CEO : Corpus-based Open-Domain Event Ontology Induction (2024)

Abstract: Existing event-centric NLP models often only apply to the pre-defined ontology, which significantly restricts their generalization capabilities.This paper presents CEO, a novel Corpus-based Event Ontology induction model to relax the restriction imposed by pre-defined event ontologies. Without direct supervision, CEO leverages distant supervision from available summary datasets to detect corpus-wise salient events and exploits external event knowledge to force events within a short distance to have close embeddings. Experiments on three popular event datasets show that the schema induced by CEO has better coverage and higher accuracy than previous methods. Moreover, CEO is the first event ontology induction model that can induce a hierarchical event ontology with meaningful names on eleven open-domain corpora, making the induced schema more trustworthy and easier to be further curated. We anonymously release our dataset, codes, and induced ontology.

## introduction extracting and understanding real-world events described in the text are crucial information extraction tasks that lay the foundations for downstream nlp applications @xcite @xcite . however, existing event-related studies are mostly restricted by the pre-defined ontology @xcite . even for the zeroshot setting, models still need a pre-defined ontology for inference @xcite . to address this limitation, the previous work @xcite proposed the event type induction task, which automatically induces event ontology from documents. however, previous work only covers verbal events while ignoring the 1 https://sites.google.com/view/ ceoeventontology s1: what is the best way to keep from spreading (v.) the virus through coughing or sneezing? ## problem definition since the majority of events are triggered by verbal and nominal predicates along with relevant arguments, we denote an event mention @xmath0, predicate, object>. for each corpus, event mentions highly relevant to its topic are considered as salient and constitute the extraction targets. to understand semantic relations between events, we aim at inducing a hierarchical event type ontology with a tree structure, where leaf nodes represent single event mentions while internal nodes are subclusters of events. task definition. given a corpus of n sentences @xmath1, . . . , s n }, event ontology induction 1) firstly extracts salient event mentions, e.g., m ij for j-th event in s i , 2) then identifies event ontology that well demonstrates correlations among all covered event types, 3) lastly infers event type names withing human readable formats from coarse-tofine granularity. ## ceo in fig. 2 , we show the overview of the proposed ceo that extracts (step 1 in §4.1) and represents salient events (step 2 in §4.2) with informative compressed event representation fine-tune l reconstruction & l triplet ontonotes nombank big bird runs for a while (verbal). big bird goes for a run (nominal). fine-tuned bert v . n. open-domain corpus longformer salient include exclude y n hypernym & hyponym sentence 1 => type name 1 sentence 2 => type name 2 sentence 3 => type name 3 sentence 4 => ? hierarchical clustering gpt-j-6b in-context learning input output infer 1. event mention extraction 2. salient event detection 3. event ontology inference 4. ontology name generation train inference l reconstruction figure 2: framework of the proposed ceo . step 1: extract events triggered by nouns or verbs; step 2: preserve salient events with distant supervision from summaries; step 3: improve event representations for hierarchical clustering with external event knowledge from wordnet; step 4: generate event type names with in-context learning. embeddings for ontology structure induction (step 3 in §4.3) and name generation (step 4 in §4.4). ## experiments in this section, we firstly introduce the utilized event datasets ( §5.1) and then quantitatively evaluate the ontology ( §5.3.1) and name ( §5.3.2) induction quality of ceo . then we evaluate the effectiveness of different techniques incorporated in ceo ( §5.4) via the ablation study. lastly, we apply ceo to perform ontology induction on eleven open-domain corpora ( §5.5) to demonstrate its effectiveness in real applications. ## conclusion to understand events expressed in open domains free from the restriction of pre-defined ontologies, we propose a new corpus-based open-domain event ontology induction strategy ceo to automatically induce hierarchical event ontology structure and provide interpretable type names for further curation. on three event datasets, we find it can capture salient events more accurately, induce ontology structures aligning well with ground truth and generate appropriate coarseto-fine type names. we also show the broad application of ceo on open domains from allsides.