Event Semantic Classification in Context (2024)

Abstract: In this work, we focus on a fundamental yet underexplored problem, event semantic classification in context, to help machines gain a deeper understanding of events. We classify events from six perspectives: modality, affirmation, specificity, telicity, durativity, and kinesis. These properties provide essential cues regarding the occurrence and grounding of events, changes of status that events can bring about, and the connection between events and time. To this end, this paper introduces a novel dataset collected for the semantic classification tasks and several effective models. By incorporating these event properties into downstream tasks, we demonstrate that understanding the fine-grained event semantics benefits downstream event understanding and reasoning via experiments on event extraction, temporal relation extraction, and subevent relation extraction.

## introduction a semantic class contains words that share a semantic feature. for example, within nouns, there are two subclasses, concrete nouns, and abstract nouns. concrete nouns include people, plants, and animals, while abstract nouns refer to concepts such as qualities, actions, and processes. in this work, instead of classifying nouns that are rather comprehensible lexemes in text, our focus is on the semantic classification of events. we perform semantic classification from multiple perspectives, which yields properties that are beneficial to comprehensive event understanding and relevant downstream tasks such as event extraction @xcite , event-event relation extraction @xcite , and event reasoning @xcite . different from conventional span classification tasks such as entity typing @xcite @xcite and event typing @xcite @xcite ) that map context: the community warmly received the refugees. event: received synset of event: receive.v.5 definition of synset (gloss): express willingness to have in one's home or environs. properties of received modality: realis affirmation: affirmative specificity: specific telicity: telic durativity: durative kinesis: non-static figure 1 : an example of event semantic classification from six perspectives. the synset of the event is drawn from wordnet @xcite . among these properties, modality, affirmation, and specificity are of great help to understanding the occurrence and grounding of an event, since modality and affirmation indicate if an event actually occurs @xcite , whereas specificity indicates whether an event is understood as a singular occurrence, a finite set of such occurrences, or others @xcite . telicity and durativity, on the other hand, are properties that connect events with time, and thus they evidently provide useful cues for temporal reasoning in narrative text. and the last property, kinesis, divides events into states and non-states. examples that belong to states include "desire," "want," "love," and so forth. they involve no dynamics and do not constitute changes themselves @xcite . there are a few works that have incidentally tagged some properties for events in the timeml @xcite , ace @xcite , masc @xcite , and uds @xcite annotations. yet only modality has been addressed with machine learning approaches in @xcite . in terms of usage of these properties, previous effort has been limited to leveraging them in feature-based statistical learning methods for the event coreference resolution task @xcite . in a nutshell, we lack the tools to obtain these useful attributes and have not fully exploited them for event understanding and reasoning tasks. in this paper, we introduce esc, the first comprehensive dataset collected for event semantic classification in both english and chinese. it contains all the wordnet @xcite example sentences for frequent verbs that feature 5,015 eventive synsets. the event mentions within these sentences are annotated with their six semantic properties. we also introduce and evaluate several models for the proposed tasks. by incorporating the event properties predicted by our best model into multiple eventrelated tasks, we demonstrate the utility of these properties through detailed experimental analysis. the contribution of this paper is threefold: ## event properties this section introduces six event properties we aim to address and why we choose them in detail. we also provide examples and analysis on how they assist event reasoning tasks. ## data annotation though there are verbal and nominal events, we believe the learning of event properties for one class can be generalized to the other with the help of current llms. we select 2,416 verbs from the 5,000 most frequent words foot_1 in the corpus of contemporary american english (coca). regarding these verbs, there are 5,015 synsets and 7,399 example sentences in wordnet @xcite ). we treat the example sentences as contexts of these verbal events. we translate the english context sentences into chinese and extract the spans of verbs using their synsets' chinese names in wordnet. we employ the data collection and labeling services from tencent cloud foot_2 for our event property annotation, in which each assignment asks six questions regarding an event and costs ¥2.0 (∼@xmath018. we require that our annotators are "master workers," indicating reliable annotation records. we identified 15 valid annotators: all of them are native chinese speakers who have received higher education and speak fluent english. before working on the annotation assignments, they are trained by experts to fully understand the instructions that provide definitions and examples of each event prop- ## classification models in this section, we introduce the models designed for the proposed classification tasks. ## enhancing event-centric nlp tasks in this section, we leverage the event properties to improve the model performances on event reasoning tasks. we study two methods to this end, one is to incorporate these properties in existing models as features, and the other is to induce constraints and incorporate the constraints into the models. we examine three event-centric nlp tasks, namely event extraction, event temporal relation extraction, and subevent relation extraction, which serve as the media for demonstrating the effectiveness of our proposed tasks and models. ## related work the study of event semantics has been the focus of both linguistics and philosophy for a long time. early effort on this topic dates back to sixty years ago: @xcite classified verbal events into four categories on whether they express "activity," "accomplishment," "achievement" or "state." and the criteria for distinguishing "accomplishment" and "achievement" from the other two is they have certain endpoints, i.e., they are telic. later, @xcite introduced durativity and kinesis to further categorize events into five classes (see tab. 2 ). though there are further efforts that classify events in finer ways @xcite , this paper focuses on how semantic classification of events supports the understanding of event-centric reasoning tasks. the most relevant work to our focus are the ten different event facets involved in the transitivity property of a clause @xcite and the seven attributes designed for examining eventive-ness @xcite (i.e., to determine whether a lexeme can be identified as an event). annotated on the masc corpus @xcite , the sitent dataset @xcite captures event vs. state distinctions. the diaspora dataset @xcite annotates phone conversations for stativity and telicity. nevertheless, these previous works have mainly established theoretical frameworks for event study and left building tools for machine reasoning as the future endeavor. recent efforts in event annotations have been made in event detection @xcite , and event-event coreferential, temporal, hierarchical, and causal relations @xcite @xcite . these corpora have enabled data-driven models to gain understanding of event semantics and how they interact with other events. however, models learned from these corpora often rely on dataset statistics @xcite and thus are biased towards prior knowledge and have limited interpretability. ## conclusion in this work, we first study six event properties that help machines gain a deep understanding of events and then introduce a novel dataset we collect for event semantic classification foot_6 . various semantic information can be inferred from these properties in that they provide the occurrence and grounding of events and their connection with time as well. we design six methods for event semantic classification, four of which involve recent large language models. experimental results demonstrate that chatgpt performs better than gpt-3 even though its response is still subject to minor perturbation of the prompt formats. on average, the model mp+gloss performs best in the proposed tasks and it is employed to predict event properties in three downstream tasks. to enhance the performances of neural models proposed for these tasks, we discuss two methodologies for incorporating useful event properties. results show that the predicted event properties are effective in enhancing the performances of existing models across three different tasks. therefore, we claim that the fundamental task of event semantic classification benefits both event understanding and reasoning.