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.
Special Interest Research Group
- Division for German and Multilingual Communication: Information Processing and Applications
- Dr. Christina Alexandris, ΕCI / QJNT, Professor, National and Kapodistrian University of Athens
- Dr. Christina Valavani (Natural Language Processing – Machine Translation and Terminology)
- Savvas Chatzipanayiotidis, MSc, PhD Candidate
- Stavros Giannakis, MSc (Opinion Mining and Sentiment Analysis)
- Vasilios Floros, MSc
- Dimitrios Mourouzidis, MSc
Detection of Implicit Information and Connotative features in Written and Spoken Journalistic Texts – Sentiment Analysis and Opinion Mining
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 “semi-professional” 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 “marked” elements, is often undetected.
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 “marked” information, alerting the user-translator to evaluate these expressions and, in the case of transcribed spoken journalistic texts, to allow the comparison of “marked” elements with prosodic and paralinguistic features in the respective multimedia files.
Data Processing, Graphic Representation and Pragmatic Evaluation of Interviews, Discussions and Speeches
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.
The interface of the annotation tool is designed to (a) to track the “local” topic discussed in a given segment of an interview or discussion or change of “local” 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.
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.
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).
- Thucydides (& Friends)
Accessing Facts and Diplomacy of the Past – Processing / Extracting Information from Ancient “Journalistic” Texts
For the International Public, ancient historical and “journalistic” texts, such the “Peloponnesian War” 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.
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 – specific parameters. For the handling of topics related to complex information such as “Diplomacy”, expert knowledge and sublanguage – specific parameters are put to use to constitute a framework replacing conventional information extraction methods and statistically-based approaches.
- 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.
- Alexandris C., Du., J. Floros V., (2023). The Context of War and Cognitive Bias: An Interactive Approach in Accessing Relations of Attitude, Behavior and Events in Ancient Texts and Online News.
In: Human-Computer Interaction Technological Innovation, M. Kurosu (Ed.): HCII 2023, Lecture Notes in Computer Science (to appear).
- Theodoropoulos, P. Alexandris, C. (2022). Fine-Grained Sentiment Analysis of Multi-domain Online Reviews.
In: Human-Computer Interaction Technological Innovation, M. Kurosu (Ed.): HCII 2022, LNCS 13303, Springer, Cham, 2022. pp. 264–278.
- Alexandris, C. (2022): Sense and Sensitivity: Knowledge Graphs as Training Data for Processing Cognitive Bias, Context and Information Not Uttered in Spoken Interaction.
In: Proceedings of “How Fair is Fair? Achieving Wellbeing AI” Session of the AAAI Spring Symposium, March 21–23, 2022, Stanford University (in print)
- Alexandris C., Du., J. Floros V., (2022). Visualizing and Processing Information Not Uttered in Spoken Political and Journalistic Data: From Graphical Representations to Knowledge Graphs in an Interactive Application.
In: Human-Computer Interaction Technological Innovation, M. Kurosu (Ed.): HCII 2022, Lecture Notes in Computer Science – LNCS 13303, 2022, Springer, Cham. pp. 211–226.
- Alexandris, C. (2021): Registering the impact of Words in Spoken Political and Journalistic Texts.
In: Journal of Human Language, Rights and Security. Peoples Friendship University (RUDN), Moscow, Russian Federation. pp 26-48.
- Alexandris C., Floros V., Mourouzidis D. (2021): Graphic Representations of Spoken Interactions from Journalistic Data: Persuasion and Negotiations.
In: Kurosu M. (eds) Human-Computer Interaction. Design and User Experience Case Studies. HCII 2021. Lecture Notes in Computer Science, vol 12764. Springer, Cham. pp 3-17.
- Giannakis S., Valavani C., Alexandris C. (2021): A Sentiment Analysis Web Platform for Multiple Social Media Types and Language-Specific Customizations.
In: Kurosu M. (eds) Human-Computer Interaction. Theory, Methods and Tools. HCII 2021. Lecture Notes in Computer Science, vol 12762. Springer, Cham. pp 318-328
- Alexandris, C, Mourouzidis, D., Floros, V. (2020): Generating Graphic Representations of Spoken Interactions Revisited: The Tension Factor and Information Not Uttered in Journalistic Data.
In: Human-Computer Interaction. Design and User Experience. HCII 2020. Lecture Notes in Computer Science, vol 12181. Springer Nature Switzerland AG 2020 M. Kurosu (Ed.): HCII 2020, LNCS 12181, pp. 523–537, 2020
- Mourouzidis, D., Floros, V., Alexandris, C. (2019): Generating Graphic Representations of Spoken Interactions from Journalistic Data.
In: M. Kurosu (Ed.): HCII 2019, Lecture Notes in Computer Science LNCS 11566, pp. 559–570, 2019, Springer Nature Switzerland AG 2019
- Alexandris, C., Mylonakis, K., Tassis, S., Nottas, M., Cambourakis, G. (2017): Implementing a Platform for Complex Information Processing from Written and Spoken Journalistic Data.
In: M. Kurosu (Ed.): Lecture Notes in Computer Science LNCS 10271, Springer, pp. 549–558.
- 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.
In: M. Kurosu (Ed.): Lecture Notes in Computer Science LNCS 10271, Springer, pp. 573–583.
- Alexandris, C., Tassis, S., Iliakis, A. (2015): Issues and Strategies for Multilingual Text Processing in the Domain of International Affairs.
Ιn: Simon T. Yates (Ed.) Machine Vision and Human-Machine Interface: Technologies, Applications and Challenges, Hauppauge, New York, NY, Nova Science Publishers, pp 27-40.
- Alexandris, C., Nottas, M., Cambourakis, G. (2015): Interactive Evaluation of Pragmatic Features in Spoken Journalistic Texts.
In: Human-Computer Interaction, HCII 2015, LNCS Lecture Notes in Computer Science pp 259-268.
Conference Papers and Technical Reports
- Alexandris, C. (2019): Evaluating Cognitive Bias in Two-Party and Multi-Party Spoken Interactions.
In: Proceedings of Interpretable AI for Well-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
Interpretable AI for Well-being: Understanding Cognitive Bias and Social Embeddedness
- Alexandris, C. (2019): “Visualizing Pragmatic Features in Spoken Interaction: Intentions, Behavior and Evaluation”.
In Proceedings of the1st International Conference on Linguistics Research on the Era of Artificial Intelligence – LREAI, Dalian, October 25-27, 2019, Dalian Maritime University (in print).
- Alexandris, C. (2018): “Measuring Cognitive Bias in Spoken Interaction and Conversation: Generating Visual Representations”
In: AAAI Spring Symposium, Stanford University, Technical Report SS-18-03, AAAI Press, Palo Alto, CA, 204-206. [mentioned in AI Magazine]
- Alexandris (2018): Interactive Multilingual Aspects of Complex Information Transfer and Information Processing in Transcribed Spoken Journalistic Texts.”
In: Proceedings of 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.
- Alexandris, C. (2015): “Signalizing and Predicting Turn-Taking in Multilingual Contexts: Using Data from Transcribed International Spoken Journalistic Texts in Human-Robot Interaction”
In: Turn-Taking and Coordination in Human-Machine Interaction Papers from the AAAI Spring Symposium, Stanford University, Technical Report SS-15-07, AAAI Press, Palo Alto, California, 71-74.