Open AccessInformation extraction using neural language models for the case of online job listings analysis
Author(s) -
Dmitriy S. Botov,
Ботов Дмитрий Сергеевич,
Julius Klenin,
Кленин Юлий Дмитриевич,
Ivan Nikolaev,
Николаев Иван Евгеньевич
Publication year - 2018
Publication title -
vestnik ûgorskogo gosudarstvennogo universiteta
Language(s) - English
Resource type - Journals
eISSN - 2078-9114
pISSN - 1816-9228
DOI - 10.17816/byusu2018037-48
Subject(s) - word2vec , computer science , artificial neural network , artificial intelligence , quality (philosophy) , natural language processing , knowledge base , domain (mathematical analysis) , machine learning , data mining , information retrieval , mathematics , mathematical analysis , philosophy , embedding , epistemology
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.