References of "Schommer, Christoph 50003041"
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See detailAn Annotation Framework for Luxembourgish Sentiment Analysis
Sirajzade, Joshgun UL; Gierschek, Daniela UL; Schommer, Christoph UL

in Besacier, Laurent; Sakti, Sakriani; Soria, Claudia (Eds.) et al Proceedings of the LREC 2020 1st Joint SLTU and CCURL Workshop (SLTU-CCURL 2020) (2020, May)

The aim of this paper is to present a framework developed for crowdsourcing sentiment annotation for the low-resource language Luxembourgish. Our tool is easily accessible through a web interface and ... [more ▼]

The aim of this paper is to present a framework developed for crowdsourcing sentiment annotation for the low-resource language Luxembourgish. Our tool is easily accessible through a web interface and facilitates sentence-level annotation of several annotators in parallel. In the heart of our framework is an XML database, which serves as central part linking several components. The corpus in the database consists of news articles and user comments. One of the components is LuNa, a tool for linguistic preprocessing of the data set. It tokenizes the text, splits it into sentences and assigns POS-tags to the tokens. After that, the preprocessed text is stored in XML format into the database. The Sentiment Annotation Tool, which is a browser-based tool, then enables the annotation of split sentences from the database. The Sentiment Engine, a separate module, is trained with this material in order to annotate the whole data set and analyze the sentiment of the comments over time and in relationship to the news articles. The gained knowledge can again be used to improve the sentiment classification on the one hand and on the other hand to understand the sentiment phenomenon from the linguistic point of view. [less ▲]

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See detailComponent Analysis of Adjectives in Luxembourgish for Detecting Sentiments
Sirajzade, Joshgun UL; Gierschek, Daniela UL; Schommer, Christoph UL

in Beermann, Dorothee; Besacier, Laurent; Sakti, Sakriani (Eds.) et al Proceedings of the LREC 2020 1st Joint SLTU and CCURL Workshop(SLTU-CCURL 2020) (2020, May)

The aim of this paper is to investigate the role of Luxembourgish adjectives in expressing sentiments in user comments written at the web presence of rtl.lu (RTL is the abbreviation for Radio Television ... [more ▼]

The aim of this paper is to investigate the role of Luxembourgish adjectives in expressing sentiments in user comments written at the web presence of rtl.lu (RTL is the abbreviation for Radio Television Lëtzebuerg). Alongside many textual features or representations, adjectives could be used in order to detect sentiment, even on a sentence or comment level. In fact, they are also by themselves one of the best ways to describe a sentiment, despite the fact that other word classes such as nouns, verbs, adverbs or conjunctions can also be utilized for this purpose. The empirical part of this study focuses on a list of adjectives that were extracted from an annotated corpus. The corpus contains the part of speech tags of individual words and sentiment annotation on the adjective, sentence, and comment level. Suffixes of Luxembourgish adjectives like -esch, -eg, -lech, -al, -el, -iv, -ent, -los, -bar and the prefix on- were explicitly investigated, especially by paying attention to their role in regards to building a model by applying classical machine learning techniques. We also considered the interaction of adjectives with other grammatical means, especially other part of speeches, e.g. negations, which can completely reverse the meaning, thus the sentiment of an utterance. [less ▲]

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See detailEine Doktorarbeit zu beginnen, ist (relativ) leicht...
Schommer, Christoph UL

in Luxemburger Wort (2020)

Vor einigen Wochen war es wieder soweit: Zwei Doktorandinnen hatten ihre wissenschaftlichen Arbeiten eingereicht, sich den Fragen einer internationalen Expertenkommission gestellt, und schließlich ihre ... [more ▼]

Vor einigen Wochen war es wieder soweit: Zwei Doktorandinnen hatten ihre wissenschaftlichen Arbeiten eingereicht, sich den Fragen einer internationalen Expertenkommission gestellt, und schließlich ihre Doktorarbeiten verteidigt. [less ▲]

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See detailSpeech Based Estimation of Parkinson’s Disease Using Gaussian Processes and Automatic Relevance Determination
Despotovic, Vladimir UL; Skovranek, Tomas; Schommer, Christoph UL

in Neurocomputing (2020), 401

Parkinson’s disease is a progressive neurodegenerative disorder often accompanied by impairment in articulation, phonation, prosody and fluency of speech. In fact, speech impairment is one of the earliest ... [more ▼]

Parkinson’s disease is a progressive neurodegenerative disorder often accompanied by impairment in articulation, phonation, prosody and fluency of speech. In fact, speech impairment is one of the earliest Parkinson’s disease symptoms, and may be used for early diagnosis. We present an experimental study of identification of Parkinson’s disease and assessment of disease progress from speech using Gaussian processes, which is further combined with Automatic Relevance Determination (ARD) for efficient feature selection. Hyperparameters of ARD covariance functions are learned for each individual feature; therefore, can be used for evaluation of their importance. In that way only a small subset of highly relevant acoustic features is selected, leading to models with better performance and lower complexity. The performance of the proposed method was assessed on two datasets: Parkinson’s disease detection dataset, which contains a range of biomedical voice measurements obtained from 31 subjects, 23 of them suffering from Parkinson’s disease and 8 healthy subjects; and Parkinson’s telemonitoring dataset, containing biomedical voice measurements collected from 42 Parkinson’s disease patients for estimation of the disease progress. Gaussian process classification with automatic relevance determination is able to successfully discriminate between Parkinson’s disease patients and healthy controls with 96.92% accuracy, outperforming Support Vector Machines and decision tree ensembles (random forests, boosted and bagged decision trees). The usability of Gaussian processes is further confirmed in regression task for tracking the progress of the disease. [less ▲]

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See detailA Personalized Sentiment Model with Textual and Contextual Information
Guo, Siwen UL; Höhn, Sviatlana UL; Schommer, Christoph UL

in The SIGNLL Conference on Computational Natural Language Learning, Hong Kong 3-4 November 2019 (2019, November)

In this paper, we look beyond the traditional population-level sentiment modeling and consider the individuality in a person's expressions by discovering both textual and contextual information. In ... [more ▼]

In this paper, we look beyond the traditional population-level sentiment modeling and consider the individuality in a person's expressions by discovering both textual and contextual information. In particular, we construct a hierarchical neural network that leverages valuable information from a person's past expressions, and offer a better understanding of the sentiment from the expresser's perspective. Additionally, we investigate how a person's sentiment changes over time so that recent incidents or opinions may have more effect on the person's current sentiment than the old ones. Psychological studies have also shown that individual variation exists in how easily people change their sentiments. In order to model such traits, we develop a modified attention mechanism with Hawkes process applied on top of a recurrent network for a user-specific design. Implemented with automatically labeled Twitter data, the proposed model has shown positive results employing different input formulations for representing the concerned information. [less ▲]

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See detailKünstliche Intelligenz für die Medizin
Schommer, Christoph UL

Article for general public (2019)

Innovative developments in Artificial Intelligence (AI), Data Science and Computer Engineering have led to far-reaching consequences for many areas of economic and social life, including medicine and ... [more ▼]

Innovative developments in Artificial Intelligence (AI), Data Science and Computer Engineering have led to far-reaching consequences for many areas of economic and social life, including medicine and healthcare. This article presents some scientific innovations, but at the same time pleads for the inclusion of important factors such as explainability, ethics, comprehensibility and others. [less ▲]

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See detailTopic-based Historical Information Selection for Personalized Sentiment Analysis
Guo, Siwen UL; Höhn, Sviatlana UL; Schommer, Christoph UL

in European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Bruges 24-26 April 2019 (2019, April)

In this paper, we present a selection approach designed for personalized sentiment analysis with the aim of extracting related information from a user's history. Analyzing a person's past is key to ... [more ▼]

In this paper, we present a selection approach designed for personalized sentiment analysis with the aim of extracting related information from a user's history. Analyzing a person's past is key to modeling individuality and understanding the current state of the person. We consider a user's expressions in the past as historical information, and target posts from social platforms for which Twitter texts are chosen as exemplary. While implementing the personalized model PERSEUS, we observed information loss due to the lack of flexibility regarding the design of the input sequence. To compensate this issue, we provide a procedure for information selection based on the similarities in the topics of a user's historical posts. Evaluation is conducted comparing different similarity measures, and improvements are seen with the proposed method. [less ▲]

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See detailLooking into the Past: Evaluating the Effect of Time Gaps in a Personalized Sentiment Model
Guo, Siwen UL; Höhn, Sviatlana UL; Schommer, Christoph UL

in ACM/SIGAPP Symposium On Applied Computing, Limassol 8-12 April 2019 (2019, April)

This paper concerns personalized sentiment analysis, which aims at improving the prediction of the sentiment expressed in a piece of text by considering individualities. Mostly, this is done by relating ... [more ▼]

This paper concerns personalized sentiment analysis, which aims at improving the prediction of the sentiment expressed in a piece of text by considering individualities. Mostly, this is done by relating to a person’s past expressions (or opinions), however the time gaps between the messages are not considered in the existing works. We argue that the opinion at a specific time point is affected more by recent opinions that contain related content than the earlier or unrelated ones, thus a sentiment model ought to include such information in the analysis. By using a recurrent neural network with an attention layer as a basic model, we introduce three cases to integrate time gaps in the model. Evaluated on Twitter data with frequent users, we have found that the performance is improved the most by including the time information in the Hawkes process, and it is also more effective to add the time information in the attention layer than at the input. [less ▲]

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See detailEin europäisches CERN für die Künstliche Intelligenz
Schommer, Christoph UL

Article for general public (2019)

Ein europäisches CERN für die Künstliche Intelligenz

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See detailThe LuNa Open Toolbox for the Luxembourgish Language
Sirajzade, Joshgun UL; Schommer, Christoph UL

in Perner, Petra (Ed.) Advances in Data Mining, Applications and Theoretical Aspects, Poster Proceedings 2019 (2019)

Despite some recent work, the ongoing research for the processing of Luxembourgish is still largely in its infancy. While a rich variety of linguistic processing tools exist, especially for English, these ... [more ▼]

Despite some recent work, the ongoing research for the processing of Luxembourgish is still largely in its infancy. While a rich variety of linguistic processing tools exist, especially for English, these software tools offer little scope for the Luxembourgish language. LuNa (a Tool for Luxembourgish National Corpus) is an Open Toolbox that allows researchers to annotate a text corpus written in Luxembourgish language and to build/query an annotated corpus. The aim of the paper is to demonstrate the components of the system and its usage for Machine Learning applications like Topic Modelling and Sentiment Detection. Overall, LuNa bases on a XML-database to store the data and to define the XML scheme, it offers a Graphical User Interface (GUI) for a linguistic data preparation such as tokenization, Part-Of-Speech tagging, and morphological analysis -- just to name a few. [less ▲]

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See detailPersonalized Sentiment Analysis and a Framework with Attention-Based Hawkes Process Model
Guo, Siwen UL; Höhn, Sviatlana UL; Xu, Feiyu et al

in Agents and Artificial Intelligence (2019)

People use different words when expressing their opinions. Sentiment analysis as a way to automatically detect and categorize people’s opinions in text, needs to reflect this diversity and individuality ... [more ▼]

People use different words when expressing their opinions. Sentiment analysis as a way to automatically detect and categorize people’s opinions in text, needs to reflect this diversity and individuality. One possible approach to analyze such traits is to take a person’s past opinions into consideration. In practice, such a model can suffer from the data sparsity issue, thus it is difficult to develop. In this article, we take texts from social platforms and propose a preliminary model for evaluating the effectiveness of including user information from the past, and offer a solution for the data sparsity. Furthermore, we present a finer-designed, enhanced model that focuses on frequent users and offers to capture the decay of past opinions using various gaps between the creation time of the text. An attention-based Hawkes process on top of a recurrent neural network is applied for this purpose, and the performance of the model is evaluated with Twitter data. With the proposed framework, positive results are shown which opens up new perspectives for future research. [less ▲]

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See detailAn Approach to Incorporate Emotions in a Chatbot with Seq2Seq Model
Zheng, Yaqiong; Guo, Siwen UL; Schommer, Christoph UL

in Benelux Conference on Artificial Intelligence, ‘s-Hertogenbosch 8-9 November 2018 (2018, November)

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See detailA Bilingual Study for Personalized Sentiment Model PERSEUS
Guo, Siwen UL; Schommer, Christoph UL

Scientific Conference (2018, September 10)

This paper investigates the significance of analyzing language preferences in personalized sentiment analysis. Motivated by the considerable amount of text generated by multilingual speakers on social ... [more ▼]

This paper investigates the significance of analyzing language preferences in personalized sentiment analysis. Motivated by the considerable amount of text generated by multilingual speakers on social platforms, we focus on constructing a single model that is able to analyze sentiments in a multilingual environment. In particular, Twitter texts are used in this research where the choice of language can be switched at a message-, sentence-, word- or topic-level. To represent and analyze the text, we extract concepts and main topics from the text and apply a recurrent neural network with attention mechanism in order to learn the relation between the lexical choices and the opinions of each sentiment holder. The personalized sentiment model PERSEUS is applied as the central structure of this research. Moreover, a language index is added to each concept to enable multilingual analysis, which provides a solution for analyzing code-switching in the text as well. In this work, English and German are chosen for a pilot study, and an artificial corpus is created to evaluate the situation with multilingual speakers. [less ▲]

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See detailMaschinen nach menschlichem Vorbild
Leyers, Pierre; Schommer, Christoph UL

Article for general public (2018)

Die Forschung im Bereich der künstlichen Intelligenz (KI) hat sich seit den Anfängen Mitte der 1950er-Jahre des vorigen Jahrhunderts erst schleppend entwickelt. Heutzutage schaffen Hochleitungsrechner ... [more ▼]

Die Forschung im Bereich der künstlichen Intelligenz (KI) hat sich seit den Anfängen Mitte der 1950er-Jahre des vorigen Jahrhunderts erst schleppend entwickelt. Heutzutage schaffen Hochleitungsrechner, die riesige Datenmengen blitzschnell verarbeiten, kombiniert mit künstlicher Intelligenz neuartige und spannende Perspektiven. Prof. Dr. Christoph Schommer beschäftigt sich an der Universität Luxemburg mit der Entwicklung von KI-Systemen, insbesondere im Bereich des maschinellen Lernens. KISysteme zeichnen sich dadurch aus, dass sie lernen und sich selbst und andere programmieren und weiterentwickeln können. Der Informatik-Experte ist überzeugt, dass die Menschen auch künftig die digitale Schlüsseltechnologie, die sie erschaffen, beherrschen können. [less ▲]

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See detailA Dynamic Associative Memory for Distant Reading
Kamlovskaya, Ekaterina UL; Schommer, Christoph UL; Sirajzade, Joshgun UL

in International Conference on Artificial Intelligence Humanities, Book of Abstracts (2018, August 16)

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See detailMind and Language. AI in an Example of Similar Patterns of Luxembourgish Language
Sirajzade, Joshgun UL; Schommer, Christoph UL

in International Conference on Artificial Intelligence Humanities, Book of Abstracts (2018, August 16)

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See detailPsychological, cognitive factors and contextual influences in pain and pain-related suffering as revealed by a combined qualitative and quantitative assessment approach
Bustan S; Gonzalez-Roldan AM; Schommer, Christoph UL et al

in PLoS ONE (2018)

Previous psychophysiological research suggests that pain measurement needs to go beyond the assessment of Pain Intensity and Unpleasantness by adding the evaluation of Pain-Related Suffering. Based on ... [more ▼]

Previous psychophysiological research suggests that pain measurement needs to go beyond the assessment of Pain Intensity and Unpleasantness by adding the evaluation of Pain-Related Suffering. Based on this three-dimensional approach, we attempted to elucidate who is more likely to suffer by identifying reasons that may lead individuals to report Pain and Pain-Related Suffering more than others. A sample of 24 healthy participants (age range 18±33) underwent four different sessions involving the evaluation of experimentally induced phasic and tonic pain. We applied two decision tree models to identify variables (selected from psychological questionnaires regarding pain and descriptors from post-session interviews) that provided a qualitative characterization of the degrees of Pain Intensity, Unpleasantness and Suffering and assessed the respective impact of contextual influences. The overall classification accuracy of the decision trees was 75% for Intensity, 77% for Unpleasantness and 78% for Pain-Related Suffering. The reporting of suffering was predominantly associated with fear of pain and active cognitive coping strategies, pain intensity with bodily competence conveying strength and resistance and unpleasantness with the degree of fear of pain and catastrophizing. These results indicate that the appraisal of the three pain dimensions was largely determined by stable psychological constructs. They also suggest that individuals manifesting higher active coping strategies may suffer less despite enhanced pain and those who fear pain may suffer even under low pain. The second decision tree model revealed that suffering did not depend on pain alone, but that the complex rating-related decision making can be shifted by situational factors (context, emotional and cognitive). The impact of coping and fear of pain on individual Pain-Related Suffering may highlight the importance of improving cognitive coping strategies in clinical settings. [less ▲]

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See detailProceedings - 2017 ILILAS Distinguished Lectures
Bouvry, Pascal UL; Bisdorff, Raymond; Schommer, Christoph UL et al

Report (2018)

The Proceedings summarizes the 12 lectures that have taken place within the ILIAS Dinstguished Lecture series 2017. It contains a brief abstract of the talks as well as some additional information about ... [more ▼]

The Proceedings summarizes the 12 lectures that have taken place within the ILIAS Dinstguished Lecture series 2017. It contains a brief abstract of the talks as well as some additional information about each speaker. [less ▲]

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See detailPERSEUS: A Personalization Framework for Sentiment Categorization with Recurrent Neural Network
Guo, Siwen UL; Höhn, Sviatlana; Xu, Feiyu et al

in International Conference on Agents and Artificial Intelligence , Funchal 16-18 January 2018 (2018, January)

This paper introduces the personalization framework PERSEUS in order to investigate the impact of individuality in sentiment categorization by looking into the past. The existence of diversity between ... [more ▼]

This paper introduces the personalization framework PERSEUS in order to investigate the impact of individuality in sentiment categorization by looking into the past. The existence of diversity between individuals and certain consistency in each individual is the cornerstone of the framework. We focus on relations between documents for user-sensitive predictions. Individual’s lexical choices act as indicators for individuality, thus we use a concept-based system which utilizes neural networks to embed concepts and associated topics in text. Furthermore, a recurrent neural network is used to memorize the history of user’s opinions, to discover user-topic dependence, and to detect implicit relations between users. PERSEUS also offers a solution for data sparsity. At the first stage, we show the benefit of inquiring a user-specified system. Improvements in performance experimented on a combined Twitter dataset are shown over generalized models. PERSEUS can be used in addition to such generalized systems to enhance the understanding of user’s opinions. [less ▲]

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