Edge-Enhanced Graph Convolution Networks for Event Detection with Syntactic Relation (2020)

Abstract: Event detection (ED), a key subtask of information extraction, aims to recognize instances of specific event types in text. Previous studies on the task have verified the effectiveness of integrating syntactic dependency into graph convolutional networks. However, these methods usually ignore dependency label information, which conveys rich and useful linguistic knowledge for ED. In this paper, we propose a novel architecture named Edge-Enhanced Graph Convolution Networks (EE-GCN), which simultaneously exploits syntactic structure and typed dependency label information to perform ED. Specifically, an edge-aware node update module is designed to generate expressive word representations by aggregating syntactically-connected words through specific dependency types. Furthermore, to fully explore clues hidden from dependency edges, a node-aware edge update module is introduced, which refines the relation representations with contextual information. These two modules are complementary to each other and work in a mutual promotion way. We conduct experiments on the widely used ACE2005 dataset and the results show significant improvement over competitive baseline methods.

## introduction event detection (ed) is an important information extraction task that seeks to recognize events of specific types from given text. specifically, each event in a sentence is marked by a word or phrase called "event trigger". the task of ed is to detect event triggers and classify them into specific types of interest. taking figure 1 as an example, ed is supposed to recognize the event trigger "visited'' and classify it to the event type meet. dependency trees convey rich structural information that is proven useful for ed @xcite @xcite . recent works on ed focus on building graph convolutional networks (gcns) over the dependency tree of a sentence to exploit syntactic dependencies @xcite @xcite . compared to sequencebased models, gcn-based models are able to capture non-local syntactic relations that are obscure from the surface form alone @xcite , and usually achieve better performance. nevertheless, existing gcn-based ed methods do not consider dependency labels, which may serve as significant indicators to reveal whether a word is a trigger or not. as shown in figure 1 , the dependency "nsubj" (nominal subject) and "dobj"(direct object) show that "putin" and "bush" are the subject and object of "visited" respectively, and the words connected to "visited" with "nmod"(noun compound modifier) dependency express when and where the event happened. apparently, such dependency labels constitute an effective evidence to predict the event type of "visited" as meet. in addition, our statistical results on the benchmark ace2005 dataset show that "nsubj", "dobj" and "nmod" take up 32.2% of triggerrelated dependency labels (2.5% for each relation on average among all 40 dependency relations), which means that simultaneously modeling syntactic structure and dependency labels can be crucial to make full use of the dependency trees to further improve the performance of ed. besides, we also observe that the same dependency label under different context may convey different signals for ed. again, taking figure 1 as an example: the dependency "nmod" connected with "ranch" indicates where the event happens but another dependency "nmod" connected with "november" points out when the event happens. such an observation demonstrates that assigning a single context-independent representation for each dependency label is not enough to express the complex relations between words. this is to say, the representations of dependency relations should be context-dependent and dynamic, calculated and updated according to a sentential context using a network structure. to model the above ideas, in this paper, we propose a novel neural architecture named edge-enhanced graph convolutional networks (ee-gcn), which explicitly takes advantage of the typed dependency labels with dynamic representations. in particular, ee-gcn transforms a sentence to a graph by treating words and dependency labels as nodes and typed edges, respectively. accordingly, an adjacency tensor is constructed to represent the graph, where syntactic structure and typed dependency labels are both captured. to encode the heterogeneous information from the adjacency tensor, ee-gcn simultaneously performs two kinds of propagation learning. for each layer, an edgeaware node update module is firstly performed for aggregating information from neighbors of each node through specific edges. then a node-aware edge update module is used to dynamically refine the edge representation with its connected node representations, making the edge representation more informative. these two modules work in a mutual promotion way by updating each other iteratively. our contributions are summarized as follows: ## related works in earlier ed studies, researchers focused on leveraging various kinds of linguistic features and manually designed feature for the task. however, all the feature-based methods depend on the quality of designed features from a pre-processing step. most recent works have focused on leveraging neural networks in this task @xcite @xcite @xcite . the existing approaches can be categorized into two classes: the first class is to improve ed through special learning techniques including adversarial training @xcite , knowledge distillation @xcite and model pretraining @xcite . the second class is to improve ed by introducing extra resource, such as argument information @xcite , document information @xcite @xcite , multi-lingual information @xcite , knowledge base @xcite and syntactic information @xcite . syntactic information plays an important role in ed. @xcite exploited a dependencybridge recurrent neural network to integrate the dependency tree into model. @xcite proposed a directed-acyclic-graph gru model to introduce syntactic structure into sequence structure. with the rise of gcn @xcite , researchers proposed to transform the syntactic dependency tree into a graph and employ gcn to conduct ed through information propagation over the graph @xcite @xcite . although these works use syntax structures, few of them take dependency label information into consideration, which we, here, demonstrate its importance. how to effectively leverage the typed dependency information still remains a challenge in this task. ## problem statement in this section, we formally describe the event detection problem. following previous works @xcite @xcite @xcite , we formulate event detection as a sequence labeling task. each word is assigned a label that contributes to event annotation. tag "o" represents the "other" tag, which means that the corresponding word is irrelevant of the target events. in addition to "o", the other tags consist of two parts: the word position in the trigger and the event type. we use the "bi" (begin, inside) signs to represent the position information of a word in the event trigger. the event type information is obtained from a pre-defined set of events. thus, the total number of tags is 2 × n eventt ype + 1, where n eventt ype is the number of predefined event types. ## methods figure 2 gives an illustration of ee-gcn based event detection architecture, which is mainly composed of three components: the input layer, the edge-enhanced gcn layer and the classification layer. next, we detail all components sequentially from bottom to top. ## analysis 6.1 ablation study to demonstrate the effectiveness of each component, we conduct an ablation study on the ace2005 dev set as ## conclusion and future works in this paper, we propose a novel model named edge-enhanced graph convolutional networks (ee-gcn) for event detection. ee-gcn introduces the typed dependency label information into the graph modeling process, and learns to update the relation representations in a context-dependent manner. experiments show that our model achieves the start-of-the-art results on the ace2005 dataset. in the future, we would like to apply ee-gcn to other information extraction tasks, such as relation extraction and aspect extraction.