Using Semantic Role Knowledge for Relevance Ranking of Key Phrases in Documents: An Unsupervised Approach
This work addresses key phrase extraction for document analysis, but it is incremental as it builds on existing PageRank methods with new features.
The paper tackled the problem of ranking key phrases in documents by integrating sentence position and semantic role information into a PageRank system, resulting in overall improvements over state-of-the-art baselines across three scientific article datasets.
In this paper, we investigate the integration of sentence position and semantic role of words in a PageRank system to build a key phrase ranking method. We present the evaluation results of our approach on three scientific articles. We show that semantic role information, when integrated with a PageRank system, can become a new lexical feature. Our approach had an overall improvement on all the data sets over the state-of-art baseline approaches.