Canada

The mythological domain has various ways of expressing events and background knowledge. Using data extracted according to the hylistic approach (Zgoll 2019), we annotated a data set of 6315 German sentences from various mythological contexts and geographical origins, like Ancient Greece and Rome or Mesopotamia, into four categories: single-point events (e.g. actions), durative-constant (background knowledge, continuous states), durative-initial, and durative-resultative. This data is used to train a classifier, which is able to reliably distinguish event types.

ACL 2023

Orpheus Came to His End by Being Struck by a Thunderbolt&#39;: Annotating Events in Mythological Sequences

religous studies

classics

ancient near eastern studies

narrative domain

situation entity types

aktionsart

hylistics

myths

states

events

annotation

### Looking for Cogsci 2023? Please [click here](https://underline.io/events/405/reception) to switch to 2023 edition.


**Dear Colleagues,**

Welcome to the 43rd Annual Conference of the Cognitive Science Society! 

This year’s conference brings together the latest research from all Cognitive Science and also highlights this year’s theme **Comparative Cognition: Animal Minds.** 

The program features four plenary speakers: Friederike Range (University of Veterinary Medicine, Vienna), Onur Güntürkün (Ruhr University Bochum), Julia Fischer (University of Göttingen), and Erich Jarvis (The Rockefeller University). The keynotes and invited symposia showcase the broad spectrum of ideas that encompass the conference theme of comparative cognition. 

Further, the program includes the Rumelhart Prize keynote presentation by Susan Goldin-Meadow (University of Chicago), and symposia to celebrate the winner of the Elman Prize: Wei Ji Ma (New York University), the Heineken Award winner Robert Zatorre (McGill University), and the Robert J. Glushko Dissertation Prizes session.

The program committee for CogSci 2021 received 1,025 submissions, including 710 full papers, 275 member abstracts, 8 publication-based talks, as well as 15 proposals for symposia, and 17 for workshops and tutorials. After a rigorous review process, the committee selected 152 papers for oral presentation and inclusion in the conference proceedings (14.8%), 332 papers for poster presentation and inclusion in the proceedings (32.4%), and 175 papers for poster presentation with inclusion of abstracts in the proceedings (17.1%) We also selected 275 submitted member abstracts. In addition, we accepted 6 publication-based talks, 12 symposia, and 9 workshops and tutorials to make for an exciting and inclusive program.

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CogSci 2021

The Cognitive Science Society brings together researchers from around the world who hold a common goal: understanding the nature of the human mind. The mission of the Society is to promote Cognitive Science as a discipline, and to foster scientific interchange among researchers in various areas of study, including Artificial Intelligence, Linguistics, Anthropology, Psychology, Neuroscience, Philosophy, and Education. The Society is a non-profit professional organization and its activities include sponsoring an annual conference and publishing the journals Cognitive Science and TopiCS.

Orpheus Came to His End by Being Struck by a Thunderbolt': Annotating Events in Mythological Sequences

workshop paper

### Welcome to ACL 2023, the 61st Annual Meeting of the Association for Computational Linguistics! 
 The conference will be held in Toronto, Canada, July 9-14, 2023. 
Following the succession of the recent conferences in our field, ACL 2023 will adopt a hybrid format.
While the impact of Covid has considerably diminished in terms of traveling, obtaining visas to Canada
entails a very long process. Moreover, the global economic conditions pose challenges for many individuals to travel to conferences. Recognizing these circumstances, we know many participants may not be
able to attend the conference in person. Therefore, we are committed to providing a great virtual platform
so everyone has the opportunity to interact with other participants and enjoy the conference. Based on the
current registered participants, approxiately 30% have chosen to attend the conference virtually. Whether
you join us in person or virtually, we sincerely hope everyone has a remarkable conference experience. 
This General Chair’s message is where I express my gratitude to the many individuals who have made
enormous contributions to the conference over the past year.

Read [**ACL 2023 General Chair's message**](https://docs.google.com/document/d/1WobYM7norbG4dI48s75HfJoD89qgX5a_F-6U8AteLSA/edit?usp=sharing/) in full.

##### **[Conference Handbook](https://2023.aclweb.org/downloads/acl2023-handbook.pdf)**

The Association for Computational Linguistics (ACL) is the premier international scientific and professional society for people working on computational problems involving human language, a field often referred to as either computational linguistics or natural language processing.

We present a novel dataset for physical and abstract plausibility of events in English. Based on naturally occurring sentences extracted from Wikipedia, we infiltrate degrees of abstractness, and automatically generate perturbed pseudo-implausible events. We annotate a filtered and balanced subset for plausibility using crowd-sourcing, and perform extensive cleansing to ensure annotation quality. In-depth quantitative analyses indicate that annotators favor plausibility over implausibility and disagree more on implausible events. Furthermore, our plausibility dataset is the first to capture abstractness in events to the same extent as concreteness, and
we find that event abstractness has an impact on plausibility ratings: 
more concrete event participants trigger a perception of implausibility.

A Dataset for Physical and Abstract Plausibility and Sources of Human Disagreement

The Turkish particle dA is a focus-associated enclitic, and it can act as a discourse connective conveying multiple senses, like additive, contrastive, causal etc. Like many other linguistic expressions, it is subject to usage ambiguity and creates a challenge in natural language automatization tasks. For the first time, we annotate the discourse and non-discourse connnective occurrences of dA in Turkish with the PDTB principles. Using a minimal set of linguistic features, we develop binary classifiers to distinguish its discourse connective usage from its other usages. We show that despite its ability to cliticize to any syntactic type, variable position in the sentence and having a wide argument span, its discourse/non-discourse connective usage can be annotated reliably and its discourse usage can be disambiguated by exploiting local cues.

Annotating and Disambiguating the Discourse Usage of the Enclitic dA in Turkish

This work presents two corpora based on excerpts from two German novels with an infor- mal narration style. We performed fine-grained multi-layer annotations of animate referents, assigning local and global prominence-lending features to the annotated referring expressions. In addition, our corpora include annotations of intra-sentential segments, which can serve as a more reliable unit of length measurement. Furthermore, we present two exemplary studies demonstrating how to use these corpora.

Multi-layered Annotation of Conversation-like Narratives in German

Most tasks in NLP require labeled data. Data labeling is often done on crowdsourcing platforms due to scalability reasons. However, publishing data on public platforms can only be done if no privacy-relevant information is included.
Textual data often contains sensitive information like person names or locations. In this work, we investigate how removing personally identifiable information (PII) as well as applying differential privacy (DP) rewriting can enable text with privacy-relevant information to be used for crowdsourcing. We find that DP-rewriting before crowdsourcing can preserve privacy while still leading to good label quality
for certain tasks and data. PII-removal led to good label quality in all examined tasks, however, there are no privacy guarantees given.

Crowdsourcing on Sensitive Data with Privacy-Preserving Text Rewriting

The availability of annotated legal corpora is crucial for a number of tasks. Annotation is mostly assumed to be a straightforward task: as long as the annotation scheme is well defined and the guidelines are clear, annotators are expected to agree on the labels. This is not always the case, especially in legal annotation, which can be extremely difficult even for expert annotators. We propose a legal annotation procedure that takes into account annotator certainty and improves it through negotiation. We also collect annotator feedback and show that our approach contributes to a positive annotation environment. Our work invites reflection on often neglected ethical concerns regarding legal annotation.

Annotators-in-the-loop: Testing a Novel Annotation Procedure on Italian Case Law

We reports on a three-part series of original methods geared towards producing semantic annotations for the decompositional marker "again". The three methods are (i) exhaustive expert annotation based on a comprehensive set of guidelines, (ii) extension of expert annotation by predicting presuppositions with a Multinomial Naïve Bayes classifier in the context of a meta-analysis to optimize feature selection and (iii) quality-controlled crowdsourcing with ensuing evaluation and KMeans clustering of annotation vectors.

Annotating Decomposition in Time: Three Approaches for Again

The annotation task we elaborated aims at describing the contextual factors that influence the appearance and interpretation of moral predicates, in newspaper articles on police brutality, in French and in English. The paper provides a brief review of the literature on moral predicates and their relation with context. The paper also describes the elaboration of the corpus and the ontology. Our hypothesis is that the use of moral adjectives and their appearance in context could change depending on the political orientation of the journal. We elaborated an annotation task to investigate the precise contexts discussed in articles on police brutality. The paper concludes by describing the study and the annotation task in details.

Pragmatic Annotation of Articles Related to Police Brutality

We present the RST Continuity Corpus (RST-CC), a corpus of discourse relations annotated for continuity dimensions. Continuity or discontinuity (maintaining or shifting deictic centres across discourse segments) is an important property of discourse relations, but the two are correlated in greatly varying ways. To analyse this correlation, the relations in the RST-CC are annotated using operationalised versions of Givón’s (1993) continuity dimensions. We also report on the inter-annotator agreement, and discuss recurrent annotation issues. First
results show substantial variation of continuity dimensions within and across relation types.

The RST Continuity Corpus

In this paper we address the scarcity of annotated data for NArabizi, a Romanized form of North African Arabic used mostly on social media, which poses challenges for Natural Language Processing (NLP). We introduce an enriched version of NArabizi Treebank (Seddah et al., 2020) with three main contributions: the addition of two novel annotation layers (named entity recognition and offensive language detection) and a re-annotation of the tokenization, morpho-syntactic and syntactic layers that ensure annotation consistency. Our experimental results, using different tokenization schemes, showcase the value of our contributions and highlight the impact of working with non-gold tokenization for NER and dependency parsing. To facilitate future research, we make these annotations publicly available. Our enhanced NArabizi Treebank paves the way for creating sophisticated language models and NLP tools for this under-represented language.

Enriching the NArabizi Treebank: A Multifaceted Approach to Supporting an Under-Resourced Language

Coreference Resolution is a well studied problem in NLP. While widely studied for English and other resource-rich languages, research on coreference resolution in Bengali largely remains unexplored due to the absence of relevant datasets. Bengali, being a low-resource language, exhibits greater morphological richness compared to English. In this article, we introduce a new dataset, BenCoref, comprising coreference annotations for Bengali texts gathered from four distinct domains. This relatively small dataset contains 5200 mention annotations forming 502 mention clusters within 48,569 tokens. We describe the process of creating this dataset and report performance of multiple models trained using BenCoref. We
expect that our work provides some valuable insights on the variations in coreference phenomena across several domains in Bengali and encourages the development of additional resources for Bengali. Furthermore, we found
poor crosslingual performance at zero-shot setting from English, highlighting the need for more language-specific resources for this task.

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A Dataset for Physical and Abstract Plausibility and Sources of Human Disagreement

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Possibility judgments may depend on assessments of similarity to known events