of deep neural networks and distributed representations in natural language processing. However, the applications of neural networks in resume parsing lack systematic investigation. In this study, we proposed an end-to-end pipeline for resume parsing based on neural networks-based classifiers and distributed embeddings. This pipeline leverages the position-wise line information and integrated meanings of each text block. The coordinated line classification by both line type classifier and line label classifier effectively segment a resume into predefined text blocks. Our proposed pipeline joints the text block segmentation with the identification of resume facts in which various sequence labelling classifiers perform named entity recognition within labelled text blocks. Comparative evaluation of four sequence labelling classifiers confirmed BLSTMCNNs-CRF’s superiority in named entity recognition task. Further comparison among three publicized resume parsers also determined the effectiveness of our text block classification method