OEE - CFC : A Dataset for Open Event Extraction from C hinese Financial Commentary (2024)

Abstract: To meet application needs, event extraction has shifted from simple entities to unconventional entities serving as event arguments. However, current corpora with unconventional entities as event arguments are limited in event types and lack rich multi-events and shared arguments. Financial commentary not only describes the basic elements of an event but also states the background, scope, manner, condition, result, and tool used for the event, as well as the tense, intensity, and emotions of actions or state changes. Therefore, it is not suitable to develop event types that include only a few specific roles, as these cannot comprehensively capture the event’s semantics. Also, there are affluent complex entities serving as event arguments, multiple events, and shared event arguments. To advance the practicality of event extraction technology, this paper first develops a general open event template from the perspective of understanding the meaning of events, aiming to comprehensively reveal useful information about events. This template includes 21 event argument roles, divided into three categories: core event roles, situational event roles, and adverbial roles. Then, based on the constructed event template, Chinese financial commentaries are collected and manually annotated to create a corpus OEE-CFC supporting open event extraction. This corpus includes 17,469 events, 44,221 arguments, 3,644 complex arguments, and 5,898 shared arguments. Finally, based on the characteristics of OEE-CFC, we design four types of prompts, and two models for event argument extraction are developed, with experiments conducted on the prompts.

## introduction recently, with the impressive advancement of event extraction task @xcite , event extraction corpora have attracted considerable attention @xcite @xcite . some scholars have annotated and released several corpora, including title2event @xcite for financial news headlines, document-level corpora (e.g., chfinann @xcite , rams @xcite , wikievent @xcite , do-cee @xcite , etc.), supporting multiple language mee @xcite , cfinee @xcite based on stock data, etc. also, there are event detection and argument extraction corpora (e.g., maven @xcite , geneva @xcite , etc.). compared to the common event extraction corpus ace2005, the aforementioned corpora not only expand the scope but also feature specific task characteristics or domain-specific traits. title2event focuses on open events in the form of triplet @xcite . chfinann is used for documentlevel multi-event extraction from chinese financial announcements, while rams and wikievent aim to support event extraction where unconventional entities serve as arguments. cfinee focuses on chinese stock headline data, adding content such as event tense, subjects, and negative phrase expressions. nevertheless, there are still some shortcomings in the existing event extraction corpora. firstly, the event ontology is not sufficiently rich. in addition to the roles of common triplet (subject, predicate, object), time, place, and tools, the background, scope, manner, conditions, and results that trigger actions or state changes, as well as the tense, intensity, and emotions of the actions or state changes, all reveal event-related information that people are interested in when events occur. this information plays an important role in understanding the event's semantics. figure 1 shows part of a financial commentary article. from figure 1 , it is clear that for sentence s 6 , the occurrence of the "低于" event is established under a certain background and measurement standard; lacking this information will directly affect the original semantic meaning of the sentence. similarly, in 不同颜色含义：红色为触发词，蓝色为主体，绿色为客体，紫色为方式，橙色为范围，粉色为背景，浅蓝为时间，淡蓝为副词，浅紫为结果， 虚框代表虚框内的整体充当一种角色，下划线表示指代涵义的词语，斜体表示受体前置 s1: 上世纪80年代末和90年代初，工业消费品价格在居民消费价格指数中的权重为40%，目前这一比重已经降到了30%左右；四是构成居民消费价格指数的服务价格 多数仍由政府管制，受生产资料价格上涨的影响相对较小。 in the late 1980s and early 1990s, the weight of industrial consumer goods prices in the consumer price index (cpi) was 40%, but this proportion has now dropped to around 30%. additionally, most of the service prices that make up the cpi are still regulated by the government, making them relatively less affected by the rise in the prices of production materials. ## models in order to verify the significance of oee-cfc, this paper constructs two event argument extraction frameworks based on the span selection strategy, including paie @xcite and tabeae @xcite . paie is a prompt-based event non-continuous prompt: background source scope time location subject manner material tool reason state degreeadverb scopeadverb timeadverb placeadverb affirmativeadverb negativeadverbs modaladverb object purpose result explicit continuous prompt: event background description event source description in the scope involved in the event in a certain time range a certain location range the subject of an event that uses some manner material or tool for some reason or change of state in degreeadverb in different scopeadverb at different timeadverb in different placeadverb affirmativeadverb negativeadverbs modaladverb and the object of the event achieves some purpose and produces some result general continuous prompt: sep background sep source sep scope sep time sep location sep subject sep manner sep material sep tool sep reason sep state sep degreeadverb sep scopeadverb sep timeadverb sep placeadverb sep affirmativeadverb sep negativeadverbs sep modaladverb sep object sep purpose sep result argument quantity-based prompt: sep subject sep object sep time sep timeadverb sep degreeadverb sep background sep scope sep modaladverb sep manner sep scopeadverb sep location sep negativeadverbs sep reason sep source sep affirmativeadverb sep state sep result sep tool sep purpose sep placeadverb sep material prompt paie tabeae head-i head-c span-i span-c head-i head-c span-i span-c due to the high generality of the open event template, where role definitions are broad and role names are not sufficiently clear in terms of semantics, this paper mainly constructs prompts from two aspects: the order of roles and the number of arguments contained by the roles. role order prompt. considering that people's sentence expressions have certain logic and style habits, this paper designs the role order prompt to capture these potential semantic information. the role order is divided into non-continuous role order prompt (direct combination of roles) and explicit continuous role order prompt (adding specific descriptive tokens between roles), based on whether there are descriptive connecting tokens between roles. additionally, to make the continuous role order prompt more general, we also design a general continuous role order prompt that uses fixed symbols to represent specific tokens. according to the common expression habits of most people, the three designed prompts are shown in figure 5 . role quantity prompt. this strategy assumes that the more frequently a role appears in docu-ments, the more significant its influence factor is, thus it should be placed earlier in the prompt. to this end, we constructed the last prompt in figure 5 according to the argument distribution in figure 3 , arranging them in descending order of quantity. ## conclusions to advance the extraction of unconventional arguments in complex scenarios and to overcome the limitation of extracting only specified event types, this paper first develops a general open event template from a linguistic perspective. this template includes three categories of roles, encompassing a total of 21 event roles. then, we annotated the event extraction corpus oee-cfc based on chinese financial commentaries. this corpus includes 4,253 documents, 17,469 events, and 44,221 arguments, with shared arguments accounting for 13.34% of the total event arguments, and documents containing five or more events making up 47.86%. finally, given the paie and tabeae, four types of prompts were designed, and the corresponding experiments were conducted. among them, the explicit continuous prompt achieved the best performance, verifying the significance of oee-cfc.