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  • 1.
    Alfalahi, Alyaa
    et al.
    Stockholm University.
    Skeppstedt, Maria
    Linnaeus University, Faculty of Technology, Department of Computer Science. Gavagai AB, Sweden.
    Ahlblom, Rickard
    Stockholm University.
    Baskalayci, Roza
    Stockholm University.
    Henriksson, Aron
    Stockholm University.
    Asker, Lars
    Stockholm University.
    Paradis, Carita
    Lund University.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Expanding a Dictionary of Marker Words for Uncertainty and Negation Using Distributional Semantics2015In: Proceedings of the 6th International Workshop on Health Text Mining and Information Analysis (Louhi '15): Short Paper Track / [ed] Cyril Grouin, Thierry Hamon, Aurélie Névéol, and Pierre Zweigenbaum, Association for Computational Linguistics , 2015, p. 90-96Conference paper (Refereed)
    Abstract [en]

    Approaches to determining the factuality of diagnoses and findings in clinical text tend to rely on dictionaries of marker words for uncertainty and negation. Here, a method for semi-automatically expanding a dictionary of marker words using distributional semantics is presented and evaluated. It is shown that ranking candidates for inclusion according to their proximity to cluster centroids of semantically similar seed words is more successful than ranking them according to proximity to each individual seed word. 

  • 2.
    Ekberg, Lena
    et al.
    Lund University.
    Paradis, CaritaVäxjö University, Faculty of Humanities and Social Sciences, School of Humanities.
    Evidentiality2009Collection (editor) (Other (popular science, discussion, etc.))
  • 3.
    Ekberg, Lena
    et al.
    Lunds Universitet.
    Paradis, Carita
    Växjö University, Faculty of Humanities and Social Sciences, School of Humanities.
    Evidentiality in language and cognition2009In: Functions of language, ISSN 0929-998X, E-ISSN 1569-9765, Vol. 16, no 1, p. 5-7Article in journal (Refereed)
  • 4.
    Engström, Robin
    et al.
    Lund University.
    Paradis, Carita
    Lund University.
    The In-group and Out-groups of the British National Party and the UK Independence Party: A Corpus-Based Discourse-Historical Analysis2015In: Journal of Language and Politics, ISSN 1569-2159, E-ISSN 1569-9862, Vol. 14, no 4, p. 501-527Article in journal (Refereed)
    Abstract [en]

    This article investigates the self-presentation and the construction of immigration discourses in articles and policy documents published by the British National Party (BNP) and the UK Independence Party (UKIP). By combining corpus analysis with the Discourse-Historical Approach to Critical Discourse Analysis, a picture emerges of two parties whose use of language is governed by the same principle of differentiation. Fundamental to the BNP’s and UKIP’s language is the dichotomy in-group/out-group. The in-group analysis investigates the parties’ choice of form of self-representation, claims to unique competence, denial of attributes and mutual perception. The out-group analysis shows how the parties construct immigration, and focuses on the aspects of legal status, quantification and origin. The analyses suggest considerable lexical and conceptual overlapping in both in-group and out-group formation.

  • 5.
    Hommerberg, Charlotte
    et al.
    Linnaeus University, Faculty of Arts and Humanities, Department of Languages.
    Paradis, Carita
    Lund University, Sweden.
    Constructing credibility in wine discourse: Modes of knowing, temporality and epistemic control2014In: Subjectivity and Epistemicity: Corpus, discourse, and literary approaches to stance / [ed] Glynn, D. & Sjölin, M., Lund: Lund University , 2014, p. 211-238Chapter in book (Refereed)
  • 6. Jones, Steven
    et al.
    Paradis, Carita
    Växjö University, Faculty of Humanities and Social Sciences, School of Humanities.
    Murphy, M. Lynne
    Willners, Caroline
    Googling for 'opposites': a web-based study of antonym canonicity2007In: Corpora, Vol. 2, no 2, p. 129-154Article in journal (Refereed)
    Abstract [en]

    This paper seeks to explain why some semantically-opposed word pairs are more likely to be seen as canonical antonyms (for example, cold/hot) than others (icy/scorching, cold/fiery, freezing/hot, etc.). Specifically, it builds on research which has demonstrated that, in discourse, antonyms are inclined to favour certain frames, such as ‘X and Y alike’, ‘from X to Y’ and ‘either X or Y’ (Justeson and Katz, 1991; etc.), and to serve a limited range of discourse functions (Jones, 2002). Our premise is that the more canonical an antonym pair is, the greater the fidelity with which it will occupy such frames. Since an extremely large corpus is needed to identify meaningful patterns of co-occurrence, we turn to Internet data for this research. As well as enabling the notion of antonym canonicity to be revisited from a more empirical perspective, this approach also allows us to evaluate the appropriateness (and assess the risks) of using the World Wide Web as a corpus for studies into certain types of low-frequency textual phenomena.

  • 7.
    Kerren, Andreas
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Kyusakova, Mimi
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Paradis, Carita
    Lund University, Centre for Languages and Literature.
    From Culture to Text to Interactive Visualization of Wine Reviews2013In: Knowledge Visualization Currents: From Text to Art to Culture, Part II / [ed] F.T. Marchese and E. Banissi, London: Springer, 2013, p. 85-110Chapter in book (Refereed)
    Abstract [en]

    On the basis of a large corpus of wine reviews, this chapter proposes a range of interactive visualization techniques that are useful for linguistic exploration and analysis of lexical, grammatical and discursive patterns in text. Our visualization tool allows linguists and others to make comparisons of visual, olfactory, gustatory and textual properties of different wines for example from different countries, from different grape varieties, or from different vintages. It also supports the visual exploration of sensory descriptions as well as confirmatory investigations of text and discourse. Besides a more technical discussion of our visualization approach, we also provide a more general overview of text and corpus visualizations and highlight linguistic challenges that we had to address during the development phase.

  • 8.
    Kerren, Andreas
    et al.
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Prangova, Mimi
    Linnaeus University, Faculty of Science and Engineering, School of Computer Science, Physics and Mathematics.
    Paradis, Carita
    Lund University, Centre for Languages and Literature.
    Visualization of Sensory Perception Descriptions2011In: Proceedings of the International Conference on Information Visualisation, IEEE, 2011, p. 135-144Conference paper (Refereed)
    Abstract [en]

    On the basis of a large corpus of wine reviews, this paper proposes a range of interactive visualization techniques that are useful for linguistic exploration and analysis of lexical, grammatical and discursive patterns in text. Our visualization tool allows linguists and others to make comparisons of visual, olfactory, gustatory and textual properties of different wines from different parts of the worlds, from different grape varieties, or from different vintages. It also supports the immediate creation of visual profiles for descriptions of sensory perceptions for exploratory purposes as well as for purposes of confirmatory investigations of linguistic patterns in text and discourse and their correlations to metadata variables. 

  • 9.
    Kucher, Kostiantyn
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Paradis, Carita
    Lund University.
    Sahlgren, Magnus
    Gavagai AB.
    Methodology and Applications of Visual Stance Analysis: An Interactive Demo2016In: International Symposium on Digital Humanities, Växjö 7-8 November 2016: Book of Abstracts, Linnaeus University , 2016, p. 56-57Conference paper (Refereed)
    Abstract [en]

    Analysis of stance in textual data can reveal the attitudes of speakers, ranging from general agreement/disagreement with other speakers to fine-grained indications of wishes and emotions. The implementation of an automatic stance classifier and corresponding visualization techniques facilitates the analysis of human communication and social media texts. Furthermore, scholars in Digital Humanities could also benefit from such an approach by applying it for literature studies. For example, a researcher could explore the usage of such stance categories as certainty or prediction in a novel. Analysis of such abstract categories in longer texts would be complicated or even impossible with simpler tools such as regular expression search.

    Our research on automatic and visual stance analysis is concerned with multiple theoretical and practical challenges in linguistics, computational linguistics, and information visualization. In this interactive demo, we demonstrate our web-based visual analytics system called ALVA, which is designed to support the text data annotation and stance classifier training stages. 

  • 10.
    Kucher, Kostiantyn
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Paradis, Carita
    Lund University.
    Sahlgren, Magnus
    Gavagai AB.
    Visual Analysis of Stance Markers in Online Social Media2014In: Poster Abstracts of IEEE VIS 2014, 2014Conference paper (Refereed)
    Abstract [en]

    Stance in human communication is a linguistic concept relating to expressions of subjectivity such as the speakers’ attitudes and emotions. Taking stance is crucial for the social construction of meaning and can be useful for many application fields such as business intelligence, security analytics, or social media monitoring. In order to process large amounts of text data for stance analyses, linguists need interactive tools to explore the textual sources as well as the results of computational linguistics techniques. Both aspects are important for refining the analyses iteratively. In this work, we present a visual analytics tool for online social media text data and corresponding time-series that can be used to investigate stance phenomena and to refine the so-called stance markers collection. 

  • 11.
    Kucher, Kostiantyn
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Paradis, Carita
    Lund University .
    Sahlgren, Magnus
    Gavagai AB.
    Visual Analysis of Text Annotations for Stance Classification with ALVA2016In: EuroVis Posters 2016 / [ed] Tobias Isenberg & Filip Sadlo, Eurographics - European Association for Computer Graphics, 2016, p. 49-51Conference paper (Refereed)
    Abstract [en]

    The automatic detection and classification of stance taking in text data using natural language processing and machine learning methods create an opportunity to gain insight about the writers’ feelings and attitudes towards their own and other people’s utterances. However, this task presents multiple challenges related to the training data collection as well as the actual classifier training. In order to facilitate the process of training a stance classifier, we propose a visual analytics approach called ALVA for text data annotation and visualization. Our approach supports the annotation process management and supplies annotators with a clean user interface for labeling utterances with several stance categories. The analysts are provided with a visualization of stance annotations which facilitates the analysis of categories used by the annotators. ALVA is already being used by our domain experts in linguistics and computational linguistics in order to improve the understanding of stance phenomena and to build a stance classifier for applications such as social media monitoring. 

  • 12.
    Kucher, Kostiantyn
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Paradis, Carita
    Lund University.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    DoSVis: Document Stance Visualization2018In: Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP '18) / [ed] Alexandru C. Telea, Andreas Kerren, and José Braz, SciTePress, 2018, Vol. 3, p. 168-175Conference paper (Refereed)
    Abstract [en]

    Text visualization techniques often make use of automatic text classification methods. One of such methods is stance analysis, which is concerned with detecting various aspects of the writer’s attitude towards utterances expressed in the text. Existing text visualization approaches for stance classification results are usually adapted to textual data consisting of individual utterances or short messages, and they are often designed for social media or debate monitoring tasks. In this paper, we propose a visualization approach called DoSVis (Document Stance Visualization) that focuses instead on individual text documents of a larger length. DoSVis provides an overview of multiple stance categories detected by our classifier at the utterance level as well as a detailed text view annotated with classification results, thus supporting both distant and close reading tasks. We describe our approach by discussing several application scenarios involving business reports and works of literature. 

  • 13.
    Kucher, Kostiantyn
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Paradis, Carita
    Lund University.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    The State of the Art in Sentiment Visualization2018In: Computer graphics forum (Print), ISSN 0167-7055, E-ISSN 1467-8659, Vol. 37, no 1, p. 71-96, article id CGF13217Article in journal (Refereed)
    Abstract [en]

    Visualization of sentiments and opinions extracted from or annotated in texts has become a prominent topic of research over the last decade. From basic pie and bar charts used to illustrate customer reviews to extensive visual analytics systems involving novel representations, sentiment visualization techniques have evolved to deal with complex multidimensional data sets, including temporal, relational, and geospatial aspects. This contribution presents a survey of sentiment visualization techniques based on a detailed categorization. We describe the background of sentiment analysis, introduce a categorization for sentiment visualization techniques that includes 7 groups with 35 categories in total, and discuss 132 techniques from peer-reviewed publications together with an interactive web-based survey browser. Finally, we discuss insights and opportunities for further research in sentiment visualization. We expect this survey to be useful for visualization researchers whose interests include sentiment or other aspects of text data as well as researchers and practitioners from other disciplines in search of efficient visualization techniques applicable to their tasks and data. 

  • 14.
    Kucher, Kostiantyn
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Paradis, Carita
    Lund University.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Visual Analysis of Sentiment and Stance in Social Media Texts2018In: EuroVis 2018 - Posters / [ed] Anna Puig and Renata Raidou, Eurographics - European Association for Computer Graphics, 2018, p. 49-51Conference paper (Refereed)
    Abstract [en]

    Despite the growing interest for visualization of sentiments and emotions in textual data, the task of detecting and visualizing various stances is not addressed well by the existing approaches. The challenges associated with this task include development of the underlying computational methods and visualization of the corresponding multi-label stance classification results. In this poster abstract, we describe the ongoing work on a visual analytics platform called StanceVis Prime, which is designed for analysis of sentiment and stance in temporal text data from various social media data sources. Our approach consumes documents from several text stream sources, applies sentiment and stance classification, and provides end users with both an overview of the resulting data series and a detailed view for close reading and examination of the classifiers’ output. The intended use case scenarios for StanceVis Prime include social media monitoring and research in sociolinguistics.

  • 15.
    Kucher, Kostiantyn
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Paradis, Carita
    Lund University.
    Sahlgren, Magnus
    Swedish Research Institute (RISE SICS).
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Active Learning and Visual Analytics for Stance Classification with ALVA2017In: ACM Transactions on Interactive Intelligent Systems (TiiS), ISSN 2160-6455, Vol. 7, no 3, article id 14Article in journal (Refereed)
    Abstract [en]

    The automatic detection and classification of stance (e.g., certainty or agreement) in text data using natural language processing and machine learning methods create an opportunity to gain insight into the speakers' attitudes towards their own and other people's utterances. However, identifying stance in text presents many challenges related to training data collection and classifier training. In order to facilitate the entire process of training a stance classifier, we propose a visual analytics approach, called ALVA, for text data annotation and visualization. ALVA's interplay with the stance classifier follows an active learning strategy in order to select suitable candidate utterances for manual annotation. Our approach supports annotation process management and provides the annotators with a clean user interface for labeling utterances with multiple stance categories. ALVA also contains a visualization method to help analysts of the annotation and training process gain a better understanding of the categories used by the annotators. The visualization uses a novel visual representation, called CatCombos, which groups individual annotation items by the combination of stance categories. Additionally, our system makes a visualization of a vector space model available that is itself based on utterances. ALVA is already being used by our domain experts in linguistics and computational linguistics in order to improve the understanding of stance phenomena and to build a stance classifier for applications such as social media monitoring.

  • 16.
    Kucher, Kostiantyn
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Schamp-Bjerede, Teri
    Lund University.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Paradis, Carita
    Lund University.
    Sahlgren, Magnus
    Gavagai AB.
    Visual Analysis of Online Social Media to Open Up the Investigation of Stance Phenomena2016In: Information Visualization, ISSN 1473-8716, E-ISSN 1473-8724, Vol. 15, no 2, p. 93-116Article in journal (Refereed)
    Abstract [en]

    Online social media are a perfect text source for stance analysis. Stance in human communication is concerned with speaker attitudes, beliefs, feelings and opinions. Expressions of stance are associated with the speakers' view of what they are talking about and what is up for discussion and negotiation in the intersubjective exchange. Taking stance is thus crucial for the social construction of meaning. Increased knowledge of stance can be useful for many application fields such as business intelligence, security analytics, or social media monitoring. In order to process large amounts of text data for stance analyses, linguists need interactive tools to explore the textual sources as well as the processed data based on computational linguistics techniques. Both original texts and derived data are important for refining the analyses iteratively. In this work, we present a visual analytics tool for online social media text data that can be used to open up the investigation of stance phenomena. Our approach complements traditional linguistic analysis techniques and is based on the analysis of utterances associated with two stance categories: sentiment and certainty. Our contributions include (1) the description of a novel web-based solution for analyzing the use and patterns of stance meanings and expressions in human communication over time; and (2) specialized techniques used for visualizing analysis provenance and corpus overview/navigation. We demonstrate our approach by means of text media on a highly controversial scandal with regard to expressions of anger and provide an expert review from linguists who have been using our tool.

  • 17.
    Martins, Rafael Messias
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Simaki, Vasiliki
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science. Lund University.
    Kucher, Kostiantyn
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Paradis, Carita
    Lund University.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    StanceXplore: Visualization for the Interactive Exploration of Stance in Social Media2017Conference paper (Refereed)
    Abstract [en]

    The use of interactive visualization techniques in Digital Humanities research can be a useful addition when traditional automated machine learning techniques face difficulties, as is often the case with the exploration of large volumes of dynamic—and in many cases, noisy and conflicting—textual data from social media. Recently, the field of stance analysis has been moving from a predominantly binary approach—either pro or con—to a multifaceted one, where each unit of text may be classified as one (or more) of multiple possible stance categories. This change adds more layers of complexity to an already hard problem, but also opens up new opportunities for obtaining richer and more relevant results from the analysis of stancetaking in social media. In this paper we propose StanceXplore, a new visualization for the interactive exploration of stance in social media. Our goal is to offer DH researchers the chance to explore stance-classified text corpora from different perspectives at the same time, using coordinated multiple views including user-defined topics, content similarity and dissimilarity, and geographical and temporal distribution. As a case study, we explore the activity of Twitter users in Sweden, analyzing their behavior in terms of topics discussed and the stances taken. Each textual unit (tweet) is labeled with one of eleven stance categories from a cognitive-functional stance framework based on recent work. We illustrate how StanceXplore can be used effectively to investigate multidimensional patterns and trends in stance-taking related to cultural events, their geographical distribution, and the confidence of the stance classifier. 

  • 18.
    Paradis, Carita
    Växjö University, Faculty of Humanities and Social Sciences, School of Humanities.
    Adjectives and boundedness2001In: Cognitive Linguistics, Vol. 12, p. 247-271Article in journal (Refereed)
  • 19.
    Paradis, Carita
    Växjö University, Faculty of Humanities and Social Sciences, School of Humanities.
    Between epistemic modality and degree: the case of really2003In: Modality in contemporary English, Mouton de Gruyter , 2003Chapter in book (Other (popular science, discussion, etc.))
  • 20.
    Paradis, Carita
    Växjö University, Faculty of Humanities and Social Sciences, School of Humanities. Engelska.
    Configurations, construals and change:: expressions of DEGREE2008In: English Language and Linguistics, ISSN 1360-6743, E-ISSN 1469-4379, Vol. 2, p. 317-343Article in journal (Refereed)
  • 21. Paradis, Carita
    Degree modifiers of adjectives in spoken British English1997Book (Other (popular science, discussion, etc.))
  • 22.
    Paradis, Carita
    Växjö University, Faculty of Humanities and Social Sciences, School of Humanities.
    Is the notion of linguistic competence relevant in Cognitive Linguistics?2003In: Annual Review of Cognitive Linguistics, Vol. 1, p. 247-271Article in journal (Refereed)
  • 23.
    Paradis, Carita
    Växjö University, Faculty of Humanities and Social Sciences, School of Humanities.
    It’s well weird. Degree modifiers of adjectives revisited: the nineties2000In: Corpora galore: analyses and techniques in describing English, 2000, p. 147-160Chapter in book (Other (popular science, discussion, etc.))
  • 24.
    Paradis, Carita
    Växjö University, Faculty of Humanities and Social Sciences, School of Humanities.
    Ontologies and construals in lexical semantic2005In: Axiomathes, Vol. 15, p. 541-573Article in journal (Refereed)
  • 25.
    Paradis, Carita
    Växjö University, Faculty of Humanities and Social Sciences, School of Humanities.
    Prime time: the middle construction in wine drinking recommendations.2009In: Corpus and discourse - and stuff: Papers in honour of Karin Aijmer / [ed] Bowen, R., Moberg, M.& S. Ohlander, 2009Chapter in book (Other (popular science, discussion, etc.))
  • 26.
    Paradis, Carita
    Växjö University, Faculty of Humanities and Social Sciences, School of Humanities.
    Reinforcing adjectives. A cognitive semantic perspective on grammaticalization.2000In: Generative theory and corpus studies. Topics in English Linguistics, Mouton de Gruyter , 2000, p. 233-258Chapter in book (Other (popular science, discussion, etc.))
  • 27.
    Paradis, Carita
    Växjö University, Faculty of Humanities and Social Sciences, School of Humanities.
    "This beauty should drink well for 10-12 years": a note on recommendations as semantic middles2009In: Text & Talk, ISSN 1860-7330, E-ISSN 1860-7349, Vol. 29, no 1, p. 53-73Article in journal (Refereed)
    Abstract [en]

    This paper capitalizes on the types of portrayal of the event in recommendations of prime drinking time using data from wine tasting notes. It argues that the weakly deontic nature of recommendation fosters semantic middles; not only the middle construction proper such as This beauty should drink well for 10–12 years, but recommendation as such is characterized by a mid-degree of transfer of action in the utterances. In spite of the fact that the event expressed in recommendations involves highly transitive structures, i.e., an actor, an undergoer, and a dynamic event, the actual staging of the recommendations at the time of use is similar to the staging of the middle construction. The various formal differences between the recommendations are examined in terms of the relative salience of the roles played by the semantic participants and the dynamicity of the event. The upshot of the study is that the middle quality is directly derived from the discourse function of recommendation.

  • 28.
    Paradis, Carita
    Växjö University, Faculty of Humanities and Social Sciences, School of Humanities.
    Where does metonymy stop? Senses, facets and active zones.2004In: metaphor and Symbol, Vol. 19, no 4, p. 245-264Article in journal (Refereed)
  • 29.
    Paradis, Carita
    et al.
    Växjö University, Faculty of Humanities and Social Sciences, School of Humanities.
    Ekberg, Lena
    Functions of Language: Evidentiality i language and cognition2009Collection (editor) (Other academic)
  • 30.
    Paradis, Carita
    et al.
    Lund University.
    Hommerberg, Charlotte
    Linnaeus University, Faculty of Arts and Humanities, Department of Languages.
    We drink with our eyes first: The web of sensory perceptions, aesthetic experiences and mixed imagery in wine reviews2016In: Mixing metaphor / [ed] Raymond W. Gibbs Jr., Amsterdam & Philadelpia: John Benjamins Publishing Company, 2016, p. 179-201Chapter in book (Refereed)
    Abstract [en]

    This chapter analyzes the language resources that writers have at their disposal to describe their experience of the web of sensory perceptions that are evoked in the wine tasting practice. The task of the writer is to provide a mental understanding of the sensations as well as a prehension of the experiences. We show that this involves the weaving together of the senses, starting with the sight of the wine, followed by a description that is iconic with the wine tasting procedure. The descriptors are systematically used cross-modally both through ontological crossovers and through longer stretches of mixed imageryWe also show how the socio-cultural context of wine consumption correlates with the types of imagery used in wine descriptions.

  • 31.
    Paradis, Carita
    et al.
    Växjö University, Faculty of Humanities and Social Sciences, School of Humanities.
    Murphy, Lynen
    University of Sussex.
    Willners, Caroline
    Lund University.
    Jones, Steven
    University of Manchester.
    Discourse functions of antonymy: a cross-linguistic investigation of Swedish and English2009In: Journal of Pragmatics, ISSN 0378-2166, E-ISSN 1879-1387, Vol. 41, no 11, p. 2159-2184Article in journal (Refereed)
    Abstract [en]

    Jones (2002) identified several discourse functions of antonymy, each of which is loosely associated with a number of contrastive constructions in written English. Subsequent work (Jones, 2006; Jones and Murphy, 2005; Murphy and Jones, 2008) demonstrated that these functions are found in other modalities/registers of English, albeit with some differences in distribution. This article takes a first step in exploring discourse functions of antonymy in a language other than English. Because binary contrast has the potential to interact in different ways with the values and thought patterns of different cultures, we hypothesized that other languages differ from English in the ways in which antonyms are used in discourse. In this study of antonyms in Swedish, translational near-equivalents of pairs used by Jones were searched in the Swedish Parole corpus, and more than 4300 instances of co-occurring antonyms were found and analyzed in their sentential contexts. While the same range of antonym discourse functions is found in English and Swedish, the proportions of those functions differ significantly between the two languages. This paper both describes their functions (and the form of the functions) in Swedish and reflects on the similarities and differences with English. We ascribe some of the differences to the idiomaticity of certain componential expressions and discuss the possibility that certain cultural values affect some categories.

  • 32.
    Paradis, Carita
    et al.
    Växjö University, Faculty of Humanities and Social Sciences, School of Humanities.
    Murphy, Lynne
    University of Sussex.
    Willners, Caroline
    Lund University.
    Introduction: lexical contrast in discourse2009In: Journal of Pragmatics, ISSN 0378-2166, E-ISSN 1879-1387, Vol. 41, no 11, p. 2137-2139Article in journal (Refereed)
  • 33.
    Paradis, Carita
    et al.
    Växjö University, Faculty of Humanities and Social Sciences, School of Humanities.
    Willners, Caroline
    Antonyms in dictionary entries: methodological aspects2007In: Studia Linguistica, Vol. 61, no 3, p. 261-277Article in journal (Refereed)
    Abstract [en]

    This paper takes the treatment of antonymy in Collins COBUILD Advanced Learner’s English Dictionary (2003) as the point of departure for a discussion about the principles of antonym inclusion in dictionaries and corpus methodologies in lexicology. Ccaled includes canonical antonyms such as good/bad and dead/alive, as well as more contextually restricted pairings such as hot/mild and flat/fizzy. The vast majority of the antonymic pairings in the dictionary are adjectives. Most of the antonyms are morphologically different from the headwords they define and typically do not involve antonymic affixes such as non–, un– or –less. Only just over one-third of the total number of pairs is given in both directions. The principles for when antonyms are included in ccaled are not transparent. We propose an initial top-down corpus-driven method to support decisions about antonym selection and inclusion.

  • 34.
    Paradis, Carita
    et al.
    Växjö University, Faculty of Humanities and Social Sciences, School of Humanities. Engelska.
    Willners, Caroline
    antonymy and negation: the boundedness hypothesis2006In: Journal of Pragmatics, Vol. 38, no 7, p. 1051-1080Article in journal (Other (popular science, discussion, etc.))
    Abstract [en]

    Abstract

    This paper investigates the interpretation of unbounded (scalar) adjective antonyms with and without

    negation such as (not) narrow – (not) wide and bounded adjective antonyms with and without negation such

    as (not) dead – (not) alive as well as their interpretations with approximating degree modifiers, fairly and

    almost, respectively. The investigation was designed to test the boundedness hypothesis, namely that the

    negator is sensitive to the configuration of the adjective in terms of BOUNDEDNESS. The data are Swedish and

    the results of the experiments show that negated unbounded adjectives do not evoke the interpretation of

    their antonyms, i.e. not wide does not equal ‘narrow’. The results of the experiments with bounded

    adjectives with and without negation showed that some of the negated adjectives were interpreted as

    synonyms of their antonyms, i.e. not alive equals ‘dead’. However, this pattern was not consistent across the

    bounded adjectives, since a number of them readily lent themselves to partial readings. Four types of

    bounded antonyms emerged from the participants’ judgements. For both unbounded and bounded

    adjectives, the interpretations of the approximating degree modifiers and the adjectives were not significantly

    different from the negated adjectives.

    # 2006 Elsevier B.V. All rights reserved.

  • 35.
    Paradis, Carita
    et al.
    Växjö University, Faculty of Humanities and Social Sciences, School of Humanities.
    Willners, Caroline
    Selecting antonyms for dictionary entries: methodlogical aspects2006Report (Other academic)
    Abstract [en]

    This paper investigates the treatment of antonymy in Collins COBUILD Advanced Learner’s English Dictionary (2003) in order to find out what kinds of headwords are provided with antonyms as part of their definitions and also discusses the principles for antonym inclusion in the entries. CCALED includes canonical antonyms such as good/bad and dead/alive, as well as more contextually restricted pairings such as hot/mild and flat/fizzy. The vast majority of the antonymic pairings in the dictionary are adjectives. Most of the antonyms are morphologically different from the headwords they define and typically do not involve antonymic affixes such as non-, un- or -less. Only just over one-third of the total number of pairs is given in both directions. The principles for when antonyms are included in CCALED are not transparent to us. We propose a corpus-based method to support decisions about antonym selection and inclusion.

  • 36.
    Paradis, Carita
    et al.
    Växjö University, Faculty of Humanities and Social Sciences, School of Humanities.
    Willners, Caroline
    What a corpus-based dictionary tells us about antonyms2006In: Proceedings XII EURALEX International Congress / [ed] E. Corino, C. Maraello, C. Onesti, 2006, p. 213-220Conference paper (Other (popular science, discussion, etc.))
    Abstract [en]

    This paper investigates the treatment of antonymy in Collins COBUILD Advanced Learner’s English Dictionary (2003) in order to find out what kinds of headwords are provided with antonyms as part of their definitions and also discusses the principles for antonym inclusion in the entries. CALED includes canonical antonyms such as good/bad and dead/alive, as well as more contextually restricted pairings such as hot/mild and flat/fizzy. The vast majority of the antonymic pairings in the dictionary are adjectives. Most of the antonyms are morphologically different from the headwords they define and typically do not involve antonymic affixes such as non-, un- or -less. Only just over one-third of the total number of pairs are given in both directions. The principles for when antonyms are included in CCALED are not transparent to us.

  • 37.
    Paradis, Carita
    et al.
    Växjö University, Faculty of Humanities and Social Sciences, School of Humanities.
    Willners, Caroline
    Lund University.
    Jones, Steven
    University of Manchester.
    Good and bad opposites: using textual and experimental techniques to measure antonym canonicity2009In: The Mental Lexicon, ISSN 1871-1340, Vol. 4, no 3, p. 380-429Article in journal (Refereed)
    Abstract [en]

    The goal of this paper is to combine corpus methodology with experimentalmethods to gain insights into the nature of antonymy as a lexico-semantic relationand the degree of antonymic canonicity of word pairs in language and inmemory. Two approaches to antonymy in language are contrasted, the lexicalcategorical model and the cognitive prototype model. The results of the investigationsupport the latter model and show that different pairings have differentlevels of lexico-semantic affinity. At this general level of categorization, empiricalmethods converge; however, since they measure slightly different aspect of lexico-semantic opposability and affinity, and since the techniques of investigationare different in nature, we obtain slightly conflicting results at the more specificlevels. We conclude that some antonym pairs can be diagnosed as “canonical”on the strength of three indicators: textual co-occurrence, individual judgementabout “goodness” of opposition, and elicitation evidence.

  • 38.
    Paradis, Carita
    et al.
    Växjö University, Faculty of Humanities and Social Sciences, School of Humanities.
    Willners, CarolineLund Univeristy.Murphy, LynneUniveristy of Sussex.
    Journal of Pragmatics: Lexical contrast in discourse2009Collection (editor) (Other (popular science, discussion, etc.))
  • 39.
    Rahimi, Afshin
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Sahlgren, Magnus
    Gavagai AB, Sweden.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Paradis, Carita
    Lund University, Sweden.
    The StaViCTA Group Report for RepLab 2014: Reputation Dimensions Task2014In: Working Notes for CLEF 2014 Conference: Sheffield, UK, September 15-18, 2014 / [ed] Linda Cappellato, Nicola Ferro, Martin Halvey, Wessel Kraaij, CEUR-WS.org , 2014, p. 1519-1527Conference paper (Refereed)
    Abstract [en]

    In this paper we present our experiments on the RepLab 2014 Reputation Dimension task. RepLab is a competitive challenge for Reputation Management Systems. RepLab 2014’s reputation dimensions task focuses on categorization of Twitter messages with regard to standard reputation dimensions (such as performance, leadership, or innovation). Our approach only relies on the textual content of tweets and ignores both metadata and the content of URLs within tweets. We carried out several experiments focusing on different feature sets including bag of n-grams, distributional semantics features, and deep neural network representations. The results show that bag of bigram features with minimum frequency thresholding work quite well in reputation dimension task especially with regards to average F1 measure over all dimensions where two of our four submitted runs achieve highest and second highest scores. Our experiments also show that semi-supervised recursive autoencoders outperform other feature sets used in our experiments with regards to accuracy measure and is a promising subject of future research for improvements. 

  • 40.
    Schamp-Bjerede, Teri
    et al.
    Lund University.
    Paradis, Carita
    Lund University.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Sahlgren, Magnus
    Gavagai AB.
    Kucher, Kostiantyn
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Rahimi, Afshin
    Hedges and Tweets: Certainty and Uncertainty in Epistemic Markers in Microblog Feeds2014In: Book of abstracts: 47th Annual Meeting of the Societas Linguistica Europaea 11–14 September 2014, Adam Mickiewicz University, Poznań, Poland, 2014, p. 199-199Conference paper (Refereed)
  • 41.
    Schamp-Bjerede, Teri
    et al.
    Lund University, Sweden.
    Paradis, Carita
    Lund University, Sweden.
    Kucher, Kostiantyn
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Sahlgren, Magnus
    Gavagai AB, Sweden.
    New Perspectives on Gathering, Vetting and Employing Big Data from Online Social Media: An Interdisciplinary Approach2015In: Abstracts Booklet, ICAME 36: Words, Words, Words – Corpora and Lexis, 2015, p. 153-155Conference paper (Refereed)
  • 42.
    Schamp-Bjerede, Teri
    et al.
    Lund University.
    Paradis, Carita
    Lund University.
    Kucher, Kostiantyn
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Sahlgren, Magnus
    Gavagai AB.
    The Signifier, Signified and Stance: Happy/Sad Emoticons as Emotionizers2014In: Book of Abstracts, IACS 2014, 2014, p. 219-219Conference paper (Refereed)
  • 43.
    Schamp-Bjerede, Teri
    et al.
    Lund University.
    Paradis, Carita
    Lund University.
    Kucher, Kostiantyn
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Sahlgren, Magnus
    Gavagai AB.
    Turning Face: Emoticons as Reinforcers/Attenuators2014Conference paper (Refereed)
  • 44.
    Simaki, Vasiliki
    et al.
    Lancaster University, UK ; Lund University.
    Panagiotis, Simakis
    XPLAIN, Greece.
    Paradis, Carita
    Lund University.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    Detection of Stance-Related Characteristics in Social Media Text2018In: Proceedings of the 10th Hellenic Conference on Artificial Intelligence (SETN '18), ACM Publications, 2018, article id 38Conference paper (Refereed)
    Abstract [en]

    In this paper, we present a study for the identification of stance-related features in text data from social media. Based on our previous work on stance and our findings on stance patterns, we detected stance-related characteristics in a data set from Twitter and Facebook. We extracted various corpus-, quantitative- and computational-based features that proved to be significant for six stance categories (contrariety, hypotheticality, necessity, prediction, source of knowledge, and uncertainty), and we tested them in our data set. The results of a preliminary clustering method are presented and discussed as a starting point for future contributions in the field. The results of our experiments showed a strong correlation between different characteristics and stance constructions, which can lead us to a methodology for automatic stance annotation of these data.

  • 45.
    Simaki, Vasiliki
    et al.
    Lancaster University, UK ; Lund University.
    Paradis, Carita
    Lund University.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM).
    A two-step procedure to identify stance constructions in discourse from political blogs2019In: Corpora, ISSN 1749-5032, E-ISSN 1755-1676, Vol. 14, no 3Article in journal (Refereed)
    Abstract [en]

    The Brexit Blog Corpus (BBC) is a collection of texts extracted from political blogs, which, in a recent study, was annotated according to a cognitive-functional stance framework by two independent annotators (Annotator A and B) using semantic criteria (Simaki et al. 2017). The goal was to label the stance or stances taken based on the overall meaning of a set of utterances. The annotators were not instructed to identify the lexical forms that were used to express the stances. In this study, we make use of those stance labelled utterances as a springboard to approach stance-taking in text from the opposite point of view, namely from how stance is realised through language. Our aim is to provide a description of the specific lexical elements used to express six stance categories, i.e., CONTRARIETY, HYPOTHETICALITY,  NECESSITY, PREDICTION, SOURCE OF KNOWLEDGE, and UNCERTAINTY. To this end, we followed a two-step experimental procedure. First, we performed a quantitative analysis of the stance labelled utterances in order to identify the lexical realisations of each stance category. Second, we carried out a meta-annotation of the data. Annotator B was instructed to single out the actual lexical forms of the constructions that triggered his semantic stance category decisions. This meta-annotation procedure made it possible for us to sift out the most salient lexical realisations of the constructions of each of the six category types on the basis of the qualitative assessments made by Annotator B. We then compared the results of the quantitative and the qualitative approaches, and we present a list of shared stance expressions for each stance category type.

  • 46.
    Simaki, Vasiliki
    et al.
    Lancaster University,UK ; Lund University.
    Paradis, Carita
    Lund University.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Evaluating stance-annotated sentences from the Brexit Blog Corpus: A quantitative linguistic analysis2018In: ICAME Journal/International Computer Archive of Modern English, ISSN 0801-5775, E-ISSN 1502-5462, Vol. 42, no 1, p. 133-166Article in journal (Refereed)
    Abstract [en]

    This paper offers a formally driven quantitative analysis of stance-annotated sentences in the Brexit Blog Corpus (BBC). Our goal is to highlight linguistic features that determine the formal profiles of six stance categories (contrariety, hypotheticality, necessity, prediction, source of knowledge and uncertainty) in a subset of the BBC. The study has two parts: firstly, it examines a large number of formal linguistic features that occur in the sentences in order to describe the specific characteristics of each category, and secondly, it compares characteristics in the entire data set in order to determine linguistic similarities throughout the data set. We show that among the six stance categories in the corpus, contrariety and necessity are the most discriminative ones, with the former using longer sentences, more conjunctions, more repetitions and shorter forms than the sentences expressing other stances. The latter has longer lexical forms but shorter sentences, which are syntactically more complex. We show that stance in our data set is expressed in sentences with around 21 words per sentence. The sentences consist mainly of alphabetical characters forming a varied vocabulary without special forms, such as digits or special characters.

  • 47.
    Simaki, Vasiliki
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science. Lund University.
    Paradis, Carita
    Lund University.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Stance Classification in Texts from Blogs on the 2016 British Referendum2017In: Speech and Computer: 19th International Conference, SPECOM 2017, Hatfield, UK, September 12-16, 2017, Proceedings / [ed] Alexey Karpov, Rodmonga Potapova, and Iosif Mporas, Springer International Publishing , 2017, p. 700-709Conference paper (Refereed)
    Abstract [en]

    The problem of identifying and correctly attributing speaker stance in human communication is addressed in this paper. The data set consists of political blogs dealing with the 2016 British referendum. A cognitive-functional framework is adopted with data annotated for six notional stance categories: concession/contrariness, hypotheticality, need/ requirement, prediction, source of knowledge, and uncertainty. We show that these categories can be implemented in a text classification task and automatically detected. To this end, we propose a large set of lexical and syntactic linguistic features. These features were tested and classification experiments were implemented using different algorithms. We achieved accuracy of up to 30% for the six-class experiments, which is not fully satisfactory. As a second step, we calculated the pair-wise combinations of the stance categories. The concession/contrariness and need/requirement binary classification achieved the best results with up to 71% accuracy. 

  • 48.
    Simaki, Vasiliki
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science. Lund University.
    Paradis, Carita
    Lund University.
    Skeppstedt, Maria
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Sahlgren, Magnus
    Swedish Research Institute (RISE SICS).
    Kucher, Kostiantyn
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Annotating speaker stance in discourse: the Brexit Blog Corpus2017In: Corpus linguistics and linguistic theory, ISSN 1613-7027, E-ISSN 1613-7035Article in journal (Refereed)
    Abstract [en]

    The aim of this study is to explore the possibility of identifying speaker stance in discourse, provide an analytical resource for it and an evaluation of the level of agreement across speakers. We also explore to what extent language users agree about what kind of stances are expressed in natural language use or whether their interpretations diverge. In order to perform this task, a comprehensive cognitive-functional framework of ten stance categories was developed based on previous work on speaker stance in the literature. A corpus of opinionated texts was compiled, the Brexit Blog Corpus (BBC). An analytical protocol and interface (ALVA) for the annotations was set up and the data were independently annotated by two annotators. The annotation procedure, the annotation agreements and the co-occurrence of more than one stance in the utterances are described and discussed. The careful, analytical annotation process has returned satisfactory inter- and intra-annotation agreement scores, resulting in a gold standard corpus, the final version of the BBC. 

  • 49.
    Simaki, Vasiliki
    et al.
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science. Lund University, Sweden.
    Simakis, Panagiotis
    XPLAIN, Greece.
    Paradis, Carita
    Lund University, Sweden.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of computer science and media technology (CM), Department of Computer Science.
    Identifying the Authors' National Variety of English in Social Media Texts2017In: Proceedings of the International Conference on Recent Advances in Natural Language Processing, RANLP 2017 / [ed] Galia Angelova, Kalina Bontcheva, Ruslan Mitkov, Ivelina Nikolova, and Irina Temnikova, Stroudsburg, PA: Association for Computational Linguistics, 2017, p. 671-678Conference paper (Refereed)
    Abstract [en]

    In this paper, we present a study for the identification of authors’ national variety of English in texts from social media. In data from Facebook and Twitter, information about the author’s social profile is annotated, and the national English variety (US, UK, AUS, CAN, NNS) that each author uses is attributed. We tested four feature types: formal linguistic features, POS features, lexicon-based features related to the different varieties, and databased features from each English variety. We used various machine learning algorithms for the classification experiments, and we implemented a feature selection process. The classification accuracy achieved, when the 31 highest ranked features were used, was up to 77.32%. The experimental results are evaluated, and the efficacy of the ranked features discussed.

  • 50.
    Skeppstedt, Maria
    et al.
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Kucher, Kostiantyn
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Paradis, Carita
    Lund University.
    Kerren, Andreas
    Linnaeus University, Faculty of Technology, Department of Computer Science.
    Language Processing Components of the StaViCTA Project2017In: Proceedings of the Workshop on Logic and Algorithms in Computational Linguistics 2017 (LACompLing 2017) / [ed] Roussanka Loukanova and Kristina Liefke, Stockholm University ; KTH , 2017, p. 137-138Conference paper (Refereed)
    Abstract [en]

    The StaViCTA project is concerned with visualising the expression of stance in written text, and is therefore dependent on components for stance detection. These components are to (i) download and extract text from any HTML page and segment it into sentences, (ii) classify each sentence with respect to twelve different, notionally motivated, stance categories, and (iii) provide a RESTful HTTP API for communication with the visualisation components. The stance categories are certainty, uncertainty, contrast, recommendation, volition, prediction, agreement, disagreement, tact, rudeness, hypotheticality, and source of knowledge. 

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