JSEEG raph: Joint Structured Event Extraction as Graph Parsing (2023)

Abstract: We propose a graph-based event extraction framework JSEEGraph that approaches the task of event extraction as general graph parsing in the tradition of Meaning Representation Parsing. It explicitly encodes entities and events in a single semantic graph, and further has the flexibility to encode a wider range of additional IE relations and jointly infer individual tasks. JSEEGraph performs in an end-to-end manner via general graph parsing: (1) instead of flat sequence labelling, nested structures between entities/triggers are efficiently encoded as separate nodes in the graph, allowing for nested and overlapping entities and triggers; (2) both entities, relations, and events can be encoded in the same graph, where entities and event triggers are represented as nodes and entity relations and event arguments are constructed via edges; (3) joint inference avoids error propagation and enhances the interpolation of different IE tasks. We experiment on two benchmark datasets of varying structural complexities; ACE05 and Rich ERE, covering three languages: English, Chinese, and Spanish. Experimental results show that JSEEGraph can handle nested event structures, that it is beneficial to solve different IE tasks jointly, and that event argument extraction in particular benefits from entity extraction. Our code and models are released as open-source.

## introduction event extraction (ee) deals with the extraction of complex, structured representations of events from text, including overlapping and nested structures @xcite . while there are existing datasets annotated with such rich representations @xcite , a majority of current approaches model this task using simplified versions of these datasets or sequence-labeling-based encodings which are figure 1 : example of nested and overlapping events in the sentence "i, purposely buy things made in canada or usa.", taken from rich ere @xcite . event extraction is a subtask of a wider set of information extraction (ie) tasks, jointly dealing with extracting various types of structured information from unstructured texts, from named entities, relations, to events. there have been continued efforts in creating benchmark datasets that can be used for evaluating a wide range of ie tasks. both ace05 @xcite 2 and rich @xcite . ere @xcite foot_1 provide consistent annotations of entities, relations, and events. while there are clear inter-relations between these different elements, and despite the availability of rich annotations, existing works often deal with individual tasks, such as named entity recognition (ner) @xcite or event extraction (ee) @xcite @xcite . recently there have been some efforts in jointly modelling multiple ie tasks @xcite @xcite , but these methods explicitly avoid nested instances. we here propose to represent events, along with entities and relations, as general graphs and approach the task of event extraction as meaning representation parsing @xcite . as shown in figure 2 , in such an information graph, event triggers and entities are represented as nodes; event types, argument roles, and relations are constrained edges; and nested/overlapped structures are straightforwardly represented, since a surface string can be abstracted into an unlimited number of nodes, as illustrated by the two separate nodes for the event triggers for "cost". our approach does not rely on ontology-or language-specific features or any external syntactic/semantic parsers, but directly parses raw text into an information graph. we experiment on the benchmark datasets ace05 @xcite and rich ere @xcite , zooming in on nested structures. our results show jsee-graph to be versatile in solving entity, relation, and event extraction jointly, even for heavily nested instances and across three different languages. abla-tion studies consistently show that event extraction especially benefits from entity extraction. the paper is structured as follows: section 2 provides the relevant background for our work, and section 3 further describes the tasks addressed and the datasets we employ, focusing in particular on their complexity, as measured by level of nesting. section 4 presents the jsee graph parsing framework and section 5 the experimental setup for evaluating the jsee parser. section 6 presents the results of our evaluations and provides a study of the performance for nested structures, as well as an ablation study assessing the effect of joint ie modeling and an error analysis. finally we provide conclusions (section 7) and discuss limitations of our work. ## related work event extraction is commonly approached as supervised classification, even though other approaches relying e.g. on generation @xcite @xcite or prompt tuning inspired by natural language understanding tasks @xcite @xcite also are gaining ground. classification-based methods break event extraction into several subtasks (trigger detection/classification, argument detection/classification), and either solve them separately in a pipeline-based manner @xcite @xcite @xcite or jointly infer them as multiple subtasks @xcite @xcite @xcite . classification-based joint methods typically apply sequence-labeling-based encoding and extract all event components in one pass, whereas pipeline methods break the problem into separate stages which are performed sequentially. whereas sequence-labeling approaches cannot distinguish overlapping events/arguments by the nature of the bio-encoding, pipeline methods may in principle detect these. however, they typically suffer from error propagation and are not equipped to model the interactions between the different event elements (triggers, arguments). nested events some previous work addresses the problem of overlapping or nested arguments in ee. @xcite address overlapping arguments in the chinese part of the ace05 dataset and jointly perform predictions for event triggers and argu-ments based on common feature representations derived from a pre-trained language model. @xcite propose a joint framework with cascaded decoding to tackle overlapping events, and sequentially perform type detection, event and argument extraction in a chinese financial event dataset. they deal with cases of both "overlapping events" and "overlapping arguments", however, their approach may suffer from error propagation due to the cascading approach. @xcite distinguish between overlapped and nested events and propose the oneee tagging scheme which formulates ee as a word-to-word relation recognition, distinguishing separate span and role relations. oneee is evaluated on the fewfc chinese financial event dataset and the biomedical event datasets genia11 and genia13. while specifically focusing on nested events, these previous works are limited by focusing only on one language or on specialized (financial/biomedical) domains. in this work we aim to provide a more comprehensive evaluation over two datasets in several versions with increasing levels of structural complexity (see below) and across three different languages. @xcite propose the dygie++ model which approaches joint modeling of ie entities and relations via span-based prediction of entities and event triggers, and subsequent dynamic graph propagation based on relations. they evaluate on ace05 and genia datasets and limit their experiments to english only. their approach is restricted to a certain span width, limiting the length of possible entities. oneie @xcite ) is a joint system for ie using global features to model cross-subtask or cross-instance interactions between the subtasks and predict an information graph. they propose the e+ extension of ace05 which includes multi-token events (e + ) as we do. as in our work, they also present results on spanish and chinese as well and develop a multilingual model, but their experiments avoid nested structures, by using only the head of entity mentions and specifically removing overlapped entities. @xcite model joint ie in a two-stage procedure which first identifies entities and event triggers and subsequently classify relations between these starting from a fully connected dependency graph; a gcn is employed to encode the resulting dependency graphs for computation of the joint distribution. while the approach is shown to be effective, it is still a pipeline approach which can suffer from error propagation. since it relies on sequence labeling for entity/event detection, it cannot identify overlapping entities/event triggers. furthermore, the approach relies on syntactic information from an external parser and focuses only on english and spanish in the light ere dataset @xcite . ## task and data while the main focus of this work is on event extraction, we hypothesize that our graph-based approach lends itself to dealing with two challenging aspects of current research on this task: the processing of nested and overlapping event structures, and the joint modeling of inter-related ie structures. in the following we quantify the level of nesting in two widely used datasets which contain rich annotations for both entities, events, and relations. we further propose two versions of each dataset with varying potential for nesting, which allows us to focus on this aspect during evaluation. event extraction is the task of extracting events into structured forms, namely event triggers and their arguments. an event trigger is the word(s) that most clearly describes an event, such as "buy", which evokes a transferownership and an transfermoney event in figure 1 . event arguments are the participants and attributes of an event, and can be tagged as entities at the same time, as demonstrated in figure 2 . we use the benchmark datasets ace05 @xcite and rich ere @xcite 2015), both containing consistent annotations for entities, relations, and events, for joint evaluation of multiple ie tasks and in multiple languages (ace05 in english and chinese, and ere in english, chinese, and spanish). we measure the nested instances in ace05 and rich ere as a way to showcase different levels of complexity for extracting entities, relations, and events. more specifically, we quantify nested instances in two versions of each dataset, one using only the head of an entity mention (when it is annotated), and the other with the entire mention text. 1 ); when only considering the head of an entity, ace05 exhibits very little nesting, but rich ere exhibits a considerable amount of nesting within entities, as well as between entity-trigger. the reason for this is that in rich ere, only certain nominal mentions are marked with explicit heads; when the full entity mentions are considered, both datasets are heavily nested. as mentioned above, this work deals with three ie tasks, as exemplified by figure 2 : entities, relations, and events. given a sentence, our jsee-graph framework extracts its entity mentions, relations, and event mentions. in addition to event extraction, we thus target two additional ie tasks in our graph-based model: entity extraction is to identify entity mentions from text and classify them into types according to a pre-defined ontology. for example, in figure 2 , "district" is an organization (org) entity. relation extraction aims to assign a relation type to an ordered pair of entity mentions, based on a pre-defined relation ontology. for example, in figure 2 , the relation between per "officials" and org "district" is orgaffiliation. ## graph parsing framework our jseegraph framework is a text-to-graph parser tailored for ee tasks, additionally with different ie components explicitly encoded in a single graph, as shown in figure 2 . our framework builds on @xcite who developed the perin parser in the context of meaning representation parsing @xcite , as well as @xcite who applied perin to the task of event extraction. we here extend this parser to the ie graphs shown in figure 2 in a multilingual setting. given a sentence, as the example shown in figure 3 , jseegraph encodes the input tokens with the pre-trained language model xlm-r (conneau et al., 2020) to obtain the contextualized embeddings and further maps the embeddings onto queries; nodes (triggers and entities) are predicted by classifying the queries and anchored to surface tokens via a deep biaffine classifier @xcite ; edges are constructed between nodes with two biaffine classifiers, assigning arguments to predicted events and relations to entity pairs. we describe each module in detail in what follows. ## results and discussion we here present the results for our jseegraph model for the ee task, as well as its performance for the additional ie components: entities and relations, evaluated as described above. we further zoom in on the nested structures identified in section 3 and assess the performance of our system on these rich structures which have largely been overlooked in previous work on event extraction. we go on to assess the influence of inter-related ie components in an ablation study. finally we provide an error analysis of our model's predictions. ## conclusion in this paper, we have proposed jseeg, a graphbased approach for joint structured event extraction, alongside entity, and relation extraction. we experiment on two benchmark datasets ace05 and rich ere, covering the three languages english, chinese, and spanish. we find that our proposed jseegraph is robust in solving nested event structures, and is especially strong in event argument extraction. we further demonstrate that it is beneficial to jointly perform ee with other ie tasks, and event argument extraction especially gains from entity extraction.