The definition of rigorous and well-structured annotation schemes is a key element in the advancement of Natural Language Processing (NLP). This paper aims to compare the performance of a general-purpose annotation scheme — Text2Story, based on the ISO 24617-1 standard — with that of a domain-specific scheme — i2b2 — in the context of clinical narrative annotation; and to assess the feasibility of harmonizing ISO 24617-1, originally designed for general-domain applications, with a specialized extension tailored to the medical domain. Based on the results of this comparative analysis, we present Med2Story, a medical-specific extension of ISO 24617-1 developed to address the particularities of clinical text annotation.

We present an annotation scheme designed to capture information related to the maintenance or change in the price of some goods (fuels, water, and vehicles) in news articles in Portuguese. The methodology we used involved adapting an existing annotation scheme, the Text2Story scheme (Silvano et al., 2021; Leal et al., 2022), which is based on different parts of ISO 24617 to capture the essential information for this project. Adaptations were needed to accommodate specific information, namely, information related to quantitative data and comparative relations that are abundant in this type of news. In this paper, we provide an overview of the annotation scheme, highlighting attributes and values of the entity and link structures specifically designed to capture financial information, as well as some problems we had to overcome in the process of building it and the rationale of some decisions behind its overall architecture.

High-quality annotation is paramount for effective predictions of machine learning models. When the annotation is dense, achieving superior human labeling can be challenging since the most used annotation tools present an overloaded visualization of labels. Thus, we present a tool for viewing annotations made in corpora, specifically for temporal relations between events and temporal expressions, filling a gap in this type of tool. We focus on narrative text, which is a rich source for these types of elements.

The main objective of this study is to contribute to multilingual discourse research by employing ISO-24617 Part 8 (Semantic Relations in Discourse, Core Annotation Schema – DR-core) for annotating discourse relations. Centering around a parallel discourse relations corpus that includes English, Polish, and European Portuguese, we initiate one of the few ISO-based comparative analyses through a multilingual corpus that aligns discourse relations across these languages. In this paper, we discuss the project’s contributions, including the annotated corpus, research findings, and statistics related to the use of discourse relations. The paper further discusses the challenges encountered in complying with the ISO standard, such as defining the scope of arguments and annotating specific relation types like Expansion. Our findings highlight the necessity for clearer definitions of certain discourse relations and more precise guidelines for argument spans, especially concerning the inclusion of connectives. Additionally, the study underscores the importance of ongoing collaborative efforts to broaden the inclusion of languages and more comprehensive datasets, with the objective of widening the reach of ISO-guided multilingual discourse research.

Recent advances in deep learning have promoted the advent of many computational systems capable of performing intelligent actions that, until then, were restricted to the human intellect. In the particular case of human languages, these advances allowed the introduction of applications like ChatGPT that are capable of generating coherent text without being explicitly programmed to do so. Instead, these models use large volumes of textual data to learn meaningful representations of human languages. Associated with these advances, concerns about copyright and data privacy infringements caused by these applications have emerged. Despite these concerns, the pace at which new natural language processing applications continued to be developed largely outperformed the introduction of new regulations. Today, communication barriers between legal experts and computer scientists motivate many unintentional legal infringements during the development of such applications. In this paper, a multidisciplinary team intends to bridge this communication gap and promote more compliant Portuguese NLP research by presenting a series of everyday NLP use cases, while highlighting the Portuguese legislation that may arise during its development.

Story components, namely, events, time, participants, and their relations are present in narrative texts from different domains such as journalism, medicine, finance, and law. The automatic extraction of narrative elements encompasses several NLP tasks such as Named Entity Recognition, Semantic Role Labeling, Event Extraction, Coreference resolution, and Temporal Inference. The text2story python, an easy-to-use modular library, supports the narrative extraction and visualization pipeline. The package contains an array of narrative extraction tools that can be used separately or in sequence. With this toolkit, end users can process free text in English or Portuguese and obtain formal representations, like standard annotation files or a formal logical representation. The toolkit also enables narrative visualization as Message Sequence Charts (MSC), Knowledge Graphs, and Bubble Diagrams, making it useful to visualize and transform human-annotated narratives. The package combines the use of off-the-shelf and custom tools and is easily patched (replacing existing components) and extended (e.g. with new visualizations). It includes an experimental module for narrative element effectiveness assessment and being is therefore also a valuable asset for researchers developing solutions for narrative extraction. To evaluate the baseline components, we present some results of the main annotators embedded in our packages for datasets in English and Portuguese. We also compare the results with the extraction of narrative elements by GPT-3, a robust LLM model.

Narratives have been the subject of extensive research across various scientific fields such as linguistics and computer science. However, the scarcity of freely available datasets, essential for studying this genre, remains a significant obstacle. Furthermore, datasets annotated with narratives components and their morphosyntactic and semantic information are even scarcer. To address this gap, we developed the Text2Story Lusa datasets, which consist of a collection of news articles in European Portuguese. The first datasets consists of 357 news articles and the second dataset comprises a subset of 117 manually densely annotated articles, totaling over 50 thousand individual annotations. By focusing on texts with substantial narrative elements, we aim to provide a valuable resource for studying narrative structures in European Portuguese news articles. On the one hand, the first dataset provides researchers with data to study narratives from various perspectives. On the other hand, the annotated dataset facilitates research in information extraction and related tasks, particularly in the context of narrative extraction pipelines. Both datasets are made available adhering to FAIR principles, thereby enhancing their utility within the research community.

Reasoning about spatial information is fundamental in natural language to fully understand relationships between entities and/or between events. However, the complexity underlying such reasoning makes it hard to represent formally spatial information. Despite the growing interest on this topic, and the development of some frameworks, many problems persist regarding, for instance, the coverage of a wide variety of linguistic constructions and of languages. In this paper, we present a proposal of integrating ISO-Space into a ISO-based multilayer annotation scheme, designed to annotate news in European Portuguese. This scheme already enables annotation at three levels, temporal, referential and thematic, by combining postulates from ISO 24617-1, 4 and 9. Since the corpus comprises news articles, and spatial information is relevant within this kind of texts, a more detailed account of space was required. The main objective of this paper is to discuss the process of integrating ISO-Space with the existing layers of our annotation scheme, assessing the compatibility of the aforementioned parts of ISO 24617, and the problems posed by the harmonization of the four layers and by some specifications of ISO-Space.

Studies in Social Sciences have revealed that when people evaluate someone else, their evaluations often reflect their biases. As a result, rater bias may introduce highly subjective factors that make their evaluations inaccurate. This may affect automated essay scoring models in many ways, as these models are typically designed to model (potentially biased) essay raters. While there is sizeable literature on rater effects in general settings, it remains unknown how rater bias affects automated essay scoring. To this end, we present a new annotated corpus containing essays and their respective scores. Different from existing corpora, our corpus also contains comments provided by the raters in order to ground their scores. We present features to quantify rater bias based on their comments, and we found that rater bias plays an important role in automated essay scoring. We investigated the extent to which rater bias affects models based on hand-crafted features. Finally, we propose to rectify the training set by removing essays associated with potentially biased scores while learning the scoring model.

Several methods for automatic essay scoring (AES) for English language have been proposed. However, multi-aspect AES systems for other languages are unusual. Therefore, we propose a multi-aspect AES system to apply on a dataset of Brazilian Portuguese essays, which human experts evaluated according to five aspects defined by Brazilian Government to the National Exam to High School Student (ENEM). These aspects are skills that student must master and every skill is assessed apart from each other. Besides the prediction of each aspect, the feature analysis also was performed for each aspect. The AES system proposed employs several features already employed by AES systems for English language. Our results show that predictions for some aspects performed well with the features we employed, while predictions for other aspects performed poorly. Also, it is possible to note the difference between the five aspects in the detailed feature analysis we performed. Besides these contributions, the eight millions of enrollments every year for ENEM raise some challenge issues for future directions in our research.