# Automatic Design of Semantic Similarity Ensembles Using Grammatical Evolution

arXiv: https://arxiv.org/abs/2307.00925 · HTML: https://mlsanity.com/paper/2307.00925/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 59 references that are in the map.
- First posted on arXiv 3 Jul 2023.

## Builds on (most cited first, 9 of 9)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [AutoML: A Survey of the State-of-the-Art](https://mlsanity.com/paper/1908.00709/) (2019)
- [SimLex-999: Evaluating Semantic Models with (Genuine) Similarity Estimation](https://mlsanity.com/paper/1408.3456/) (2014)
- [Wikipedia-based Semantic Interpretation for Natural Language Processing](https://mlsanity.com/paper/1401.5697/) (2014)
- [PonyGE2: Grammatical Evolution in Python](https://mlsanity.com/paper/1703.08535/) (2017)
- [An evaluative baseline for geo-semantic relatedness and similarity](https://mlsanity.com/paper/1402.3371/) (2014)
- [The semantic similarity ensemble](https://mlsanity.com/paper/1401.2517/) (2014)

## Abstract (arXiv)

Semantic similarity measures are a key component in natural language processing tasks such as document analysis, requirement matching, and user input interpretation. However, the performance of individual measures varies considerably across datasets. To address this, ensemble approaches that combine multiple measures are often employed. This paper presents an automated strategy based on grammatical evolution for constructing semantic similarity ensembles. The method evolves aggregation functions that maximize correlation with human-labeled similarity scores. Experiments on standard benchmark datasets demonstrate that the proposed approach outperforms existing ensemble techniques in terms of accuracy. The results confirm the effectiveness of grammatical evolution in designing adaptive and accurate similarity models. The source code that illustrates our approach can be downloaded from https://github.com/jorge-martinez-gil/sesige.