103 research outputs found
Detecting Event-Related Links and Sentiments from Social Media Texts
Nowadays, the importance of Social Media is constantly growing, as people often use such platforms to share mainstream media news and comment on the events that they relate to. As such, people no loger remain mere spectators to the events that happen in the world, but become part of them, commenting on their developments and the entities involved, sharing their opinions and distributing related content. This paper describes a system that links the main events detected from clusters of newspaper articles to tweets related to them, detects complementary information sources from the links they contain and subsequently applies sentiment analysis to classify them into positive, negative and neutral. In this manner, readers can follow the main events happening in the world, both from the perspective of mainstream as well as social media and the public's perception on them. This system is part of a media monitoring framework working live and it will be demonstrated using Google Earth.JRC.G.2-Global security and crisis managemen
A combined qualitative-quantitative approach for the identification of highly co-creative technology-driven firms
JRC's Participation in the Guided Summarization Task at TAC 2010
In this paper we describe our participation in the Guided Summarization Task at the Text Analysis Conference 2010 (TAC'10). The goal of the task was to encourage a deeper semantic analysis of the source documents instead of relying only on document word frequencies to select important concepts. We used the output of our event extraction system and automatic learning of semantically-related terms to capture the required aspects of each particular article category. We submitted two runs: the first uses information extraction tools in combination with co-occurrence of features, the second uses only co-occurrence information. In the following sections we describe our runs and discuss the results attained.JRC.DG.G.2-Global security and crisis managemen
Acronym recognition and processing in 22 languages
We are presenting work on recognising acronyms of the form Long-Form
(Short-Form) such as "International Monetary Fund (IMF)" in millions of news
articles in twenty-two languages, as part of our more general effort to
recognise entities and their variants in news text and to use them for the
automatic analysis of the news, including the linking of related news across
languages. We show how the acronym recognition patterns, initially developed
for medical terms, needed to be adapted to the more general news domain and we
present evaluation results. We describe our effort to automatically merge the
numerous long-form variants referring to the same short-form, while keeping
non-related long-forms separate. Finally, we provide extensive statistics on
the frequency and the distribution of short-form/long-form pairs across
languages
Extracting and Learning Social Networks out of Multilingual News
Various kinds of social networks can be derived from the analysis of news articles. We present here our experience in building social networks by the extraction of relationships between entities all automatically derived from multilingual news articles. Unqualified relationships between persons can be extracted through simple co-occurrence statistics. Qualified relationships can be extracted using linguistic patterns. Our highly redundant sources (50,000 daily articles in 40 languages) are used to both validate our algorithms and strengthen pertinent relationships. Due to the amount of data we process these social networks provide a complex challenge for their useful visualization and navigation.JRC.G.2-Support to external securit
Automated Extraction of Socio-political Events from News (AESPEN): Workshop and Shared Task Report
We describe our effort on automated extraction of socio-political events from
news in the scope of a workshop and a shared task we organized at Language
Resources and Evaluation Conference (LREC 2020). We believe the event
extraction studies in computational linguistics and social and political
sciences should further support each other in order to enable large scale
socio-political event information collection across sources, countries, and
languages. The event consists of regular research papers and a shared task,
which is about event sentence coreference identification (ESCI), tracks. All
submissions were reviewed by five members of the program committee. The
workshop attracted research papers related to evaluation of machine learning
methodologies, language resources, material conflict forecasting, and a shared
task participation report in the scope of socio-political event information
collection. It has shown us the volume and variety of both the data sources and
event information collection approaches related to socio-political events and
the need to fill the gap between automated text processing techniques and
requirements of social and political sciences
Challenges and Applications of Automated Extraction of Socio-political Events from Text (CASE 2022): Workshop and Shared Task Report
We provide a summary of the fifth edition of the CASE workshop that is held
in the scope of EMNLP 2022. The workshop consists of regular papers, two
keynotes, working papers of shared task participants, and task overview papers.
This workshop has been bringing together all aspects of event information
collection across technical and social science fields. In addition to the
progress in depth, the submission and acceptance of multimodal approaches show
the widening of this interdisciplinary research topic.Comment: to appear at CASE 2022 @ EMNLP 202
Crowdsourcing Cybersecurity: Cyber Attack Detection using Social Media
Social media is often viewed as a sensor into various societal events such as
disease outbreaks, protests, and elections. We describe the use of social media
as a crowdsourced sensor to gain insight into ongoing cyber-attacks. Our
approach detects a broad range of cyber-attacks (e.g., distributed denial of
service (DDOS) attacks, data breaches, and account hijacking) in an
unsupervised manner using just a limited fixed set of seed event triggers. A
new query expansion strategy based on convolutional kernels and dependency
parses helps model reporting structure and aids in identifying key event
characteristics. Through a large-scale analysis over Twitter, we demonstrate
that our approach consistently identifies and encodes events, outperforming
existing methods.Comment: 13 single column pages, 5 figures, submitted to KDD 201
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