FP7Реинтеграция2014–2018

MediaGist · Summarisation and Sentiment Analysis for Evolving Multilingual Media Content

7РП — „Хора“ (Действия „Мария Кюри“)

Период
2014-03-01 → 2018-02-28
Финансиране от ЕС
100 000 €
Участници
1
Схема
MC-CIG

Линиите свързват координатора с партньорите.

Накратко на български

Мул изображениени системи за анализ на новини и коментари на различни езици помагат за автоматичното обобщаване на текстове и определяне на емоционалния им тон. Това помага на журналистите да откриват теми, които се обсъждат противоречиво в различни държави.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

Summarisation and Sentiment Analysis for Evolving Multilingual Media Content

The integration project of Josef Steinberger started in March 2014. Since the beginning, he has been involved in teaching, supervising and research of the Natural language processing group at the host institution. His research goal was to make significant advances in multilingual summarisation and sentiment analysis research so as to extract and present the GIST of online multilingual news and the corresponding commentaries. He created an online system for crosslingual analysis of aggregated news and commentaries (the MediaGist system). It uses output from both objectives: summarization and sentiment analysis. It is designed to assist journalists to detect and explore news topics, which are controversially reported or discussed in different countries. News articles from current week are clustered separately in currently 5 languages and the clusters are then linked across languages. Sentiment analysis provides a basis to compute controversy scores and summaries help to explore the differences. Recognized entities play an important role in most of the system’s modules and provide another way to explore the data. He presented a demo of the developed system at the most important event of 2016 – the ACL conference. The system runs at: http://mediagist.kiv.zcu.cz/ and video describing its functionality can be found at: https://www.youtube.com/watch?v=ONtKw_l6_X4. He published 2 journal papers, 1 book chapter and 14 conference papers during the project. He currently has H-index 11 and 352 citations in Scopus, H-index 6 and 154 citation in Web of Science and H-index 18 and 1 265 citations in Google Scholar. He taught 10 courses during the project, 608 students in total and prepared a new course on Information retrieval. He supervised 7 undergraduate students, 7 master students and 2 PhD students. He was involved in many international activities. He reviewed 9 journal papers and 75 conference papers and took part in 29 conference program committees. He co-organized 4 workshops of main NLP conferences: two workshop of Balto-Slavic NLP (at RANLP’2015 and at EACL’2017) and two multilingual summarization workshops (Multiling at SigDial’2015 and EACL’2017). He was coordinating several shared task, experiments preceding the workshops. Researcher’s involvement in the international research includes common efforts with the FP7/SENSEI consortium (mainly University of Essex, UK) and EC’s Joint Research Centre, Italy. He also contributed to the systems that took part in shared tasks of the community (SemEval’14, Multiling’15, SemEval’16, BSNLP’17, Multiling’17 and SemEval’18). These systems were ranked among the best ones in many cases. He also chaired a conference with more than 60 attendees (a Czech and Slovak data community). He went on 16 missions, 6 collaboration missions, 9 conferences and took part in the Czech chapter of MCAA as well. The total cost of the project was EUR 202k (with EU Contribution of EUR 100k and host institution contribution of EUR 102k).

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

News aggregators gather thousands of news articles every day from across the world and cluster them into news stories comprising large numbers of articles about the same event. This is even augmented by the increasing amount of information in social media (e.g., Twitter, Facebook) where mass opinions about news events can be monitored. A promising way to reduce this bulk of highly redundant data is offered by the language technologies known as multi-document text summarisation and sentiment analysis. A major problem is that news articles are in many languages while current technology has mostly dealt with English, and the question of how current research can be applied in a heavily multilingual context has barely been addressed.Most multi-document summarisation research has focused on the news domain and MediaGist will do likewise in order to build on existing techniques and resources. However, summarising posts in social media, representing complementary and unbiased information, will be considered as well. Media professionals, however, will want to go beyond summaries from sources in one language and consider how news events are reported in other countries and from other perspectives. Identifying differences in opinion towards entities and events may provide some clues as the disagreements in reporting across languages. MediaGist will perform multilingual sentiment analysis and will thus make possible the generation of summaries that reveal these disagreements.The goal of MediaGist is to make significant advances in multilingual research so as to extract and present the GIST (the main content and opinions) of online multilingual news and the corresponding content in social media.

Оригинален текст от CORDIS (на английски).

Участници

  • ZAPADOCESKA UNIVERZITA V PLZNI · PilsenКоординаторЧехия

Връзки

Данни: CORDIS, © Европейски съюз