Information extraction using neural language models for the case of online job listings analysis
In this article we discuss the approach to information extraction (IE) using neural language models. We provide a detailed overview of modern IE methods: both supervised and unsupervised. The proposed method allows to achieve a high quality solution to the problem of analyzing the relevant labor market requirements without the need for a time consuming labelling procedure. In this experiment, professional standards act as a knowledge base of the labor domain. Comparing the descriptions of work actions and requirements from professional standards with the elements of job listings, we extract four entity types. The approach is based on the classification of vector representations of texts, generated using various neural language models: averaged word2vec, SIF weighted averaged word2vec, TF IDF weighted averaged word2vec, paragraph2vec. Experimentally, the best quality was shown by the averaged word2vec (CBOW) model.