# A Neural Approach for Detecting Morphological Analogies

arXiv: https://arxiv.org/abs/2108.03945 · HTML: https://mlsanity.com/paper/2108.03945/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 33 references that are in the map.
- First posted on arXiv 9 Aug 2021.

## Builds on (most cited first, 5 of 5)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Abstraction and Analogy-Making in Artificial Intelligence](https://mlsanity.com/paper/2102.10717/) (2021)
- [Analogical Dissimilarity: Definition, Algorithms and Two Experiments in Machine Learning](https://mlsanity.com/paper/1401.3427/) (2014)
- [Evaluating vector-space models of analogy](https://mlsanity.com/paper/1705.04416/) (2017)

## Built on by (most cited first, 6 of 6)

- [AnaloBench: Benchmarking the Identification of Abstract and Long-context Analogies](https://mlsanity.com/paper/2402.12370/) (2024)
- [Relevant Entity Selection: Knowledge Graph Bootstrapping via Zero-Shot Analogical Pruning](https://mlsanity.com/paper/2306.16296/) (2023)
- [Galois theory for analogical classifiers](https://mlsanity.com/paper/2205.04593/) (2022)
- [Solving morphological analogies: from retrieval to generation](https://mlsanity.com/paper/2303.18062/) (2023)
- [Tackling Morphological Analogies Using Deep Learning -- Extended Version](https://mlsanity.com/paper/2111.05147/) (2021)
- [Measuring the Feasibility of Analogical Transfer using Complexity](https://mlsanity.com/paper/2206.11753/) (2022)

## Abstract (arXiv)

Analogical proportions are statements of the form "A is to B as C is to D" that are used for several reasoning and classification tasks in artificial intelligence and natural language processing (NLP). For instance, there are analogy based approaches to semantics as well as to morphology. In fact, symbolic approaches were developed to solve or to detect analogies between character strings, e.g., the axiomatic approach as well as that based on Kolmogorov complexity. In this paper, we propose a deep learning approach to detect morphological analogies, for instance, with reinflexion or conjugation. We present empirical results that show that our framework is competitive with the above-mentioned state of the art symbolic approaches. We also explore empirically its transferability capacity across languages, which highlights interesting similarities between them.