“Who is the Madonna of I talian- A merican Literature?”: Target Entity Extraction and Analysis of Vossian Antonomasia (2023)

Abstract: In this paper, we present approaches for the automated extraction and disambiguation of a part of the stylistic device Vossian Antonomasia (VA), namely the target entity that is described by the expression. We model the problem as a coreference resolution task and a question answering task and also combine both tasks. To tackle these tasks, we utilize state-of-the-art models in these areas. In addition, we visualize the connection between the source and target entities of VA in a web demo to get a deeper understanding of the interaction of entities used in VA expressions.

## introduction vossian antonomasia (or va for short) is a popular stylistic device used to describe an entity by refering to another entity, typically in a witty and resourceful way. structure-wise, a va expression consists of three parts: target (trg), source (src), and modifier (mod). the combination of source and modifier is used to describe the target. take, for instance, the sentence "it is the madonna of italian-american literature in that it shows the transition from the italian immigrant to american citizen like no other book of its genre." (nyt 1991/08/07/1128838). 1 the author uses "madonna", the popular american singer, as source and transfers a set of characteristics of madonna to the target, helen barolini's novel "umbertina". the modifier "italian-american literature" projects these characteristics onto the target. in general, the source consists of a universally known, famous named entity, from which one or more typical traits or characteristics are to be invoked. the target, on the other hand, does not necessarily have to be a named entity (e.g., "rally trg car trg racing trg is the david src hasselhoff src of motor mod sports mod " (nyt 2006/08/02/1780256)). 1 to avoid an excessively long reference list, all examples taken from the new york times corpus @xcite are cited using the pattern "nyt year/month/day/article-id". it is also possible that no specific target is meant, in which case the va expression would be hypothetical, for instance, "we're waiting for the raffi src of our mod industry mod ." (nyt 1989/06/11/0257799), or there is a target, but it is not explicitly mentioned in the article content. the task of extracting va expressions focusing on source and modifier has already been covered @xcite @xcite . the models are also able to identify a reference of the target inside a sentence where source and modifier appear, but in most cases, these references are in the form of pronouns or other mentions of the entity that cannot be linked to a knowledge base for disambiguation. to the best of our knowledge, there exists no method for extracting the target entity from texts. as the phrase consisting of source and modifier, such as "the madonna of italian-american literature", is a specific mention of the target entity ("umbertina"), it should appear in the reference chain of the target when using coreference resolution. however, as we will later show, coreference resolution often fails to detect these complex mentions. for a deeper understanding of va expressions and their meaning, the target entity is an essential part. we need to identify it to comprehend the transferred characteristics. after its identification, we can compute va chains where the source of one va expression is the target of another to track the transfer of characteristics across multiple entities and also analyze the assignments of characteristics to entities. thus, in this paper, we tackle two tasks: target extraction: the automatic extraction of the full name of the target entity inside a text. target linking: disambiguation and linking to wikidata. in addition, we visualize the results in a web demo for exploration and visualization. 2 our annotated data @xcite and code are freely available. 2 ## related work the detection and extraction of va has recently been worked on. while @xcite ; fischer and @xcite used semi-automated approaches to detect va expressions, @xcite developed the first automated approach for the detection of va expressions on the sentencelevel. they developed a finer extraction approach on the word-level @xcite . in particular, they employed pre-trained contextual language models, for instance, bert @xcite , and fine-tuned them on an annotated dataset modeling the problem as a sequence tagging task. however, all models lack the ability to identify the target entity within the article. the automated detection and extraction of other stylistic devices, such as metaphors, has been covered widely. the extraction of va expressions consisting of source and modifier is closely related to metaphor detection. however, our focus is on the extraction of the target entity, so we do not consider such related work. ## conclusion we have shown that the extraction of the target name and its linking is not a trivial task, and that state-of-the-art coreference resolution models, which should cover this task, do not perform as well as they do on common datasets in their domain. however, our idea of modeling the problem as a question-answering task by employing the annotation of source and modifier shows better results and the concatenation of both models, first ele s followed by lf shows promising results. notably, re-formulating the va expression into a qa problem works on all syntactic forms of va expressions and is not limited to the syntax in the dataset. these findings also show that the annotated va dataset that emerged from the target annotations, even though it is a specific device, can be used as an out-of-domain evaluation dataset for qa and coref models in general. with the disambiguation of the target entity we are able to deepen the understanding of va. the ability to track va chains is a completely new field that can lead to many interesting insights into the function and use of va, for example, regarding the transfer of characteristics. our web demo greatly simplifies the exploration of connections between source and target entities.