Document-level Event Extraction via Heterogeneous Graph-based Interaction Model with a Tracker (2021)

Abstract: Document-level event extraction aims to recognize event information from a whole piece of article. Existing methods are not effective due to two challenges of this task: a) the target event arguments are scattered across sentences; b) the correlation among events in a document is non-trivial to model. In this paper, we propose Heterogeneous Graph-based Interaction Model with a Tracker (GIT) to solve the aforementioned two challenges. For the first challenge, GIT constructs a heterogeneous graph interaction network to capture global interactions among different sentences and entity mentions. For the second, GIT introduces a Tracker module to track the extracted events and hence capture the interdependency among the events. Experiments on a large-scale dataset (Zheng et al, 2019) show GIT outperforms the previous methods by 2.8 F1. Further analysis reveals is effective in extracting multiple correlated events and event arguments that scatter across the document.

## introduction event extraction (ee) is one of the key and challenging tasks in information extraction (ie), which aims to detect events and extract their arguments from the text. most previous methods @xcite @xcite @xcite focus on sentence-level ee, extracting events from a single sentence. the sentence-level model, however, fails to extract events whose arguments spread in multiple sentences, which is much more common in real-world scenarios. hence, extracting events at the document-level is critical. it has attracted much attention recently @xcite @xcite . ## preliminaries we first clarify some important notions. a) entity mention: a text span within document that refers to an entity object; b) event argument: an entity playing a specific event role. event roles are predefined for each event type; c) event record: an entry of a specific event type containing arguments for different roles in the event. for simplicity, we use record for short in the following sections. following @xcite , given a document composed of sentences @xmath0 ## methodology as shows in figure 2 , git first extracts candidate entities through sentence-level neural extractor (sec 3.1). then we construct a heterogeneous graph to model the interactions among sentences and entity mentions (sec 3.2), and detect event types expressed by the document (sec 3.3). finally we introduce a tracker module to continuously track all the records with global memory, in which we utilize the global interdependency among records for multi-event extraction (sec 3.4). ## conclusion although promising in practical application, document-level ee still faces some challenges such as arguments-scattering phenomenon and multiple correlated events expressed by a single document. to tackle the challenges, we introduce heterogeneous graph-based interaction model with a tracker (git). git uses a heterogeneous graph interaction network to model global interactions among sentences and entity mentions. git also uses a tracker to track the extracted records to consider global interdependency during extraction. experiments on large-scale public dataset @xcite show git outperforms previous stateof-the-art by 2.8 f1. further analysis verifies the effectiveness of git especially in cross-sentence events extraction and multi-event scenarios.