Croatia

Scientific writing is assumed to have become
more informationally dense over time (Halliday,
1988; Biber and Gray, 2016). Given that
scientific writing is intended for communication
between experts, we hypothesize a tendency
towards optimizing language use by striving
for a balance between highly informative
content and a conventionalized style of writing.
We study this by means of fractal analysis, asking
whether the degree of informativity has become
more persistent with predictable patterns
of gradual changes between high vs. low informational
content, indicating a trend towards
an optimal code for scientific communication.
Specifically, surprisal is used to measure informativity
and the Hurst exponent is used as a
long-term dependence measure for fractality
analysis, quantifying the degree of persistence
of informativity in scientific texts.

EACL 2023

Fractality of informativity in 300 years of English scientific writing

fractality

scientific writing

information theory

The 45th Annual Meeting of the Cognitive Science Society will take place in Sydney, Australia as a modified hybrid conference. Presentations from the main-session room will be live-streamed every day for all events. This will include all invited talks and award sessions. Recordings of all CogSci 2023 sessions will be made available for on-demand viewing after the conference. 
The in-person component will be held at the International Convention Centre Sydney at Darling Harbour. The Conference will take place Wednesday July 26th – Saturday July 29th, 2023 (Wednesday, July 26th is the Workshops Day).

Join us!

 [**The Program at a Glance can be found here**](https://cognitivesciencesociety.org/wp-content/uploads/2023/07/COgSci-PAAG-V2.pdf)
 [**The Detailed Program can be found here**](https://online.fliphtml5.com/ebtyf/mswz/)
 

#### **CogSci 2023 Program Book**
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CogSci 2023

The 45th Annual Meeting of the Cognitive Science Society presents the latest research across cognitive science and highlights the theme of Cognition in Context.

workshop paper

### Welcome to the 17th Conference of the European Chapter of the Association for Computational Linguistics (EACL) 
Continuing its mission of expanding and involving the science community of all European countries, EACL had selected the Ukrainian community for the 16th EACL, which took place online due to the COVID pandemic. Unfortunately, the ongoing war made the organisation in Kyiv impossible. Considering the importance of physical interaction among researchers, especially after the restrictions imposed by the COVID pandemic, in addition to an online mode, the conference will be held in Dubrovnik, Croatia, from 2 to 6 of May, 2023. The original aim of strengthening the connection with the Ukrainian community will not change as our program will feature a dedicated session and a workshop to highlight work on Ukrainian language technologies. As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

You need to register for the conference in order to access this site. Please visit https://2023.eacl.org/registration for more information.

As the flagship European conference in the field of computational linguistics, EACL welcomes European and international researchers covering a broad spectrum of research areas that are concerned with computational approaches to natural language.

Emotion regulation is a crucial element in dealing with emotional events and has positive effects on mental health. This paper aims to provide a more comprehensive understanding of emotional events by introducing a new French corpus of emotional narratives collected using a questionnaire for emotion regulation. We follow the theoretical framework of the Component Process Model which considers emotions as dynamic processes composed of four interrelated components (behavior, feeling, thinking and territory). Each narrative is related to a discrete emotion and is structured based on all emotion components by the writers. We study the interaction of components and their impact on emotion classification with machine learning methods and pre-trained language models. Our results show that each component improves prediction performance, and that the best results are achieved by jointly considering all components. Our results also show the effectiveness of pre-trained language models in predicting discrete emotion from certain components, which reveal differences in how emotion components are expressed.

Emotion Recognition based on Psychological Components in Guided Narratives for Emotion Regulation

This presentation describes the process of interlinking the Neulateinische Wortliste, a a lexical resource consisting of a list of more than 20,000 Neo-Latin words, with other resources for Latin. The resources are made interoperable thanks to their linking to the LiLa Knowledge Base, which applies Linguistic Linked Open Data practices and data categories to describe and publish on the Web both textual and lexical resources for the Latin language

Linking the Neulateinische Wortliste to the anonymous Knowledge Base of Interoperable Resources for Latin

Towards computational systems capable of dealing with complex and general linguistic phenomena, it is essential to understand figurative language, which verbal humor is an instance of. This paper reports state-of-the-art results for Humor Recognition in Portuguese, specifically, an F1-score of 99.6% with a BERTbased classifier. However, following the surprising high performance in such a challenging task, we further analyzed what was actually learned by the classifiers. Our main conclusions were that classifiers based on contentfeatures achieve the best performance, but rely mostly on stylistic aspects of the text, not necessarily related to humor, such as punctuation and question words. On the other hand, for humor-related features, we identified some important aspects, such as the presence of named entities, ambiguity and incongruity.

What do Humor Classifiers Learn? An Attempt to Explain Humor Recognition Models

In this paper, we present approaches for the automated extraction and disambiguation of a part of the stylistic device Vossian Antonomasia (VA), namely the target entity described by the expression. We model the problem as a coreference resolution and a question answering task and also combine both. To tackle the tasks at hand, we utilize state-of-the-art models in these areas. In addition, we visualize the connection between source and target entities of VA in a web demo to provide a deeper understanding of their mutual relationship.

Who is the Madonna of Italian-American Literature?: Extracting and Analyzing Target Entities of Vossian Antonomasia

In this paper we present the first information extraction system able to capture smell events, including smell words, smell sources and qualities. We then apply the system to four English corpora, covering a time period between 15th and 20th century. Then, starting from the extracted data, we adapt an existing approach to semantic shift detection to capture which smell sources underwent a change in the way their odour was perceived before and after 1800. We find correspondences between the extracted items and the output of historical research concerning the smell of tobacco and tea. Despite the limited amount of data, the results are promising and indicate that this research can yield valuable insights in the area of diachronical sensory analysis. In the future, we intend to broaden the scope of our data and conduct more comprehensive analyses on a greater variety of smell sources.

Scent Mining: Extracting Olfactory Events, Smell Sources and Qualities

This study relies on natural language processing to explore the nature of online communication in Russia during the war on Ukraine in 2022. The analysis of a large corpus of publications in traditional media and on social media identifies massive state interventions aimed at manipulating public opinion. The study relies on expertise in media studies and political science to trace the major themes and strategies of propagandist narratives on three major Russian social media platforms over several months as well as their perception by the users. Distributions of several keyworded pro-war and anti-war topics are examined to reveal the cross-platform specificity of social media audiences. We release WarMM-2022, a 1.7 M posts corpus. This corpus includes publications related to the Russia-Ukraine war, which appeared in Russian mass media (February to September 2022) and on social networks (July to September 2022). The corpus can be useful for the development of NLP approaches to propaganda detection and subsequent studies of propaganda campaigns in social sciences in addition to traditional methods, such as content analysis, focus groups, surveys.

Wartime Media Monitor (WarMM-2022): A Study of Information Manipulation on Russian Social Media during the Russia-Ukraine War

Named Entity Annotation Projection Applied to Classical Languages

This paper investigates the use of standard and non-standard adverbial markers in modern Chinese literature. In Chinese, adverbials can be derived from many adjectives, adverbs and verbs with the suffix ``de''. The suffix has a standard and a non-standard written form, both of which are frequently used. Contrastive research on these two competing forms has mostly been qualitative or limited to small text samples. In this first large-scale quantitative study, we present statistics on 346 adverbial types from an 8-million-character text corpus drawn from Chinese literature in the 20th century. We present a semantic analysis of the verbs modified by adverbs with standard and non-standard markers, and a chronological analysis of marker choice among six prominent modern Chinese authors. We show that the non-standard form is more frequently used when the adverbial modifies an emotion verb. Further, we demonstrate that marker choice is correlated to text genre and register, as well as the writing style of the author.

Standard and Non-standard Adverbial Markers: a Diachronic Analysis in Modern Chinese Literature

Quotes are universally appealing. Humans recognize good quotes and save them for later reference. However, it may pose a challenge for machines. In this work, we build a new corpus of quotes and propose a new task, quote detection, as a type of span detection. We retrieve the quote set from Goodreads and collect the spans through a custom search on the Gutenberg Book Corpus. We run two types of baselines for quote detection: Conditional random field (CRF) and summarization with pointer-generator networks and Bidirectional and Auto-Regressive Transformers (BART). The results show that the neural sequence-to-sequence models perform substantially better than CRF. From the viewpoint of neural extractive summarization, quote detection seems easier than news summarization. Moreover, model fine-tuning on our corpus and the Cornell Movie-Quotes Corpus introduces incremental performance boosts. Finally, we provide a qualitative analysis to gain insight into the performance.

Quote Detection: A New Task and Dataset for NLP

Authorship verification is used to link texts written by the same author without needing a model per author, making it useful for de-anonymizing users spreading text with malicious intent. Recent advances in Transformer-based language models hold great promise for author verification, though short context lengths and non-diverse training regimes present challenges for their practical application. In this work, we investigate the effect of these challenges in the application of a Cross-Encoder Transformer-based author verification system under multiple conditions. We perform experiments with four Transformer backbones using differently tuned variants of fanfiction data and found that our BigBird pipeline outperformed Longformer, RoBERTa, and ELECTRA and performed competitively against the official top ranked system from the PAN evaluation.
We also examined the effect of authors and fandoms not seen in training on model performance. Through this, we found fandom has the greatest influence on true trials, pairs of text written by the same author, and that a balanced training dataset in terms of class and fandom performed the most consistently.

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