Entity Disambiguation with Entity Definitions (2023)

Abstract: Local models have recently attained astounding performances in Entity Disambiguation (ED), with generative and extractive formulations being the most promising research directions. However, previous works have so far limited their studies to using, as the textual representation of each candidate, only its Wikipedia title. Although certainly effective, this strategy presents a few critical issues, especially when titles are not sufficiently informative or distinguishable from one another. In this paper, we address this limitation and investigate the extent to which more expressive textual representations can mitigate it. We evaluate our approach thoroughly against standard benchmarks in ED and find extractive formulations to be particularly well-suited to such representations. We report a new state of the art on 2 out of the 6 benchmarks we consider and strongly improve the generalization capability over unseen patterns. We release our code, data and model checkpoints at https://github.com/SapienzaNLP/extend .

## introduction being able to pair a mention in a given text with its correct entity out of a set of candidates is a crucial problem in natural language processing (nlp), referred to as entity disambiguation @xcite . indeed, since ed enables the identification of the actors involved in human language, it is often considered a necessary building block for a wide range of downstream applications, including information extraction @xcite @xcite , question answering @xcite and semantic parsing @xcite . ed generally occurs as the last step in an entity linking pipeline @xcite , preceded by mention detection and candidate gen-eration, and its approaches have traditionally been divided into two groups, depending on whether co-occurring mentions are disambiguated independently (local methods; @xcite al. (2019); wu et al. (2020); tedeschi et al. (2021) ), or not (global methods; @xcite ; @xcite ; @xcite ; @xcite ). despite the limiting operational hypothesis of independence between co-occurring mentions, local methods have nowadays achieved performances that are either on par or above those attained by their global counterparts, mainly thanks to the advent of large pre-trained language models. in particular, among these methods, generative @xcite and extractive @xcite formulations are arguably the most promising directions, having resulted in large performance improvements across multiple benchmarks. regardless of their modeling differences, the key idea behind these methods is to part away from the previous classification-based approaches and, instead, adopt formulations that better leverage the original pre-training of the underlying language models. on the one hand, generative formulations tackle ed as a text generation problem and train neural architectures to generate auto-regressively, given a mention and its context, a textual representation of the correct entity. on the other hand, extractive approaches frame ed as extractive question answering: they first concatenate a textual representation of each entity candidate to the original input and then train a model to extract the span corresponding to the correct entity. although they have admittedly attained great improvements, both in-and out-of-domain, to the best of our knowledge, previous works on both these formulations have limited their studies to a single type of textual representation for entities, that is, their title in wikipedia. this strategy, however, presents a number of issues @xcite and, in particular, often results in representations that are either insufficiently informative, or are even virtually indistinguishable from one another. in contrast to this trend, we address this limitation and explore the effect of more expressive textual representations on state-of-the-art local methods. to this end, we propose complementing wikipedia titles with their description in wikidata, such that, for instance, the candidates for ronaldo in ronaldo scored two goals for portugal would be cristiano ronaldo: portuguese association football player and ronaldo: brazilian association football player, rather than the less informative cristiano ronaldo and ronaldo, respectively. we test our novel representations on generative and extractive formulations, and evaluate against standard benchmarks in ed, both in and out of domain, reporting statistically significant improvements for the latter group. ## method we now formally introduce ed and the textual representation strategy we put forward. then, we describe the two formulations with which we implement and test our proposal. ed with entity definitions. given a mention m occurring in a context c m , entity disambiguation is formally defined as the task of identifying, out of a set of candidates e 1 , . . . , e n , the correct entity e * that m refers to. each candidate e typically corresponds to a page in wikipedia, and, in generative and extractive formulations, is additionally associated with a textual representation ê describing its meaning. whereas previous works have considered the title that e has in wikipedia as ê (e.g. cristiano ronaldo), here we focus on more expressive alternatives and leverage wikidata to achieve this objective. specifically, we first retrieve the wikidata description of e (e.g. portuguese association football player). then, we define as the new representation of e the colon-separated concatenation of its wikipedia title and its wikidata description (e.g., cristiano ronaldo: portuguese association football player). generative modeling. in our first formulation, we follow de @xcite and frame ed as a text generation problem. starting from a mention m and its context c m , we first wrap the span corresponding to m in c m between two special symbols, @xmath0; we denote this modified sequence by cm . then, we train a sequence-tosequence model to generate the textual represen-tation ê * of the correct entity e * by learning the following probability: extractive modeling. additionally, we also consider the formulation recently presented by @xcite that frames ed as extractive question answering. here, cm , defined in the same way as it was for generative modeling, above, represents the query, whereas the context is built by concatenating the textual representations ê1 , . . . , ên of the candidates e 1 , . . . , e n . a model is then trained to extract the text span that corresponds to e * . following the same efficiency reasoning of the authors, we use as our underlying model the longformer @xcite , whose linear attention better scales to this type of long-input formulations. compared to the above generative method, the benefits of this approach lie in i) dropping the need for a potentially slow auto-regressive decoding process, and ii) enabling full joint contextualization both between context and candidates, and across candidates themselves. ## experiments and results in order to assess the impact of our proposal in ed, we evaluate how the performances of generative and extractive formulations change when moving from wikipedia titles to our alternative. to this end, in this section, we first describe our experimental setting, discussing the datasets, evaluation strategy and comparison systems we adopt. then, we describe the architecture we use for the two formulations. finally, we present our findings. ## qualitative analysis we report in sentence: hugh waddell is a scottish [...] professional rugby league footballer [...] has played [...] at club level for [...] leeds [...] previous candidates: ✘ leeds ✓ leeds rhinos new candidates: ✘ leeds: city in west yorkshire, england ✓ leeds rhinos: english rugby league football club sentence: world without superman is a superman comic book story arc published by dc comics. previous candidates: ✓ superman ✘ superman (comic book) new candidates: ✓ superman: superhero appearing in dc comics ✘ superman: comic book series featuring superman sentence: frank mortimer born [...] in wakefield was an english professional rugby league footballer [...] previous candidates: ✓ wakefield ✘ wakefield, massachusetts new candidates: ✓ wakefield: city in west yorkshire, england ✘ wakefield, massachusetts: town in massachusetts due to space limitations, out of the 100 candidates all these three examples have, we only report the first two, which always include the correct one (denoted by ✓, as opposed to the incorrect alternative marked by ✘). ## conclusion in this work, we focus on a shortcoming of generative and extractive formulations in entity disambiguation, namely their usage of wikipedia titles, which are often insufficiently informative, and explore the effect of more expressive representations on these formulations. while we do not witness positive gains for generative formulations, at least in the limited data and computational regime we consider, we report strong improvements on extractive formulations. specifically, our extractive approach sets a new state of the art on 2 out of the 6 benchmarks under consideration, and, more interestingly, shows better scalability over unseen patterns, especially unseen entities.