Extracting Message Sequence Charts from H indi Narrative Text (2020)

Abstract: In this paper, we propose the use of Message Sequence Charts (MSC) as a representation for visualizing narrative text in Hindi. An MSC is a formal representation allowing the depiction of actors and interactions among these actors in a scenario, apart from supporting a rich framework for formal inference. We propose an approach to extract MSC actors and interactions from a Hindi narrative. As a part of the approach, we enrich an existing event annotation scheme where we provide guidelines for annotation of the mood of events (realis vs irrealis) and guidelines for annotation of event arguments. We report performance on multiple evaluation criteria by experimenting with Hindi narratives from Indian History. Though Hindi is the fourth most-spoken first language in the world, from the NLP perspective it has comparatively lesser resources than English. Moreover, there is relatively less work in the context of event processing in Hindi. Hence, we believe that this work is among the initial works for Hindi event processing.

## introduction narratives are used to communicate complex ideas, detailed accounts of complex events or arguments about one's beliefs @xcite . moreover, a narrative is a powerful tool not just from entertainment perspective but is one of the core component of human memory, knowledge and intelligence @xcite . narrative texts are common in history where they mostly describe events that have happened in the past. narrative text can also be routinely seen in news articles reporting events that have happened (or about to happen) in various political, corporate and social walks of a country. for multiple applications in text analysis, it becomes important to hindi is an indo-aryan language spoken by around 300 million people in india. additionally, hindi is the fourth most-spoken first language in the world foot_0 . in comparison to english, hindi has different linguistic characteristics leading to a different set of nlp challenges. first of all, hindi is a subject-object-verb (sov) language with relatively free word order, as against the svo order in english. secondly, hindi does not have high accuracy nlp toolkits such as stanford corenlp @xcite . in this paper, we make one of the first attempts to facilitate event processing in hindi by proposing annotation guidelines for events as well as their arguments. we propose to represent a hindi narrative using message sequence charts (msc) @xcite figure 1: msc of example text narrative described in we also address the problem of automatically extracting the msc representation from a given narrative text. msc extends the notion of a single event-timeline @xcite for a narrative by providing a timeline per actor (entity of interest). msc representation captures all the actors and interactions in an easy to visualize manner and hence make the text more comprehensible. further, the representation's support for inference mechanisms opens up possibilities of tackling natural language understanding problems like text comprehension and question answering. our work is similar to @xcite to represent a english narrative using msc. however, due to intricacies of events in hindi language, their approach cannot be used for construction of msc of a hindi narrative. the key contributions of the paper are (i) we extend an existing scheme of annotation of events @xcite ; we provide guidelines for annotation of mood of events (realis vs irrealis) and guidelines for annotation of event arguments, (ii) we propose an approach to identify event predicates and their arguments, (iii) msc based visu-alization of actors and interactions, (iv) we report performance on multiple evaluation criteria by experimenting with four hindi narratives from indian history. ## proposed annotation guidelines and approach as highlighted earlier, an msc consists of a set of actors and interactions among them. the proposed approach is accordingly divided into two major stages: (1) extraction of actors and their coreference resolution; ## related work automatic extraction of mscs from narratives is studied for english language. recently, @xcite proposed a semantic role labelling and dependency parsing based approach to extract messages discussed in a narrative. they further use the document level time-anchors algorithm for temporal ordering of extracted messages. several researchers such as (rao and devi, 2017, 2018) have focused on extraction of events from hindi. however, these works are focused on news and social media texts. @xcite propose a neural network based approach for event extraction. however, the approach is supervised and needs labelled data. on the other hand, our approach does not need labelled data. ## conclusions in this paper we proposed the use of a knowledge representation known as message sequence charts (msc) for visualizing hindi narratives by identifying and depicting the multiple actors and interactions involved. as per our knowledge this is the first attempt to visualize hindi narratives and represent them formally. we extend a set of annotation guidelines for marking events and their arguments pivoted on verbs. we also propose linguistic knowledge based approach for actor and interaction identification. we report results on four state-of-the-art hindi narrative datasets and present a brief analysis of the approaches. as future work, we would like to extend this work on lines of semantic role labelling (srl) for hindi including nominal predicates and more arguments. we also aim to work on the temporal ordering aspect of hindi narratives which would allow us to depict interactions in the true chronological order.