A Novel Framework for Detecting Important Subevents from Crisis Events via Dynamic Semantic Graphs (2021)

Abstract: Social media is an essential tool to share information about crisis events, such as natural disasters. Event Detection aims at extracting information in the form of an event, but considers each event in isolation, without combining information across sentences or events. Many posts in Crisis NLP contain repetitive or complementary information which needs to be aggregated (e.g., the number of trapped people and their location) for disaster response. Although previous approaches in Crisis NLP aggregate information across posts, they only use shallow representations of the content (e.g., keywords), which cannot adequately represent the semantics of a crisis event and its sub-events. In this work, we propose a novel framework to extract critical sub-events from a large-scale crisis event by combining important information across relevant tweets. Our framework first converts all the tweets from a crisis event into a temporally-ordered set of graphs. Then it extracts sub-graphs that represent semantic relationships connecting verbs and nouns in 3 to 6 node sub-graphs. It does this by learning edge weights via Dynamic Graph Convolutional Networks (DGCNs) and extracting smaller, relevant sub-graphs. Our experiments show that our extracted structures (1) are semantically meaningful sub-events and (2) contain information important for the large crisis-event. Furthermore, we show that our approach significantly outperforms event detection baselines, highlighting the importance of aggregating information across tweets for our task.

## introduction social media is widely used for informing humanitarian aid efforts in crisis events @xcite . during a large-scale crisis event, there is a large set of smaller events in duration and impact that are essential components of the larger event, which are called sub-events. a sub-event is a structure that represents an action, and thus has a temporal dimension and a list of entities involved. detecting important sub-events that occur during a crisis (e.g., road blocks, people trapped) can aid authorities to prevent and respond to urgent situations (e.g., rescue efforts) @xcite . however, this requires connecting information from multiple posts as they contain repetitive or complementary information which needs to be aggregated (e.g., the number of trapped people and their location) for disaster response. several approaches in crisis nlp aggregate information across multiple tweets in the form of clusters, where each cluster is considered a subevent @xcite @xcite . however, these methods have several shortcomings. first, the output clusters may not refer to a single sub-event, but to a list of sub-events that share similar information types. for example, consider the tweets 25 people killed in everest base-camp and 200 people killed in gorkha. they contain the same information type (i.e., number of people killed), but clearly refer to two different sub-events. this results in large, non-interpretable clusters that lack cohesion @xcite . second, most work ignores or uses heuristics to model the temporal dependencies of sub-events, without explicitly modeling timesensitive information that gets updated, such as the number of injured people. third, tweet content is represented by shallow semantics, such as bagof-words or verb-noun pairs. such representations miss information that distinguishes between different sub-events of the same information type and are inadequate to model semantic dependencies across sub-events. to provide an example, consider the nepal earthquake in april 2015. in representing events in text is a complex problem. most work on event detection relies on the semantic frame theory @xcite , according to which an event is represented by the predicate, typically a verb or noun denoting an action, and the event arguments, a list of entities related to the predicate via a specific set of relations (e.g., agent, patient,..). in this work, we use the same notion of event representations. thus, we can distinguish different sub-events even if they have the same predicate and/or partially share entities, as in the example discussed earlier. recent work on social event understanding proposed a method to model event dependencies. they construct a sequence of graphs representing all the documents @xcite . they preserve temporal dependencies of events by using a dynamic graph convolutional network (dgcn); a model that learns an expressive graph representation of nodes not only from their connections in a certain time-step, but also from the dynamic context of the previous time-step. in our framework we exploit the expressive power of a dgcn to aggregate tweet content and model large-scale crisis events, by learning graph edge weights. these weights let us identify important nodes and relations; a critical step for sub-event extraction @xcite . we propose sd 2 sg, (sub-event detection via dynamic semantic graphs): a novel framework to extract important sub-events from a temporallyordered group of tweets related to a crisis event. our approach combines information across tweets into a set of temporally-ordered graphs, which are used to extract sub-events. since we have limited data, we impose structural (entities can be connected only via a predicate) and semantic constraints (predicates are defined via the framenet ontology) in each graph. thanks to these constraints, our model learns valid relations instead of coincidental co-occurrences of words via the use of a dgcn model. finally, we exploit the same structural constraints another time to rank 3 to 6 node subgraphs (sub-events) from the learned graph of a crisis event. the contributions of our proposed framework are summarized as follows: ## related work relevant research focuses on two main directions: (i) information extraction or classification in the tweet/sentence level and (ii) information aggregation across documents / posts. ## proposed framework: sd 2 sg in this section, we discuss our approach on extracting sub-events from a stream of temporally-ordered tweets related to a crisis event. our framework consists of the following steps, as shown in figure 1: (i) construct the initial dynamic semantic graph, (ii) learn the graph's weights via a graph neural net-algorithm 1: steps of sd 2 sg input : @xmath0, tweet1, . . . , tweetn in a temporal order, @xmath1, @xmath2, pre-trained embeddings output : extracted sub-events for each time-step t: s1, s2, . . . , st from each crisis event. 1. for each crisis event, @xmath3, irrelevant} do divide tweet1, tweet2, . . . , tweetn into groups of equal size, @xmath4, d2, ..., dt; for each @xmath5; choose top k; work, and (iii) extract sub-events from the learned graph via random walks that satisfy our semantic constraints. the pseudocode of our framework's steps is in algorithm 1. ## training & evaluation crisis event dataset. to train the model described in algorithm 1, we use a subset of the combined dataset described in alam et al., which consists of twitter data from 59 crisis events, including natural and man-made disasters. the tweets in this dataset were all manually annotated by alam et al. as either being related or unrelated to their corresponding crisis event. the statistics of the dataset are shown in once the dgcn is trained, we execute step 3 in algorithm 1 and extract sub-graphs of interest for our evaluation. we evaluate our extracted subevents with respect to two factors: (i) validity and (ii) importance in the context of a large-scale crisis event. ## discussion in the previous section we show that despite sd 2 sg performed significantly better than our baselines, our numbers are overall low; 45.5% of our extracted sub-events are valid and only 25% important. in this section we identify and discuss a set of reasons why sub-event extraction of tweets is a challenging problem and how we can improve. the first step of our analysis is to compare our extracted sub-events to an existing resource manually curated by experts in crisis nlp. we used the emterms (emergency management terms) ontology @xcite ; a resource of 7,000 manually annotated terms that are used in twitter to describe crisis events, classified into 23 information-specific categories. based on this lexicon and our extracted sub-events, we estimate the percentage of predicates and arguments that exist in the em terms by a partial string matching (many emterms are phrases of 2-3 words). in ## conclusion in this paper, we propose a novel framework to extract sub-events from a large-scale crisis event. contrary to earlier views of sub-events as clusters of unrelated words or phrases, our methodology aims at extracting sub-events in the form of a predicate and its arguments. our framework aggregates information across a set of tweets into dynamic graph representations, while maintaining semantic constraints. through an extensive qualitative analysis of our extracted sub-events, we show that our approach performs better than other baselines and highlight the challenges of our task.