Martin Atkinson

Near real time information mining in multilingual news

This paper presents a near real-time multilingual news monitoring and analysis system that forms the backbone of our research work. The system integrates technologies to address the problems related to information extraction and analysis of open source intelligence on the World Wide Web. By chaining together different techniques in text mining, automated machine learning and statistical analysis, we can automatically determine who, where and, to a certain extent, what is being reported in news articles.

Cluster-Centric Approach to News Event Extraction

This paper presents a real-time and multilingual news event extraction system developed at the Joint Research Centre of the European Commission. It is capable of accurately and efficiently extracting violent and natural disaster events from online news. In particular, a linguistically relatively lightweight approach is deployed, in which clustered news are heavily exploited at all stages of processing. The paper focuses on the systems architecture, real-time news clustering, geolocating clusters, event extraction grammar development, adapting the system to the processing of new languages, cluster-level information fusion, visual event tracking and accuracy evaluation.

Multilingual Real-time Event Extraction for Border Security Intelligence Gathering

This chapter gives an overview of tools developed for Frontex, the European Agency for the Management of Operational Cooperation at the External Borders of the Member States of the European Union, to facilitate the process of extracting structured information on events related to border security from on-line news articles, with a particular focus on incidents and developments in the context of illegal migration, cross-border crime, and related crisis situations at the EU external borders and in third countries. A hybrid event extraction system has been constructed, which consists of two core event extraction engines, namely, NEXUS, developed at the Joint Research Centre (JRC) of the European Commission and PULS, developed at the University of Helsinki. These systems are applied to the stream of news articles continuously gathered and pre-processed by the Europe Media Monitor (EMM) – a large-scale multilingual news aggregation engine, developed at the JRC. In order to bridge the automated analysis phase with in-depth human analysis phase an event moderation tool has been developed, which allows the user to access the database of automatically extracted event descriptions and to clean, validate, group, enhance, and export them into other knowledge repositories.

Online news event extraction for global crisis surveillance

This article presents a real-time and multilingual news event extraction system developed at the Joint Research Centre of the European Commission. It is capable of accurately and efficiently extracting violent and natural disaster events from online news. In particular, a linguistically relatively lightweight approach is deployed, in which clustered news are heavily exploited at all stages of processing. Furthermore, the technique applied for event extraction assumes the inverted-pyramid style of writing news articles, i.e., the most important parts of the story are placed in the beginning and the least important facts are left toward the end. The article focuses on the systems architecture, real-time news clustering, geo-locating and geocoding clusters, event extraction grammar development, adapting the system to the processing of new languages, cluster-level information fusion, visual event tracking, event extraction accuracy evaluation, and detecting event reporting boundaries in news article streams. This article is an extended version of [20].

NewsGist: a multilingual statistical news summarizer

In this paper we present NewsGist, a multilingual, multidocument news summarization system underpinned by the Singular Value Decomposition (SVD) paradigm for document summarization and purpose-built for the Europe Media Monitor (EMM). The summarization method employed yielded state-ofthe-art performance for English at the Update Summarization task of the last Text Analysis Conference (TAC) 2009 and integrated with EMM represents the first online summarization system able to produce summaries for so many languages. We discuss the context and motivation for developing the system and provide an overview of its architecture. The paper is intended to serve as accompaniment of a live demo of the system, which can be of interest to researchers and engineers working on multilingual open-source news analysis and mining.

Tracking Media Reports on the Shiga Toxin-Producing Escherichia coli O104: H4 Outbreak in Germany

In May 2011, an outbreak of enterohemorrhagic Escherichia coli (EHEC) occurred in northern Germany. The Shiga toxin-producing strain O104:H4 infected several thousand people, frequently leading to haemolytic uremic syndrome (HUS) and gastroenteritis (GI). First reports about the outbreak appeared in the German media on Saturday 21st of May 2011; the media attention rose to high levels in the following two weeks, with up to 2000 articles categorized per day by the automatic threat detection system MedISys (Medical Information System). In this article, we illustrate how MedISys detected the sudden increase in reporting on E. coli on 21st of May and how automatic analysis of the reporting provided epidemic intelligence information to follow the event. Categorization, filtering and clustering allowed identifying different aspects within the unfolding news event, analyzing general media and official sites in parallel.

Real-time text mining in multilingual news for the creation of a pre-frontier intelligence picture

This paper presents an endeavor aiming at construction of a real-time event extraction system for border security-related intelligence gathering from online news. First, the background and motivation behind the presented work is given. Next, the paper describes the event extraction processing chain, the specifics of the domain, i.e., illegal migration and related cross-border crime, and event moderation and visualisation aspects of the system.

Frontex real-time news event extraction framework

An ever-growing amount of information relevant for early detection of certain threats can be extracted from on-line news. This led to an emergence of news mining tools to help analysts to digest the overflow of information and to extract valuable knowledge from on line news sources. This paper gives an overview of the fully operational Real-time News Event Extraction Framework developed for Frontex, the EU Border Agency, to facilitate the process of extracting structured information on border security-related events from on-line news. In particular, a hybrid event extraction system has been constructed, which is applied to the stream of news articles continuously gathered and pre-processed by the Europe Media Monitor - a large-scale multilingual news aggregation engine. The framework consists also of an earth browser, in which events are visualized and an event moderation tool, which allows to access the database of automatically extracted event descriptions and to clean, validate, group, enhance and export them into other knowledge repositories.

Techniques for Multilingual Security-Related Event Extraction from Online News

This chapter presents a number of techniques for multilingual event extraction, the main task is to accurately and efficiently detect key information about security-related events from electronic news media and summarize it in the form of database-like structures. Gathering such information over time is an important task for developing global news surveillance systems, particularly in the context of security threats and mass emergencies. In particular, this chapter describes novel techniques for dealing with specific extraction tasks, including: an event type classification method based on domain-specific inference rules, an approach to event geo-tagging based on utilisation of lexico-semantic patterns, a simple method for cross-lingual event information fusion, and techniques for scoring the relevance rank of automatically extracted facts.

News mining for border security Intelligence

This presentation gives an overview of an effort to construct OSINT (Open-Source Intelligence) tools for Frontex, the European Agency for the Management of Operational Cooperation at the External Borders of the Member States of the European Union, to facilitate automating the process of extracting structured knowledge from on-line news articles on border-security related events at the EU borders and in related third countries. A particular focus is on incidents and developments which are of relevance in the context of illegal migration. This includes: (a) illegal migration incidents (e.g., illegal border crossing attempts), (b) related cross-border crime (e.g., human/arms/drug trafficking), (c) related crisis events (e.g., terrorist attacks, outbreaks of infectious disease).