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  • Выявление искаженной информации: подход с использованием дискурсивных связей
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News
October 6, 2026
International N5 Symposium ‘Neural Networks and Nonlinearity in Nizhny Novgorod Brings Together Scientists from Russia and Serbia
The International N5 Symposium ‘Neural Networks and Nonlinearity in Nizhny Novgorod’ was held at the Nizhny Novgorod House of Scientists from September 23 to 26. The event was organised by HSE University–Nizhny Novgorod and the Nizhny Novgorod House of Scientists, with the participation of Sberbank and the Institute of Physics Belgrade. The symposium was held for the second time: the first conference took place in 2025 and attracted considerable interest from the academic community.
October 5, 2026
‘The Climate Transition Is Not Necessarily a Limitation for Business
Linara Khadimullina works in the field of low-carbon development. In an interview with the Young Scientists of HSE project, she spoke about why nature is not just a beautiful backdrop, her research on the role of sustainable corporate governance in reducing greenhouse gas emissions, and growing plants as a source of inspiration.
October 5, 2026
Africa, Youth, and Civic Dialogue: Public Diplomacy Discussed at HSE University
In late September, HSE University hosted a roundtable discussion titled Civil Society in African Countries and Youth Participation in Public Diplomacy. Representatives of non-governmental organisations from Ghana, Ethiopia, and Russia, along with students from HSE University’s Bachelor’s Programme in Public Administration, discussed how young people without official diplomatic status can influence relations between countries and how the nonprofit sector can remain sustainable amid declining grant funding.

 

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Выявление искаженной информации: подход с использованием дискурсивных связей

P. 23–32.
Galitsky B., Ilvovsky D.

A linguistic method for determining whether given text is a rumor or disinformation is proposed, based on web mining and linguistic technology comparing two text fragments. We hypothesize about a family of content generation algorithms which are capable of producing deception from a portion of genuine, original text. We then propose a disinformation detection algorithm which finds a candidate source of text on the web and compares it with the given text, applying parse thicket technology. Parse thicket is a graph combined from a sequence of parse trees augmented with inter-sentence relations for anaphora and rhetoric structures. We evaluate our algorithm in the domain of customer reviews, considering a product review as an instance of possible deception. It is confirmed as a plausible way to detect rumor and deception in a web document.

Language: English
Full text
Keywords: web miningparse thicketчаща разбора
Publication based on the results of:
Mining Data with Complex Structure and Semantic Technologies (2016)

In book

Пятнадцатая национальная конференция по искусственному интеллекту с международным участием КИИ-2016 (3-7 октября 2016г., г.Смоленск, Россия): Труды конференции
Т. 1. , Смоленск: Универсум, 2016.
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