Zhang, Z., Petrak, J. & Maynard, D. Adapted TextRank for Term Extraction: A Generic Method of Improving Automatic Term Extraction Algorithms. in SEMANTiCS 2018 – 14th International Conference on Semantic Systems 00, 0-000 (2018).
In this paper, a novel method of ATE which addresses two major limitations is presented. First, the fact that no single ATE method consistently performs well across all domains, and secondly that the majority of ATE methods are unsupervised. These are addressed by investigating a generic method that can improve the accuracy of existing ATE methods, and by arguing that it is possible to use existing lexical resources to support ATE.
- Selects a subset of words based on their semantic relatedness to a set of seed words or phrases relevant to the domain, but not necessarily representative of the terms within the target text.
- It then applies an adapted TextRank algorithm to create a graph for these words, and computes a text-level TextRank score for each selected word.
- Finally, these scores are used to revise the score of a term candidate previously computed by an ATE method.
A sample of performances in different datasets and using different ATE techniques. The base performance of the ATE method is represented by the black horizontal line. The horizontal axis represents the semantic similarity threshold used in step 1. The vertical axis shows average P@K for all five K’s considered.