Event Detection between Literary Studies and NLP. A Survey, a Narratological Reflection, and a Case Study

2.1 Event Categories

Events can be considered the smallest units that make up a narrative. An event can also be seen as a change of state, i.e., any type of expressed change that contributes to the narration (Hühn 2013). To define what can be considered as a change of state, and therefore an event, Hühn (2013) distinguishes two types of events, based on the context in which the concept of event is used: (1) “a type of narration that can be described linguistically and manifests itself in predicates that express changes (event I), and (2) an interpretation- and context-dependent type of narration that implies changes of a special kind (event II), on the other.” Both event I and event II portray a basic type of narration and are characterized by a change of state, the transition from one situation to another, usually in relation to a character. Event I and event II are distinguished by the degree of specificity of the change of state. Event I changes of state consist of any change of state that contributes to the narrative, defining narrativity as the “relation of changes of any kind” (Hühn 2013). Event I concerns every type of change of state expressed in a text, whereas event II refers to specific changes of state that meet additional conditions, such as changes that are decisive, unpredictable turns in the narration, or a deviation from the norm of what is expected. The evaluation of the additional conditions of event II is a matter of interpretation, and therefore event II is a hermeneutic category. On the contrary, event I can be evaluated rather objectively.

The definition of narrativity used in event II differs from the definition of narrativity used in event I. In event II, narration is considered to be the “representation of changes with certain qualities” (Hühn 2013). Whether these qualities are present is dependent on context and interpretation of the events in relation to the whole text. For example, “Mary stepped onto the ship” contains a type I event, namely the change of state of the character Mary by moving from the bank to the ship, resulting in a change of surroundings. However, in the context of a particular literary or cultural context, such as emigration, this can also be a type II event. Emigration can be seen as a new beginning and is therefore a deviation from what is expected. Therefore, this example can also be an event II change of state, depending on the literary and cultural context. Event II changes of state are considered to be more or less eventful, according to what extent they meet the following five criteria: relevance, unpredictability, effect, irreversibility, and non-iterativity (Hühn 2013). These additional criteria are also predominantly dependent on cultural, historical, or literary context. Therefore, the eventfulness of a change can be interpreted differently by different readers. Besides different event types, different event sequences have been conceptualized, too.

2.2 Fabula and Syuzhet

The Russian formalist Viktor Shklovsky introduced the terms fabula and syuzhet (Scheffel 2013) based on an analysis of the difference between chains of events in “actual life” and in art. Shklovsky argues that to understand the “aesthetic laws” of artistic narrative, the distinction between fabula and syuzhet is necessary. He defines syuzhet as “the material of the fabula in the artistic form.” In other words, the fabula represents what has happened or what was in the narrated world, whereas syuzhet is the artistic form in which the fabula is presented to the reader. Fabula is defined as “the material for syuzhet formation,” a chronological chain of events.

The fabula/syuzhet distinction is similar to the story/plot and histoire/discourse distinction (Pier 2003; Scheffel 2013). Story is “a narrative of events arranged in their time sequence” (Scheffel 2013). For instance, dinner comes after breakfast and Tuesday after Monday. Plot is a narrative of events focused on causality, for example, “The king died, and then the queen died of grief.” In the plot a causal relation between events is established, whereas in the story, the relationship is only chronological. More broadly, plot involves the transformation of “happenings” into a sequence of structured events that form a narrative (Xin 2022).

Similar to Shklovsky, Todorov identifies two aspects of literary works: histoire and discourse. A literary work is

at the same time a story [histoire] and a discourse [discours]. It is story, in the sense that it evokes a certain reality […]. But the work is at the same time discourse […]. At this level, it is not the events reported which count but the manner in which the narrator makes them known to us (Scheffel 2013).

The difference between fabula/syuzhet and histoire/discours is mainly found in the artistic value prescribed to the different terms. Todorov considers both histoire and discours as important aspects of a literary work. Histoire is necessary to create a certain reality for the reader. Discours is important since literariness is not solely about the events reported, but also about the manner in which the narrator presents them to the reader. Discours also considers features such as perspective, style, and mode, whereas the syuzhet primarily focuses on the order of events represented in a text. Additionally, histoire contains the continuum of the narrated world, in contrast to fabula that only contains the parts of the narrated world that are relevant to the plot. Due to their broader definitions, histoire and discours are considered to be of equal literary value, whereas the fabula is considered not to be of literary value, and the artistic value of a text is represented solely in the syuzhet. Moreover, the interplay of the two sequences, with flashback and anticipations, generates a narrative tension, the narrativity that keeps readers engaged (Baroni 2007; Sternberg 1992). The automatic detection of both sequences is a difficult task, but computational literary studies have a unique interest in the way in which events are presented and can complement NLP efforts to detect the “histoire” of news. However, it is also worth noting that there are NLP works that consider aspects of the “syuzhet,” mostly in relation to the framing of events (Hamborg 2023; Minnema et al. 2022a).

3.1 Operationalizing the Narrativity of Event Representation

Given the specific interest of computational literary studies in the way in which events are presented, an operational model for the automatic detection of events in literary texts should enable the extraction of information not only about the semantics of events but also their rhetorical, narratological, and literary functions. To this end, Gius and Vauth (2022) started from operationalizing the narrativity of event representation at the level of discourse, using German prose as a case study.

Gius and Vauth (2022) define four different event categories that can be called event I in the context of Hühn:

1. Changes of state are physical or mental states’ changes of animate or inanimate entities

   “As Gregor Samsa awoke one morning from uneasy dreams;”

2. Process events are actions or happenings that do not result in a change of state, such as moving, thinking, feeling

   “he found himself transformed in his bed into a gigantic insect;”

3. Stative events are physical and mental states of animate or inanimate objects

   “His room lay quiet within its four familiar walls;”

4. Non-events do not relate to facts in the narrated world, such as general statements, questions or hypothetical situations

   “She would have closed the door to the apartment.”

The four different types of events were specified in order to differentiate them for narrativity analysis and define events as “any change of state explicitly or implicitly represented in a text.” The events are ordered by degree of narrativity, with changes of state having the highest degree of narrativity and stative events the lowest. Non-events do not contribute to narrativity, but are included for comprehensive annotation. Gius and Vauth (2022) consider the whole text when annotating events. However, they aim to avoid “relatively strong interpretations necessary when primarily relating to the story world ‘behind’ its representation in the narrative” (Gius and Vauth 2022). The narrativity hierarchy of the four types of event categories ensures that the representation of eventfulness in discourse is reflected in the annotation. This indirect annotation of eventfulness is more aligned with the different types of eventfulness related to event II (Hühn 2013). One of the approaches of eventfulness discussed by Hühn (2013) requires that a change actually takes place in the narrated world (thus, it should be a fact in the narrated world) and that it reaches a conclusion (thus, the change cannot be described as only begun or in progress). This definition of eventfulness is similar to Gius and Vauth (2022)’s definition of change of state. However, as they annotate every event occurring in the text, and additionally non-events, their overall definition of event categories is broader than that allowed by event II, aligning more closely with that of event I.

The model with four event types has been used by Vauth et al. (2021) to annotate four German prose texts (Vauth and Gius 2021) and automatically classify events by following a two-step process. First, they extract verb phrases, which are then labeled with an event type in the second step. Since the annotation guidelines in Vauth and Gius (2021) focus on the finite verb of the sentence, the verb phrase extraction is done by selecting the finite verbs in each sentence using a pre-trained tagger. Then, for each verb, the dependency tree of a pre-trained parser is used to identify all tokens they cover, by traversing the tree. Relative clauses are not considered when moving down the dependency tree, and neither are conjunctions if their children consist of full verbs. On unseen data, the model reached a 0.71 F1 score in identifying the correct span and a 0.78 F1 score in classifying the event type. However, Vauth et al. (2021) only used German prose and therefore relied on a German pre-trained tagger and parser. Suitable pre-trained taggers and parsers will need to be selected for other languages to test this approach.

3.2 Literary Events as realia

Sims et al. (2019) define a literary event as an event that is actually happening in the story (realis), with the goal of analyzing the narrative plot. In this model, there are no stative events, they only consider activities, achievements, accomplishments, and changes of state, following Vendler (1957). A phrase is considered an event by Sims et al. (2019) if either (1) a change of state has occurred, (2) the cause of a state can be deduced, or (3) the phrase refers to an acute mental state, such as acute short-lasting responses like shocked or astonished. This specification of the types of events is in line with event II, in which an event is defined as a “representation of changes with certain qualities” (Hühn 2013). As a consequence of these requirements, fewer phrases are annotated as events than in Vauth and Gius (2021). For example, the sentence “at the end of the path is a cave” is a stative event for Vauth and Gius (2021), but it would not be an event according to Sims et al. (2019).

Similar to the guidelines by Vauth and Gius (2021), events must have occurred, thus negations are not considered to be events, nor are possible future events. Generic phrases are also considered not to be events in both guidelines. However, Sims et al. (2019) do not treat wishes and desires as events, whereas Vauth et al. (2021) consider the act of wishing as stative events. Another difference is that Sims et al. (2019) consider single words as events, whereas for Vauth et al. (2021), all words that can be assigned to a finite verb are included in the annotation span of an event. Lastly, Sims et al. (2019) define their event triggers more broadly, including not only verbs but also adjectives and nouns. This approach has the advantage of being extendable to languages whose syntax does not rely on verbs as much as English does, but it also has the limitation that Vendler’s verb classes are not applicable to many languages.

For event detection, Sims et al. (2019) use an LSTM and five BiLSTMs that are trained on the annotated verbs only (baseline), as well as on a featurized model containing six extra token features: lemma, part of speech tag, context, syntax, WordNet synset and hyponymy information, word embeddings, and bare plurals as subjects. The differences between the BiLSTMs are the context included in the BiLSTM, including a sentence CNN, document context, and BERT contextual representations. The BiLSTM with BERT representations on the featurized model has the highest performance, with an F-score of 73.9.

3.3 Hylistic Analysis

Pannach (2023) analyses events in folktales using the hylistic theory (Zgoll 2020). Folktales are considered as sequences of hylemes. A hyleme is an individual statement containing events and states in chronological order. For example, the statement “Orpheus came to his end by being struck by a thunderbolt” results in the following hyleme sequence, which consists of three parts: (1) “Orpheus is struck by a thunderbolt,” (2) “Orpheus dies,” and (3) “Orpheus is dead.” This model does not include aspects related to how the events are presented, it rather focuses on achieving the best possible comparability between different variants of the same folktale, even across languages. That is why the events are translated into present-tense statements that describe precise actions or states. Additionally, Pannach uses four main categories in her hylistic analysis: single-point (punctual), durative-constant, durative-initial and durative-resultative, which are mainly associated to verbs in a phrase. Single-point hylemes consist of active actions, passive experiences, reactions, perceptions, or feelings. The beginning and end of the event are both included in the hyleme sequence. Durative hylemes are true for part of the sequence or the entire hyleme sequence. Durative-initial hylemes are true at the beginning of a sequence, durative-constant are true during the entire sequence, and durative-resultative at the end of the sequence.

Pannach (2023) compares this approach to the event model of Gius and Vauth (2022). The change-of-state event category of Gius and Vauth (2022) corresponds to the single-point category used by Pannach (2023). Process events are also considered to be single-point. However, when the property of the event is iterative, such as “Charon works the sails,” the phrase would be considered to be durative-constant. Stative events correspond to the three durative hylistic classes, which class it belongs to depends on the context. Non-events are not annotated in the hylistic classes.

The vast majority of the annotated data consists of single-point statements. Due to the unequal distribution, as well as the similarity between the three different durative hylistic classes, a multinomial naive Bayes model was used, with a TF-IDF vectorizer. Three classifiers were implemented, one binary classifier distinguishing single-point and durative hylemes, one classifying durative-initial, durative-constant and durative-resultative hylemes, and one considering all four classes. The binary classifier reached a 0.79 F1 score for the durative hylemes and a 0.92 F1 score for the single-point hylemes. The second classifier obtained a 0.32 F1 score for the durative initial statements, a 0.85 F1 score for the durative constant, and a 0.56 F1 score for the durative-resultative. It is important to note that 69% of this test set consists of durative-constant hylemes, and 24% of durative-resultative hylemes. For the third classifier, the durative-initial hylemes obtained a 0.25 F1 score, the durative-constant hylemes a 0.69 F1 score, the durative-resultative hylemes a 0.43 F1 score, and a 0.93 F1 score on the single-point statements. In this test set, the distribution across the different classes is again unbalanced, as the test set only contains 30 durative initial hylemes and 1,151 single-point statements. As it is unclear whether the class imbalance in the test set of the second and the third classifier is reflected in the respective training sets, it is hard to determine how this imbalance has influenced the results, and whether this influences the strong preference for the single-point hylemes by the third classifier.

3.4 Analyzing Narrative Discourse with Large Language Models

Piper and Bagga (2024) use large language models (LLMs) to analyze narrative discourse within the framework of Genette (1980)’s narrative triangle concerning story, discourse, and narrating instance. They use three categories to analyze narrative discourse: (1) POV (point of view), focused on the experiencing agent; (2) time, including use of tense, anachrony, flashbacks, eventfulness, and event sequences; and (3) setting, including location and concreteness (realized and tangible space). Thus, they explicitly use event sequences and eventfulness as features to capture dimensions of time.

Piper and Bagga (2024) prompt LLMs to estimate the degree of presence of a given feature using a three-point scale. The dataset of Piper and Bagga (2022) is used to collect 13,543 passages from 18 genres, including contemporary novels, short stories, folktales, and non-fiction, such as memoirs and stories from AskReddit. The experiments were run on a subset of passages with a manually annotated narrativity score higher than 3.0. The evaluation consists of four steps: (1) replication, (2) honeypot, (3) inter-annotator agreement, and (4) model performance. First, 15 iterations are run on half of the validation data. For the best model, 95.6% replication occurs in all documents. Second, a nonsensical “honeypot” feature is used, for which the answer should never be positive. This feature is used to measure to what extent a model is randomly guessing. In the best model, all nonsensical prompts were answered negatively. Third, three annotators answered identical prompts the models received. The inter-annotator agreement is fair, with a Fleiss’s kappa of 0.38 and a universal agreement rate of 43%. Lastly, the model’s accuracy is evaluated by comparing the model’s results to the majority vote of the human annotators and the minimum match, where the results are compared with any human answer regardless of the majority vote.

There is a variance in the models’ F1 score, from 0.28 to 0.79 of the majority vote, but a higher performance for the minimum match, with a maximum F1 score of 0.95, and four out of six models with an F1 score of 0.87 or higher. The annotator agreement correlates strongly with model performance. Thus, LLMs are a promising tool in the analysis of narrative discourse, specifically since the results show that the features “event sequences” and “eventfulness” can have different weights in classifying narrative. As the high variance across models is also seen between human annotators, the results emphasize the subjectivity and ambiguity in the task.

4.1 News

In this section, we discuss the issues identified concerning the comparability between different works and corpora in two recent surveys (Caselli and Bos 2023; Norambuena et al. 2023) and by discussing the data structure of events proposed in Vossen et al. (2021), since it is one of the most elaborate narratology-based frameworks.

Caselli and Bos (2023) find that variation in the definition of events and the annotation of linguistic realizations, and the assignment of events to specific semantic classes, make most of the event-labeled corpora incompatible with each other. They give an overview of six event-annotated news corpora, which all use a different event definition. The majority of these corpora restrict the annotation of events by solely annotating events that occur in given event classes. These restrictions make these frameworks unsuitable for literature. For example, ACE (Doddington et al. 2004) only annotates events in news articles that fall under one of eight semantic classes (life, movement, conflict, business, contact, personnel, justice, transaction). In contrast, TimeML (Pustejovsky et al. 2003) rejects restrictions on semantic classes and linguistic realizations of events, as annotations are based on the lexical aspect and their contextual syntactic structure. As TimeML is aimed at portraying events as temporal expressions relative to each other, this approach is not applicable in the analysis of the syuzhet.

Norambuena et al. (2023) identify two fundamental units in news narrative extraction, events and entities, i.e., the actions and happenings in the text and the characters and other entities that are related to the events. Focusing on the former, they define computational narrative representation as a discrete story structure, such as a graph or a timeline of events. They observe that the most common and simple way to computationally represent a narrative is as a linear sequence of events, such as a timeline.

Since the survey only analyses research using news corpora, they assume that each text (news article) focuses on one single main event. Previous or secondary events, which can, for example, be used to link articles together, are not taken into account in this survey. As previous and secondary events are crucial in fiction, this assumption is not applicable to literary event detection. They identify three scopes: events as sentences, events as entire documents, and events as a cluster of documents. This is a broader view of events than in many other approaches. For example, TimeML defines events as more specific than an action, such as a perception.

Among these seemingly incompatible approaches, there are also two that leverage insight coming from narratological scholarship. The first one is Vossen et al. (2021), who propose a framework informed by narratology and argue that a plot structure is composed of three elements: (1) an exposition, in which the characters and the setting are introduced, (2) a predicament, which consists of a set of struggles or problems that an actor has to go through, and (3) the extrication, which is the end of the predicament. The predicament itself consists of three elements: (1) rising action, which consists of events that increase the tension, (2) climax, which consists of events where the tension reaches its maximum, and (3) falling action, which consists of events that resolve the climax and lower tension. Besides these dynamic patterns, they also define three data structures: the timeline based on the fabula, which they define as a chronological timeline; the causeline, related to the plot, which they define as a set of loose and strict causal relations; and the storyline, which they define as a set of (pairwise) relations between events according to the patterns mentioned above; it is associated to the plot structure. The storyline includes the explanatory causal relations between events related to a climax event by the strongest connection. The events in the storyline are chronologically ordered. In annotation, every event mention is associated with a temporal expression or is directly temporally related to other events in the timeline. In the causeline only events that express a loose causal relation are included. Based on the causeline, the storyline depicts explicit additional explanatory relations that may lead to a climax event. In section 6, we will compare this framework to our approach of analyzing narrative events.

Another NLP work looking at narratology – as well as at Critical Discourse Analysis (CDA) – is by Huang and Usbeck (2024), who propose a theoretical framework to construct new narratives from an author-focused perspective. CDA considers news narrators as a dominant group that shapes a narrated world encoded in language, in which real-world events are portrayed to the public. Therefore, the focus is on how real-world events are organized to shape a narrated world, using an adapted definition of fabula and discourse by Gervás and Calle (2024). They consider the information flow from a real-world event to a news item as follows: First, based on a real-world event, a subset of an organized event sequence forms the fabula; then, the discourse is created through narrative composition, simplified as causal relations between the events in the fabula; and, lastly, the discourse is used to form textualized narratives in natural language. They define fabula as “the actual sequence of events, that is chronologically and causally ordered” and discourse as “the product of the telling, which reorganizes the chronological and causal order of this sequence.” They view the narrated world as event-event causal relations and narration as a function that shapes the narrated world. They consider events as the smallest unit in a narrative, but do not consider all events in a text to be part of the narrated world. Indeed, they make a distinction between constituent and supplementary events, of which only the former are represented as event-event causal relations. The proposed theoretical framework represents this information flow as the narrated world logic, which can be used to extract the core story of events told by a news narrator. As this is a proposal for a theoretical framework that has not been evaluated yet, it is unclear how effective it is and whether this framework is applicable to literature.

To conclude, in the task of event detection in news, there is no general consensus on the definition of event. This lack of consensus shows that relying on existing frameworks and corpora does not lead to broadly applicable annotations, as the different corpora are hard to compare and relate to each other (Caselli and Bos 2023). Moreover, most corpora restrict events to certain semantic event classes, but this is too restrictive for a comprehensive analysis of the syuzhet.

4.2 Historical Texts

The lack of a general consensus on the definition of events does not only occur with event extraction in news texts, but also with historical texts. Additionally, the aim of event extraction from historical texts is not focused on information extraction only, but also on the analysis and interpretation of events. To address the difference in objectives between fields, and to make NLP techniques applicable to historical texts in such a way that it will lead to a more homogeneous usage of event extraction in historical research, Sprugnoli and Tonelli (2017) suggest using the expertise of historians for the linguistic annotation of events.

Sprugnoli and Tonelli (2019) conclude from their discussions with historians that the semantic type of an event is the most relevant information for annotation, that multi-token annotation of event phrases should be possible, and that events can have different syntactical forms and grammatical classes. Accordingly, they define 22 relevant semantic classes, based on the semantic categories of the Historical Thesaurus of the Oxford English Dictionary (HTOED), aiming to avoid too much granularity while at the same time ensuring broad informativeness. The latter is important due to the diverse topics and genres in historical texts.

They consider three different types of event spans: (1) single-token, (2) multi-token, and (3) discontinuous expressions. Events can be verbs, past participles, present participles, adjectives, nouns, and pronouns. Multi-token events are restricted to seven types of linguistic construction, such as phrasal verb constructions, final and non-finite verbs, and nouns.

The resulting annotated corpus, the Histo Corpus (Sprugnoli and Tonelli 2019), is used to train two types of classifiers: CRF classifiers and a BiLSTM. Two CRF classifiers were implemented: one to identify the event span and the other to predict the correct event class on unseen text. The BiLSTM is used for sequence tagging as well as event detection and event classification. Overall, the BiLSTM outperforms the CRF classifiers in event classification, except for the event class ‘physical sensations.’

In another project (Verkijk and Vossen 2023), historians have been involved in the development of an ontology that can be used for event extraction from the archives of the Dutch East India Company (VOC). The ontology enables the extraction of implied events, as this is deemed to be important by experts. They use the CEO ontology (Segers et al. 2017), which models semantic circumstantial relations between event classes, as the basis for the definition of event classes, since they want to be able to annotate static events. They identify three relevant types of observable events: ship movement, trade, and (geo)political/social relations. More detailed classes, for example, whether an action is legal or illegal, depend on the context and the interpretation of an expert, and are therefore not considered as observable events. Building on FrameNet (Ruppenhofer et al. 2010) and CEO, they define participants specific to each event class. Other event arguments are spatial or temporal. Roles can be recycled from one event to another; for example, the agent in an Attacking event is a patient in the state BeingInConflict. Results show good agreement between human annotators for the labeling of event triggers, but poor performance of fine-tuned models for automated event detection (Verkijk et al. 2024), as the highest precision achieved is 0.55 using the model GysBERT (Manjavacas and Fonteyn 2022) and the highest recall is 0.43, obtained using the model XLM-R (Conneau et al. 2019).

From this type of research, we can observe that event annotation in historical texts differs greatly from approaches to annotate events in literature. Both Sprugnoli and Tonelli (2019) and Verkijk and Vossen (2023) use predefined semantic classes and themes to identify and analyze events, while considering a multitude of syntactical forms and grammatical classes. However, for research on literature, all events in the text are relevant because they can fulfill different functions that cannot be defined in advance (Pianzola 2018). Some events contribute to creating the setting for the story, other events contribute to the progression of the plot, and others contribute to show the personality of the fictional characters. All events potentially play a role in the cognitive and aesthetic processing of literary text by readers (Caracciolo 2014).

4.3 Review Summary

As can be seen in Table 1, multiple projects in different domains operationalize event detection without using a concrete definition of what should be considered to be an event. Instead, some use strictly defined semantic classes in their annotation guidelines (Sprugnoli and Tonelli 2017; Verkijk and Vossen 2023), some use narrative statements to detect events (Pannach 2023), and some use existing datasets and their respective definitions to analyze events (Piper and Bagga 2024; Vossen et al. 2021). These diverse approaches show that in the operationalization of events and related theoretical concepts, strict event definitions are not a prerequisite for implementing computational pipelines for event detection.

6.1 Timeline, Causeline, and Storyline

Figure 1 shows the news sample from Vossen et al. (2021). The temporal relation between all events is expressed in the timeline, whereas only the loose causal relations are included in the causeline, and only explicit explanatory relations that may lead to the climax event are included in the storyline. Figure 2 shows the fiction sample, annotated according to Vossen et al. (2021). Figure 2 shows that timelines, causelines, and storylines do not fully reflect the story presented in fictional texts. First, fiction contains more description (of, for example, surroundings) than news. The timeline of the news sample shows a clear temporal order of events in the text, whereas the temporal order for the description of the grove and the way in which the wolf is stretched out are not explicitly expressed. It can be assumed that the splitting of the grove was created before the stone was placed there, however, it is also possible that the stone was first placed there and the trees grew around it. In genres such as science fiction and fantasy, the environment is not necessarily static, thus complicating expressing all events in a timeline.

Figure 1: Example of the timeline, causeline, and storyline framework applied on news from Vossen et al. (2021).

Figure 2: Timeline, causeline, and storyline framework by Vossen et al. (2021) applied on fiction.

Second, the causeline does not contain the description of the grove, the stone, and the way in which the wolf is stretched out. Therefore, this description is not included in the storyline, as the storyline is based on the causeline. However, despite not being part of the causal relations between events, the description of the grove, the stone, and the wolf does contribute to the narrative, since it helps the reader to imagine the scene and contributes to the build-up of suspense, the tension leading to the climax.

Lastly, the storyline that can be derived from the causeline stops at the event froze_e7, which is the climax of the storyline. Half of the events occurring in the sample, namely those related to the description of the grove and the wolf, are not included in the storyline. However, due to the emphasis on the description of the grove, the stone, and the wolf, the wolf dying appears to be crucial to the narrative. The description of the scene also contributes to the build-up of suspense, thus the event froze_e7 is not actually a climax (according to Vossen et al. (2021)), as there is no falling of action or resolution afterward. Additionally, readers could conclude from this excerpt that the death of the wolf is more important to the narrative than Wilson walking towards and discovering the dead wolf, whereas the storyline only portrays the movements of Wilson.

6.2 Narrative Events

In Figure 3 and Figure 4, the two samples are annotated following our definition of narrative events. When comparing the storylines of the news and fiction sample to the annotation of narrative events, it is evident that the build-up and rise in action to a climax (as defined by Vossen et al. (2021)) can be related to the narrative event model. According to this model, all events are processes, except for Gray standing outside and the firing of the shots. Thus, process events in the text seem to build up to the same climax event, which is annotated as a change of state. In Figure 1 the storyline starts with noticed_e4, whereas [Police say] is annotated as a process event.

Figure 3: News example of the annotation of narrative events. The bold verbs are the finite verbs per annotation span. Note that the word event is omitted from the annotation labels for abbreviation.

Figure 4: Fiction example of the annotation of narrative events. The bold verbs are the finite verbs per annotation span. Note that some events contain implied finite verbs and that the word event is omitted from the annotation labels for abbreviation.

The firing of the shots is described as a change of state, which puts the emphasis on the police agents shooting at Gray. It is a change of state as the finite verb of the sentence is fired. One of the distinguishing properties between changes of state and process events is irreversibility. If the finite verb expresses an irreversible change, the corresponding phrase is a change of state, as the irreversible change has led to a permanent property change of an entity. Firing shots is such an irreversible change, as one cannot reverse firing a shot. An alternative phrasing of the event reported in the last sentence could have a different event type. For example, the same event could be presented from the perspective of Gray (like in the second sentence “he noticed”): “Gray heard gunshots.” This sentence would be annotated as a process event, as the finite verb is heard and emphasizes describing a perception.

This can be related to research in which semantic frames are used to analyze perspective and framing in news (Minnema et al. 2022b). For example, in the headline “Cyclist, 70s, seriously injured following collision in Dublin,” the word collision triggers the frame impact, showing that the main event in the sentence describes the impact on the cyclist. The same event has also been described with the following sentence: “Driver hits pedestrian with his car, sending the 70-year old man to hospital with heavy injuries.” In this headline, hits is the trigger of the frame cause_impact, which shows that the main event in this headline expresses the cause of the impact, namely the driver causing the injures.

The first headline would be annotated as a stative event according to our framework, as the finite verb injured describes the physical state the cyclist is in. The low level of narrativity corresponding with this narrative event also corresponds with the frame impact, as the impact is described without naming the agent that has caused the accident. The second headline is a process event, as the finite verb is hits, which describes a motion. This corresponds with a higher level of narrativity, which fits with the frame cause_impact, as this emphasizes the action that caused the impact.

In the fiction sample, the different event categories fluctuate (see Figure 4). The text starts with a process event, then the level of narrativity moves up to a change of state, and then goes down again to a process event and two non-events. Next, a stative event is followed by a change of state. Then several stative events and two changes of state conclude the paragraph. This fluctuation in level of narrativity cannot be seen in the storyline in Figure 2, as only the first change of state is shown in the storyline.