pwc-archive/papers-with-abstracts huggingface dataset:
Matrix and Double-Array Representations for Efficient Finite State Tokenization
Authors: Nils Diewald
Date: 2022-06-01 00:00:00
Proceeding: CMLC (LREC) 2022 6

This paper presents an algorithm and implementation for efficient tokenization of space-delimited languages based on a deterministic finite state automaton. Two representations of the underlying data structure are presented and a model implementation for German is compared with state-of-the-art approaches. The presented solution is faster than other tools while maintaining comparable quality.
Count-Based and Predictive Language Models for Exploring DeReKo
Authors: Peter Fankhauser, Marc Kupietz
Date: 2022-06-01 00:00:00
Proceeding: CMLC (LREC) 2022 6
Tasks: Word Embeddings

We present the use of count-based and predictive language models for exploring language use in the German Reference Corpus DeReKo. For collocation analysis along the syntagmatic axis we employ traditional association measures based on co-occurrence counts as well as predictive association measures derived from the output weights of skipgram word embeddings. For inspecting the semantic neighbourhood of words along the paradigmatic axis we visualize the high dimensional word embeddings in two dimensions using t-stochastic neighbourhood embeddings. Together, these visualizations provide a complementary, explorative approach to analysing very large corpora in addition to corpus querying. Moreover, we discuss count-based and predictive models w.r.t. scalability and maintainability in very large corpora.
“The word expired when that world awoke.” New Challenges for Research with Large Text Corpora and Corpus-Based Discourse Studies in Totalitarian Times
Authors: Hanno Biber
Date: 2022-06-01 00:00:00
Proceeding: CMLC (LREC) 2022 6

In the following poster proposal a report will be given on the prospects of a promising corpus project initiated by one of the large digital text corpora hosted by the Austrian Academy of Sciences. First, the resources of the AAC-Austrian Academy Corpus, that has been founded in 2001, which is one of the very valuable examples of digital diachronic text corpora suitable for corpus-based discourse studies and lexicography based upon historical sources, can be used as a basis for trying to answer new questions concerning the challenges for doing linguistic research with large digital text corpora in the context of studying totalitarian language use. The questions, as well as the chances and limits of such an approach, have very obvious actual references to the historic events unfolding today as well as a clearly historical dimension, precisely because the digital text sources that have been created to analyse the German language use of the Nazi-period from 1933 to 1945 can be understood as a model to deal with related questions of contemporary language use, particularly in the context of the new war of extermination of Russia in Ukraine of the year 2022 and how it is represented in contemporary media.
The Annohub Web Portal
Authors: Frank Abromeit
Date: 2022-06-01 00:00:00
Proceeding: LDL (ACL) 2022 6

We introduce the Annohub web portal, specialized on metadata for annotated language resources like corpora, lexica and linguistic terminologies. The new portal provides easy access to our previously released Annohub Linked Data set, by allowing users to explore the annotation metadata in the web browser. In addition, we added features that will allow users to contribute to Annohub by means of uploading language data, in RDF, CoNNL or XML formats, for annotation scheme and language analysis. The generated metadata is finally available for personal use, or for release in Annohub.
From ELTeC Text Collection Metadata and Named Entities to Linked-data (and Back)
Authors: Milica Ikonić Nešić, Ranka Stanković, Christof Schöch, Mihailo Skoric
Date: 2022-06-01 00:00:00
Proceeding: LDL (ACL) 2022 6
Tasks: Entity Linking, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Retrieval

In this paper we present the wikification of the ELTeC (European Literary Text Collection), developed within the COST Action “Distant Reading for European Literary History” (CA16204). ELTeC is a multilingual corpus of novels written in the time period 1840—1920, built to apply distant reading methods and tools to explore the European literary history. We present the pipeline that led to the production of the linked dataset, the novels’ metadata retrieval and named entity recognition, transformation, mapping and Wikidata population, followed by named entity linking and export to NIF (NLP Interchange Format). The speeding up of the process of data preparation and import to Wikidata is presented on the use case of seven sub-collections of ELTeC (English, Portuguese, French, Slovenian, German, Hungarian and Serbian). Our goal was to automate the process of preparing and importing information, so OpenRefine and QuickStatements were chosen as the best options. The paper also includes examples of SPARQL queries for retrieval of authors, novel titles, publication places and other metadata with different visualisation options as well as statistical overviews.
IMTVault: Extracting and Enriching Low-resource Language Interlinear Glossed Text from Grammatical Descriptions and Typological Survey Articles
Authors: Sebastian Nordhoff, Thomas Krämer
Date: 2022-06-01 00:00:00
Proceeding: LDL (ACL) 2022 6
Tasks: Articles, Translation

Many NLP resources and programs focus on a handful of major languages. But there are thousands of languages with low or no resources available as structured data. This paper shows the extraction of 40k examples with interlinear morpheme translation in 280 different languages from LaTeX-based publications of the open access publisher Language Science Press. These examples are transformed into Linked Data. We use LIGT for modelling and enrich the data with Wikidata and Glottolog. The data is made available as HTML, JSON, JSON-LD and N-quads, and query facilities for humans (Elasticsearch) and machines (API) are provided.
Linking the LASLA Corpus in the LiLa Knowledge Base of Interoperable Linguistic Resources for Latin
Authors: Margherita Fantoli, Marco Passarotti, Francesco Mambrini, Giovanni Moretti, Paolo Ruffolo
Date: 2022-06-01 00:00:00
Proceeding: LDL (ACL) 2022 6

This paper describes the process of interlinking the 130 Classical Latin texts provided by an annotated corpus developed at the LASLA laboratory with the LiLa Knowledge Base, which makes linguistic resources for Latin interoperable by following the principles of the Linked Data paradigm and making reference to classes and properties of widely adopted ontologies to model the relevant information. After introducing the overall architecture of the LiLa Knowledge Base and the LASLA corpus, the paper details the phases of the process of linking the corpus with the collection of lemmas of LiLa and presents a federated query to exemplify the added value of interoperability of LASLA’s texts with other resources for Latin.
Use Case: Romanian Language Resources in the LOD Paradigm
Authors: Verginica Barbu Mititelu, Elena Irimia, Vasile Pais, Andrei-Marius Avram, Maria Mitrofan
Date: 2022-06-01 00:00:00
Proceeding: LDL (ACL) 2022 6
Tasks: Word Embeddings

In this paper, we report on (i) the conversion of Romanian language resources to the Linked Open Data specifications and requirements, on (ii) their publication and (iii) interlinking with other language resources (for Romanian or for other languages). The pool of converted resources is made up of the Romanian Wordnet, the morphosyntactic and phonemic lexicon RoLEX, four treebanks, one for the general language (the Romanian Reference Treebank) and others for specialised domains (SiMoNERo for medicine, LegalNERo for the legal domain, PARSEME-Ro for verbal multiword expressions), frequency information on lemmas and tokens and word embeddings as extracted from the reference corpus for contemporary Romanian (CoRoLa) and a bi-modal (text and speech) corpus. We also present the limitations coming from the representation of the resources in Linked Data format. The metadata of LOD resources have been published in the LOD Cloud. The resources are available for download on our website and a SPARQL endpoint is also available for querying them.
Fuzzy Lemon: Making Lexical Semantic Relations More Juicy
Authors: Fernando Bobillo, Julia Bosque-Gil, Jorge Gracia, Marta Lanau-Coronas
Date: 2022-06-01 00:00:00
Proceeding: LDL (ACL) 2022 6

The OntoLex-Lemon model provides a vocabulary to enrich ontologies with linguistic information that can be exploited by Natural Language Processing applications. The increasing uptake of Lemon illustrates the growing interest in combining linguistic information and Semantic Web technologies. In this paper, we present Fuzzy Lemon, an extension of Lemon that allows to assign an uncertainty degree to lexical semantic relations. Our approach is based on an OWL ontology that defines a hierarchy of data properties encoding different types of uncertainty. We also illustrate the usefulness of Fuzzy Lemon by showing that it can be used to represent the confidence degrees of automatically discovered translations between pairs of bilingual dictionaries from the Apertium family.
A Cheap and Dirty Cross-Lingual Linking Service in the Cloud
Authors: Christian Chiarcos, Gilles Sérasset
Date: 2022-06-01 00:00:00
Proceeding: LDL (ACL) 2022 6
Tasks: Knowledge Graphs

In this paper, we describe the application of Linguistic Linked Open Data (LLOD) technology for dynamic cross-lingual querying on demand. Whereas most related research is focusing on providing a static linking, i.e., cross-lingual inference, and then storing the resulting links, we demonstrate the application of the federation capabilities of SPARQL to perform lexical linking on the fly. In the end, we provide a baseline functionality that uses the connection of two web services – a SPARQL end point for multilingual lexical data and another SPARQL end point for querying an English language knowledge graph – in order to perform querying an English language knowledge graph using foreign language labels. We argue that, for low-resource languages where substantial native knowledge graphs are lacking, this functionality can be used to lower the language barrier by allowing to formulate cross-linguistically applicable queries mediated by a multilingual dictionary.
Spicy Salmon: Converting between 50+ Annotation Formats with Fintan, Pepper, Salt and Powla
Authors: Christian Fäth, Christian Chiarcos
Date: 2022-06-01 00:00:00
Proceeding: LDL (ACL) 2022 6

Heterogeneity of formats, models and annotations has always been a primary hindrance for exploiting the ever increasing amount of existing linguistic resources for real world applications in and beyond NLP. Fintan - the Flexible INtegrated Transformation and Annotation eNgineering platform introduced in 2020 is designed to rapidly convert, combine and manipulate language resources both in and outside the Semantic Web by transforming it into segmented RDF representations which can be processed in parallel on a multithreaded environment and integrating it with ontologies and taxonomies. Fintan has recently been extended with a set of additional modules increasing the amount of supported non-RDF formats and the interoperability with existing non-JAVA conversion tools, and parts of this work are demonstrated in this paper. In particular, we focus on a novel recipe for resource transformation in which Fintan works in tandem with the Pepper toolset to allow computational linguists to transform their data between over 50 linguistic corpus formats with a graphical workflow manager.
A Survey of Guidelines and Best Practices for the Generation, Interlinking, Publication, and Validation of Linguistic Linked Data
Authors: Fahad Khan, Christian Chiarcos, Thierry Declerck, Maria Pia di Buono, Milan Dojchinovski, Jorge Gracia, Giedre Valunaite Oleskeviciene, Daniela Gifu
Date: 2022-06-01 00:00:00
Proceeding: LDL (ACL) 2022 6
Tasks: Survey

This article discusses a survey carried out within the NexusLinguarum COST Action which aimed to give an overview of existing guidelines (GLs) and best practices (BPs) in linguistic linked data. In particular it focused on four core tasks in the production/publication of linked data: generation, interlinking, publication, and validation. We discuss the importance of GLs and BPs for LLD before describing the survey and its results in full. Finally we offer a number of directions for future work in order to address the findings of the survey.
Computational Morphology with OntoLex-Morph
Authors: Christian Chiarcos, Katerina Gkirtzou, Fahad Khan, Penny Labropoulou, Marco Passarotti, Matteo Pellegrini
Date: 2022-06-01 00:00:00
Proceeding: LDL (ACL) 2022 6
Tasks: Descriptive, MORPH

This paper describes the current status of the emerging OntoLex module for linguistic morphology. It serves as an update to the previous version of the vocabulary (Klimek et al. 2019). Whereas this earlier model was exclusively focusing on descriptive morphology and focused on applications in lexicography, we now present a novel part and a novel application of the vocabulary to applications in language technology, i.e., the rule-based generation of lexicons, introducing a dynamic component into OntoLex.
Detecting Users Prone to Spread Fake News on Arabic Twitter
Authors: Zien Sheikh Ali, Abdulaziz Al-Ali, Tamer Elsayed
Date: 2022-06-01 00:00:00
Proceeding: OSACT (LREC) 2022 6
Tasks: Misinformation

The spread of misinformation has become a major concern to our society, and social media is one of its main culprits. Evidently, health misinformation related to vaccinations has slowed down global efforts to fight the COVID-19 pandemic. Studies have shown that fake news spreads substantially faster than real news on social media networks. One way to limit this fast dissemination is by assessing information sources in a semi-automatic way. To this end, we aim to identify users who are prone to spread fake news in Arabic Twitter. Such users play an important role in spreading misinformation and identifying them has the potential to control the spread. We construct an Arabic dataset on Twitter users, which consists of 1,546 users, of which 541 are prone to spread fake news (based on our definition). We use features extracted from users’ recent tweets, e.g., linguistic, statistical, and profile features, to predict whether they are prone to spread fake news or not. To tackle the classification task, multiple learning models are employed and evaluated. Empirical results reveal promising detection performance, where an F1 score of 0.73 was achieved by the logistic regression model. Moreover, when tested on a benchmark English dataset, our approach has outperformed the current state-of-the-art for this task.
AraSAS: The Open Source Arabic Semantic Tagger
Authors: Mahmoud El-Haj, Elvis de Souza, Nouran Khallaf, Paul Rayson, Nizar Habash
Date: 2022-06-01 00:00:00
Proceeding: OSACT (LREC) 2022 6
Tasks: Diversity, TAG

This paper presents (AraSAS) the first open-source Arabic semantic analysis tagging system. AraSAS is a software framework that provides full semantic tagging of text written in Arabic. AraSAS is based on the UCREL Semantic Analysis System (USAS) which was first developed to semantically tag English text. Similarly to USAS, AraSAS uses a hierarchical semantic tag set that contains 21 major discourse fields and 232 fine-grained semantic field tags. The paper describes the creation, validation and evaluation of AraSAS. In addition, we demonstrate a first case study to illustrate the affordances of applying USAS and AraSAS semantic taggers on the Zayed University Arabic-English Bilingual Undergraduate Corpus (ZAEBUC) (Palfreyman and Habash, 2022), where we show and compare the coverage of the two semantic taggers through running them on Arabic and English essays on different topics. The analysis expands to compare the taggers when run on texts in Arabic and English written by the same writer and texts written by male and by female students. Variables for comparison include frequency of use of particular semantic sub-domains, as well as the diversity of semantic elements within a text.
AraNPCC: The Arabic Newspaper COVID-19 Corpus
Authors: Abdulmohsen Al-Thubaity, Sakhar Alkhereyf, Alia O. Bahanshal
Date: 2022-06-01 00:00:00
Proceeding: OSACT (LREC) 2022 6

This paper introduces a corpus for Arabic newspapers during COVID-19: AraNPCC. The AraNPCC corpus covers 2019 until 2021 via automatically-collected data from 12 Arab countries. It comprises more than 2 billion words and 7.2 million texts alongside their metadata. AraNPCC can be used for several natural language processing tasks, such as updating available Arabic language models or corpus linguistics tasks, including language change over time. We utilized the corpus in two case studies. In the first case study, we investigate the correlation between the number of officially reported infected cases and the collective word frequency of “COVID” and “Corona.” The data shows a positive correlation that varies among Arab countries. For the second case study, we extract and compare the top 50 keywords in 2020 and 2021 to study the impact of the COVID-19 pandemic on two Arab countries, namely Algeria and Saudi Arabia. For 2020, the data shows that the two countries’ newspapers strongly interacted with the pandemic, emphasizing its spread and dangerousness, and in 2021 the data suggests that the two countries coped with the pandemic.
Pre-trained Models or Feature Engineering: The Case of Dialectal Arabic
Authors: Kathrein Abu Kwaik, Stergios Chatzikyriakidis, Simon Dobnik
Date: 2022-06-01 00:00:00
Proceeding: OSACT (LREC) 2022 6
Tasks: Dialect Identification, Feature Engineering, Sentiment Analysis, Word Embeddings

The usage of social media platforms has resulted in the proliferation of work on Arabic Natural Language Processing (ANLP), including the development of resources. There is also an increased interest in processing Arabic dialects and a number of models and algorithms have been utilised for the purpose of Dialectal Arabic Natural Language Processing (DANLP). In this paper, we conduct a comparison study between some of the most well-known and most commonly used methods in NLP in order to test their performance on different corpora and two NLP tasks: Dialect Identification and Sentiment Analysis. In particular, we compare three general classes of models: a) traditional Machine Learning models with features, b) classic Deep Learning architectures (LSTMs) with pre-trained word embeddings and lastly c) different Bidirectional Encoder Representations from Transformers (BERT) models such as (Multilingual-BERT, Ara-BERT, and Twitter-Arabic-BERT). The results of the comparison show that using feature-based classification can still compete with BERT models in these dialectal Arabic contexts. The use of transformer models have the ability to outperform traditional Machine Learning approaches, depending on the type of text they have been trained on, in contrast to classic Deep Learning models like LSTMs which do not perform well on the tasks
A Context-free Arabic Emoji Sentiment Lexicon (CF-Arab-ESL)
Authors: Shatha Ali A. Hakami, Robert Hendley, Phillip Smith
Date: 2022-06-01 00:00:00
Proceeding: OSACT (LREC) 2022 6
Tasks: Sentiment Analysis

Emoji can be valuable features in textual sentiment analysis. One of the key elements of the use of emoji in sentiment analysis is the emoji sentiment lexicon. However, constructing such a lexicon is a challenging task. This is because interpreting the sentiment conveyed by these pictographic symbols is highly subjective, and differs depending upon how each person perceives them. Cultural background is considered to be one of the main factors that affects emoji sentiment interpretation. Thus, we focus in this work on targeting people from Arab cultures. This is done by constructing a context-free Arabic emoji sentiment lexicon annotated by native Arabic speakers from seven different regions (Gulf, Egypt, Levant, Sudan, North Africa, Iraq, and Yemen) to see how these Arabic users label the sentiment of these symbols without a textual context. We recruited 53 annotators (males and females) to annotate 1,069 unique emoji. Then we evaluated the reliability of the annotation for each participant by applying sensitivity (Recall) and consistency (Krippendorff’s Alpha) tests. For the analysis, we investigated the resulting emoji sentiment annotations to explore the impact of the Arabic cultural context. We analyzed this cultural reflection from different perspectives, including national affiliation, use of colour indications, animal indications, weather indications and religious impact.
Sa‘7r: A Saudi Dialect Irony Dataset
Authors: Halah AlMazrua, Najla AlHazzani, Amaal AlDawod, Lama AlAwlaqi, Noura AlReshoudi, Hend Al-Khalifa, Luluh AlDhubayi
Date: 2022-06-01 00:00:00
Proceeding: OSACT (LREC) 2022 6
Tasks: Deep Learning, Sentiment Analysis

In sentiment analysis, detecting irony is considered a major challenge. The key problem with detecting irony is the difficulty to recognize the implicit and indirect phrases which signifies the opposite meaning. In this paper, we present Sa‘7r ساخرthe Saudi irony dataset, and describe our efforts in constructing it. The dataset was collected using Twitter API and it consists of 19,810 tweets, 8,089 of them are labeled as ironic tweets. We trained several models for irony detection task using machine learning models and deep learning models. The machine learning models include: K-Nearest Neighbor (KNN), Logistic Regression (LR), Support Vector Machine (SVM), and Naïve Bayes (NB). While the deep learning models include BiLSTM and AraBERT. The detection results show that among the tested machine learning models, the SVM outperformed other classifiers with an accuracy of 0.68. On the other hand, the deep learning models achieved an accuracy of 0.66 in the BiLSTM model and 0.71 in the AraBERT model. Thus, the AraBERT model achieved the most accurate result in detecting irony phrases in Saudi Dialect.
Classifying Arabic Crisis Tweets using Data Selection and Pre-trained Language Models
Authors: Alaa Alharbi, Mark Lee
Date: 2022-06-01 00:00:00
Proceeding: OSACT (LREC) 2022 6
Tasks: Humanitarian

User-generated Social Media (SM) content has been explored as a valuable and accessible source of data about crises to enhance situational awareness and support humanitarian response efforts. However, the timely extraction of crisis-related SM messages is challenging as it involves processing large quantities of noisy data in real-time. Supervised machine learning methods have been successfully applied to this task but such approaches require human-labelled data, which are unlikely to be available from novel and emerging crises. Supervised machine learning algorithms trained on labelled data from past events did not usually perform well when classifying a new disaster due to data variations across events. Using the BERT embeddings, we propose and investigate an instance distance-based data selection approach for adaptation to improve classifiers’ performance under a domain shift. The K-nearest neighbours algorithm selects a subset of multi-event training data that is most similar to the target event. Results show that fine-tuning a BERT model on a selected subset of data to classify crisis tweets outperforms a model that has been fine-tuned on all available source data. We demonstrated that our approach generally works better than the self-training adaptation method. Combing the self-training with our proposed classifier does not enhance the performance.