H indi Causal T ime B ank: an Annotated Causal Event Corpus (2023)

Abstract: Events and states have gained importance in NLP and information retrieval for being semantically rich temporal and spatial information indicators. Event causality helps us identify which events are necessary for another event to occur. The cause-effect event pairs can be relevant for multiple NLP tasks like question answering, summarization, etc. Multiple efforts have been made to identify causal events in documents but very little work has been done in this field in the Hindi language. We create an annotated corpus for detecting and classifying causal event relations on top of the Hindi Timebank (Goel et al., 2020), the ‘Hindi Causal Timebank’ (Hindi CTB). We introduce semantic causal relations like Purpose, Reason, and Enablement inspired from Bejan and Harabagiu (2008)’s annotation scheme and add some special cases particular to Hindi language.

## introduction events and the relations between events form an important part of textual and verbal communication. a dynamic event (or event, by timeml guidelines @xcite ) as defined by @xcite is a cover term for situations that happen, occur, hold, or take place. stative events (or states) are the predicates describing states or circumstances in which something obtains or holds. detecting events, states, temporal expressions, and their relations provides a rich source of information and represents real-world information in the text. causal relations between events is a comparatively recent task that detects the presence of causality between two events-in other words, detecting whether an event needs to occur for another event to take place or if an event 'causes' another event. event causality plays a significant role in nlp applications. question answering, summarization, information extraction, and knowledge graphs are some of the many downstream tasks that can be solved better with the help of causality. we extend the idea of event causality to the hindi language by building upon the initial seed dataset of events and states @xcite and annotate causal event and state relations in an augmented dataset of 1,000 hindi news articles. we mark the causal event pairs and classify them into different relation types. we provide a comprehensive set of guidelines for identifying and classifying event relations based on the previous work @xcite . to our knowledge, this is the first work in the field of event causality in the hindi language. event causality is a difficult task as causality is a psychological concept. it involves a lot of implicit connotations and real world context. one cannot simply look at the structure of a sentence and determine whether causality exists or not. for example, in figure 1 the two sentences follow the same structure, have two events but only one sentence exhibits causality. the second sentence simply shows a temporal relationship. this is because in the first sentence, the real world context tells us that a thief would probably want to stay away from the police and hence the first event of seeing the police would have caused the second event of running away to occur. thus, manually identifying causal relations by two annotators can vary as they might not share the same real world context as they have different experiences. therefore, while creating this dataset, multiple annotators annotated the data and we only marked the causal relations where majority of the annotators agreed upon the existence of causality between two events. the paper is divided into the following sections; section 2 talks about the previous work and gives brief overview of the existing datasets with annotated causal event relations. we introduce the dataset and present the annotation guidelines in section 3. in section 4, we conduct baseline experiments on our dataset, which is followed by conclusion in section 5. ## related work the first work on events dates back to 1978 @xcite . ever since, there have been multiple datasets with events and event extraction guidelines. the timeml annotation guidelines in 2004 @xcite introduced a rule-based approach for the event and state extraction. the existing largescale datasets are all in the english language. following are some of the widely used datasets for event causality based on news events. : we build the hindi causal timebank (hindictb) on top of the hindi timebank by annotating causal relations between events. the further sub-sections talk about the annotation guidelines for the same.