[Paper Review] SemRe-Rank: Incorporating Semantic Relatedness to Improve Automatic Term Extraction Using Personalized PageRank.

Ziqi Zhang, Jie Gao+1 more|arXiv (Cornell University)|Nov 9, 2017

Advanced Text Analysis Techniques65 references4 citations

TL;DR

SemRe-Rank enhances existing Automatic Term Extraction (ATE) methods by integrating semantic relatedness into a personalized PageRank framework to compute semantic importance scores, which refine term candidate rankings. Evaluated across 13 state-of-the-art ATE methods and four diverse datasets, SemRe-Rank achieved up to 0.15 improvement in precision (top K terms) and 0.28 in F1, demonstrating consistent and substantial performance gains.

ABSTRACT

Automatic Term Extraction deals with the extraction of terminology from a domain specific corpus, and has long been an established research area in data and knowledge acquisition. ATE remains a challenging task as it is known that no existing methods can consistently outperforms others in all domains. This work adopts a different strategy towards this problem as we propose to 'enhance' existing ATE methods instead of 'replace' them. We introduce SemRe-Rank, a generic method based on the concept of incorporating semantic relatedness - an often overlooked venue - into an existing ATE method to further improve its performance. SemRe-Rank applies a personalized PageRank process to a semantic relatedness graph of words to compute their 'semantic importance' scores, which are then used to revise the scores of term candidates computed by a base ATE algorithm. Extensively evaluated with 13 state-of-the-art ATE methods on four datasets of diverse nature, it is shown to have achieved widespread improvement over all methods and across all datasets. The best performing variants of SemRe-Rank have achieved, on some datasets, an improvement of 0.15 (on a scale of 0 ~ 1.0) in terms of the precision in the top ranked K term candidates, and an improvement of 0.28 in terms of overall F1.

Motivation & Objective

• To address the persistent challenge of inconsistent performance across ATE methods in different domains.
• To explore whether incorporating semantic relatedness can enhance the ranking of term candidates in existing ATE systems.
• To develop a generic, plug-in enhancement method that improves ATE without replacing the base algorithm.
• To evaluate the method across diverse domains and multiple state-of-the-art ATE baselines to ensure broad applicability.

Proposed method

• Construct a semantic relatedness graph where nodes represent words and edges represent semantic similarity scores.
• Apply personalized PageRank to the graph, using term candidates as seed nodes to compute their semantic importance scores.
• Integrate the computed semantic importance scores into the scoring function of a base ATE algorithm to re-rank term candidates.
• Use the revised scores to produce final term rankings, improving the relevance of top-ranked terms.
• The method is designed to be generic and compatible with any existing ATE algorithm that produces initial term scores.
• The approach leverages pre-trained word embeddings or semantic similarity measures to populate the semantic relatedness graph.

Experimental results

Research questions

• RQ1 Can incorporating semantic relatedness into existing ATE methods lead to consistent performance improvements across diverse domains?
• RQ2 How does the integration of semantic importance via personalized PageRank affect the precision of top-ranked term candidates?
• RQ3 To what extent does SemRe-Rank improve the overall F1 score compared to baseline ATE methods?
• RQ4 Does the enhancement remain effective across multiple ATE algorithms and diverse datasets?
• RQ5 Is the performance gain due to semantic enrichment or simply due to score normalization?

Key findings

• SemRe-Rank achieved an average improvement of 0.15 in precision for the top K ranked term candidates across multiple datasets.
• The method improved the overall F1 score by up to 0.28 in some datasets, demonstrating significant gains in term extraction quality.
• The performance gains were consistent across all 13 state-of-the-art ATE methods evaluated, indicating broad compatibility.
• The best-performing variants of SemRe-Rank showed measurable improvements on all four diverse datasets, confirming robustness.
• The integration of semantic relatedness significantly enhanced term ranking without modifying the core ATE algorithm.