Véronique Hoste

• Ghent University

Classical Chinese poetry has a long history, dating back to the 11th century BC. By investigating the sentiment expressed in the poetry, we can gain more insights in the emotional life and history development in ancient Chinese culture. To help improve the sentiment analysis performance in the field of classical Chinese poetry, we propose to utiliz...

As one of the important features that differentiates humans from machines, emotion is complicated not only in its wide varieties but also in its expression channels, including both verbal and non-verbal language. Different modalities contribute in unique ways to the integrated expression of emotion. However, in most of the existing multimodal datas...

Due to the rise of user-generated content, social media is increasingly adopted as a channel to deliver customer service. Given the public character of online platforms, the automatic detection of emotions forms an important application in monitoring customer satisfaction and preventing negative word-of-mouth. This paper introduces EmoTwiCS, a corp...

The present research investigates the effects of Personality Traits (PTs) and Game Design Elements (GDEs) on Player Enjoyment (PE) in the context of serious games. Three Games With A Purpose (GWAPs) were created to revise and correct automatically tagged Parts-of-Speech (PoS) of the Corpus Oral y Sonoro del Español Rural (COSER, [5], ‘Audible Corpu...

This demo paper presents three Games With A Purpose (GWAPs) created to revise and correct automatically tagged part-of-speech (PoS) of the most extensive collection of Spanish oral data, known as the Corpus Oral y Sonoro del Español Rural (COSER, [7], “Audible Corpus of Spoken Rural Spanish”). The goal is to create a morpho-syntactically annotated...

Most datasets for multimodal emotion recognition only have one emotion annotation for all the modalities combined, which serves as a gold standard for single modalities. This procedure ignores, however, the fact that each modality constitutes a unique perspective that contains its own clues. Moreover, as in unimodal emotion analysis, the perspectiv...

In this paper, we explore the feasibility of irony detection in Dutch social media. To this end, we investigate both transformer models with embedding representations, as well as traditional machine learning classifiers with extensive feature sets. Our feature-based methodology implements a variety of information sources including lexical, semantic...

Fine-grained sentiment analysis, known as Aspect-Based Sentiment Analysis (ABSA), establishes the polarity of a section of text concerning a particular aspect. Aspect, sentiment, and emotion categorisation are the three steps that make up the configuration of ABSA, which we looked into for the dataset of English reviews. In this work, due to the fu...

In this paper, we present a benchmark result for end-to-end cross-document event coreference resolution in Dutch. First, the state of the art of this task in other languages is introduced, as well as currently existing resources and commonly used evaluation metrics. We then build on recently published work to fully explore end-to-end event corefere...

News organizations increasingly tailor their news offering to the reader through personalized recommendation algorithms. However, automated recommendation algorithms reflect a commercial logic based on calculated relevance to the user, rather than aiming at a well-informed citizenry. In this paper, we introduce the EventDNA corpus, a dataset of 177...

In this paper, we explore the feasibility of irony detection in Dutch social media. To this end, we investigate both transformer models with embedding representations, as well as traditional machine learning classifiers with extensive feature sets. Our feature-based methodology implements a variety of information sources including lexical, semantic...

Background Headache medicine is largely based on detailed history taking by physicians analysing patients’ descriptions of headache. Natural language processing (NLP) structures and processes linguistic data into quantifiable units. In this study, we apply these digital techniques on self-reported narratives by patients with headache disorders to r...

This contribution presents version 1.5 of the Annotated Corpora for Term Extraction Research (ACTER) dataset. It includes domain-specific corpora in three languages (English, French, and Dutch) and four domains (corruption, dressage (equitation), heart failure, and wind energy). Manual annotations are available of terms and Named Entities for each...

This pilot study employs the Wizard of Oz technique to collect a corpus of written human-computer conversations in the domain of customer service. The resulting dataset contains 192 conversations and is used to test three hypotheses related to the expression and annotation of emotions. First, we hypothesize that there is a discrepancy between the e...

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 t...

Event coreference resolution is a task in which different text fragments that refer to the same real-world event are automatically linked together. This task can be performed not only within a single document but also across different documents and can serve as a basis for many useful Natural Language Processing applications. Resources for this typ...

We present SENTiVENT, a corpus of fine-grained company-specific events in English economic news articles. The domain of event processing is highly productive and various general domain, fine-grained event extraction corpora are freely available but economically-focused resources are lacking. This work fills a large need for a manually annotated dat...

In an era where user-generated content becomes ever more prevalent, reliable methods to judge emotional properties of these kinds of complex texts are needed, for example for developing corpora in machine learning contexts. In this study, we focus on Dutch Twitter messages, a genre which is high in emotional content and frequently investigated in t...

Emotion detection has become a growing field of study, especially seeing its broad application potential. Research usually focuses on emotion classification, but performance tends to be rather low, especially when dealing with more advanced emotion categories that are tailored to specific tasks and domains. Therefore, we propose the use of the dime...

The field of sentiment analysis is currently dominated by the detection of attitudes in lexically explicit texts such as user reviews and social media posts. In objective text genres such as economic news, indirect expressions of sentiment are common. Here, a positive or negative attitude toward an entity must be inferred from connotational or real...

Social media are an essential source of meaningful data that can be used in different tasks such as sentiment analysis and emotion recognition. Mostly, these tasks are solved with deep learning methods. Due to the fuzzy nature of textual data, we consider using classification methods based on fuzzy rough sets.

The detection of online cyberbullying has seen an increase in societal importance, popularity in research, and available open data. Nevertheless, while computational power and affordability of resources continue to increase, the access restrictions on high-quality data limit the applicability of state-of-the-art techniques. Consequently, much of th...

A core task in information extraction is event detection that identifies event triggers in sentences that are typically classified into event types. In this study an event is considered as the unit to measure diversity and similarity in news articles in the framework of a news recommendation system. Current typology-based event detection approaches...

Automatic term extraction (ATE) is an important task within natural language processing, both separately, and as a preprocessing step for other tasks. In recent years, research has moved far beyond the traditional hybrid approach where candidate terms are extracted based on part-of-speech patterns and filtered and sorted with statistical termhood a...

Emotion detection is an important task that can be applied to social media data to discover new knowledge. While the use of deep learning methods for this task has been prevalent, they are black-box models, making their decisions hard to interpret for a human operator. Therefore, in this paper, we propose an approach using weighted $k$ Nearest Neig...

Social media are an essential source of meaningful data that can be used in different tasks such as sentiment analysis and emotion recognition. Mostly, these tasks are solved with deep learning methods. Due to the fuzzy nature of textual data, we consider using classification methods based on fuzzy rough sets. Specifically, we develop an approach f...

In online domain-specific customer service applications, many companies struggle to deploy advanced NLP models successfully, due to the limited availability of and noise in their datasets. While prior research demonstrated the potential of migrating large open-domain pretrained models for domain-specific tasks, the appropriate (pre)training strateg...

Waar taal en technologie samenkomen, ontstaat het domein van Natural Language Processing (NLP). Met technieken uit machine learning, zoals het trainen van modellen om tekst, afbeeldingen of geluidsfrequenties te herkennen, kunnen we computers op een intelligente manier laten werken met taal. Bekende NLP-toepassingen van machine learning zijn bijvoo...

As with many tasks in natural language processing, automatic term extraction (ATE) is increasingly approached as a machine learning problem. So far, most machine learning approaches to ATE broadly follow the traditional hybrid methodology, by first extracting a list of unique candidate terms, and classifying these candidates based on the predicted...

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 si...

Social media texts have become one of the most used forms of written language and a valuable source of information for companies. However, despite the wealth of information hidden in social media language for profiling, information extraction and sentiment analysis purposes, this user-generated content is also characterized by non-standard language...

Successful prevention of cyberbullying depends on the adequate detection of harmful messages. Given the impossibility of human moderation on the Social Web, intelligent systems are required to identify clues of cyberbullying automatically. Much work on cyberbullying detection focuses on detecting abusive language without analyzing the severity of t...

Automatic term extraction is a productive field of research within natural language processing, but it still faces significant obstacles regarding datasets and evaluation, which require manual term annotation. This is an arduous task, made even more difficult by the lack of a clear distinction between terms and general language, which results in lo...

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 whic...

Concerns about selective exposure and filter bubbles in the digital news environment trigger questions regarding how news recommender systems can become more citizen-oriented and facilitate – rather than limit – normative aims of journalism. Accordingly, this chapter presents building blocks for the construction of such a news algorithm as they are...

When developing an algorithm that uses news diversity as a key driver for personalized news recommendation it is crucial to focus on means to cluster news articles in a fine-grained manner, ideally by leveraging the content of the text. In this paper we investigate semantic classification of news articles in an unfiltered news stream. We first pres...

We investigate the cost-effectiveness of special-purpose crawled corpora versus more focused corpora for automatic terminology extraction (ATE). Our focus is on medical terminology on heart failure for two languages, viz. English for which we have more web and specialized resources at our disposal and the less resourced Dutch. We show that, althoug...

The detection of online cyberbullying has seen an increase in societal importance, popularity in research, and available open data. Nevertheless, while computational power and affordability of resources continue to increase, the access restrictions on high-quality data limit the applicability of state-of-the-art techniques. Consequently, much of th...

One of the main characteristics of social media data is the use of non-standard language. Since NLP tools have been trained on traditional text material their performance drops when applied to social media data. One way to overcome this is to first perform text normalization. In this work, we apply text normalization to noisy English and Dutch text...

When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technol...

When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technol...

Various studies show that statistical machine translation (SMT) systems suffer from fluency errors, especially in the form of grammatical errors and errors related to idiomatic word choices. In this study, we investigate the effectiveness of using monolingual information contained in the machine-translated text to estimate word-level quality of SMT...

Tools that automatically extract terms and their equivalents in other languages from parallel corpora can contribute to multilingual professional communication in more than one way. By means of a use case with data from a medical web site with point of care evidence summaries (Ebpracticenet), we illustrate how hybrid multilingual automatic term ext...

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 articl...

With the improved quality of Machine Translation (MT) systems in the last decades, post-editing (the correction of MT errors) has gained importance in Computer-Assisted Translation (CAT) workflows. Depending on the number and the severity of the errors in the MT output, the effort required to post-edit varies from sentence to sentence. The existing...

Keywords: terminology; automatic term extraction; ATR;

While social media offer great communication opportunities, they also increase the vulnerability of young people to threatening situations online. Recent studies report that cyberbullying constitutes a growing problem among youngsters. Successful prevention depends on the adequate detection of potentially harmful messages and the information overlo...

To push the state of the art in text mining applications, research in natural language processing has increasingly been investigating automatic irony detection, but manually annotated irony corpora are scarce. We present the construction of a manually annotated irony corpus based on a fine-grained annotation scheme for irony that allows to identify...

Terms are notoriously difficult to identify, both automatically and manually. This complicates the evaluation of the already challenging task of automatic term extraction. With the advent of multilingual automatic term extraction from comparable corpora, accurate evaluation becomes increasingly difficult, since term linking must be evaluated as wel...

Online communication platforms are increasingly used to express suicidal thoughts. There is considerable interest in monitoring such messages, both for population-wide and individual prevention purposes, and to inform suicide research and policy. Online information overload prohibits manual detection, which is why keyword search methods are typical...

While social media offer great communication opportunities, they also increase the vulnerability of young people to threatening situations online. Recent studies report that cyberbullying constitutes a growing problem among youngsters. Successful prevention depends on the adequate detection of potentially harmful messages and the information overlo...

In the past decade, sentiment analysis research has thrived, especially on social media. While this data genre is suitable to extract opinions and sentiment, it is known to be noisy. Complex normalisation methods have been developed to transform noisy text into its standard form, but their effect on tasks like sentiment analysis remains underinvest...

"But I don’t know how to work with [name of tool or resource]" is something one often hears when researchers in Human and Social Sciences (HSS) are confronted with language technology, be it written or spoken, tools or resources. The TTNWW project shows that these researchers do not need to be experts in language or speech technology, or to know al...

Quality Estimation (QE) and error analysis of Machine Translation (MT) output remain active areas in Natural Language Processing (NLP) research. Many recent efforts have focused on Machine Learning (ML) systems to estimate the MT quality, translation errors, post-editing speed or post-editing effort. As the accuracy of such ml tasks relies on the a...

Syntactically annotated corpora are highly important for enabling large-scale diachronic and diatopic language research. Such corpora have recently been developed for a variety of historical languages, or are still under development. One of those under development is the fully tagged and parsed Corpus of Historical Low German (CHLG), which is aimed...

In this paper we present a Neural Network (NN) architecture for detecting grammatical er- rors in Statistical Machine Translation (SMT) using monolingual morpho-syntactic word rep- resentations in combination with surface and syntactic context windows. We test our approach on two language pairs and two tasks, namely detecting grammatical errors and...

CLIN27 conference poster with intermediate results on cyberbullying detectection in the AMiCA project.

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 valid...

We describe an educational demonstration interface and tools for stylometry (authorship attribution and profiling) and readability research for Dutch. The Stylene system consists of a popularisation interface for learning about stylometric analysis, and of web-based interfaces to software for readability and stylometry research aimed at researchers...

Social media, and in particular Twitter, are increasingly being utilized during crises. It has been shown that tweets offer valuable real-time information for decision-making. Given the vast amount of data available on the Web, there is a need for intelligent ways to select and retrieve the desired information. Analyzing sentiment and emotions in o...

Readability research has a long and rich tradition, but there has been too little focus on general readability prediction without targeting a specific audience or text genre. Moreover, although NLP-inspired research has focused on adding more complex readability features, there is still no consensus on which features contribute most to the predicti...

This chapter introduces one of the early and most influential machine learning approaches to coreference resolution, the mention-pair model. Initiated in the mid-1990s and further developed into a more generic resolver by Soon et al. in 2001 and many others, the simple model still remains a popular benchmark in the learning-based resolution researc...

As social media constitutes a valuable source for data analysis for a wide range of applications, the need for handling such data arises. However, the nonstandard language used on social media poses problems for natural language processing (NLP) tools, as these are typically trained on standard language material. We propose a text normalization app...

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 task...

Despite the recent advances in the field of machine translation (MT), MT systems cannot guarantee that the sentences they produce will be fluent and coherent in both syntax and semantics. Detecting and highlighting errors in machine-translated sentences can help post-editors to focus on the erroneous fragments that need to be corrected. This paper...

This paper describes the submission of the UGENT-LT3 SCATE system to the WMT16 Shared Task on Quality Estimation (QE), viz. English-German word and sentence-level QE. Based on the observation that the data set is homogeneous (all sentences belong to the IT domain), we performed bilingual terminology extraction and added features derived from the re...

The recent development of social media poses new challenges to the research community in analyzing online interactions between people. Social networking sites offer great opportunities for connecting with others, but also increase the vulnerability of young people to undesirable phenomena, such as cybervictimization. Recent research reports that on...

In the current era of online interactions, both positive and negative experiences are abundant on the Web. As in real life, negative experiences can have a serious impact on youngsters. Recent studies have reported cybervictimization rates among teenagers that vary between 20% and 40%. In this paper, we focus on cyberbullying as a particular form o...

We present a fine-grained scheme for the annotation of polar sentiment in text, that accounts for explicit sentiment (so-called private states), as well as implicit expressions of sentiment (polar facts). Polar expressions are annotated below sentence level and classified according to their subjectivity status. Additionally, they are linked to one...

This paper outlines current work on the construction of a high-quality, richly-annotated and register-diversified parallel corpus for the English-Spanish language pair, as currently carried out within the framework of the MULTINOT project. The corpus consists of original and translated texts in both directions and is designed as a multifunctional r...

This article investigates whether the integration of a domain-specific, bilingual glossary supports audiovisual translators of documentaries in terms of translation process time and terminological errors. After a short review of issues typical of documentary translation and a discussion of the use of translation-memory software in general, the refe...

This paper focuses on topic-specific and more specifically company-specific sentiment analysis in financial newswire text. This application is of great use to researchers in the financial domain who study the impact of news (media) on the stock markets. We investigate the viability of a new fine-grained sentiment annotation scheme. Most of the curr...