Event Schema Research Articles

Modeling human activity comprehension at human scale: prediction, segmentation, and categorization.

Humans form sequences of event models-representations of the current situation-to predict how activity will unfold. Multiple mechanisms have been proposed for how the cognitive system determines when to segment the stream of behavior and switch from one active event model to another. Here, we constructed a computational model that learns knowledge about event classes (event schemas), by combining recurrent neural networks for short-term dynamics with Bayesian inference over event classes for event-to-event transitions. This architecture represents event schemas and uses them to construct a series of event models. This architecture was trained on one pass through 18 h of naturalistic human activities. Another 3.5 h of activities were used to test each variant for agreement with human segmentation and categorization. The architecture was able to learn to predict human activity, and it developed segmentation and categorization approaching human-like performance. We then compared two variants of this architecture designed to better emulate human event segmentation: one transitioned when the active event model produced high uncertainty in its prediction and the other transitioned when the active event model produced a large prediction error. The two variants learned to segment and categorize events, and the prediction uncertainty variant provided a somewhat closer match to human segmentation and categorization-despite being given no feedback about segmentation or categorization. These results suggest that event model transitioning based on prediction uncertainty or prediction error can reproduce two important features of human event comprehension.

Conceptual Metaphors and Event Schemas in the Conceptualization of Neoliberalism and Socialism in Presidential Inauguration Speeches: A Multidisciplinary Approach to Social Progress

Introduction: Neoliberalism and socialism are two political ideologies that have been widely debated and conceptualized in various ways. This study investigates how these ideologies are represented through metaphors in the political discourse of Rafael Correa, Ecuador's ex-president. Objective: To explore how neoliberalism and socialism are conceptualized using metaphors in Correa's presidential inauguration speeches. Methods: The study applied the Conceptual Metaphor Theory and the Event Schemas Classification System to analyze the corpus of Correa's speeches. A bibliographic review was conducted by identifying relevant sources, while those not focused on political metaphor analysis were discarded. Development: The results revealed a clear contrast in the conceptualization of both ideologies. Neoliberalism was metaphorically represented as WAR, CHAOS, CONFLICT, and DESTRUCTION, invoking images of bombs and disasters. In contrast, socialism was associated with metaphors of SHIELD, SWORD, and PROTECTION, framed as a battle to protect society. Correa strategically used these metaphors to persuade his audience and reinforce his political stance. Conclusions: The study concluded that Correa's use of metaphor served as a persuasive tool to polarize neoliberalism and socialism. The conceptualization of these ideologies reflects socio-cognitive and political perspectives, influencing audience perception and aligning with the ideological goals of his administration

Graph-based event schema induction in open-domain corpus

Graph-based event schema induction in open-domain corpus

Remembering what we imagine: the role of event schemas in shaping how imagined autobiographical events are recalled.

Much like recalling autobiographical memories, constructing imagined autobiographical events depends on episodic memory processes. The ability to imagine events contributes to several future-oriented behaviors (e.g., decision-making, problem solving), which relies, in part, on the ability to remember the imagined events. A factor affecting the memorability of such events is their adherence to event schemas-conceptualizations of how events generally unfold. In the current study, we examined how two aspects of event schemas-event expectancy and familiarity-affect the ability to recall imagined events. Participants first imagined and described in detail autobiographical events that either aligned with or deviated from an event, expected to occur in a context (e.g., a kitchen) that was either familiar or unfamiliar. This resulted in imaginations ranging from maximally schema-congruent (expected events in a familiar context) to maximally novel (unexpected events in an unfamiliar context). Twenty-four hours later, participants recalled these imagined events. Recollections were scored for the number of reinstated details from the imaginations and the number of newly added details. We found greater reinstatement of details for both the maximally congruent and maximally novel events, while maximally novel events were recalled more precisely than other events (i.e., fewer added details). Our results indicate a complementary benefit to remembering schematic and novel imagined events, which may guide equally important but distinct future-oriented behaviors.

CHRONOS: A Schema-Based Event Understanding and Prediction System

Chronological and Hierarchical Reasoning Over Naturally Occurring Schemas (CHRONOS) is a system that combines language model-based natural language processing with symbolic knowledge representations to analyze and make predictions about newsworthy events. CHRONOS consists of an event-centric information extraction pipeline and a complex event schema instantiation and prediction system. Resulting predictions are detailed with arguments, event types from Wikidata, schema-based justifications, and source document provenance. We evaluate our system by its ability to capture the structure of unseen events described in news articles and make plausible predictions as judged by human annotators.

Open‐domain event schema induction via weighted attentive hypergraph neural network

SummaryEvent schema refers to the use of a template to depict similar events, and it is a necessary prerequisite for event causality extractions. The induction of event schemas is a difficult task, especially for texts in the open domain, due to the complex and diverse manifestations of events. Previous models considered participants in event mentions are independent or compositional, ignoring the high‐order correlations among participants, which limit their capability of induce event schema. To remedy this, we propose constructing an Event Structure Hypergraph (ESH) to better utilizes the event structural information for event schema induction. In particular, we first extract event mentions from the open‐domain corpus. and then construct an ESH by representing event mentions as a hyperedges. ESH contains high‐order information between participants in event mention. To, learn event mentions representation based on ESH, we propose a weighted attentive hypergraph neural network (WHGNN) to model event high‐order correlations and then integrate node‐category weight matrix into the training of network by improving event representation. By applying jointly cluster algorithm on the event mentions representation, we can induce reliable event schemas. Experimental results on three datasets demonstrate that our approach can induce salient and high‐quality event schemas on open‐domain corpus.

Patient safety near misses – Still missing opportunities to learn

Objectives A patient safety near miss is a safety event that had the potential to cause harm, but did not reach the patient. For over 20 years healthcare has been exhorted to learn from patient safety near misses to support improvements in patient safety. The belief is that, by addressing the factors that contribute to patient safety near misses, harmful incidents will be avoided. However, there seems to have been little progress made to learn from patient safety near misses. This study aimed to explore why there has been limited progress, and how best patient safety near misses may be learned from. Methods A qualitative case study was undertaken to explore the learning from patient safety near misses in different National Health Service contexts. Semi-structured interviews were conducted with patient safety leads in secondary care, primary care, and regional/national bodies. Interviews were recorded, transcribed, and thematically analysed. Results Seventeen interviews were undertaken across the National Health Service, with further data collected from policy, guidance, field notes, and research memos. Thematic analysis identified the following: variations in safety event schema; limited processes for patient safety near misses; unsupportive reporting contexts; and assumed, but non-evidenced improvements in patient safety. Participants also shared their thoughts on how learning from patient safety near misses could be improved. Conclusion A lack of progress has been made to learn from patient safety near misses in the National Health Service. This is contributed to by a lack of agreement about what is and how best to learn from a patient safety near miss. The learning value of patient safety near misses lies in the focus they place on controls to hazards, but they should not be learned from in isolation.

An APT Event Extraction Method Based on BERT-BiGRU-CRF for APT Attack Detection

Advanced Persistent Threat (APT) seriously threatens a nation’s cyberspace security. Current defense technologies are typically unable to detect it effectively since APT attack is complex and the signatures for detection are not clear. To enhance the understanding of APT attacks, in this paper, a novel approach for extracting APT attack events from web texts is proposed. First, the APT event types and event schema are defined. Secondly, an APT attack event extraction dataset in Chinese is constructed. Finally, an APT attack event extraction model based on the BERT-BiGRU-CRF architecture is proposed. Comparative experiments are conducted with ERNIE, BERT, and BERT-BiGRU-CRF models, and the results show that the APT attack event extraction model based on BERT-BiGRU-CRF achieves the highest F1 value, indicating the best extraction performance. Currently, there is seldom APT event extraction research, the work in this paper contributes a new method to Cyber Threat Intelligence (CTI) analysis. By considering the multi-stages, complexity of APT attacks, and the data source from huge credible web texts, the APT event extraction method enhances the understanding of APT attacks and is helpful to improve APT attack detection capabilities.

Evolutionary combination of connected event schemas into meaningful plots

Many of the stories we are exposed to are built from small schemas of connected events involving a set of characters–boy meets girl leads to a relationship or crime leads to revenge. The present paper proposes an evolutionary solution to the task of putting together a story by combining a set of such schemas. This approach presents three challenges: how to mix up the elements in the different schemas, how to instantiate the characters across the schemas and how to tell acceptable combinations from the rest. The present paper applies an evolutionary solution that relies on a genetic representation for these combinations of schemas, and applies as fitness functions a set of metrics on compatibility constraints across schema combinations. Outputs of this procedure are evaluated by human judges in comparison with baseline solutions in which the values for genes are assigned at random. The proposed solution generates a population of story drafts that resemble plot descriptions for simple stories. The results of the comparative evaluation by human judges are positive. The genetic representation of pattern combinations and the metrics on compatibility across pattern pairs provide a valid evolutionary solution for constructing simple plots.

Semi-automatic representation of design code based on knowledge graph for automated compliance checking

Automated compliance checking (ACC) intends to verify the compliance of designs in construction industry by design codes. The ability to interpret and represent semantic information of design codes determines the maximum application scope of ACC. However, design codes are clause texts written in natural languages and existing ACC studies usually use relatively low-complexity code clause samples. At present, the lack of an accurate representation model for design codes leads to difficulties in representing the implicit information, nested logic, and complex relations contained in high-complexity clauses in codes. To address this problem, this research establishes a new representation model based on knowledge graph (KG). Four schemas are proposed into the model including order, complex, event and integration schemas. Further, an accompanying methodology for semi-automatic construction of design code KG (DCKG) is proposed. It includes four parts: interpretation, reconstruction, organization, and implementation. Where the implementation part develops a code annotation platform. In the case study and experiment, a scenario of checking a building information model (BIM) of metro station by GB50157–2013 Code for Design of Metro is adopted to validate the newly proposed representation model and the automated compliance process. The results show that the proposed model and method are correct and feasible, and our model outperforms other models in the representation ability of design codes.

A cognitive approach to the allegedly left-headed prefix verbs in German:

In this article, the much discussed problem of apparently left-headed German prefix verbs (e. g. besohlen ‘sole’, erschneidern ‘obtain by tailoring’, verschimmeln ‘become mouldy’) will be addressed from a cognitive perspective. It will be argued that nominal bases are metonymically reconceptualized as events inside the derivatives. This proposal has two advantages over previous approaches. First, it is not necessary to assign verbal or verbalizing properties to the prefixes be-, ent-, er-, ver-, and zer-, which have their origin in prepositions or adverbs. Secondly, an overgeneration of virtual verbs, i. e. of verbs which are not or no longer attested (e. g. *dachen ‘roof’, †sohlen ‘sole’) is avoided because the metonymic and hence purely conceptual verbalization process takes place in the context of the inseparable prefixes. The prefixes neither change the word-class of their bases nor provide argument structures, which are predicted by a small set of conceptual event schemata. The function of the prefixes is rather to add semantic or aspectual information. Synchronic support for the proposal to be presented first of all comes from the fact that conversion, which historically preceded prefixation in German, is still active. Contrastive analyses of English and German non-derived denominal verbs will illustrate this point. Moreover, many prefix verbs still co-exist with non-derived denominal verbs (e. g. schottern (< Schotter ‘gravel, ballast’) vs. beschottern, entschottern, verschottern). A more general observation is that the compatibility of denominal verbs with the holistic (or ‘synoptic’) perspective conveyed by the inseparable prefixes depends on the metonymic patterns underlying these verbs.

Flexible reuse of cortico-hippocampal representations during encoding and recall of naturalistic events

Although every life event is unique, there are considerable commonalities across events. However, little is known about whether or how the brain flexibly represents information about different event components at encoding and during remembering. Here, we show that different cortico-hippocampal networks systematically represent specific components of events depicted in videos, both during online experience and during episodic memory retrieval. Regions of an Anterior Temporal Network represented information about people, generalizing across contexts, whereas regions of a Posterior Medial Network represented context information, generalizing across people. Medial prefrontal cortex generalized across videos depicting the same event schema, whereas the hippocampus maintained event-specific representations. Similar effects were seen in real-time and recall, suggesting reuse of event components across overlapping episodic memories. These representational profiles together provide a computationally optimal strategy to scaffold memory for different high-level event components, allowing efficient reuse for event comprehension, recollection, and imagination.

Novel objects with causal event schemas elicit selective responses in tool- and hand-selective lateral occipitotemporal cortex.

Tool-selective lateral occipitotemporal cortex (LOTC) responds preferentially to images of tools (hammers, brushes) relative to non-tool objects (clocks, shoes). What drives these responses? Unlike other objects, tools exert effects on their surroundings. We tested whether LOTC responses are influenced by event schemas that denote different temporal relations. Participants learned about novel objects embedded in different event sequences. Causer objects moved prior to the appearance of an environmental event (e.g. stars), while Reactor objects moved after an event. Visual features and motor association were controlled. During functional magnetic resonance imaging, participants viewed still images of the objects. We localized tool-selective LOTC and non-tool-selective parahippocampal cortex (PHC) by contrasting neural responses to images of familiar tools and non-tools. We found that LOTC responded more to Causers than Reactors, while PHC did not. We also measured responses to images of hands, which elicit overlapping responses with tools. Across inferior temporal cortex, voxels' tool and hand selectivity positively predicted a preferential response to Causers. We conclude that an event schema typical of tools is sufficient to drive LOTC and that category-preferential responses across the temporal lobe may reflect relational event structures typical of those domains.

Cross-National Rivalry: National Identity in Sports (A Case Study of English and Russian)

Nationalism and sport are often interwoven and, subsequently, the competitive nature of sport competition can also mirror the contentious nature between international athletes. Evidence of such inter-group conflict may manifest itself through ethnolinguistics and is reinforced through social identity theory. Data analysis for the English and Russian languages was evaluated in four categories. Data includes Word Association Network entries for the four opposites of the sport event schema in Russian and English: 1) strong – weak; 2) success – failure; 3) ahead – behind; 4) winner – loser. Semantic analysis established asymmetries of the lexical oppositions relative to sport competition, which reinforce the manifestation of social identity in ways that elevate the status of one group while degrading the perception of the other. The authors believe that this study exposes that the congruence between semantics and ethno-linguistics which is rooted in social identity. The four authors have equally contributed to this study. The contribution included a literature review on the subject of the study and showing how rivalry in sport is influenced by social identity and ethno-linguistics, which helped to identify the dearth of research into cultural implications underlying sports. The authors also collected dictionary definitions of the items of the sports event schema and performed analysis of the data in the English and Russian languages.

Troubled Images: Analysing the Republican Use of Visual Metaphors in Wall Paintings and Pictures in Northern Ireland

The republican movement in Northern Ireland was visually manifested in images drawn on walls and on the gable ends of houses in the towns of Belfast and (London) Derry. As well as being an iconographic expression of the social injustice they suffered (Rapp & Rhomberg, 2013; Goalwin, 2013), these wall paintings were employed by the republicans to convey political and ideological messages in order to heighten awareness and to mobilize people. Blank spaces on walls were increasingly exploited by republican groups and were converted into a visual medium sui generis for their political and ideological claims and demands. Over the years republicans developed this novel communication strategy geared to expressing highly emotional content that served to reflect and influence the sentiments of the communities involved in the conflict. It also served to channel collective memory, recording key events and contributing to the formation of an identity. Intense political disagreements and armed conflict between the Catholic and Protestant communities from 1968 to 1998 led to violent clashes during the period known as “the Troubles”. This complicated time frame has been metaphorically represented in various ways in wall paintings and posters in Northern Ireland. The aim of this paper is to analyze the political and ideological use of visual metaphors in the images of the republican movement during the “Troubles”. More specifically, by applying the most recent methodological tool derived from a socio-cognitive model of discourse analysis, namely Conceptual Metaphor Theory (Lakoff & Johnson, 1980), the purpose is to analyse, both from a qualitative and quantitative point of view, the three different types of Conceptual Metaphorical Schemas: Propositional, Image and Event Schema (Soares da Silva, 2016).

From cognitive maps to spatial schemas.

A schema refers to a structured body of prior knowledge that captures common patterns across related experiences. Schemas have been studied separately in the realms of episodic memory and spatial navigation across different species and have been grounded in theories of memory consolidation, but there has been little attempt to integrate our understanding across domains, particularly in humans. We propose that experiences during navigation with many similarly structured environments give rise to the formation of spatial schemas (for example, the expected layout of modern cities) that share properties with but are distinct from cognitive maps (for example, the memory of a modern city) and event schemas (such as expected events in a modern city) at both cognitive and neural levels. We describe earlier theoretical frameworks and empirical findings relevant to spatial schemas, along with more targeted investigations of spatial schemas in human and non-human animals. Consideration of architecture and urban analytics, including the influence of scale and regionalization, on different properties of spatial schemas may provide a powerful approach to advance our understanding of spatial schemas.

Document-level event argument extraction with self-augmentation and a cross-domain joint training mechanism

Document-level event argument extraction (EAE), a critical task for event knowledge acquisition, seeks to find event arguments beyond the sentence level. Previous approaches for this task adopt supervised learning, which suffers from the data scarcity issue, especially in low-resource situations. To address this challenge, we propose a new method for document-level EAE that addresses the data scarcity from two perspectives: self-augmentation and cross-domain joint training. On the one hand, our method can generate additional training samples from an existing dataset using pre-trained language models, considerably expanding the training set; on the other hand, our method can learn with out-of-domain datasets with varying event schema, using a cross-domain joint training framework. To address data quality problems and domain mismatches, we also provide a new noise filtering method based on a teacher–student framework. The extensive experiments demonstrate that our approach delivers state-of-the-art performance on benchmark datasets, and that it is especially effective in low-resource situations.

Under the surface

AbstractThrough a survey of crosslinguistic empirical studies on narratives and in the domain of motion events, we argue that the principles of language use pose a hurdle for second language acquisition, an issue that has not been given adequate attention in earlier research. These principles are part of our pragmatic knowledge which encompasses language-specific information organization at the discourse level as well as event schemata at the level of construing reportable units. The findings show how highly competent L2 speakers activate L1-based knowledge that reflects deeply entrenched principles and seems resistant to change.

Metonymic event-based time interval concepts in Mandarin Chinese-Evidence from time interval words.

Starting from the overwhelming view that time is metaphorically conceptualized in terms of space, this study will, on the one hand, take the time interval words into minute analysis to confirm our view of event conceptualization of time at a more basic level along with space–time metaphoric conceptualization of time at a relational level. In alignment with the epistemology of the time–space conflation of the Chinese ancestors, our view is supported by the systematic examination of evidence related to the cultural origins of the conceptualization of time, through a scrutiny of the original meanings and construction of words related to intervals of time in Mandarin Chinese. This study offers a new explanation of how: (1) the conceptualization of time in Chinese is realized through metonymic cognition and (2) words related to specific intervals of time are coined based on the metonymic conceptualization of related events or a corresponding event schema. Five major types of event-based metonymies are identified, and their interactive functions are illustrated. Based on this evidence, we argue that the double nature of both metaphoric and metonymic time conceptualization in Mandarin Chinese lies in the fact that time interval words can be used in its time categorial sense or as a time entity which suggests the etymological origins of Chinese as ideograph. It is concluded therefore that the event-based metonymy conceptualization of time can provide better insights into the characteristics of Chinese modes of thinking and its influences on the perception of and interaction with the world. This study can also serve as good evidence for the shaping effect of language on cognition.

Conceptual Blending Across Ontological Domains—References to Time and Space in Motion Events by Tunisian Arabic Speakers of L2 German

Patterns of information selection and verbal encoding may rely on an interdependence between the spatial and temporal conceptual domain in the context of motion events. This has been shown, e.g., for Tunisian Arabic (TA), a language with a highly differentiated aspectual system. We address the question whether this interdependence can also be observed when L1 speakers of TA describe motion events in their L2 German, a language without grammaticalized aspect. Data obtained in an unscripted language production experiment in which L1 and highly advanced L2 speakers of German describe videos showing different types of motion events (one type showing boundary crossing at a goal likea woman entering a supermarket, the other type showing motion along a path with no evident goal such asa car driving along a road)suggest that this is indeed the case. The L2 speakers deviate systematically from the L1 speakers of German in the information they select for verbal encoding, but show clearly similar patterns to those used when describing the same scenes in the L1 (TA). The differences can be interpreted as pointing to the high relevance attributed to the temporal dimension of the events shown in the videos by the L2 speakers. The results are placed in the theoretical framework of schema theory. The findings for Arabic speakers of L2 German can be explained by assuming that the same event schema is activated in the context of L2 use as in the context of L1 use.