{"id":8304,"date":"2018-06-14T09:56:37","date_gmt":"2018-06-14T09:56:37","guid":{"rendered":"https:\/\/web10.gr\/eci\/?page_id=8304"},"modified":"2025-05-18T20:08:02","modified_gmt":"2025-05-18T18:08:02","slug":"journalism-computational-linguistics-lab-jcl-lab","status":"publish","type":"page","link":"https:\/\/eci-org.eu\/?page_id=8304&lang=en","title":{"rendered":"Journalism Computational Linguistics (JCL) Research Lab"},"content":{"rendered":"<section class=\"wpb-content-wrapper\">[vc_row row_type=&#8221;row&#8221; text_align=&#8221;left&#8221; css_animation=&#8221;&#8221;][vc_column][vc_column_text]\n<p><a href=\"https:\/\/eci-org.eu\/wp-content\/uploads\/2024\/01\/JCL-Lab.jpg\"><img loading=\"lazy\" class=\"wp-image-13806 aligncenter\" src=\"https:\/\/eci-org.eu\/wp-content\/uploads\/2024\/01\/JCL-Lab-300x172.jpg\" alt=\"\" width=\"571\" height=\"328\" srcset=\"https:\/\/eci-org.eu\/wp-content\/uploads\/2024\/01\/JCL-Lab-300x172.jpg 300w, https:\/\/eci-org.eu\/wp-content\/uploads\/2024\/01\/JCL-Lab-768x440.jpg 768w, https:\/\/eci-org.eu\/wp-content\/uploads\/2024\/01\/JCL-Lab-345x198.jpg 345w, https:\/\/eci-org.eu\/wp-content\/uploads\/2024\/01\/JCL-Lab.jpg 800w\" sizes=\"(max-width: 571px) 100vw, 571px\" \/><\/a><\/p>\n<h3>Mission<\/h3>\n<ul>\n<li><span style=\"font-weight: 400;\">To provide a Journalists Workbench and Tools for the direct access, efficient processing, in-depth analysis and complete evaluation of content and complex information in monolingual and multilingual written and spoken political and journalistic texts.<\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3>Projects (Continuing)<\/h3>\n<ul>\n<li><strong>Imply<br \/>\n<\/strong>Detection of Implicit Information and Connotative features in Written and Spoken Journalistic Texts &#8211; Sentiment Analysis and Opinion Mining<\/li>\n<li><strong>Prag-Graph<br \/>\n<\/strong>Data Processing, Graphic Representation and Pragmatic Evaluation of Interviews, Discussions and Speeches<\/li>\n<li><strong>Thucydides (&amp; Friends)<br \/>\n<\/strong>Accessing Facts and Diplomacy of the Past \u2013 Processing \/ Extracting\u00a0Information from Ancient \u201cJournalistic\u201d Texts<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3>Special Interest Research Group<\/h3>\n<ul>\n<li><span style=\"font-weight: 400;\">Division for German and Multilingual Communication: Information Processing and Applications<\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3>Head<\/h3>\n<ul>\n<li><span style=\"font-weight: 400;\">Dr.<strong>\u00a0Christina Alexandris<\/strong>, \u0395CI \/ QJNT, Professor, National and Kapodistrian University of Athens<\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3>External Researchers<\/h3>\n<ul>\n<li><span style=\"font-weight: 400;\">Dr.<strong>\u00a0Christina Valavani<\/strong>\u00a0(Natural Language Processing &#8211; Machine\u00a0Translation and Terminology)<\/span><\/li>\n<li><span style=\"font-weight: 400;\"><strong>Savvas Chatzipanayiotidis<\/strong>, MSc, PhD Candidate<\/span><\/li>\n<li><span style=\"font-weight: 400;\"><strong>Stavros Giannakis<\/strong>, MSc (Opinion Mining and Sentiment Analysis)<\/span><\/li>\n<li><span style=\"font-weight: 400;\"><strong>Vasilios Floros<\/strong>, MSc<\/span><\/li>\n<li><span style=\"font-weight: 400;\"><strong>Dimitrios Mourouzidis<\/strong>, MSc<\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3>Projects (continuing)<\/h3>\n<ul>\n<li><span style=\"font-size: 120%; font-weight: 400;\">Imply<\/span><br \/>\n<h5>Detection of Implicit Information and Connotative features in Written and Spoken Journalistic Texts &#8211; Sentiment Analysis and Opinion Mining<\/h5>\n<p><span style=\"font-weight: 400;\">The present approach targets to facilitate the translation, the detailed processing and the correct transfer of opinions, style and overall spirit of written and spoken online journalistic texts. Here, we present the integration of an annotation strategy for written and spoken journalistic texts detecting elements with explicit and implicit connotative features. The proposed annotation strategy is morphologically based and related to a controlled-language-like framework, functioning as a checklist and targeting to address re-occurring problems encountered mainly by \u201csemi-professional\u201d translators, namely journalists, economists and other professionals working with multilingual written and transcribed journalistic texts available from the media and the web. Most of these professionals, usually having an above-average fluency of one or more foreign languages, often lack the necessary exposure to the culture(s) related to the foreign language(s) concerned, especially due to distance or frequent change of location. Thus, essential information presented either in a subtle form or in an indirect way, constituting emotionally and socio-culturally \u201cmarked\u201d elements, is often undetected.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The designed user-oriented module is aimed to be integrated in a annotation tool targeting to indicate the largest possible percentage of the points in the texts signalizing \u201cmarked\u201d information, alerting the user-translator to evaluate these expressions and, in the case of transcribed spoken journalistic texts, to allow the comparison of \u201cmarked\u201d elements with prosodic and paralinguistic features in the respective multimedia files.<\/span><\/li>\n<li><span style=\"font-size: 120%; font-weight: 400;\">Prag-Graph<\/span><br \/>\n<h5>Data Processing, Graphic Representation and Pragmatic Evaluation of\u00a0Interviews, Discussions and Speeches<\/h5>\n<p><span style=\"font-weight: 400;\">The designed annotation tool targets (1) to provide the User-Journalist with the tracked indications of the topics handled in the interview or discussion and (2) to view the graphic pattern of the discourse structure of the interview or discussion, (3) to evaluate the discourse structure, (4) to allow the User to compare the discourse structure of conversations and interviews with similar topics or the same participants \/ participant and (5) to indicate the largest possible percentage of the points in the texts signalizing information with implied information and connotative features.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The interface of the annotation tool is designed to (a) to track the \u201clocal\u201d topic discussed in a given segment of an interview or discussion or change of \u201clocal\u201d topic in an interview or discussion and (b) annotate and highlight all the points possibly containing connotative features information, alerting the User to evaluate the parts of the text containing these expressions.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The designed tool allows the tracking of any change of topic or the same or a similar answer, as well as associations and generalizations related to the same topic.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">Incoming texts to be processed constitute transcribed data from journalistic texts. The interactive annotation tool is designed to operate with most commercial transcription tools, some of which are available online. The designed tool may also be adapted to downloaded written texts from the internet (blog).<\/span><\/li>\n<li><span style=\"font-size: 120%; font-weight: 400;\">Thucydides (&amp; Friends)<\/span><br \/>\n<h5>Accessing Facts and Diplomacy of the Past \u2013 Processing \/ Extracting\u00a0Information from Ancient \u201cJournalistic\u201d Texts<\/h5>\n<p><span style=\"font-weight: 400;\">For the International Public, ancient historical and \u201cjournalistic\u201d texts, such the \u201cPeloponnesian War\u201d of the Ancient Greek historian Thucydides, may allow an insight for the understanding of current international and national political affairs and international political and economic relations. The present approach targets to facilitate the accessibility of such texts for non-experts in the International Public, especially journalists, translators and students. Specifically, the basic issue to be addressed here is the possibility to access complex information in the Ancient Text related to diplomacy and to compare it to passages from online journalistic texts (1) and to directly find out respective passages in the original texts along with a translation in English (2) as well as a second type of translation containing structures close to the original text, minimizing language-specific interference and parameters of translations (3). The latter possibility (3) provides a closer look to the content and structure of the original text and is less dependent on language-specific parameters interfering in the English translation.<\/span><\/p>\n<p><span style=\"font-weight: 400;\">The present approach concerns the integration of expert knowledge within a System-controlled framework for the detection of information concerning diplomacy, especially cause and result relations contained in the online Ancient Text. The module presented here is designed to make use of already-existing tools and mechanisms, the construction of a database and interface with low computational cost, combined with expert knowledge and sublanguage \u2013 specific parameters. For the handling of topics related to complex information such as \u201cDiplomacy\u201d, expert knowledge and sublanguage \u2013 specific parameters are put to use to constitute a framework replacing conventional information extraction methods and statistically-based approaches.<\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3>Book<\/h3>\n<ul>\n<li><span style=\"font-weight: 400;\">Alexandris, C. (2020): Issues in Multilingual Information Processing of Spoken Political and Journalistic Texts in the Media and Broadcast News, Newcastle upon Tyne, UK, Cambridge Scholars.<\/span><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3>Publications<\/h3>\n<ul>\n<li style=\"font-weight: 400;\">Alexandris, C., Trachanas, G., Chatzipanayiotidis, S. (2024). Of Politics, Behavior and Commands: Processing Information Unspoken for Sentiment Analysis and Spoken Interaction Applications.<br \/>\nIn: Kurosu, M., Hashizume, A. (eds) Human-Computer Interaction. HCII 2024. Lecture Notes in Computer Science, vol 14684. Springer, Cham.<br \/>\n<a href=\"https:\/\/doi.org\/10.1007\/978-3-031-60405-8_15\" target=\"_blank\" rel=\"noopener\">https:\/\/doi.org\/10.1007\/978-3-031-60405-8_15<\/a><\/li>\n<li style=\"font-weight: 400;\">Trachanas, G., Valavani, C., Alexandris, C., Giannakis, S. (2024). Vocal Minority Versus Silent Majority: Twitter Data for Greek General Elections and Tweets on German Foreign Policy.<br \/>\nIn: Kurosu, M., Hashizume, A. (eds) Human-Computer Interaction. HCII 2024. Lecture Notes in Computer Science, vol 14684. Springer, Cham.<br \/>\n<a href=\"https:\/\/doi.org\/10.1007\/978-3-031-60405-8_25\" target=\"_blank\" rel=\"noopener\">https:\/\/doi.org\/10.1007\/978-3-031-60405-8_25<\/a><\/li>\n<li style=\"font-weight: 400;\">Alexandris C. (2024) GenAI and Socially Responsible AI in Natural Language Processing Applications: A Linguistic Perspective. In: Proceedings of the AAAI Spring Symposium Series 2024, Vol. 3, No. 1, Stanford University, Palo Alto, CA. pp. 330-337.<br \/>\n<a href=\"https:\/\/doi.org\/10.1609\/aaaiss.v3i1.31230\" target=\"_blank\" rel=\"noopener\">https:\/\/doi.org\/10.1609\/aaaiss.v3i1.31230<\/a><\/li>\n<li style=\"font-weight: 400;\">Alexandris C. (2023). Processing Information Unspoken: New Insights from Crowd-Sourced Data for Sentiment Analysis and Spoken Interaction Applications.<br \/>\nIn: Socially Responsible AI for Well-being (SS-23-09), Papers from the AAAI Spring Symposium, San Francisco, CA.<br \/>\n<a href=\"https:\/\/ceur-ws.org\/Vol-3527\/Paper_456.pdf\">https:\/\/ceur-ws.org\/Vol-3527\/Paper_456.pdf<\/a><\/li>\n<li style=\"font-weight: 400;\">Alexandris C., Du., J. Floros V., (2023). The Context of War and Cognitive\u00a0Bias: An Interactive Approach in Accessing Relations of Attitude, Behavior and Events in Ancient Texts and Online News.<br \/>\nIn: Kurosu, M., Hashizume, A. (eds) Human-Computer Interaction. HCII 2023. Lecture Notes in Computer Science, vol 14014. Springer, Cham.<br \/>\n<a href=\"https:\/\/doi.org\/10.1007\/978-3-031-35572-1_14\">https:\/\/doi.org\/10.1007\/978-3-031-35572-1_14<\/a><\/li>\n<li style=\"font-weight: 400;\">Theodoropoulos, P. Alexandris, C. (2022). Fine-Grained Sentiment Analysis\u00a0of Multi-domain Online Reviews.<br \/>\nIn: Human-Computer Interaction\u00a0Technological Innovation, M. Kurosu (Ed.): HCII 2022, LNCS 13303,\u00a0Springer, Cham, 2022. pp. 264\u2013278.<br \/>\n<a href=\"https:\/\/doi.org\/10.1007\/978-3-031-05409-9_20\">https:\/\/doi.org\/10.1007\/978-3-031-05409-9_20<\/a><\/li>\n<li style=\"font-weight: 400;\">Alexandris, C. (2022): Sense and Sensitivity: Knowledge Graphs as Training\u00a0Data for Processing Cognitive Bias, Context and Information Not Uttered in\u00a0Spoken Interaction.<br \/>\nIn: Proceedings of &#8220;How Fair is Fair? Achieving\u00a0Wellbeing AI&#8221; Session of the AAAI Spring Symposium, March 21\u201323, 2022,\u00a0Stanford University (in print)<\/li>\n<li style=\"font-weight: 400;\">Alexandris C., Du., J. Floros V., (2022). Visualizing and Processing\u00a0Information Not Uttered in Spoken Political and Journalistic Data: From\u00a0Graphical Representations to Knowledge Graphs in an Interactive\u00a0Application.<br \/>\nIn: Human-Computer Interaction Technological Innovation, M.\u00a0Kurosu (Ed.): HCII 2022, Lecture Notes in Computer Science &#8211; LNCS 13303,\u00a02022, Springer, Cham. pp. 211\u2013226.<br \/>\n<a href=\"https:\/\/doi.org\/10.1007\/978-3-031-05409-9_16\">https:\/\/doi.org\/10.1007\/978-3-031-05409-9_16<\/a><\/li>\n<li style=\"font-weight: 400;\">Alexandris, C. (2021): Registering the impact of Words in Spoken Political\u00a0and Journalistic Texts.<br \/>\nIn: Journal of Human Language, Rights and\u00a0Security. Peoples Friendship University (RUDN), Moscow, Russian\u00a0Federation. pp 26-48.<br \/>\n<a href=\"https:\/\/doi.org\/10.22363\/2713-0614-2021-1-1-26-48\">https:\/\/doi.org\/10.22363\/2713-0614-2021-1-1-26-48<\/a><\/li>\n<li style=\"font-weight: 400;\">Alexandris C., Floros V., Mourouzidis D. (2021): Graphic Representations of\u00a0Spoken Interactions from Journalistic Data: Persuasion and Negotiations.<br \/>\nIn: Kurosu M. (eds) Human-Computer Interaction. Design and User Experience\u00a0Case Studies. HCII 2021. Lecture Notes in Computer Science, vol 12764.\u00a0Springer, Cham. pp 3-17.<br \/>\n<a href=\"https:\/\/doi.org\/10.1007\/978-3-030-78468-3_1\">https:\/\/doi.org\/10.1007\/978-3-030-78468-3_1<\/a><\/li>\n<li style=\"font-weight: 400;\">Giannakis S., Valavani C., Alexandris C. (2021): A Sentiment Analysis Web\u00a0Platform for Multiple Social Media Types and Language-Specific\u00a0Customizations.<br \/>\nIn: Kurosu M. (eds) Human-Computer Interaction. Theory,\u00a0Methods and Tools. HCII 2021. Lecture Notes in Computer Science, vol\u00a012762. Springer, Cham. pp 318-328<br \/>\n<a href=\"https:\/\/doi.org\/10.1007\/978-3-030-78462-1_24\">https:\/\/doi.org\/10.1007\/978-3-030-78462-1_24<\/a><\/li>\n<li style=\"font-weight: 400;\">Alexandris, C, Mourouzidis, D., Floros, V. (2020):\u00a0Generating Graphic\u00a0Representations of Spoken Interactions Revisited: The Tension Factor and\u00a0Information Not Uttered in Journalistic Data.<br \/>\nIn: Human-Computer\u00a0Interaction. Design and User Experience. HCII 2020. Lecture Notes in\u00a0Computer Science, vol 12181. Springer Nature Switzerland AG 2020 M. Kurosu\u00a0(Ed.): HCII 2020, LNCS 12181, pp. 523\u2013537, 2020<br \/>\n<a href=\"https:\/\/doi.org\/10.1007\/978-3-030-49059-1_39\" target=\"_blank\" rel=\"noopener noreferrer\">https:\/\/doi.org\/10.1007\/978-3-030-49059-1_39<\/a><\/li>\n<li style=\"font-weight: 400;\">Mourouzidis, D., Floros, V., Alexandris, C. (2019): Generating Graphic Representations of Spoken Interactions from Journalistic Data.<br \/>\nIn: M. Kurosu (Ed.):\u00a0 HCII 2019, Lecture Notes in Computer Science\u00a0LNCS 11566, pp. 559\u2013570, 2019, Springer Nature Switzerland AG 2019<br \/>\n<a href=\"https:\/\/doi.org\/10.1007\/978-3-030-22646-6_42\">https:\/\/doi.org\/10.1007\/978-3-030-22646-6_42<\/a><\/li>\n<li style=\"font-weight: 400;\">Alexandris, C., Mylonakis, K., Tassis, S., Nottas, M., Cambourakis, G. (2017): Implementing a Platform for Complex Information Processing from Written and Spoken Journalistic Data.<br \/>\nIn: M. Kurosu (Ed.): Lecture Notes in\u00a0Computer Science LNCS 10271, Springer, pp. 549\u2013558.<\/li>\n<li style=\"font-weight: 400;\">Du, J., Alexandris, C., Mourouzidis, D., Floros, V.,Iliakis, A. (2017): Controlling Interaction in Multilingual Conversation Revisited: A Perspective for Services and Interviews in Mandarin Chinese.<br \/>\nIn: M. Kurosu (Ed.): Lecture Notes in Computer Science LNCS 10271, Springer, pp. 573\u2013583.<\/li>\n<li style=\"font-weight: 400;\">Alexandris, C., Tassis, S., Iliakis, A. (2015): Issues and Strategies for Multilingual Text Processing in the Domain of International Affairs.<br \/>\n\u0399n:\u00a0Simon T. Yates (Ed.) Machine Vision and Human-Machine Interface: Technologies, Applications and Challenges, Hauppauge, New York, NY, Nova Science Publishers, pp 27-40.<\/li>\n<li style=\"font-weight: 400;\">Alexandris, C., Nottas, M., Cambourakis, G.\u00a0(2015): Interactive Evaluation of Pragmatic Features in Spoken Journalistic Texts.<br \/>\nIn:\u00a0Human-Computer Interaction, HCII 2015, LNCS Lecture Notes in Computer Science pp 259-268.<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n<h3>Conference Papers and Technical Reports<\/h3>\n<ul>\n<li style=\"font-weight: 400;\">Alexandris, C. (2019): Evaluating Cognitive Bias in Two-Party and Multi-Party Spoken Interactions.<br \/>\nIn: Proceedings of Interpretable AI for\u00a0Well-being: Understanding Cognitive Bias and Social Embeddedness (IAW 2019) in conjunction with AAAI Spring Symposium (SS-19-03), Stanford University, Palo Alto, CA. IAW 2019<br \/>\nInterpretable AI for Well-being: Understanding Cognitive Bias and Social Embeddedness<br \/>\n<a href=\"http:\/\/ceur-ws.org\/Vol-2448\/\">http:\/\/ceur-ws.org\/Vol-2448\/<\/a><br \/>\n<a href=\"http:\/\/ceur-ws.org\/Vol-2448\/SSS19_Paper_Upload_211.pdf\">http:\/\/ceur-ws.org\/Vol-2448\/SSS19_Paper_Upload_211.pdf<\/a><\/li>\n<li style=\"font-weight: 400;\">Alexandris, C. (2019): \u201cVisualizing Pragmatic Features in Spoken Interaction: Intentions, Behavior and Evaluation\u201d.<br \/>\nIn Proceedings of the1st International Conference on Linguistics Research on the Era of Artificial Intelligence \u2013 LREAI, Dalian, October 25-27, 2019, Dalian Maritime University (in print).<\/li>\n<li style=\"font-weight: 400;\">Alexandris, C. (2018): \u201cMeasuring Cognitive Bias in Spoken Interaction and Conversation: Generating Visual Representations\u201d<br \/>\nIn: AAAI Spring Symposium, Stanford University, Technical Report SS-18-03, AAAI Press, Palo Alto, CA, 204-206. [mentioned in AI Magazine]<\/li>\n<li style=\"font-weight: 400;\">Alexandris (2018): Interactive Multilingual Aspects of Complex Information Transfer and Information Processing in Transcribed Spoken Journalistic Texts.\u201d<br \/>\nIn: Proceedings of\u00a0 the International Conference in Society and Languages in the Third Millenium, Communication, Education, Translation, RUDN University, Institute of of Law, Moscow, May 2018, pp 10-21.<\/li>\n<li style=\"font-weight: 400;\">Alexandris, C. (2015): \u201cSignalizing and Predicting Turn-Taking in Multilingual Contexts: Using Data from Transcribed International Spoken Journalistic Texts in Human-Robot Interaction\u201d<br \/>\nIn: Turn-Taking and\u00a0Coordination in Human-Machine Interaction Papers from the AAAI Spring Symposium, Stanford University, Technical Report SS-15-07, AAAI Press, Palo Alto, California, 71-74.<\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n[\/vc_column_text][\/vc_column][\/vc_row]\n<\/section>","protected":false},"excerpt":{"rendered":"<p>[vc_row row_type=&#8221;row&#8221; text_align=&#8221;left&#8221; css_animation=&#8221;&#8221;][vc_column][vc_column_text] Mission To provide a Journalists Workbench and Tools for the direct access, efficient processing, in-depth analysis and complete evaluation of content&#8230;<\/p>\n","protected":false},"author":4,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":[],"_links":{"self":[{"href":"https:\/\/eci-org.eu\/index.php?rest_route=\/wp\/v2\/pages\/8304"}],"collection":[{"href":"https:\/\/eci-org.eu\/index.php?rest_route=\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/eci-org.eu\/index.php?rest_route=\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/eci-org.eu\/index.php?rest_route=\/wp\/v2\/users\/4"}],"replies":[{"embeddable":true,"href":"https:\/\/eci-org.eu\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=8304"}],"version-history":[{"count":20,"href":"https:\/\/eci-org.eu\/index.php?rest_route=\/wp\/v2\/pages\/8304\/revisions"}],"predecessor-version":[{"id":14188,"href":"https:\/\/eci-org.eu\/index.php?rest_route=\/wp\/v2\/pages\/8304\/revisions\/14188"}],"wp:attachment":[{"href":"https:\/\/eci-org.eu\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=8304"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}