Les parseurs de discours ont suscité un intérêt considérable dans les récentes applications de traitement automatique du langage naturel. Cette approche dépasse les limites traditionnelles de la phrase et peut s’étendre pour englober l’identification de relation de discours. Il existe plusieurs parseurs spécialisés dans le traitement autmatique du discours, mais ces derniers ont été principalement évalués sur des corpus anglais. Par conséquent, il n’est pas évident de bien cerner les éléments linguistiques importants sur lesquels les parseurs se basent pour classifier les relations de discours en dehors de l’anglais. Cet article évalue les performances du parseur DMRST sur le corpus RST-DT traduit en français. Nous constatons que les performances de classification des relations de discours en français sont comparables à celles obtenues pour d’autres langues. En analysant les succès et échecs de la classification des relations, nous soulignons l’impact des marqueurs de discours et des structures syntaxiques sur la précision du parseur.

This study introduces an approach for evaluating the importance of signals proposed by Das and Taboada in discourse parsing. Previous studies using other signals indicate that discourse markers (DMs) are not consistently reliable cues and can act as distractors, complicating relations recognition. The study explores the effectiveness of alternative signal types, such as syntactic and genre-related signals, revealing their efficacy even when not predominant for specific relations. An experiment incorporating RST signals as features for a parser error / success prediction model demonstrates their relevance and provides insights into signal combinations that prevents (or facilitates) accurate relation recognition. The observations also identify challenges and potential confusion posed by specific signals. This study resulted in producing publicly available code and data, contributing to an accessible resources for research on RST signals in discourse parsing.

The Event Causality Identification Shared Task of CASE 2023 is the second iteration of a shared task centered around the Causal News Corpus. Two subtasks were involved: In Subtask 1, participants were challenged to predict if a sentence contains a causal relation or not. In Subtask 2, participants were challenged to identify the Cause, Effect, and Signal spans given an input causal sentence. For both subtasks, participants uploaded their predictions for a held-out test set, and ranking was done based on binary F1 and macro F1 scores for Subtask 1 and 2, respectively. This paper includes an overview of the work of the ten teams that submitted their results to our competition and the six system description papers that were received. The highest F1 scores achieved for Subtask 1 and 2 were 84.66% and 72.79%, respectively.

Despite the importance of understanding causality, corpora addressing causal relations are limited. There is a discrepancy between existing annotation guidelines of event causality and conventional causality corpora that focus more on linguistics. Many guidelines restrict themselves to include only explicit relations or clause-based arguments. Therefore, we propose an annotation schema for event causality that addresses these concerns. We annotated 3,559 event sentences from protest event news with labels on whether it contains causal relations or not. Our corpus is known as the Causal News Corpus (CNC). A neural network built upon a state-of-the-art pre-trained language model performed well with 81.20% F1 score on test set, and 83.46% in 5-folds cross-validation. CNC is transferable across two external corpora: CausalTimeBank (CTB) and Penn Discourse Treebank (PDTB). Leveraging each of these external datasets for training, we achieved up to approximately 64% F1 on the CNC test set without additional fine-tuning. CNC also served as an effective training and pre-training dataset for the two external corpora. Lastly, we demonstrate the difficulty of our task to the layman in a crowd-sourced annotation exercise. Our annotated corpus is publicly available, providing a valuable resource for causal text mining researchers.

The Event Causality Identification Shared Task of CASE 2022 involved two subtasks working on the Causal News Corpus. Subtask 1 required participants to predict if a sentence contains a causal relation or not. This is a supervised binary classification task. Subtask 2 required participants to identify the Cause, Effect and Signal spans per causal sentence. This could be seen as a supervised sequence labeling task. For both subtasks, participants uploaded their predictions for a held-out test set, and ranking was done based on binary F1 and macro F1 scores for Subtask 1 and 2, respectively. This paper summarizes the work of the 17 teams that submitted their results to our competition and 12 system description papers that were received. The best F1 scores achieved for Subtask 1 and 2 were 86.19% and 54.15%, respectively. All the top-performing approaches involved pre-trained language models fine-tuned to the targeted task. We further discuss these approaches and analyze errors across participants’ systems in this paper.

In the scope of WNUT-2020 Task 2, we developed various text classification systems, using deep learning models and one using linguistically informed rules. While both of the deep learning systems outperformed the system using the linguistically informed rules, we found that through the integration of (the output of) the three systems a better performance could be achieved than the standalone performance of each approach in a cross-validation setting. However, on the test data the performance of the integration was slightly lower than our best performing deep learning model. These results hardly indicate any progress in line of integrating machine learning and expert rules driven systems. We expect that the release of the annotation manuals and gold labels of the test data after this workshop will shed light on these perplexing results.

This paper introduces a new CLARIN Knowledge Center which is the K-Centre for Atypical Communication Expertise (ACE for short) which has been established at the Centre for Language and Speech Technology (CLST) at Radboud University. Atypical communication is an umbrella term used here to denote language use by second language learners, people with language disorders or those suffering from language disabilities, but also more broadly by bilinguals and users of sign languages. It involves multiple modalities (text, speech, sign, gesture) and encompasses different developmental stages. ACE closely collaborates with The Language Archive (TLA) at the Max Planck Institute for Psycholinguistics in order to safeguard GDPR-compliant data storage and access. We explain the mission of ACE and show its potential on a number of showcases and a use case.

We report on a case study of text and images that reveals the inadequacy of simplistic assumptions about their connection and interplay. The context of our work is a larger effort to create automatic systems that can extract event information from online news articles about flooding disasters. We carry out a manual analysis of 1000 articles containing a keyword related to flooding. The analysis reveals that the articles in our data set cluster into seven categories related to different topical aspects of flooding, and that the images accompanying the articles cluster into five categories related to the content they depict. The results demonstrate that flood-related news articles do not consistently report on a single, currently unfolding flooding event and we should also not assume that a flood-related image will directly relate to a flooding-event described in the corresponding article. In particular, spatiotemporal distance is important. We validate the manual analysis with an automatic classifier demonstrating the technical feasibility of multimedia analysis approaches that admit more realistic relationships between text and images. In sum, our case study confirms that closer attention to the connection between text and images has the potential to improve the collection of multimodal information from news articles.

This paper presents our submissions to SemEval-2019 Task9, Suggestion Mining. Our system is one in a series of systems in which we compare an approach using expert-defined rules with a comparable one using machine learning. We target tasks with a syntactic or semantic component that might be better described by a human understanding the task than by a machine learner only able to count features. For Semeval-2019 Task 9, the expert rules clearly outperformed our machine learning model when training and testing on equally balanced testsets.

We present the results and the findings of the Second VarDial Evaluation Campaign on Natural Language Processing (NLP) for Similar Languages, Varieties and Dialects. The campaign was organized as part of the fifth edition of the VarDial workshop, collocated with COLING’2018. This year, the campaign included five shared tasks, including two task re-runs – Arabic Dialect Identification (ADI) and German Dialect Identification (GDI) –, and three new tasks – Morphosyntactic Tagging of Tweets (MTT), Discriminating between Dutch and Flemish in Subtitles (DFS), and Indo-Aryan Language Identification (ILI). A total of 24 teams submitted runs across the five shared tasks, and contributed 22 system description papers, which were included in the VarDial workshop proceedings and are referred to in this report.

With the goal of discovering differences between Belgian and Netherlandic Dutch, we participated as Team Taurus in the Dutch-Flemish Subtitles task of VarDial2018. We used a rather simple marker-based method, but a wide range of features, including lexical, lexico-syntactic and syntactic ones, and achieved a second position in the ranking. Inspection of highly distin-guishing features did point towards differences between the two language varieties, but because of the nature of the experimental data, we have to treat our observations as very tentative and in need of further investigation.

In sources used in oral history research (such as interviews with eye witnesses), passages where the degree of personal emotional involvement is found to be high can be of particular interest, as these may give insight into how historical events were experienced, and what moral dilemmas and psychological or religious struggles were encountered. In a pilot study involving a large corpus of interview recordings with Dutch war veterans, we have investigated if it is possible to develop a method for automatically identifying those passages where the degree of personal emotional involvement is high. The method is based on the automatic detection of exceptionally large silences and filled pause segments (using Automatic Speech Recognition), and cues taken from specific n-grams. The first results appear to be encouraging enough for further elaboration of the method.

In the context of ongoing developments as regards the creation of a sustainable, interoperable language resource infrastructure and spreading ideas of the need for open access, not only of research publications but also of the underlying data, various issues present themselves which require that different stakeholders reconsider their positions. In the present paper we relate the experiences from the CLARIN-NL data curation service (DCS) over the two years that it has been operational, and the future role we envisage for expertise centres like the DCS in the evolving infrastructure.

In this paper we present the first freely available corpus of Dutch text messages containing data originating from the Netherlands and Flanders. This corpus has been collected in the framework of the SoNaR project and constitutes a viable part of this 500-million-word corpus. About 53,000 text messages were collected on a large scale, based on voluntary donations. These messages will be distributed as such. In this paper we focus on the data collection processes involved and after studying the effect of media coverage we show that especially free publicity in newspapers and on social media networks results in more contributions. All SMS are provided with metadata information. Looking at the composition of the corpus, it becomes visible that a small number of people have contributed a large amount of data, in total 272 people have contributed to the corpus during three months. The number of women contributing to the corpus is larger than the number of men, but male contributors submitted larger amounts of data. This corpus will be of paramount importance for sociolinguistic research and normalisation studies.

In this paper we report on the experiences gained in the recent construction of the SoNaR corpus, a 500 MW reference corpus of contemporary, written Dutch. It shows what can realistically be done within the confines of a project setting where there are limitations to the duration in time as well to the budget, employing current state-of-the-art tools, standards and best practices. By doing so we aim to pass on insights that may be beneficial for anyone considering to undertake an effort towards building a large, varied yet balanced corpus for use by the wider research community. Various issues are discussed that come into play while compiling a large corpus, including approaches to acquiring texts, the arrangement of IPR, the choice of text formats, and steps to be taken in the preprocessing of data from widely different origins. We describe FoLiA, a new XML format geared at rich linguistic annotations. We also explain the rationale behind the investment in the high-quali ty semi-automatic enrichment of a relatively small (1 MW) subset with very rich syntactic and semantic annotations. Finally, we present some ideas about future developments and the direction corpus development may take, such as setting up an integrated work flow between web services and the potential role for ISOcat. We list tips for potential corpus builders, tricks they may want to try and further recommendations regarding technical developments future corpus builders may wish to hope for.

For mining intellectual property texts (patents), a broad-coverage lexicon that covers general English words together with terminology from the patent domain is indispensable. The patent domain is very diffuse as it comprises a variety of technical domains (e.g. Human Necessities, Chemistry & Metallurgy and Physics in the International Patent Classification). As a result, collecting a lexicon that covers the language used in patent texts is not a straightforward task. In this paper we describe the approach that we have developed for the semi-automatic construction of a broad-coverage lexicon for classification and information retrieval in the patent domain and which combines information from multiple sources. Our contribution is twofold. First, we provide insight into the difficulties of developing lexical resources for information retrieval and text mining in the patent domain, a research and development field that is expanding quickly. Second, we create a broad coverage lexicon annotated with rich lexical information and containing both general English word forms and domain terminology for various technical domains.

In The Low Countries, a major reference corpus for written Dutch is being built. We discuss the interplay between data acquisition and data processing during the creation of the SoNaR Corpus. Based on developments in traditional corpus compiling and new web harvesting approaches, SoNaR is designed to contain 500 million words, balanced over 36 text types including both traditional and new media texts. Beside its balanced design, every text sample included in SoNaR will have its IPR issues settled to the largest extent possible. This data collection task presents many challenges because every decision taken on the level of text acquisition has ramifications for the level of processing and the general usability of the corpus. As far as the traditional text types are concerned, each text brings its own processing requirements and issues. For new media texts - SMS, chat - the problem is even more complex, issues such as anonimity, recognizability and citation right, all present problems that have to be tackled. The solutions actually lead to the creation of two corpora: a gigaword SoNaR, IPR-cleared for research purposes, and the smaller - of commissioned size - more privacy compliant SoNaR, IPR-cleared for commercial purposes as well.

The computational linguistics community in The Netherlands and Belgium has long recognized the dire need for a major reference corpus of written Dutch. In part to answer this need, the STEVIN programme was established. To pave the way for the effective building of a 500-million-word reference corpus of written Dutch, a pilot project was established. The Dutch Corpus Initiative project or D-Coi was highly successful in that it not only realized about 10% of the projected large reference corpus, but also established the best practices and developed all the protocols and the necessary tools for building the larger corpus within the confines of a necessarily limited budget. We outline the steps involved in an endeavour of this kind, including the major highlights and possible pitfalls. Once converted to a suitable XML format, further linguistic annotation based on the state-of-the-art tools developed either before or during the pilot by the consortium partners proved easily and fruitfully applicable. Linguistic enrichment of the corpus includes PoS tagging, syntactic parsing and semantic annotation, involving both semantic role labeling and spatiotemporal annotation. D-Coi is expected to be followed by SoNaR, during which the 500-million-word reference corpus of Dutch should be built.

For research and development of an approach for automatically answering why-questions (why-QA) a data collection was created. The data set was obtained by way of elicitation and comprises a total of 395 why-questions. For each question, the data set includes the source document and one or two user-formulated answers. In addition, for a subset of the questions, user-formulated paraphrases are available. All question-answer pairs have been annotated with information on topic and semantic answer type. The resulting data set is of importance not only for our research, but we expect it to contribute to and stimulate other research in the field of why-QA.

The Dutch Language Corpus Initiative (D-Coi) project aims to specify the design of a 500-million-word reference corpus of written Dutch, and to put the tools and procedures in place that are needed to actually construct such a corpus. One of the tasks in the project is to conduct a user requirements study that should provide the basis for the eventual design of the 500-million-word reference corpus. The present paper outlines the user requirements analysis and reports the results so far.

After the successful completion of the Spoken Dutch Corpus (1998 -- 2003) the time is ripe to take some time to sit back and reflect on our achievements and the procedures underlying them in order to learn from our experiences. In this paper we will in particular pay attention to issues affecting the levels of linguistic annotation, but some more general issues deserve to be treated as well (bug reporting, consistency). We will try to come up with solutions, but sometimes we want to invite further discussion from other researchers.

In this paper, we investigate whether a dataset derived from a multi-purpose corpus such as the Spoken Dutch Corpus may be considered appropriate for developing a taxonomy of wh-questions, and a model of the way in which these questions are integrated in spoken discourse. We compare the results obtained from the Spoken Dutch Corpus with a similar analysis of a large random collection of FAQs from the internet. We find substantial differences between the questions in spoken discourse and FAQs. Therefore, it may not be trivial to use a general purpose corpus as a starting point for developing models for human-computer interaction.