O nto ED : Low-resource Event Detection with Ontology Embedding (2021)

Abstract: Event Detection (ED) aims to identify event trigger words from a given text and classify it into an event type. Most current methods to ED rely heavily on training instances, and almost ignore the correlation of event types. Hence, they tend to suffer from data scarcity and fail to handle new unseen event types. To address these problems, we formulate ED as a process of event ontology population: linking event instances to pre-defined event types in event ontology, and propose a novel ED framework entitled OntoED with ontology embedding. We enrich event ontology with linkages among event types, and further induce more event-event correlations. Based on the event ontology, OntoED can leverage and propagate correlation knowledge, particularly from data-rich to data-poor event types. Furthermore, OntoED can be applied to new unseen event types, by establishing linkages to existing ones. Experiments indicate that OntoED is more predominant and robust than previous approaches to ED, especially in data-scarce scenarios.

## introduction event detection (ed) @xcite is the task to extract structure information of events from unstructured texts. for example, in the event mention "jack is married to the iraqi microbiologist known as dr. germ.", an ed model should identify the event type as 'marry' where the word 'married' triggers the event. the extracted events with canonical structure facilitate various social applications, such as biomedical science @xcite , financial analysis @xcite , fake news detection @xcite and so on. as a non-trivial task, ed suffers from the lowresource issues. on the one hand, the maldistribution of samples is quite serious in ed benchmark datasets, e.g., fewevent @xcite and maven @xcite , where a large portion of event types contain relatively few training instances. as shown in figure 1 , the sample size of two event types attack and riot differs greatly @xcite . in low-resource scenarios, supervised ed models @xcite @xcite are prone to overfitting since they require sufficient training instances for all event types. on the other hand, real-world applications tend to be open and evolve promptly, and accordingly there can be numerous new unseen event types. handling new event types may even entail starting over, without being able to re-use annotations from previous ones @xcite . regarding low-resource ed, huang et al. (2018) take a fresh look at ed, by mapping each event mention to a specific type in a target event ontology, which can train from few seen event types and then transfer knowledge to new unseen ones. however, the event ontology here merely considers the intrastructure for each event mention and event type. in this paper, we enrich the event ontology with more inter-structures of event types, such as temporal, causal and hierarchical event-event relations @xcite . for example, as seen in figure 1 , our key intention is to fully utilize the event ontology and leverage correlation knowledge from datarich event types (i.e., attack) to data-poor ones (i.e., sentence, acquit and riot). besides, new event types (i.e., be-born) can be learned with correlation (i.e., cosuper) of existing ones (i.e., injure). as the first attempt to construct such event ontology, we propose a novel ed framework with ontology embedding called ontoed. first, we establish the initial event ontology with event instances and types. we capture semantic features and relations of event instances with bert @xcite and utilize prototypes @xcite to represent event types, where a prototype is the average of its instance embeddings. second, we extend the event ontology with event-event relations based on extracted relations among event instances, and then learn ontology embedding by aggregating neighbor prototypes for each prototype w.r.t. correlations among event types. in this way, semantically similar event types in vector space will be closer, thus, improving the discrimination of dissimilar event types. third, we design an event correlation inference mechanism to induce new event correlations based on symbolic rules, e.g., (sentence, before, acquit) ∧ (acquit, before, pardon) → (sentence, before, pardon). thus, we can induce new eventevent relations to further enrich the event ontology. to the best of our knowledge, it is the first work to explicitly model correlations among event types with event ontology in low-resource ed. our contributions can be summarized as follows: extensive experimental results demonstrate that our model can achieve better performance on the overall, few-shot, and zero-shot setting. traditional approaches to ed are mostly based on neural networks @xcite @xcite @xcite @xcite , and ignore correlation knowledge of event types, especially in low-resource scenarios. most previous low-resource ed methods @xcite have been based on supervised learning. however, supervised-based methods are too dependent on data, and fail to be applied to new types without additional annotation efforts. another popular methods for low-resource ed are based on meta learning. deng et al. (2020); lai et al. (2020); shen et al. (2021) reformulate ed as a few-shot learning problem to extend ed with limited labeled samples to new event types, and propose to resolve few-shot ed with meta learning. besides, knowledge enhancement and transfer learning are applied to tackle low-resource ed problems. tong et al. (2020) leverage open-domain trigger knowledge to address long-tail issues in ed. liu et al. (2020); du and cardie (2020) propose to handle few-shot and zero-shot ed tasks by casting it as a machine reading comprehension problem. huang et al. (2018) propose to tackle zero-shot ed problem by mapping each event mention to a specific type in a target event ontology. note that huang @xcite establish the event ontology merely with intra-structure of events, while we extend it with inter-structure of event correlations. though these methods are suitable for low-resource scenarios, they mostly ignore implicit correlation among event types and lack reasoning ability. in order to utilize correlation knowledge among event types, @xcite propose a new event graph schema, where two event types are connected through multiple paths involving entities. however, it requires various annotations of entities and entity-entity relations, which is complicated and demanding. different from @xcite , we propose to revisit the ed task as an ontology learning process, inspired by relation extraction (re) tasks based on ontology and logic-based learning. @xcite present a logic-based relational learning approach to re that uses inductive logic programming for generating information extraction (ie) models in the form of symbolic rules, demonstrating that ontology-based ie approaches are advantageous in capturing correlation among classes, and succeed in symbolic reasoning. ## experiments the experiments seek to: (1) demonstrate that on-toed with ontology embedding can benefit both standard and low-resource ed, and (2) assess the effectiveness of different modules in ontoed and provide error analysis. to this end, we verify the effectiveness of ontoed in three types of evaluation: ontoevent is established based on two newly proposed datasets for ed: maven @xcite and fewevent @xcite . they are constructed from wikipedia documents or based on existing event datasets, such as ace-2005 foot_2 and tac-kbp-2017 foot_3 . in terms of event-event relation annotation in ontoevent, we jointly use two models: tcr @xcite is applied to extract temporal and causal relations, and jcl @xcite is used for extract hierarchical relations. the code of ontoed and ontoevent dataset can be obtained from github foot_4 . ## conclusion and future work this paper proposes a novel event detection framework with ontology embedding called ontoed. we revisit the ed task by linking each event instance to a specific type in a target event ontology. to facilitate the linkage, we enrich the event ontology with event-event relations, such as temporal, causal and hierarchical correlation, and induce more event correlations based on existing ones. the key insight is that event ontology can help to reduce model dependence on instance data, especially in low-resource scenarios. as data-rich event types can propagate correlation knowledge to data-poor ones, and new event types can establish linkages to the event ontology. we demonstrate the effectiveness of ontoed in three settings: overall, few-shot as well as zeroshot, and experiments show that ontoed excels previous methods with great robustness. in the future, we intend to extend our work in several aspects. first, we would improve the event ontology and consider more event correlations. second, we would explore if low-resource ed can also boost to identify event correlation. third, we would develop more neuro-symbolic methods for ed.