Unsupervised Technique for Automatically Extracting Components of References

Abstract

The automatic extraction of bibliographic data remains a difficult task to the present day, when it's realized that the scientific publications are not in a standard format and every publications has its own template. There are many “regular expression” techniques and “supervised machine learning” techniques for extracting the entire details of the references mentioned within the bibliographic section. But there's no much difference within the percentage of their success. Our idea is to seek out whether unsupervised machine learning techniques can help us in increasing the share of success. This paper presents a technique for segregating and automatically extracting the individual components of references like Authors, Title of the references, publications details, etc., using “Unsupervised technique”, “Named-Entity recognition”(NER) technique and link these references to their corresponding full text article with the assistance of google.