ED e R : Towards Understanding Dependency Relations Between Events (2023)

Abstract: Relation extraction is a crucial task in natural language processing (NLP) and information retrieval (IR). Previous work on event relation extraction mainly focuses on hierarchical, temporal and causal relations. Such relationships consider two events to be independent in terms of syntax and semantics, but they fail to recognize the interdependence between events. To bridge this gap, we introduce a human-annotated Event Dependency Relation dataset (EDeR). The annotation is done on a sample of documents from the OntoNotes dataset, which has the additional benefit that it integrates with existing, orthogonal, annotations of this dataset. We investigate baseline approaches for EDeR’s event dependency relation prediction. We show that recognizing such event dependency relations can further benefit critical NLP tasks, including semantic role labelling and co-reference resolution.

## introduction events play a critical role in enabling ai agents to understand and perceive the world, as they provide information on what happened and the entities involved. an event is composed of a predicate (i.e., verb) and arguments, where the predicate indicates the event's action and the arguments represent the subject, object and so on of the predicate @xcite . previous work on relation extraction mainly focuses on investigating the relationships between entities @xcite @xcite rather than events. in the relatively limited research work that studies the relations between events, the types of relations considered are causal @xcite @xcite , temporal @xcite , and hierarchical @xcite . such relationships consider two events † corresponding author. to be independent in terms of syntax and semantics but overlook the inter-dependence between them. this disregard for interdependence can potentially yield incomplete or erroneous narrative interpretations. take the sentence "he tried to forgive his father one time." from figure 1 , e2 with the predicate "forgive" is the action that he "tried" (e1) to take. however, there is no temporal or causal or hierarchical relation between e2 and e1; rather, e2 is the object (syntactically depending) and patient (semantically depending) of e1. in fact, e2 didn't actually happen. recognizing such relations, that reveal both semantic and syntactic dependencies, is an essential, yet challenging task for ai researchers. correctly identifying these dependency relations can enhance language understanding and provide valuable information for various related nlp tasks. motivated by these, we investigate how an event may be an argument of another as representing the event dependency relations, rather than being regarded as independent. additionally, we distinguish argument into required argument and optional argument events. required arguments, like e2 ("forgive") in e1 ("tried") from figure 1 , are present for an event to be complete and meaningful (to "try to", one has to try to do something). optional arguments, such as e13 ("see") in e12 ("carried"), enhance or clarify the event that it is an argument of (for example indicating the purpose), and their occurrence isn't explicitly stated. non-argument events, which are conjunctive or conditional, are often mislabelled as arguments in the event extraction task, like e9 ("arrived"), a temporal modifier for e10 ("did not @xcite anything"), but is independent. argument-event dependency relations are frequent in natural language texts, especially in narrative texts such as news articles, conversations, and similar. hence, we present a human-annotated event dependency relation dataset (eder) that (1) extracts event dependency information based on a sample of 275 documents spanning seven genres from ontonotes @xcite and integrates with orthogonal annotations of this dataset, and (2) provides refined semantic rolelabelled event representations based on this information. we build the eder dataset with 11,852 high-quality annotations, according to the proposed event relation taxonomy. it can serve as the basis for an effective automatic classification process. we apply both eder human-annotated and model-predicted event dependency relations to enhance a state-of-the-art semantic role labeling (srl) model @xcite 's event representation generation. experimental results prove these relations improve event extraction, yielding updated representations from eder. these refined event representations were further applied to the co-reference resolution (cr) task. a performance comparison taking the original and our updated event representations as inputs on a cr model @xcite affirms the effectiveness and validity of eder's refined representations. we release eder (e.g., dataset, baseline code, and model) at https://github.com/ richielee93/eder . ## dataset we utilize a subset of ontonotes documents with its human-annotated and predicate-argument formatted events to extract candidate event pairs, where one event's predicate is contained in the span of another. these pairs, denoted as event1 and event2, are labelled by human annotators to indicate whether event2 is a required argument of, an optional argument of, a condition of, or independent from event1. we next detail the way we collect and pre-process the candidate event pairs and the human annotation process and construction of the dataset. ## experiment: event dependency relation prediction given two events e i and e j from a sentence x, the basic task is to predict whether e j is an argument of e i or not. in this section, we evaluate the performance of several baseline methods on this task. the best achieves an accuracy of just over 82%. in section 5, we show that even this binary classification of events into dependent and independent is sufficient to improve performance in two further nlp tasks: event representation extraction and co-reference resolution. the task can be refined into a 3-way classification task, by distinguishing required and optional arguments, and into a 4-way classification task by distinguishing also the two types of non-argument events: conditions and independent events. the 3-and 4-way classification tasks are significantly harder. a summary of results is presented in section 6, and full details in appendix a.2. ## conclusion in this paper, we introduced eder, a highquality human-annotated event dependency relation dataset. we presented a thorough description of the dataset construction process followed by a detailed analysis. we implement various language models and one unsupervised rule-based method, to establish benchmark performance against the dataset. the experimental results of these competitive baselines, along with the evident improvements in some related nlp tasks such as semantic role labelling and cor-reference resolution, demonstrate the utility and benefit of the eder dataset. we hope that eder facilitates future research in uncovering potential relations among events, thereby enriching the understanding of our linguistic world.