While persuasion has been extensively examined in the context of politicians’ speeches, there exists a notable gap in the understanding of the pathos role in user-generated argumentation. This paper presents an exploratory study into the pathos dimension of user-generated arguments and formulates ideas on how pathos could be incorporated in argument mining. Using existing sentiment and emotion detection tools, this research aims to obtain insights into the role of emotion in argumentative public discussion on controversial topics, explores the connection between sentiment and stance, and detects frequent emotion-related words for a given topic.

This paper presents a detailed description and results of the first shared task on explainability for cross-lingual emotion in tweets. Given a tweet in one of the five target languages (Dutch, Russian, Spanish, English, and French), systems should predict the correct emotion label (Task 1), as well as the words triggering the predicted emotion label (Task 2). The tweets were collected based on a list of stop words to prevent topical or emotional bias and were subsequently manually annotated. For both tasks, only a training corpus for English was provided, obliging participating systems to design cross-lingual approaches. Our shared task received submissions from 14 teams for the emotion detection task and from 6 teams for the trigger word detection task. The highest macro F1-scores obtained for both tasks are respectively 0.629 and 0.616, demonstrating that cross-lingual emotion detection is still a challenging task.

This paper provides an overview of the Shared Task for Cross-lingual Classification of CSR Themes and Topics. We framed the task as two separate sub-tasks: one cross-lingual multi-class CSR theme recognition task for English, French and simplified Chinese and one multi-label fine-grained classification task of CSR topics for Environment (ENV) and Labor and Human Rights (LAB) themes in English. The participants were provided with URLs and annotations for both tasks. Several teams downloaded the data, of which two teams submitted a system for both sub-tasks. In this overview paper, we discuss the set-up of the task and our main findings.

This study explores aspect-based sentiment analysis (ABSA) methodologies for literary-historical research, aiming to address the limitations of traditional sentiment analysis in understanding nuanced aspects of literature. It evaluates three ABSA toolchains: rule-based, machine learning-based (utilizing BERT and MacBERTh embeddings), and a prompt-based workflow with Mixtral 8x7B. Findings highlight challenges and potentials of ABSA for literary-historical analysis, emphasizing the need for context-aware annotation strategies and technical skills. The research contributes by curating a multilingual corpus of travelogues, publishing an annotated dataset for ABSA, creating openly available Jupyter Notebooks with Python code for each modeling approach, conducting pilot experiments on literary-historical texts, and proposing future endeavors to advance ABSA methodologies in this domain.

The study of ancient Middle Eastern cultures is dominated by the vast number of cuneiform texts. Multiple languages and language families were expressed in cuneiform. The most dominant language written in cuneiform is the Semitic Akkadian, which is the focus of this paper. We are specifically focusing on letters written in the dialect used in modern-day Baghdad and south towards the Persian Gulf during the Old Babylonian period (c. 2000-1600 B.C.E.). The Akkadian language was rediscovered in the 19th century and is now being scrutinised by Natural Language Processing (NLP) methods. However, existing Akkadian text publications are not always suitable for digital editions. We therefore risk applying NLP methods onto renderings of Akkadian unfit for the purpose. In this paper we want to investigate the input material and try to initiate a discussion about best-practices in the crossroad where NLP meets cuneiform studies. Specifically, we want to question the use of pre-trained embeddings, sentence segmentation and the type of cuneiform input used to fine-tune language models for the task of fine-grained part-of-speech tagging. We examine the issues by theoretical and practical approaches in a way that we hope spurs discussions that are relevant for automatic processing of other ancient languages.

In this paper, we examine the recognition of irony by both humans and automatic systems. We achieve this by enhancing the annotations of an English benchmark data set for irony detection. This enhancement involves a layer of human-annotated irony likelihood using a 7-point Likert scale that combines binary annotation with a confidence measure. Additionally, the annotators indicated the trigger words that led them to perceive the text as ironic, which leveraged necessary theoretical insights into the definition of irony and its various forms. By comparing these trigger word spans across annotators, we determine the extent to which humans agree on the source of irony in a text. Finally, we compare the human-annotated spans with sub-token importance attributions for fine-tuned transformers using Layer Integrated Gradients, a state-of-the-art interpretability metric. Our results indicate that our model achieves better performance on tweets that were annotated with high confidence and high agreement. Although automatic systems can identify trigger words with relative success, they still attribute a significant amount of their importance to the wrong tokens.

This paper presents preliminary experiments for the lemmatisation of unedited, Byzantine Greek epigrams. This type of Greek is quite different from its classical ancestor, mostly because of its orthographic inconsistencies. Existing lemmatisation algorithms display an accuracy drop of around 30pp when tested on these Byzantine book epigrams. We conducted seven different lemmatisation experiments, which were either transformer-based or based on neural edit-trees. The best performing lemmatiser was a hybrid method combining transformer-based embeddings with a dictionary look-up. We compare our results with existing lemmatisers, and provide a detailed error analysis revealing why unedited, Byzantine Greek is so challenging for lemmatisation.

Given the omnipresence of social media in our society, thoughts and opinions are being shared online in an unprecedented manner. This means that both positive and negative emotions can be equally and freely expressed. However, the negativity bias posits that human beings are inherently drawn to and more moved by negativity and, as a consequence, negative emotions get more traffic. Correspondingly, when writing about emotions this negativity bias could lead to expressions of negative emotions that are linguistically more complex. In this paper, we attempt to use readability and linguistic complexity metrics to better understand the manifestation of emotions on social media platforms like Reddit based on the widely-used GoEmotions dataset. We demonstrate that according to most metrics, negative emotions indeed tend to generate more complex text than positive emotions. In addition, we examine whether a higher complexity hampers the automatic identification of emotions. To answer this question, we fine-tuned three state-of-the-art transformers (BERT, RoBERTa, and SpanBERT) on the same emotion detection dataset. We demonstrate that these models often fail to predict emotions for the more complex texts. More advanced LLMs like RoBERTa and SpanBERT also fail to improve by significant margins on complex samples. This calls for a more nuanced interpretation of the emotion detection performance of transformer models. We make the automatically annotated data available for further research at: https://huggingface.co/datasets/pranaydeeps/CAMEO

In this paper we investigate potential bias in fine-tuned transformer models for irony detection. Bias is defined in this research as spurious associations between word n-grams and class labels, that can cause the system to rely too much on superficial cues and miss the essence of the irony. For this purpose, we looked for correlations between class labels and words that are prone to trigger irony, such as positive adjectives, intensifiers and topical nouns. Additionally, we investigate our irony model’s predictions before and after manipulating the data set through irony trigger replacements. We further support these insights with state-of-the-art explainability techniques (Layer Integrated Gradients, Discretized Integrated Gradients and Layer-wise Relevance Propagation). Both approaches confirm the hypothesis that transformer models generally encode correlations between positive sentiments and ironic texts, with even higher correlations between vividly expressed sentiment and irony. Based on these insights, we implemented a number of modification strategies to enhance the robustness of our irony classifier.

In this paper, we present the interim results of a transformer-based annotation pipeline for Ancient and Medieval Greek. As the texts in the Database of Byzantine Book Epigrams have not been normalised, they pose more challenges for manual and automatic annotation than Ancient Greek, normalised texts do. As a result, the existing annotation tools perform poorly. We compiled three data sets for the development of an automatic annotation tool and carried out an inter-annotator agreement study, with a promising agreement score. The experimental results show that our part-of-speech tagger yields accuracy scores that are almost 50 percentage points higher than the widely used rule-based system Morpheus. In addition, error analysis revealed problems related to phenomena also occurring in current social media language.

This paper investigates whether adding data of typologically closer languages improves the performance of transformer-based models for three different downstream tasks, namely Part-of-Speech tagging, Named Entity Recognition, and Sentiment Analysis, compared to a monolingual and plain multilingual language model. For the presented pilot study, we performed experiments for the use case of Slovene, a low(er)-resourced language belonging to the Slavic language family. The experiments were carried out in a controlled setting, where a monolingual model for Slovene was compared to combined language models containing Slovene, trained with the same amount of Slovene data. The experimental results show that adding typologically closer languages indeed improves the performance of the Slovene language model, and even succeeds in outperforming the large multilingual XLM-RoBERTa model for NER and PoS-tagging. We also reveal that, contrary to intuition, distantly or unrelated languages also combine admirably with Slovene, often out-performing XLM-R as well. All the bilingual models used in the experiments are publicly available at https://github.com/pranaydeeps/BLAIR

This paper reports on the results of a comparative evaluation in view of the development of a new lemmatizer for unedited, Byzantine Greek texts. For the experiment, the performance of four existing lemmatizers, all pre-trained on Ancient Greek texts, was evaluated on how well they could handle texts stemming from the Middle Ages and displaying quite some peculiarities. The aim of this study is to get insights into the pitfalls of existing lemmatistion approaches as well as the specific challenges of our Byzantine Greek corpus, in order to develop a lemmatizer that can cope with its peculiarities. The results of the experiment show an accuracy drop of 20pp. on our corpus, which is further investigated in a qualitative error analysis.

This paper describes the system we developed for the shared task ‘Hero, Villain and Victim: Dissecting harmful memes for Semantic role labelling of entities’ organised in the framework of the Second Workshop on Combating Online Hostile Posts in Regional Languages during Emergency Situation (Constraint 2022). We present an ensemble approach combining transformer-based models and linguistic information, such as the presence of irony and implicit sentiment associated to the target named entities. The ensemble system obtains promising classification scores, resulting in a third place finish in the competition.

This paper reports on experiments for cross-lingual transfer using the anchor-based approach of Schuster et al. (2019) for English and a low-resourced language, namely Hindi. For the sake of comparison, we also evaluate the approach on three very different higher-resourced languages, viz. Dutch, Russian and Chinese. Initially designed for ELMo embeddings, we analyze the approach for the more recent BERT family of transformers for a variety of tasks, both mono and cross-lingual. The results largely prove that like most other cross-lingual transfer approaches, the static anchor approach is underwhelming for the low-resource language, while performing adequately for the higher resourced ones. We attempt to provide insights into both the quality of the anchors, and the performance for low-shot cross-lingual transfer to better understand this performance gap. We make the extracted anchors and the modified train and test sets available for future research at https://github.com/pranaydeeps/Vyaapak

This contribution presents D-Terminer: an open access, online demo for monolingual and multilingual automatic term extraction from parallel corpora. The monolingual term extraction is based on a recurrent neural network, with a supervised methodology that relies on pretrained embeddings. Candidate terms can be tagged in their original context and there is no need for a large corpus, as the methodology will work even for single sentences. With the bilingual term extraction from parallel corpora, potentially equivalent candidate term pairs are extracted from translation memories and manual annotation of the results shows that good equivalents are found for most candidate terms. Accompanying the release of the demo is an updated version of the ACTER Annotated Corpora for Term Extraction Research (version 1.5).

As emotion analysis in text has gained a lot of attention in the field of natural language processing, differences in emotion expression across languages could have consequences for how emotion detection models work. We evaluate the language-dependence of an mBERT-based emotion detection model by comparing language identification performance before and after fine-tuning on emotion detection, and performing (adjusted) zero-shot experiments to assess whether emotion detection models rely on language-specific information. When dealing with typologically dissimilar languages, we found evidence for the language-dependence of emotion detection.

In this paper, we present the SentEMO platform, a tool that provides aspect-based sentiment analysis and emotion detection of unstructured text data such as reviews, emails and customer care conversations. Currently, models have been trained for five domains and one general domain and are implemented in a pipeline approach, where the output of one model serves as the input for the next. The results are presented in three dashboards, allowing companies to gain more insights into what stakeholders think of their products and services. The SentEMO platform is available at https://sentemo.ugent.be

This paper presents the results of a replication experiment for automatic irony detection in Dutch social media text, investigating both a feature-based SVM classifier, as was done by Van Hee et al. (2017) and and a transformer-based approach. In addition to building a baseline model, an important goal of this research is to explore the implementation of common-sense knowledge in the form of implicit sentiment, as we strongly believe that common-sense and connotative knowledge are essential to the identification of irony and implicit meaning in tweets. We show promising results and the presented approach can provide a solid baseline and serve as a staging ground to build on in future experiments for irony detection in Dutch.

In this research, we present pilot experiments to distil monolingual models from a jointly trained model for 102 languages (mBERT). We demonstrate that it is possible for the target language to outperform the original model, even with a basic distillation setup. We evaluate our methodology for 6 languages with varying amounts of resources and language families.

Internet memes have become ubiquitous in social media networks today. Due to their popularity, they are also a widely used mode of expression to spread disinformation online. As memes consist of a mixture of text and image, they require a multi-modal approach for automatic analysis. In this paper, we describe our contribution to the SemEval-2021 Detection of Persuasian Techniques in Texts and Images Task. We propose a Multi-Modal learning system, which incorporates “memebeddings”, viz. joint text and vision features by combining them with compact bilinear pooling, to automatically identify rhetorical and psychological disinformation techniques. The experimental results show that the proposed system constantly outperforms the competition’s baseline, and achieves the 2nd best Macro F1-score and 14th best Micro F1-score out of all participants.

This paper presents a pilot study to automatic linguistic preprocessing of Ancient and Byzantine Greek, and morphological analysis more specifically. To this end, a novel subword-based BERT language model was trained on the basis of a varied corpus of Modern, Ancient and Post-classical Greek texts. Consequently, the obtained BERT embeddings were incorporated to train a fine-grained Part-of-Speech tagger for Ancient and Byzantine Greek. In addition, a corpus of Greek Epigrams was manually annotated and the resulting gold standard was used to evaluate the performance of the morphological analyser on Byzantine Greek. The experimental results show very good perplexity scores (4.9) for the BERT language model and state-of-the-art performance for the fine-grained Part-of-Speech tagger for in-domain data (treebanks containing a mixture of Classical and Medieval Greek), as well as for the newly created Byzantine Greek gold standard data set. The language models and associated code are made available for use at https://github.com/pranaydeeps/Ancient-Greek-BERT

The TermEval 2020 shared task provided a platform for researchers to work on automatic term extraction (ATE) with the same dataset: the Annotated Corpora for Term Extraction Research (ACTER). The dataset covers three languages (English, French, and Dutch) and four domains, of which the domain of heart failure was kept as a held-out test set on which final f1-scores were calculated. The aim was to provide a large, transparent, qualitatively annotated, and diverse dataset to the ATE research community, with the goal of promoting comparative research and thus identifying strengths and weaknesses of various state-of-the-art methodologies. The results show a lot of variation between different systems and illustrate how some methodologies reach higher precision or recall, how different systems extract different types of terms, how some are exceptionally good at finding rare terms, or are less impacted by term length. The current contribution offers an overview of the shared task with a comparative evaluation, which complements the individual papers by all participants.

This paper presents two different systems for the SemEval shared task 7 on Assessing Humor in Edited News Headlines, sub-task 1, where the aim was to estimate the intensity of humor generated in edited headlines. Our first system is a feature-based machine learning system that combines different types of information (e.g. word embeddings, string similarity, part-of-speech tags, perplexity scores, named entity recognition) in a Nu Support Vector Regressor (NuSVR). The second system is a deep learning-based approach that uses the pre-trained language model RoBERTa to learn latent features in the news headlines that are useful to predict the funniness of each headline. The latter system was also our final submission to the competition and is ranked seventh among the 49 participating teams, with a root-mean-square error (RMSE) of 0.5253.

Internet memes have become a very popular mode of expression on social media networks today. Their multi-modal nature, caused by a mixture of text and image, makes them a very challenging research object for automatic analysis. In this paper, we describe our contribution to the SemEval-2020 Memotion Analysis Task. We propose a Multi-Modal Multi-Task learning system, which incorporates “memebeddings”, viz. joint text and vision features, to learn and optimize for all three Memotion subtasks simultaneously. The experimental results show that the proposed system constantly outperforms the competition’s baseline, and the system setup with continual learning (where tasks are trained sequentially) obtains the best classification F1-scores.

This paper describes our contribution to the SemEval-2020 Task 9 on Sentiment Analysis for Code-mixed Social Media Text. We investigated two approaches to solve the task of Hinglish sentiment analysis. The first approach uses cross-lingual embeddings resulting from projecting Hinglish and pre-trained English FastText word embeddings in the same space. The second approach incorporates pre-trained English embeddings that are incrementally retrained with a set of Hinglish tweets. The results show that the second approach performs best, with an F1-score of 70.52% on the held-out test data.

This paper investigates the validity of combining more traditional orthographic information with cross-lingual word embeddings to identify cognate pairs in English-Dutch and French-Dutch. In a first step, lists of potential cognate pairs in English-Dutch and French-Dutch are manually labelled. The resulting gold standard is used to train and evaluate a multi-layer perceptron that can distinguish cognates from non-cognates. Fifteen orthographic features capture string similarities between source and target words, while the cosine similarity between their word embeddings represents the semantic relation between these words. By adding domain-specific information to pretrained fastText embeddings, we are able to obtain good embeddings for words that did not yet have a pretrained embedding (e.g. Dutch compound nouns). These embeddings are then aligned in a cross-lingual vector space by exploiting their structural similarity (cf. adversarial learning). Our results indicate that although the classifier already achieves good results on the basis of orthographic information, the performance further improves by including semantic information in the form of cross-lingual word embeddings.

This paper investigates the use of unsupervised cross-lingual embeddings for solving the problem of code-mixed social media text understanding. We specifically investigate the use of these embeddings for a sentiment analysis task for Hinglish Tweets, viz. English combined with (transliterated) Hindi. In a first step, baseline models, initialized with monolingual embeddings obtained from large collections of tweets in English and code-mixed Hinglish, were trained. In a second step, two systems using cross-lingual embeddings were researched, being (1) a supervised classifier and (2) a transfer learning approach trained on English sentiment data and evaluated on code-mixed data. We demonstrate that incorporating cross-lingual embeddings improves the results (F1-score of 0.635 versus a monolingual baseline of 0.616), without any parallel data required to train the cross-lingual embeddings. In addition, the results show that the cross-lingual embeddings not only improve the results in a fully supervised setting, but they can also be used as a base for distant supervision, by training a sentiment model in one of the source languages and evaluating on the other language projected in the same space. The transfer learning experiments result in an F1-score of 0.556, which is almost on par with the supervised settings and speak to the robustness of the cross-lingual embeddings approach.

One of the major challenges currently facing the field of argumentation mining is the lack of consensus on how to analyse argumentative user-generated texts such as online comments. The theoretical motivations underlying the annotation guidelines used to generate labelled corpora rarely include motivation for the use of a particular theoretical basis. This pilot study reports on the annotation of a corpus of 100 Dutch user comments made in response to politically-themed news articles on Facebook. The annotation covers topic and aspect labelling, stance labelling, argumentativeness detection and claim identification. Our IAA study reports substantial agreement scores for argumentativeness detection (0.76 Fleiss’ kappa) and moderate agreement for claim labelling (0.45 Fleiss’ kappa). We provide a clear justification of the theories and definitions underlying the design of our guidelines. Our analysis of the annotations signal the importance of adjusting our guidelines to include allowances for missing context information and defining the concept of argumentativeness in connection with stance. Our annotated corpus and associated guidelines are made publicly available.

This paper describes our contribution to the SemEval-2019 Task 5 on the detection of hate speech against immigrants and women in Twitter (hatEval). We considered a supervised classification-based approach to detect hate speech in English tweets, which combines a variety of standard lexical and syntactic features with specific features for capturing offensive language. Our experimental results show good classification performance on the training data, but a considerable drop in recall on the held-out test set.

This paper presents proof-of-concept experiments for combining orthographic and semantic information to distinguish cognates from non-cognates. To this end, a context-independent gold standard is developed by manually labelling English-Dutch pairs of cognates and false friends in bilingual term lists. These annotated cognate pairs are then used to train and evaluate a supervised binary classification system for the automatic detection of cognates. Two types of information sources are incorporated in the classifier: fifteen string similarity metrics capture form similarity between source and target words, while word embeddings model semantic similarity between the words. The experimental results show that even though the system already achieves good results by only incorporating orthographic information, the performance further improves by including semantic information in the form of embeddings.

Traditional approaches to automatic term extraction do not rely on machine learning (ML) and select the top n ranked candidate terms or candidate terms above a certain predefined cut-off point, based on a limited number of linguistic and statistical clues. However, supervised ML approaches are gaining interest. Relatively little is known about the impact of these supervised methodologies; evaluations are often limited to precision, and sometimes recall and f1-scores, without information about the nature of the extracted candidate terms. Therefore, the current paper presents a detailed and elaborate analysis and comparison of a traditional, state-of-the-art system (TermoStat) and a new, supervised ML approach (HAMLET), using the results obtained for the same, manually annotated, Dutch corpus about dressage.

Although common sense and connotative knowledge come naturally to most people, computers still struggle to perform well on tasks for which such extratextual information is required. Automatic approaches to sentiment analysis and irony detection have revealed that the lack of such world knowledge undermines classification performance. In this article, we therefore address the challenge of modeling implicit or prototypical sentiment in the framework of automatic irony detection. Starting from manually annotated connoted situation phrases (e.g., “flight delays,” “sitting the whole day at the doctor’s office”), we defined the implicit sentiment held towards such situations automatically by using both a lexico-semantic knowledge base and a data-driven method. We further investigate how such implicit sentiment information affects irony detection by assessing a state-of-the-art irony classifier before and after it is informed with implicit sentiment information.

We present the highlights of the now finished 4-year SCATE project. It was completed in February 2018 and funded by the Flemish Government IWT-SBO, project No. 130041.1

This paper presents a dataset and supervised classification approach for economic event detection in English news articles. Currently, the economic domain is lacking resources and methods for data-driven supervised event detection. The detection task is conceived as a sentence-level classification task for 10 different economic event types. Two different machine learning approaches were tested: a rich feature set Support Vector Machine (SVM) set-up and a word-vector-based long short-term memory recurrent neural network (RNN-LSTM) set-up. We show satisfactory results for most event types, with the linear kernel SVM outperforming the other experimental set-ups

This paper presents the first shared task on irony detection: given a tweet, automatic natural language processing systems should determine whether the tweet is ironic (Task A) and which type of irony (if any) is expressed (Task B). The ironic tweets were collected using irony-related hashtags (i.e. #irony, #sarcasm, #not) and were subsequently manually annotated to minimise the amount of noise in the corpus. Prior to distributing the data, hashtags that were used to collect the tweets were removed from the corpus. For both tasks, a training corpus of 3,834 tweets was provided, as well as a test set containing 784 tweets. Our shared tasks received submissions from 43 teams for the binary classification Task A and from 31 teams for the multiclass Task B. The highest classification scores obtained for both subtasks are respectively F1= 0.71 and F1= 0.51 and demonstrate that fine-grained irony classification is much more challenging than binary irony detection.

This paper presents an integrated ABSA pipeline for Dutch that has been developed and tested on qualitative user feedback coming from three domains: retail, banking and human resources. The two latter domains provide service-oriented data, which has not been investigated before in ABSA. By performing in-domain and cross-domain experiments the validity of our approach was investigated. We show promising results for the three ABSA subtasks, aspect term extraction, aspect category classification and aspect polarity classification.

Breaking news on economic events such as stock splits or mergers and acquisitions has been shown to have a substantial impact on the financial markets. As it is important to be able to automatically identify events in news items accurately and in a timely manner, we present in this paper proof-of-concept experiments for a supervised machine learning approach to economic event detection in newswire text. For this purpose, we created a corpus of Dutch financial news articles in which 10 types of company-specific economic events were annotated. We trained classifiers using various lexical, syntactic and semantic features. We obtain good results based on a basic set of shallow features, thus showing that this method is a viable approach for economic event detection in news text.

Handling figurative language like irony is currently a challenging task in natural language processing. Since irony is commonly used in user-generated content, its presence can significantly undermine accurate analysis of opinions and sentiment in such texts. Understanding irony is therefore important if we want to push the state-of-the-art in tasks such as sentiment analysis. In this research, we present the construction of a Twitter dataset for two languages, being English and Dutch, and the development of new guidelines for the annotation of verbal irony in social media texts. Furthermore, we present some statistics on the annotated corpora, from which we can conclude that the detection of contrasting evaluations might be a good indicator for recognizing irony.

Recognising and understanding irony is crucial for the improvement natural language processing tasks including sentiment analysis. In this study, we describe the construction of an English Twitter corpus and its annotation for irony based on a newly developed fine-grained annotation scheme. We also explore the feasibility of automatic irony recognition by exploiting a varied set of features including lexical, syntactic, sentiment and semantic (Word2Vec) information. Experiments on a held-out test set show that our irony classifier benefits from this combined information, yielding an F1-score of 67.66%. When explicit hashtag information like #irony is included in the data, the system even obtains an F1-score of 92.77%. A qualitative analysis of the output reveals that recognising irony that results from a polarity clash appears to be (much) more feasible than recognising other forms of ironic utterances (e.g., descriptions of situational irony).

In this research, we evaluate different approaches for the automatic extraction of hypernym relations from English and Dutch technical text. The detected hypernym relations should enable us to semantically structure automatically obtained term lists from domain- and user-specific data. We investigated three different hypernymy extraction approaches for Dutch and English: a lexico-syntactic pattern-based approach, a distributional model and a morpho-syntactic method. To test the performance of the different approaches on domain-specific data, we collected and manually annotated English and Dutch data from two technical domains, viz. the dredging and financial domain. The experimental results show that especially the morpho-syntactic approach obtains good results for automatic hypernym extraction from technical and domain-specific texts.

Wikipedia pages typically contain inter-language links to the corresponding pages in other languages. These links, however, are often incomplete. This paper describes a set of experiments in which the viability of discovering such missing inter-language links for ambiguous nouns by means of a cross-lingual Word Sense Disambiguation approach is investigated. The input for the inter-language link detection system is a set of Dutch pages for a given ambiguous noun and the output of the system is a set of links to the corresponding pages in three target languages (viz. French, Spanish and Italian). The experimental results show that although it is a very challenging task, the system succeeds to detect missing inter-language links between Wikipedia documents for a manually labeled test set. The final goal of the system is to provide a human editor with a list of possible missing links that should be manually verified.

Given the recent trend to evaluate the performance of word sense disambiguation systems in a more application-oriented set-up, we report on the construction of a multilingual benchmark data set for cross-lingual word sense disambiguation. The data set was created for a lexical sample of 25 English nouns, for which translations were retrieved in 5 languages, namely Dutch, German, French, Italian and Spanish. The corpus underlying the sense inventory was the parallel data set Europarl. The gold standard sense inventory was based on the automatic word alignments of the parallel corpus, which were manually verified. The resulting word alignments were used to perform a manual clustering of the translations over all languages in the parallel corpus. The inventory then served as input for the annotators of the sentences, who were asked to provide a maximum of three contextually relevant translations per language for a given focus word. The data set was released in the framework of the SemEval-2010 competition.

In this paper, we investigate the use of a machine-learning based approach to the specific problem of scientific term detection in patient information. Lacking lexical databases which differentiate between the scientific and popular nature of medical terms, we used local context, morphosyntactic, morphological and statistical information to design a learner which accurately detects scientific medical terms. This study is the first step towards the automatic replacement of a scientific term by its popular counterpart, which should have a beneficial effect on readability. We show a F-score of 84% for the prediction of scientific terms in an English and Dutch EPAR corpus. Since recasting the term extraction problem as a classification problem leads to a large skewedness of the resulting data set, we rebalanced the data set through the application of some simple TF-IDF-based and Log-likelihood-based filters. We show that filtering indeed has a beneficial effect on the learner’s performance. However, the results of the filtering approach combined with the learning-based approach remain below those of the learning-based approach.