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.
Previous research in the field of text reuse has also focused on the more specific problem of finding allusive references. One of the first studies @cite_4 looked at allusion detection in literary text using an IR approach exploiting textual features at a diversity of levels (including morphology and syntax) but collected only qualitative evidence on the efficiency of such approach. More ambitiously, approached the task of finding allusive references across texts in different languages using string alignment algorithms from machine translation. Besides the afore-mentioned work by , the work by is highly related to the present study, since the authors also worked on allusive reuse from the Bible in the works of Bernard. In their work, the authors focused on modeling text reuse patterns based on a set of transformation rules defined over string case, lemmata, POS tags and synset relationships: (syno- hypo- co-hypo-)nymy. More recently, conducted a quantitative comparison of such transformation rules with paraphrase detection methods on the task of predicting paraphrase relation between text pairs but do not evaluate the method in an IR setup.
@cite_4
1545953061
We describe here a method for discovering imitative textual allusions in a large collection of Classical Latin poetry. In translating the logic of literary allusion into computational terms, we include not only traditional IR variables such as token similarity and ngrams, but also incorporate a comparison of syntactic structure as well. This provides a more robust search method for Classical languages since it accomodates their relatively free word order and rich inflection, and has the potential to improve fuzzy string searching in other languages as well.