Extracting Events from Industrial Incident Reports (2021)

Abstract: Incidents in industries have huge social and political impact and minimizing the consequent damage has been a high priority. However, automated analysis of repositories of incident reports has remained a challenge. In this paper, we focus on automatically extracting events from incident reports. Due to absence of event annotated datasets for industrial incidents we employ a transfer learning based approach which is shown to outperform several baselines. We further provide detailed analysis regarding effect of increase in pre-training data and provide explainability of why pre-training improves the performance.

## introduction the industrial revolution 1 has had a profound effect on the socio-political fabric of the world. economic progress of societies has been highly correlated with their degree of industrialization. however, one of the flip sides of this progress has been the cost of large industrial accidents in terms of injuries to workers, damage to material and property as well as the irreparable loss of innocent human lives. such major industrial incidents have had large social and political impacts and have prompted policy makers to devise multiple regulations towards prevention of such incidents. as an instance, the huge social uproar after the bhopal gas leakage tragedy 2 had many political ramifications and resulted in creation of many new acts, rules and institutions in india and internationally. governmental agencies in-charge of industrial safety (osha; minerva) as well as the industrial enterprises themselves try and minimize the possibility of recurrence of industrial incidents. for this 1 https://en.wikipedia.org/wiki/ industrial_revolution 2 https://en.wikipedia.org/wiki/bhopal_ disaster on february 1, 2014, at approximately 11:37 a.m., a 340 ft.-high guyed telecommunication tower, suddenly collapsed during upgrading activities. four employees were working on the tower removing its diagonals. in the process, no temporary supports were installed. as a result of the tower 's collapse , two employees were killed and two others were badly injured. ## related work this section discusses important related work on two important aspects -automated analysis of textual incident reports/descriptions and unsupervised or weakly supervised event extraction approaches. as per the best of our knowledge, this is the first work on labelling and predicting events (a token level object) from incident report text. however, there are multiple papers which analyze incident reports at the document or sentence level for various tasks such as classification, cause-effect extraction and incident similarity. tanguy et al.(2016) use nlp techniques to analyze aviation safety re-ports. the authors focus on classification of reports into different categories as well as use probabilistic topic models to analyze different aspects of incidents. the authors also propose the timeplot system to identify similar incident reports. similar to @xcite , @xcite ) perform text classification of event reports in nuclear power plants. however, both @xcite and @xcite do not focus on extraction of specific events from incident reports. @xcite use neural network techniques to extract occupational health and safety related information from news articles related to industrial incidents. specifically, they focus on extraction of target organization, safety issues, geographical location of the incident and penalty mentioned in the article. in the context of event extraction approaches, multiple state-of-the-art supervised approaches have been proposed in the literature recently. however, the complex neural network architectures demand significant amounts of training data which is not available in the current scenario of event extraction in incident reports. hence, we discuss two event extraction approaches which are weakly supervised in nature. in @xcite , the authors propose a rule based approach which considers all past tense verbs as events with a wordnet based filter retaining only "action" or "communication" events. there is no support for extraction of nominal events proposed by the authors. (araki and @xcite propose an open domain event extraction approach which uses linguistic resources like word-net and wikipedia to generate training data in a distantly supervised manner and then train a bil-stm based supervised event detection model using this data. @xcite propose a weakly supervised approach for event detection. the authors first construct a large-scale event-related candidate set and then use an adversarial training mechanism to identify events. we use the first two approaches - @xcite and @xcite as our baselines and discuss them in detail in section 4. the third approach @xcite based on adversarial training is evaluated on closeddomain datasets and hence it would be difficult to tune it and use it as a baseline for an open-domain event extraction task like ours. ## event extraction in incident reports events are specific occurrences that appear in the text to denote happenings or changes in states of the involved participants. multiple guidelines defining events and their extents in text are proposed in the literature @xcite . it is important to note that no event annotated data is available for any incident text dataset and this compels us to consider event extraction approaches which are either unsupervised or involve minimal training data. we make a two fold contribution in this regard. firstly, we annotate a moderately sized incident text dataset foot_1 for evaluation and weak supervision. secondly, we propose a transfer learning approach based on the standard bilstm sequence labelling architecture and compare with three baselines from literature. ## conclusion and future work in this paper we focused on extracting events from reports on incidents in aviation and construction domains. as there is no dataset of incident reports comprising of annotations for event extraction, we contributed by proposing modifications to a set of existing event guidelines and accordingly preparing a small annotated dataset. keeping in mind the limited data settings, we proposed a transfer learning approach over the existing bilstm-crf based sequence labelling approach and experimented with different static and contextual embeddings. we observed that pretraining improves performance of event extraction for all combinations of domains and embeddings. as part of the analysis, we showed the impact of employing varying amounts of pretraining data. we also performed a novel clustering based analysis to explain why pretraining improves performance of event extraction. we also propose a novel ensemble approach motivated from a practical viewpoint. as future work, we plan to pursue other important stages of the incident report analysis pipeline such as (i) entity/actor identification which involves finding the important participants in an incident, (ii) event argument identification which involves finding participants which are agents or experiencers of the event, (iii) state/condition identification which involve finding expressions describing long-running state-like conditions and (iv) eventevent relation identification which involves establishing of relation links between events.