Event Relation Recognition by Multi Part of Speech Association Distribution Characteristics

Event relation recognition, as one of natural language processing technologies, faces information stream of texts detecting event relation. By analyzing the influence of the words of different parts of speech on the relevance of events. And use the form of lexical chain to extract and store the relevant vocabulary between events, this paper propose an event relation recognization method based on lexical chain to detect latent semantic relation between events: whether events hold logical relation or not. Cornpared with the method based on dependency cue inference, the proposed method achieves 7. 68% improvement

Identifying Conspiracy Theories News based on Event Relation Graph

Conspiracy theories, as a type of misinformation, are narratives that explains an event or situation in an irrational or malicious manner. While most previous work examined conspiracy theory in social media short texts, limited attention was put on such misinformation in long news documents. In this paper, we aim to identify whether a news article contains conspiracy theories. We observe that a conspiracy story can be made up by mixing uncorrelated events together, or by presenting an unusual distribution of relations between events. Achieving a contextualized understanding of events in a story is essential for detecting conspiracy theories. Thus, we propose to incorporate an event relation graph for each article, in which events are nodes, and four common types of event relations, coreference, temporal, causal, and subevent relations, are considered as edges. Then, we integrate the event relation graph into conspiracy theory identification in two ways: an event-aware language model is developed to augment the basic language model with the knowledge of events and event relations via soft labels; further, a heterogeneous graph attention network is designed to derive a graph embedding based on hard labels. Experiments on a large benchmark dataset show that our approach based on event relation graph improves both precision and recall of conspiracy theory identification, and generalizes well for new unseen media sources.Comment: Accepted to EMNLP 2023 Finding

ProtoEM: A Prototype-Enhanced Matching Framework for Event Relation Extraction

Event Relation Extraction (ERE) aims to extract multiple kinds of relations among events in texts. However, existing methods singly categorize event relations as different classes, which are inadequately capturing the intrinsic semantics of these relations. To comprehensively understand their intrinsic semantics, in this paper, we obtain prototype representations for each type of event relation and propose a Prototype-Enhanced Matching (ProtoEM) framework for the joint extraction of multiple kinds of event relations. Specifically, ProtoEM extracts event relations in a two-step manner, i.e., prototype representing and prototype matching. In the first step, to capture the connotations of different event relations, ProtoEM utilizes examples to represent the prototypes corresponding to these relations. Subsequently, to capture the interdependence among event relations, it constructs a dependency graph for the prototypes corresponding to these relations and utilized a Graph Neural Network (GNN)-based module for modeling. In the second step, it obtains the representations of new event pairs and calculates their similarity with those prototypes obtained in the first step to evaluate which types of event relations they belong to. Experimental results on the MAVEN-ERE dataset demonstrate that the proposed ProtoEM framework can effectively represent the prototypes of event relations and further obtain a significant improvement over baseline models.Comment: Work in progres

Response of immune system after ultra endurance event: relation between magnesium and immunological response

Magnesium (Mg) is one of the most important micronutrients, and therefore its role in biological system has been extensively investigated. Particularly, Mg has a strong regulatory role in the immune system. The aim of this study was to investigate the behavior of plasma magnesium and the immune response after half-ironman triathlon. Blood samples from six athletes were collected before and immediately after the triathlon competition. Magnesium plasma concentration and immune parameters were analyzed (total white blood cells (WBC), neutrophils, (NE), lymphocytes (LY), eosinophils (EO) monocytes (MO) and basophils (BA)). Pre and post race values were compared by paired t-tests. Pearson\u27s product–moment correlation coefficients were used to examine potential relationships between magnesium concentration, WBC and finishing time. Significant changes after triathlon completion were found for Mg, WBC, NE, LY, EO and no significant changes were found for MO and BA over time. There was no correlation between Mg, WBC and finishing time, possibly due to type β error. We can conclude that long duration exercise cause a depletion of magnesium reserves in the organism that may produce hypomagnesaemia. Magnesium deficit has been show to be related to impaired cellular and humoral immune function. Significant increase in WBC, which could lead to an increased susceptibility to infection after ultraendurance exercise due to duration, intensity and the extreme ambient conditions, commonly observed in ultra-endurance events. There is significant evidence in the literature that immune response is influenced by transient magnesium deficiency

SPEECH: Structured Prediction with Energy-Based Event-Centric Hyperspheres

Event-centric structured prediction involves predicting structured outputs of events. In most NLP cases, event structures are complex with manifold dependency, and it is challenging to effectively represent these complicated structured events. To address these issues, we propose Structured Prediction with Energy-based Event-Centric Hyperspheres (SPEECH). SPEECH models complex dependency among event structured components with energy-based modeling, and represents event classes with simple but effective hyperspheres. Experiments on two unified-annotated event datasets indicate that SPEECH is predominant in event detection and event-relation extraction tasks.Comment: Accepted by ACL 2023 Main Conference. Code is released at \url{https://github.com/zjunlp/SPEECH

Event-Object Reasoning with Curated Knowledge Bases: Deriving Missing Information

The broader goal of our research is to formulate answers to why and how questions with respect to knowledge bases, such as AURA. One issue we face when reasoning with many available knowledge bases is that at times needed information is missing. Examples of this include partially missing information about next sub-event, first sub-event, last sub-event, result of an event, input to an event, destination of an event, and raw material involved in an event. In many cases one can recover part of the missing knowledge through reasoning. In this paper we give a formal definition about how such missing information can be recovered and then give an ASP implementation of it. We then discuss the implication of this with respect to answering why and how questions.Comment: 13 page