Unsupervised key-phrases extraction from scientific papers using domain and linguistic knowledge

The domain of Digital Libraries presents specific challenges for unsupervised information extraction to support both the automatic classification of documents and the enhancement of users’ navigation in the digital content. In this paper, we propose a combined use of machine learning techniques (i.e. Support Vector Machines) and Natural Language Processing techniques (i.e. Stanford NLP parser) to tackle the problem of unsupervised key-phrases extraction from scientific papers. The proposed method strongly depends on the robust structural properties of a scientific paper as well as on the lexical knowledge that we are able to mine from its text. For the experimental assessment we have use a subset of ACM1 papers in the Computer Science domain containing 400 documents. Preliminary evaluation of the approach shows promising result that improves – on the same data-set – on state-of-the-art Bayesian learning system KEA2 from a minimum 27% to a maximum 77% depending on KEA parameters ...

• Type: Conference
• Year: 2008
• Where: ICDIM
• Authors: Mikalai Krapivin, Maurizio Marchese, Andrei Yadrantsau, Yanchun Liang
• Keywords: ICDIM 2008, Information Management, Scientific Paper, Unsupervised Information Extraction, Unsupervised Key-phrases Extraction