Temporal reasoning has been the focus of several studies during the past years, both in linguistics and computational studies. Although advances on this topic are undeniable, there are still improvements to be made and new avenues to pursue. One relevant problem concerns the temporal ordering of the events, particularly asserting and representing how events are temporally related and how the story told in the narrative evolves. This paper aims to analyse the temporal structure of narratives present in news articles with the aid of di erent visualisations. To this end, we annotated a dataset of 119 news articles in European Portuguese following an annotation scheme that combines di erent parts of ISO 24617-Language Resource Management - Semantic Annotation Framework (SemAF). The temporal layer of this annotation scheme identi es the events and their main features, as well as the temporal links between the events. The annotation provided us with paramount information about the temporal characteristics of news at two levels: the story and the report levels. The visualisations that we propose facilitate the process of understanding how news are temporally organised, providing a more practical means to observe them.
Understanding the temporal dimension of a text requires more than merely identifying events
and temporal expressions. Eventually, this understanding of text requires building structured
information about events and inferring their temporal relations before constructing a timeline
of events. Such tasks are still very challenging in Natural Language Processing (NLP) and
Information Retrieval (IR), and they can surely bene t from the input of linguistic analysis.
Currently, temporal extraction comprises three phases: (i) recognition of events and temporal
expressions; (ii) recognition of temporal relations between them; and (iii) time-line constructions
based on the temporal relations [
Determining the temporal organisation of events becomes even more challenging when
dealing with texts that present events in a non-chronological order, like news articles, which
display an intricate temporal structure compared to other types of narratives [
Moreover, as demonstrated by [
Regarding time-line constructed based on temporal relations, researchers have explored
di erent formats. For instance, [
For this study, we de ned three objectives. First, we aim to analyse the intricate relations
between the events throughout the narrative of the news annotated according to ISO-24617-1
[13]. Second, we work towards assessing how the story events are organised within each report
and across the reports. Finally, we seek to determine associations between the temporal relations
and some of the grammatical features of the events. The representation of the temporal relations
in two formats, the Bubble data structure, proposed in [
The paper is organised as follows. Section 2 reviews some proposals about the temporal structure of news. Section 3 describes the study, beginning with the description of the problem and the research questions (3.1), followed by relevant information about the dataset and the annotation scheme (3.2) and the study’s methodology (3.3). Subsection 3.4 reports on the results of our study. Finally, Section 4, presents the overall conclusions.
From a solely linguistic approach, several research works have yielded signi cant ndings
regarding news temporal structure [
From a computational point of view, some studies propose annotation schemes, methods and
techniques to identify and retrieve temporal information from news [
Moreover, the complexity of temporal information retrieval of news is ampli ed by the
presence of reports with the mention of sources or attributions, which create a second narrative
layer, the narrative of the report. Each report comprises several events from the story [
In this study, our objective is to characterise the temporal structure of narrative news. However,
since news comprise two intertwined levels, the level of the story being told and the level of
the report that tells that story [
RQ1: How are the events temporally organised at the story level? RQ2: What is the report level’s role in the temporal organisation of the story? RQ3: What is the association between the temporal relations and some of the events’ grammatical features?
The three research questions were created to help de ne the typical structure of news, and, ultimately, assist in the development of automatic methods for information extraction. The rst question aims to de ne the prototypical temporal structure of news and provide important input for automatic forms of temporal analysis. Extracting temporal information from unstructured data can greatly bene t journalists and other stakeholders. However, it is a challenging task. Creating timelines from text is relatively easy if temporal expressions unambiguously locate situations. But it becomes more complicated if the text lacks temporal expressions. Therefore, understanding how situations are organised throughout the news can help with automatic extraction. The second question is related to the rst one because to extract information and organise it temporally, we need to distinguish between situations that are part of the story being narrated and those that provide information about the sources used by the journalist to write the news. Both types of situations occur alternately in the news, making it di cult to automatically classify them without some linguistic input. The third question aims to deepen our understanding of news events characteristics, which will impact both theoretical linguistics and computer science. Determining the linguistic features of events is essential for automatic methods of extracting and organising information, particularly in cases where there are no explicit temporal adverbials or discourse markers. For example, in the sequence of sentences "The boys played football. The girls swam in the pool.", the preferred temporal connection between the situations is simultaneity, whereas in "The boys broke the window glass. The girls tore the curtain.", the preferred temporal relationship is successivity. Understanding which grammatical elements contribute to these temporal relations is key to temporal information extraction.
For this study, we utilized the T2S Lusa Annotated Dataset 1, which is a set of 119 news texts retrieved from the T2S Lusa dataset 2. The T2S Lusa dataset contains 360 news articles collected from the general news feed of Lusa, a Portuguese news agency. First, the team of linguists conducted a preliminary manual analysis of a small set of Lusa news, and identi ed a set of keywords that were more frequent in the news with a narrative nature, such as "assaults", "robberies", "accidents", or "police interventions". Afterwards, Lusa collected the news containing these keywords with a length restriction of 50 to 200 words. Finally, the linguists manually checked this collection, resulting in a corpus of 360 news articles.
The data was annotated following a previously designed multilayered annotation scheme,
which was built up in such a way that it combines four parts of ISO-24617 Language resource
management: Part 1 – Time and Events [13]; Part 4 – Semantic Roles [
The focus of the current study is on tags that describe events and temporal links. Events are annotated to identify eventualities (i.e. events and states) and their speci c characteristics using the Event tag, which speci es values for Class, Type, Part of Speech, and Tense. Class identi es some characteristics of the lexical semantics of verbs. For example, the Reporting value is given to events in the case of verbs that denote a situation in which an entity reports a story or provides information about a particular situation. Type is related to the Aspect of the situation, which determines its internal temporal structure, and can be a State (a situation in which something obtains or holds), a Process (a durative and atelic situation), or a Transition (a situation introducing a consequent state). Temporal links (TLinks) are used to identify temporal relations between events, which are critical in narrative texts for determining the chronological order of the events. TLinks have the following values: Before, After, Includes, Is_Included, During, Simultaneous, Identity, Begins, Ends, Begun_By, and Ended_By.
The dataset of 119 news articles was annotated by a PhD student in Linguistics who
collaborated in the development of the annotation scheme. The annotator discussed problematic cases
with a team of linguists before carrying out the annotations. To ensure the accuracy of the
annotations, a second annotator followed the guidelines of the annotation manual and annotated
a sample of 10% of the dataset (19 news articles) to test inter-annotator agreement. Regarding
the inter-annotator agreement, rst, we measured the agreement of the events labelling. For
this task, the agreement between the two annotators was computed as a pairwise f1. The
choice for the f1 instead of Cohen’s Kappa in the event labelling is due to the high number of
non-labelled tokens (disregarded for this particular study), which can raise the kappa score
disproportionately [
To answer our rst research question (How are the events temporally organised at the story
level?), we focused on the temporal relations (TLinks) between all the events that comprise the
story, excluding the reporting events belonging to the report level, which the TLink Identity
connected. This strategy excluded the reporting events that are part of the second level of
discourse. As explained in [
For our second research question (What is the report level’s role in the story’s temporal organisation?), we aimed to determine the reports’ role in organising the story’s timeline. We collected two types of information: (i) TLinks between all the events within each report; and (ii) the TLinks between the rst story event of a report block B and any story event from the report block A. The example shown in Appendix A.1 illustrates this procedure. There are ve blocks, represented in the Bubble structure A.2.1: the rst and the second reports are introduced by the reporting verb disse (’said’), the third by the reporting event acrescentou (’added’), the fourth by a rmou (’stated’) and the last one by indicou ( pointed ). These ve blocks of the report form the report level. Each block, represented by a Bubble, includes multiple story events. So, rst, we extracted all the TLinks connecting these events. Second, we extracted all the TLinks between the story events embedded in the di erent Bubbles.
For our third research question (What is the association between the temporal relations and some of the events’ grammatical features?), we thoroughly analysed the features of the events that form the story level. We aimed to uncover the connection between the grammatical characteristics of the events and each temporal relation. To achieve this, we collected all the relevant information from the tags Class, Type, Part of Speech (PoS), and Tense of the events linked by each of the TLinks likely to impact temporal relations.
The extraction and visualisation of these data were made possible by the package text2story3. This is a Python package devoted to automatically extracting narratives programmatically and easily. Additionally, the package o ers three types of visualisation for narratives: Knowledge Graph (KG), Message Sequence Chart (MSC), and Bubble Diagram (BM). In this study, we only employ the MSC and BM to visualise some news story elements. The MSC represents 3https://pypi.org/project/text2story/ the events that form the story level as lifelines, i.e., the coloured rectangles in sequence with lines underneath. These events can be linked by TLinks that were annotated in the text. The sequence order of events in the MSC is the same order that they appear in the text. This arrangement allows more careful analysis of such types of events than if this information was in the annotation tool. One example can be seen in Figure 2 in Appendix A.2. Di erently, the Bubble Diagram’s main goal is to represent the reporting events and each event related to the story attached to them. In this diagram, there is the Big Bubble, which represents a reporting event. Inside each Big Bubble are little bubbles, representing events at the story’s level. The bubbles, the big ones, and the little ones are sorted clockwise, i.e., the rst event that appears in the text goes in a noon position, the second appears in a clockwise direction, and so on. The TLinks between the little bubbles are drawn in the gure as well. One example is in Figure 1 in Appendix A.2.
In the set of the 119 annotated news, 3068 events were identi ed, occurring both at the story and report levels. There is only one news article devoid of the report level, which is evidence of the prominence of this part in the overall structure of the type of news that composes the dataset. The news article in Appendix A.1 illustrates the pattern followed by the generality of the news that were annotated. In this particular example, as one can observe in the Bubble diagram of Appendix A.2.1, the news includes ve blocks of report marked by ve reporting verbs. Hence, it is expected to encounter a reasonably high number of reporting events that build the report level. We found, per news, an average of 3.50 reporting events in a total of 417 events.
At the story level, the number of events is necessarily higher, with a total of 2651 events, an average of 22.27 events per news, because the purpose of this type of journalistic text is to inform the reader about a situation. So, returning to our running example in A.1, events such as feriu, fazer disparos, recusou, parar (’wounded, shooting, refused, stop’) are part of the story level. In this example, we can observe that not all the events that compose the story are introduced by a reporting event. For instance, the information given in the third paragraph about the event atingido (’hit’) is one of those cases.
Table 1 systematises the analysis of the temporal relations across the two levels under scrutiny: the story and report levels.
Overall, the analysis of the temporal relations indicates that, for most cases, the temporal relation Identity, picked whenever two events are the same, is the most frequent, which is expected because, as explained in Section 2, prototypically, the journalist presents the main event in the news’ lead and then other events related to the main one are presented in more detail throughout the news. In our running example (A.1), this is true for the event fazer disparos ( shooting ), which is introduced in the rst report block and then resumed in the second report block (cf. Appendix A.2.1). The recurrence of the TLink Identity is also related to some annotation’s rules, such as the one about light verb constructions like zeram disparos, which stipulates that the noun (disparos) and the light verb ( zeram), must be linked by an Identity TLink. This is recommended in the ISO standard to capture the fact that, despite being two distinct words that have the potential to denote two di erent events, they represent a single event in the constructions in question.
Regarding the events that form the story, the second most common temporal relation is After, as shown in column A of Table 1. In this case, the events are presented chronologically, that is, they are described in the order they happened. This applies to most of the events depicted in paragraph six of our running example and represented in the MSC A.2.2. Our ndings align with the evidence presented by [15]’s study and described in Section 2. The same observation about the frequency of Tlinks is true concerning the story events described in di erent report blocks (cf. column C of Table 1). Although the di erences towards other temporal relations are not so notable, the rst event of report B is typically linked to an event of report A by TLink After, which means that there is temporal sucessivity. The relation between the event desconhecer (’didn’t know’) and intimidação (’intimidating’) illustrates this feature (cf. A.2.1).
The TLink Before often ensues in our dataset, which is in accordance with the general structure of news, being the third most recurrent TLink within the events that compose the story and the events across reports. In fact, news usually, after making known the main event, give an account of earlier events. In our running example in Appendix A.1, the events disparos ( ring’) and cessar ( stop ) represented in the second bubble of the picture in Appendix A.2.1 showcase this temporal relation.
The analysis of the temporal relations between the events described by each report block (cf. column B Table 1) reveals di erent results. Even though the chronological order is also frequently adopted, as one can observe in the Bubble representation in A.2.1, it comes after the simultaneity relation. The simultaneity and inclusion relations are also common (cf. Table 1), which are compatible with the fact that, again and again, the journalist provides more detail about the main event, describing either secondary events that happen at the same time or subevents that are part of an event. Once again, this is true for instances of our running example, such as the inclusion relation between shooting and refusing to stop, two secondary events reported in the rst report block (cf. rst Bubble in Appendix A.2.1).
Overall, the results disclose a wide and rich variety of temporal relations between all the story’s events. The events embedded in the reports follow the general trend of being linked by Identity, but they often describe situations that happen simultaneously more frequently than when considering only the story’s events.
Analysing the association between some of the events’ grammatical features, particularly
Class, Type, Tense, PoS, and temporal relations, leads to relevant conclusions. The rst
conclusion drawn from the results is that PoS and Tense are not pertinent factors when inferring
temporal relations. In terms of the association of Class to TLinks, since one can group the
di erent labels into eventive and stative situations, and because the tag Type is more speci c
concerning aspectual di erences, we deemed it best to analyse the latter. As a matter of fact,
regarding the label Type, there seems to be a correlation between the values attributed to events
and the temporal relation. This correlation concerns the aspectual properties of telicity and
durativity. Telicity is the property of situations that cannot last inde nitely, as they have an
intrinsic terminal point in their internal temporal structure. This aspectual property distinguishes
Transitions, i.e., dynamic events, corresponding to [
In most cases, temporal successive readings (which subsumes After and Before TLinks) involve two situations (cf. Table 2): Transitions, or at least one of them is a Transition. For example, regarding TLink_Before, there were 284 cases (50,27%) involving only Transitions, while with TLink_After there were 422 cases (61,26%). On the contrary, TLink_Before connects Transitions and States in only 25,75% of the cases and Transitions and Processes in 12,21% of the cases, whereas TLink_After connects Transitions and States in 21,48% of the cases and Transitions and Processes in 14,8% of the cases.
The number of cases in which both situations are non-telic, i.e., Processes or States is considerably smaller (cf. Table 2). For instance, the cases involving only States were 22 (3,89%) (TLink_before) and 15 (2,18%) (TLink_After), while those involving only Processes were 10 (1,77%) (TLink_Before) and 8 (1,16%) (TLink_After).
In summary, although it is not a categorical distinction, there is a clear correlation between telicity and temporal successivity.
Simultaneity readings seem to be correlated with another aspectual property – duration.
States and Processes are durative; Transitions may or may not be durative (as this tag subsumes
two aspectual types: Accomplishments, which are durative, and Achievements, which are
non-durative (cf. [
To sum up, although there are no absolute restrictions, it appears that simultaneity readings are related to durative situations, particularly when the TLinks Is_included and Includes are involved. 4
The purpose of the study presented in this paper was to investigate the temporal structure of news articles at two levels - the story and report levels. Three research questions were formulated. With respect to the rst research question - How are the events temporally organised at the story level? -, our analysis revealed that, in the majority of cases, the journalist repeats the same event introduced in the lead throughout the news, resulting in the most frequent TLink Identity. However, the additional information provided by the journalist about the main event tends to follow a chronological order, which is why TLink After is also frequently observed. Regarding RQ2, which aims to investigate the role of the report level in the temporal organisation of news stories, we can conclude that reporting events hold signi cant importance in this type of news articles. Though not all events in the story are introduced by reporting events, when they are, the most frequent relation within each block of the reporting event is simultaneity. Therefore, there is a somewhat distinct pattern of temporal organisation depending on whether we consider each reporting event or the entire news article. In relation to our third research question (RQ3), which is concerned with the relationship between the temporal relations and the grammatical characteristics of events, our study indicates that Type is the most signi cant attribute. Additionally, we have observed a correlation between telic situations and temporal successiveness, and durative situations and temporal simultaneity.
In summary, our linguistic analysis provided valuable insights into how news articles convey
4This same exact correlation between temporal readings and aspectual properties, in particular the fact that telicity
triggers temporal succession, whereas durativity triggers simultaneity, was also pointed out for independent reasons
in other works (cf. [
National Funds nance this work through the FCT - Fundação para a Ciência e a Tecnologia, I.P. (Portuguese Foundation for Science and Technology) within the project StorySense, with reference 2022.09312.PTDC. It was also funded by national funds through FCT – Fundação para a Ciência e a Tecnologia, I.P., within the project UIDB/00022/2020. [11] S. Liu, Y. Wu, E. Wei, M. Liu, Y. Liu, Story ow: Tracking the evolution of stories, IEEE
Transactions on Visualization and Computer Graphics 19 (2013) 2436–2445. [12] Q. Chen, S. Cao, J. Wang, N. Cao, How does automation shape the process of narrative visualization: A survey of tools, IEEE Transactions on Visualization and Computer Graphics (2023). [13] ISO-24617-1, Language resource management - Semantic annotation framework (SemAF)
Part 1: Time and events (SemAF-Time, ISO-TimeML), Standard, Geneva, CH, 2012. [14] A. Bell, News time, Time & Society 4 (1995) 305–328. doi:10.1177/ 0961463X95004003003. [15] C. Schokkenbroek, News stories: Structure, time and evaluation, Time & Society 8 (1999) 59–98. doi:10.1177/0961463X99008001004. [16] J. Sanders, K. van Krieken, Traveling through narrative time: How tense and temporal deixis guide the representation of time and viewpoint in news narratives, Cognitive Linguistics 30 (2019) 281–304. doi:10.1515/cog-2018-0041. [17] I. Mani, B. Schi man, Temporally anchoring and ordering events in news, 2004. [18] A. Setzer, Temporal Information in Newswire Articles: an Annotation Scheme and Corpus
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A GNR feriu ligeiramente com “bagos de borracha” (balas de borracha) um homem em Cabeceiras de Basto que estava a fazer disparos para o ar com munições reais e que se recusou a parar, disse hoje fonte policial.
O caso deu-se na freguesia de Refojos de Basto, concelho de Cabeceiras de Basto, distrito de Braga, um pouco antes das 00:00.
O homem, de 52 anos, foi atingido pelas balas de borracha nas pernas.
Fonte do Comando Territorial da GNR em Braga disse à agência Lusa que os operacionais só atingiram o homem depois de este desobedecer à ordem de cessar os disparos.
“Não parou sequer quando os militares zeram disparos de intimidação para o ar”, acrescentou a fonte, que a rmou desconhecer as causas do comportamento do detido.
Depois de tratado a ferimentos ligeiros em unidade hospitalar, foi detido no posto da GNR em Cabeceiras e será hoje presente a juiz de instrução para xação das medidas de coação tidas por convenientes.
Como o caso envolveu armas de fogo, a Polícia Judiciária vai ser informada de detalhes, indicou a fonte.
The GNR slightly wounded a man in Cabeceiras de Basto with "rubber bullets" who was shooting into the air with live ammunition and refused to stop, a police source said today.
The incident took place in the parish of Refojos de Basto, in the municipality of Cabeceiras de Basto, Braga district, just before 00:00.
The 52-year-old man was hit by rubber bullets in the legs.
A source from the GNR’s Territorial Command in Braga told Lusa news agency that the o cers only hit the man after he disobeyed the order to stop ring.
"He didn’t even stop when the soldiers red intimidating shots into the air," added the source, who said he didn’t know the cause of the detainee’s behaviour.
After being treated for minor injuries at a hospital, he was detained at the GNR post in Cabeceiras and will be brought before an investigating judge today for the coercive measures deemed appropriate.
As the case involved rearms, the Judicial Police will be informed of the details, the source said.
A.2.1. Bubble representation