ymoslem/acl-abstracts huggingface dataset:
The MOLOR Lemma Bank: a New LLOD Resource for Old Irish (2024)

This paper describes the first steps in creating a Lemma Bank for Old Irish (600-900CE) within the Linked Data paradigm, taking inspiration from a similar resource for Latin built as part of the LiLa project (2018{--}2023). The focus is on the extraction and RDF conversion of nouns from Goidelex, a novel and highly structured morphological resource for Old Irish. The aim is to strike a good balance between retaining a representative level of morphological granularity and at the same time keeping the amount of lemma variants within workable limits, to facilitate straightforward resource interlinking for Old Irish, planned as future work.
CHAMU\cCA: Towards a Linked Data Language Resource of Portuguese Borrowings in Asian Languages (2024)

This paper presents the development of CHAMU{\c{C}}A, a novel lexical resource designed to document the influence of the Portuguese language on various Asian languages, with an initial focus on the languages of South Asia. Through the utilization of linked open data and the OntoLex vocabulary, CHAMU{\c{C}}A offers structured insights into the linguistic characteristics, and cultural ramifications of Portuguese borrowings across multiple languages. The article outlines CHAMU{\c{C}}A`s potential contributions to the linguistic linked data community, emphasising its role in addressing the scarcity of resources for lesser-resourced languages and serving as a test case for organising etymological data in a queryable format. CHAMU{\c{C}}A emerges as an initiative towards the comprehensive catalogization and analysis of Portuguese borrowings, offering valuable insights into language contact dynamics, historical evolution, and cultural exchange in Asia, one that is based on linked data technology.
LODinG: Linked Open Data in the Humanities (2024)

We are presenting LODinG {--} Linked Open Data in the Humanities (abbreviated from Linked Open Data in den Geisteswissenschaften), a recently launched research initiative exploring the intersection of Linked Open Data (LOD) and a range of areas of work within the Humanities. We focus on effective methods of collecting, modeling, linking, releasing and analyzing machine-readable information relevant to (digital) humanities research in the form of LOD. LODinG combines the sources and methods of digital humanities, general and computational linguistics, digital lexicography, German and Romance philology, translatology, cultural and literary studies, media studies, information science and law to explore and expand the potential of the LOD paradigm for such a diverse and multidisciplinary field. The project`s primary objectives are to improve the methods of extracting, modeling and analyzing multilingual data in the LOD paradigm; to demonstrate the application of the linguistic LOD to various methods and domains within and beyond the humanities; and to develop a modular, cross-domain data model for the humanities.
DigItAnt: a platform for creating, linking and exploiting LOD lexica with heterogeneous resources (2024)

Over the past few years, the deployment of Linked Open Data (LOD) technologies has witnessed significant advancements across a myriad of sectors, linguistics included. This progression is characterized by an exponential increase in the conversion of resources to adhere to contemporary encoding standards. Such transformations are driven by the objectives outlined in {\textquotedblleft}ecological{\textquotedblright} methodologies, notably the FAIR data principles, which advocate for the reuse and interoperability of resources. This paper presents the DigItAnt architecture, developed in the context of a national project funded by the Italian Ministry of Research and in the service of a recently started Italian endeavor to realize a federation of infrastructures for the humanities. It details its services, utilities and data types, and shows how it manages to produce, exploit and interlink LLOD and non-LLOD datasets in ways that are meaningful to its intended target disciplinary context, i.e. historical linguistics over epigraphy data. The paper also introduces how DigItAnt services and functionalities will contribute to the empowerment of the H2IOSC Italian infrastructures cluster project, which is devoted to the construction of a nationwide research infrastructure federation for the humanities, and it will possibly contribute to its pilot project towards an authoritative LLOD platform.
Teanga Data Model for Linked Corpora (2024)

Corpus data is the main source of data for natural language processing applications, however no standard or model for corpus data has become predominant in the field. Linguistic linked data aims to provide methods by which data can be made findable, accessible, interoperable and reusable (FAIR). However, current attempts to create a linked data format for corpora have been unsuccessful due to the verbose and specialised formats that they use. In this work, we present the Teanga data model, which uses a layered annotation model to capture all NLP-relevant annotations. We present the YAML serializations of the model, which is concise and uses a widely-deployed format, and we describe how this can be interpreted as RDF. Finally, we demonstrate three examples of the use of the Teanga data model for syntactic annotation, literary analysis and multilingual corpora.
The Services of the LiLa Knowledge Base of Interoperable Linguistic Resources for Latin (2024)

This paper describes three online services designed to ease the tasks of querying and populating the linguistic resources for Latin made interoperable through their publication as Linked Open Data in the LiLa Knowledge Base. As for querying the KB, we present an interface to search the collection of lemmas that represents the core of the Knowledge Base, and an interactive, graphical platform to run queries on the resources currently interlinked. As for populating the KB with new textual resources, we describe a tool that performs automatic tokenization, lemmatization and Part-of-Speech tagging of a raw text in Latin and links its tokens to LiLa.
An Annotated Dataset for Transformer-based Scholarly Information Extraction and Linguistic Linked Data Generation (2024)

We present a manually curated and annotated, multidisciplinary dataset of 15,262 sentences from research articles (abstract and main text) that can be used for transformer-based extraction from scholarly publications of three types of entities: 1) research methods, named entities of variable length, 2) research goals, entities that appear as textual spans of variable length with mostly fixed lexico-syntactic-structure, and 3) research activities, entities that appear as textual spans of variable length with complex lexico-syntactic structure. We explore the capabilities of our dataset by using it for training/fine-tuning various ML and transformer-based models. We compare our finetuned models as well as LLM responses (chatGPT 3.5) based on 10-shot learning, by measuring F1 scores in token-based, entity-based strict and entity-based partial evaluations across interdisciplinary and discipline-specific datasets in order to capture any possible differences in discipline-oriented writing styles. Results show that fine tuning of transformer-based models significantly outperforms the performance of few- shot learning of LLMs such as chatGPT, highlighting the significance of annotation datasets in such tasks. Our dataset can also be used as a source for linguistic linked data by itself. We demonstrate this by presenting indicative queries in SPARQL, executed over such an RDF knowledge graph.
Linguistic LOD for Interoperable Morphological Description (2024)

Interoperability is a characteristic of a product or system that seamlessly works with another product or system and implies a certain level of independence from the context of use. Turning to language resources, interoperability is frequently cited as one important rationale underlying the use of LLOD representations and is generally regarded as highly desirable. In this paper we further elaborate this theme, distinguishing three different kinds of interoperability providing practical implementations with examples from morphology.
Modeling linking between text and lexicon with OntoLex-Lemon: a case study of computational terminology for the Babylonian Talmud (2024)

This paper illustrates the first steps in the creation of a computational terminology for the Babylonian Talmud. After introducing reasons and the state of the art, the paper exposes the choice of using OntoLex-Lemon and the new FrAC module for encoding the attestations and quantitative data of the terminology extraction. After that, the Talmudic terminology base is introduced and an example entry with the above-mentioned data is shown. The scheme is motivated not only by the rich representation the model allows, but also by the future management of the link between text and lexical entries.
OntoLex Publication Made Easy: A Dataset of Verbal Aspectual Pairs for Bosnian, Croatian and Serbian (2024)

This paper introduces a novel language resource for retrieving and researching verbal aspectual pairs in BCS (Bosnian, Croatian, and Serbian) created using Linguistic Linked Open Data (LLOD) principles. As there is no resource to help learners of Bosnian, Croatian, and Serbian as foreign languages to recognize the aspect of a verb or its pairs, we have created a new resource that will provide users with information about the aspect, as well as the link to a verb`s aspectual counterparts. This resource also contains external links to monolingual dictionaries, Wordnet, and BabelNet. As this is a work in progress, our resource only includes verbs and their perfective pairs formed with prefixes {\textquotedblleft}pro{\textquotedblright}, {\textquotedblleft}od{\textquotedblright}, {\textquotedblleft}ot{\textquotedblright}, {\textquotedblleft}iz{\textquotedblright}, {\textquotedblleft}is{\textquotedblright} and {\textquotedblleft}na{\textquotedblright}. The goal of this project is to have a complete dataset of all the aspectual pairs in these three languages. We believe it will be useful for research in the field of aspectology, as well as machine translation and other NLP tasks. Using this resource as an example, we also propose a sustainable approach to publishing small to moderate LLOD resources on the Web, both in a user-friendly way and according to the Linked Data principles.
Towards Semantic Interoperability: Parallel Corpora as Linked Data Incorporating Named Entity Linking (2024)

The paper presents the results of the research related to the preparation of parallel corpora, focusing on transformation into RDF graphs using NLP Interchange Format (NIF) for linguistic annotation. We give an overview of the parallel corpus that was used in this case study, as well as the process of POS tagging, lemmatization, named entity recognition (NER), and named entity linking (NEL), which is implemented using Wikidata. In the first phase of NEL main characters and places mentioned in novels are stored in Wikidata and in the second phase they are linked with the occurrences of previously annotated entities in text. Next, we describe the named entity linking (NEL), data conversion to RDF, and incorporation of NIF annotations. Produced NIF files were evaluated through the exploration of triplestore using SPARQL queries. Finally, the bridging of Linked Data and Digital Humanities research is discussed, as well as some drawbacks related to the verbosity of transformation. Semantic interoperability concept in the context of linked data and parallel corpora ensures that data exchanged between systems carries shared and well-defined meanings, enabling effective communication and understanding.
\textbfInvited paper: Computer-Assisted Language Comparison with EDICTOR 3 (2024)
Exploring Diachronic and Diatopic Changes in Dialect Continua: Tasks, Datasets and Challenges (2024)
Presence or Absence: Are Unknown Word Usages in Dictionaries? (2024)
Towards a GoldenHymns Dataset for Studying Diachronic Trends in 19th Century Danish Religious Hymns (2024)
AXOLOTL`24 Shared Task on Multilingual Explainable Semantic Change Modeling (2024)
Towards a Complete Solution to Lexical Semantic Change: an Extension to Multiple Time Periods and Diachronic Word Sense Induction (2024)
TartuNLP @ AXOLOTL-24: Leveraging Classifier Output for New Sense Detection in Lexical Semantics (2024)
Complexity and Indecision: A Proof-of-Concept Exploration of Lexical Complexity and Lexical Semantic Change (2024)
Can political dogwhistles be predicted by distributional methods for analysis of lexical semantic change? (2024)
Towards an Onomasiological Study of Lexical Semantic Change Through the Induction of Concepts (2024)
Deep-change at AXOLOTL-24: Orchestrating WSD and WSI Models for Semantic Change Modeling (2024)
A Few-shot Learning Approach for Lexical Semantic Change Detection Using GPT-4 (2024)
TreeForm: End-to-end Annotation and Evaluation for Form Document Parsing (2024)

Visually Rich Form Understanding (VRFU) poses a complex research problemdue to the documents' highly structured nature and yet highly variable style and content. Current annotation schemes decompose form understanding and omit key hierarchical structure, making development and evaluation of end-to-end models difficult. In this paper, we propose a novel F1 metric to evaluate form parsers and describe a new content-agnostic, tree-based annotation scheme for VRFU: TreeForm. We provide methods to convert previous annotation schemes into TreeForm structures and evaluate TreeForm predictions using a modified version of the normalized tree-edit distance. We present initial baselines for our end-to-end performance metric and the TreeForm edit distance, averaged over the FUNSD and XFUND datasets, of 61.5 and 26.4 respectively. We hope that TreeForm encourages deeper research in annotating, modeling, and evaluating the complexities of form-like documents.
Annotation Scheme for English Argument Structure Constructions Treebank (2024)

We introduce a detailed annotation scheme for argument structure constructions (ASCs) along with a manually annotated ASC treebank. This treebank encompasses 10,204 sentences from both first (5,936) and second language English datasets (1,948 for written; 2,320 for spoken). We detail the annotation process and evaluate inter-annotation agreement for overall and each ASC category.
A Mapping on Current Classifying Categories of Emotions Used in Multimodal Models for Emotion Recognition (2024)

In Emotion Detection within Natural Language Processing and related multimodal research, the growth of datasets and models has led to a challenge: disparities in emotion classification methods. The lack of commonly agreed upon conventions on the classification of emotions creates boundaries for model comparisons and dataset adaptation. In this paper, we compare the current classification methods in recent models and datasets and propose a valid method to combine different emotion categories. Our proposal arises from experiments across models, psychological theories, and human evaluations, and we examined the effect of proposed mapping on models.
Surveying the FAIRness of Annotation Tools: Difficult to find, difficult to reuse (2024)

In the realm of Machine Learning and Deep Learning, there is a need for high-quality annotated data to train and evaluate supervised models. An extensive number of annotation tools have been developed to facilitate the data labelling process. However, finding the right tool is a demanding task involving thorough searching and testing. Hence, to effectively navigate the multitude of tools, it becomes essential to ensure their findability, accessibility, interoperability, and reusability (FAIR). This survey addresses the FAIRness of existing annotation software by evaluating 50 different tools against the FAIR principles for research software (FAIR4RS). The study indicates that while being accessible and interoperable, annotation tools are difficult to find and reuse. In addition, there is a need to establish community standards for annotation software development, documentation, and distribution.
Automatic Annotation Elaboration as Feedback to Sign Language Learners (2024)

Beyond enabling linguistic analyses, linguistic annotations may serve as training material for developing automatic language assessment models as well as for providing textual feedback to language learners. Yet these linguistic annotations in their original form are often not easily comprehensible for learners. In this paper, we explore the utilization of GPT-4, as an example of a large language model (LLM), to process linguistic annotations into clear and understandable feedback on their productions for language learners, specifically sign language learners.
Towards Better Inclusivity: A Diverse Tweet Corpus of English Varieties (2024)

The prevalence of social media presents a growing opportunity to collect and analyse examples of English varieties. Whilst usage of these varieties is often used only in spoken contexts or hard-to-access private messages, social media sites like Twitter provide a platform for users to communicate informally in a scrapeable format. Notably, Indian English (Hinglish), Singaporean English (Singlish), and African-American English (AAE) can be commonly found online. These varieties pose a challenge to existing natural language processing (NLP) tools as they often differ orthographically and syntactically from standard English for which the majority of these tools are built. NLP models trained on standard English texts produced biased outcomes for users of underrepresented varieties (Blodgett and O`Connor, 2017). Some research has aimed to overcome the inherent biases caused by unrepresentative data through techniques like data augmentation or adjusting training models. We aim to address the issue of bias at its root - the data itself. We curate a dataset of tweets from countries with high proportions of underserved English variety speakers, and propose an annotation framework of six categorical classifications along a pseudo-spectrum that measures the degree of standard English and that thereby indirectly aims to surface the manifestations of English varieties in these tweets.
Building a corpus for the anonymization of Romanian jurisprudence (2024)

Access to jurisprudence is of paramount importance for both law professionals (judges, lawyers, law students) and for the larger public. In Romania, the Superior Council of Magistracy holds a large database of jurisprudence from different courts in the country, which is updated daily. However, granting public access requires its anonymization. This paper presents the efforts behind building a corpus for the anonymization process. We present the annotation scheme, the manual annotation methods, and the platform used.
Class Balancing for Efficient Active Learning in Imbalanced Datasets (2024)

Recent developments in active learning algorithms for NLP tasks show promising results in terms of reducing labelling complexity. In this paper we extend this effort to imbalanced datasets; we bridge between the active learning approach of obtaining diverse andinformative examples, and the heuristic of class balancing used in imbalanced datasets. We develop a novel tune-free weighting technique that canbe applied to various existing active learning algorithms, adding a component of class balancing. We compare several active learning algorithms to their modified version on multiple public datasetsand show that when the classes are imbalanced, with manual annotation effort remaining equal the modified version significantly outperforms the original both in terms of the test metric and the number of obtained minority examples. Moreover, when the imbalance is mild or non-existent (classes are completely balanced), our technique does not harm the base algorithms.
When is a Metaphor Actually Novel? Annotating Metaphor Novelty in the Context of Automatic Metaphor Detection (2024)

We present an in-depth analysis of metaphor novelty, a relatively overlooked phenomenon in NLP. Novel metaphors have been analyzed via scores derived from crowdsourcing in NLP, while in theoretical work they are often defined by comparison to senses in dictionary entries. We reannotate metaphorically used words in the large VU Amsterdam Metaphor Corpus based on whether their metaphoric meaning is present in the dictionary. Based on this, we find that perceived metaphor novelty often clash with the dictionary based definition. We use the new labels to evaluate the performance of state-of-the-art language models for automatic metaphor detection and notice that novel metaphors according to our dictionary-based definition are easier to identify than novel metaphors according to crowd-sourced novelty scores. In a subsequent analysis, we study the correlation between high novelty scores and word frequencies in the pretraining and finetuning corpora, as well as potential problems with rare words for pre-trained language models. In line with previous works, we find a negative correlation between word frequency in the training data and novelty scores and we link these aspects to problems with the tokenization of BERT and RoBERTa.
Enhancing Text Classification through LLM-Driven Active Learning and Human Annotation (2024)

In the context of text classification, the financial burden of annotation exercises for creating training data is a critical issue. Active learning techniques, particularly those rooted in uncertainty sampling, offer a cost-effective solution by pinpointing the most instructive samples for manual annotation. Similarly, Large Language Models (LLMs) such as GPT-3.5 provide an alternative for automated annotation but come with concerns regarding their reliability. This study introduces a novel methodology that integrates human annotators and LLMs within an Active Learning framework. We conducted evaluations on three public datasets. IMDB for sentiment analysis, a Fake News dataset for authenticity discernment, and a Movie Genres dataset for multi-label classification.The proposed framework integrates human annotation with the output of LLMs, depending on the model uncertainty levels. This strategy achieves an optimal balance between cost efficiency and classification performance. The empirical results show a substantial decrease in the costs associated with data annotation while either maintaining or improving model accuracy.
Using ChatGPT for Annotation of Attitude within the Appraisal Theory: Lessons Learned (2024)

We investigate the potential of using ChatGPT to annotate complex linguistic phenomena, such as language of evaluation, attitude and emotion. For this, we automatically annotate 11 texts in English, which represent spoken popular science, and evaluate the annotations manually. Our results show that ChatGPT has good precision in itemisation, i.e. detecting linguistic items in the text that carry evaluative meaning. However, we also find that the recall is very low. Besides that, we state that the tool fails in labeling the detected items with the correct categories on a more fine-grained level of granularity. We analyse the errors to find systematic errors related to specific categories in the annotation scheme.
Are You Serious? Handling Disagreement When Annotating Conspiracy Theory Texts (2024)

We often assume that annotation tasks, such as annotating for the presence of conspiracy theories, can be annotated with hard labels, without definitions or guidelines. Our annotation experiments, comparing students and experts, show that there is little agreement on basic annotations even among experts. For this reason, we conclude that we need to accept disagreement as an integral part of such annotations.