# On the Feasibility of Automated Detection of Allusive Text Reuse

arXiv: https://arxiv.org/abs/1905.02973 · HTML: https://mlsanity.com/paper/1905.02973/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2019.

## Built on by (most cited first, 8 of 8)

- [Machine-assisted quantitizing designs: augmenting humanities and social sciences with artificial intelligence](https://mlsanity.com/paper/2309.14379/) (2023)
- [Reception Reader: Exploring Text Reuse in Early Modern British Publications](https://mlsanity.com/paper/2302.04084/) (2023)
- [Graecia capta ferum victorem cepit. Detecting Latin Allusions to Ancient Greek Literature](https://mlsanity.com/paper/2308.12008/) (2023)
- [Loci Similes: A Benchmark for Extracting Intertextualities in Latin Literature](https://mlsanity.com/paper/2601.07533/) (2026)
- [Quantitative Intertextuality from the Digital Humanities Perspective: A Survey](https://mlsanity.com/paper/2510.27045/) (2025)
- [Detecting Sexual Content at the Sentence Level in First Millennium Latin Texts](https://mlsanity.com/paper/2309.14974/) (2023)
- [From transcription to semantic corpus analysis: unsupervised learning of sentence representations for ancient languages](https://mlsanity.com/paper/2607.24542/) (2026)
- [Beyond Similarity: Grounded Agentic Extraction and Expert-Adjudicated Evaluation of Intertextuality in Classical Chinese Histories](https://mlsanity.com/paper/2607.27595/) (2026)

## Abstract (arXiv)

The detection of allusive text reuse is particularly challenging due to the sparse evidence on which allusive references rely---commonly based on none or very few shared words. Arguably, lexical semantics can be resorted to since uncovering semantic relations between words has the potential to increase the support underlying the allusion and alleviate the lexical sparsity. A further obstacle is the lack of evaluation benchmark corpora, largely due to the highly interpretative character of the annotation process. In the present paper, we aim to elucidate the feasibility of automated allusion detection. We approach the matter from an Information Retrieval perspective in which referencing texts act as queries and referenced texts as relevant documents to be retrieved, and estimate the difficulty of benchmark corpus compilation by a novel inter-annotator agreement study on query segmentation. Furthermore, we investigate to what extent the integration of lexical semantic information derived from distributional models and ontologies can aid retrieving cases of allusive reuse. The results show that (i) despite low agreement scores, using manual queries considerably improves retrieval performance with respect to a windowing approach, and that (ii) retrieval performance can be moderately boosted with distributional semantics.