Towards Consistent Document-level Entity Linking: Joint Models for Entity Linking and Coreference Resolution (2022)

Abstract: We consider the task of document-level entity linking (EL), where it is important to make consistent decisions for entity mentions over the full document jointly. We aim to leverage explicit “connections” among mentions within the document itself: we propose to join EL and coreference resolution (coref) in a single structured prediction task over directed trees and use a globally normalized model to solve it. This contrasts with related works where two separate models are trained for each of the tasks and additional logic is required to merge the outputs. Experimental results on two datasets show a boost of up to +5% F1-score on both coref and EL tasks, compared to their standalone counterparts. For a subset of hard cases, with individual mentions lacking the correct EL in their candidate entity list, we obtain a +50% increase in accuracy.

## introduction in this paper we explore a principled approach to solve entity linking (el) jointly with coreference resolution (coref). concretely, we formulate coref+el as a single structured task over directed trees that conceives el and coref as two complementary components: a coreferenced cluster can only be linked to a single entity or nil (i.e., a nonlinkable entity), and all mentions linking to the same entity are coreferent. this contrasts with previous attempts to join coref+el @xcite @xcite where coref and el models are trained separately and additional logic is required to merge the predictions of both tasks. our first approach (local in fig. 1(a) ) is motivated by current state-of-the-art coreference resolution models @xcite ) that predict a single antecedent for each span to resolve. we extend this architecture by also considering entity links as potential antecendents: in the example of fig. 1 , the mention "alliance" can be either connected to its antecedent mention "nato" or to any of its candidate links (alliance or alliance,_ohio). while straightforward, this approach cannot solve cases where the first coreferenced mention does not include the correct entity in its candidate list (e.g., if the order of "nato" and "alliance" mentions in fig. 1 would be reversed). we therefor propose a second approach, global, which by construction overcomes this inherent limitation by using bidirectional connections between mentions. because that implies cycles could be formed, we resort to solving a maximum spanning tree problem. mentions that refer to the same entity form a cluster, represented as a subtree rooted by the single entity they link to. to encode the overall document's clusters in a single spanning tree, we introduce a virtual root node (see fig. 1(b) ). 2 this paper contributes: (i) 2 architectures (local and global) for joint entity linking (el) and corefence resolution, (ii) an extended aida dataset @xcite , adding new annotations of linked and nil coreference clusters, (iii) experimental analysis on 2 datasets where our joint coref+el models achieve up to +5% f1-score on both tasks compared to standalone models. we also show up to +50% in accuracy for hard cases of el where entity mentions lack the correct entity in their candidate list. ## architecture our model takes as input (i) the full document text, and (ii) an alias table with entity candidates for each of the possible spans. our end-to-end approach allows to jointly predict the mentions, entity links and coreference relations between them. ## experimental setup we considered two datasets to evaluate our proposed models: dwie (zaporojets et al., 2021) and aida @xcite . since aida essentially does not contain coreference information, we had to extend it by (i) adding missing mention links in order to make annotations consistent on the coreference cluster level, and (ii) annotating nil coreference clusters. we note this extended dataset as aida + . see as input to our models, for dwie we generate spans of up to 5 tokens. for each mention span s i , we find candidates from a dictionary of entity surface forms used for hyperlinks in wikipedia. we then keep the top-16 candidates based on the prior for that surface form, as per @xcite . each of those candidates e j is represented using a wikipedia2vec embedding e j @xcite . 5 for aida + , we use the spans, entity candidates, and entity representations from @xcite . 6 to assess the performance of our joint coref+el models local and global, we also provide stan2 )-( 4 )), while the el model is based only on the linking component (eq. ( 6 )). as performance metrics, for coreference resolution we calculate the average-f1 score of commonly used muc @xcite , b 3 @xcite and ceaf e @xcite metrics as implemented by @xcite . for el, we use (i) mention-level f1 score (el m ), and (ii) cluster-level hard f1 score (el h ) that counts a true positive only if both the coreference cluster (in terms of all its mention spans) and the entity link are correctly predicted. these el metrics are executed in a strong matching setting that requires predicted spans to exactly match the boundaries of gold mentions. furthermore, for el we only report the performance on non-nil mentions, leaving the study of nil links for future work. our experiments will answer the following research questions: (q1) how does performance of our joint coref+el models compare to standalone models? (q2) does jointly solving coreference resolution and el enable more coherent el predictions? (q3) how do our joint models perform on hard cases where some individual entity mentions do not have the correct candidate? ## results as m . we observe that the difference in performance between our joint models and standalone is bigger on m clusters (with a consistent superiority of global), indicating that our approach indeed produces more coherent predictions for mentions that refer to the same concept. further analysis reveals that this difference in performance is even higher for a more complex scenario where the clusters contain mentions with different surface forms (not shown in the table). in order to tackle research question (q3), we study the accuracy of our models on the important corner case that involves mentions without correct entity in their candidate lists. this is illustrated in ## conclusion we propose two end-to-end models to solve entity linking and coreference resolution tasks in a joint setting. our joint architectures achieve superior performance compared to the standalone counterparts. further analysis reveals that this boost in performance is driven by more coherent predictions on the level of mention clusters (linking to the same entity) and extended candidate entity coverage. xiyuan yang, xiaotao gu, sheng lin, siliang tang, yueting zhuang, fei wu, zhigang chen, guoping hu, and xiang ren. 2019. learning dynamic context augmentation for global entity linking. in proceedings of the 2019 conference on empirical methods in natural language processing and the international joint conference on natural language processing (emnlp-ijcnlp 2019), pages 271-281. klim zaporojets, johannes deleu, chris develder, and thomas demeester. 2021. dwie: an entity-centric dataset for multi-task document-level information extraction. information processing & management, 58(4):102563.