=Paper=
{{Paper
|id=None
|storemode=property
|title=Cross-lingual Detection of World Events from News Articles
|pdfUrl=https://ceur-ws.org/Vol-1272/paper_19.pdf
|volume=Vol-1272
|dblpUrl=https://dblp.org/rec/conf/semweb/LebanFBG14
}}
==Cross-lingual Detection of World Events from News Articles==
https://ceur-ws.org/Vol-1272/paper_19.pdf
Cross-lingual detection of world events from
news articles
Gregor Leban, Blaž Fortuna, Janez Brank, and Marko Grobelnik
Jožef Stefan Institute
Ljubljana, Slovenia
{firstname.surname}@ijs.si
Abstract. In this demo we describe a system called Event Registry
(http://eventregistry.org) that can identify world events from news arti-
cles. Events can be detected in different languages. For each event, the
system can extract core event information and store it in a structured
form that allows advanced search options. Numerous visualizations are
provided for visualizing search results.
Keywords: information extraction; cross-linguality; events; named entity de-
tection; clustering; news
1 Introduction
Each day there are thousands of small, medium and large events that are hap-
pening in the world. These events range from insignificant meetings, conferences
and sport events to important natural disasters and decisions made by world
leaders. The way we learn about these events is from different media channels.
The unstructured nature of content provided on these channels is suitable for
humans, but hard to understand by computers.
Identifying events described in the news and extracting main information
about these events is the main goal of the system presented in this paper. Event
Registry [2] is able to process news articles published in different languages world-
wide. By analyzing the articles it can identify the mentioned events and extract
main event information. Extracted event information is stored in a structured
way that provides unique features such as searching for events by date, location,
entity, topic and other event properties. Beside finding events of interest, Event
Registry also provides user interface with numerous visualizations showing date,
location, concept and topic aggregates of events that match the search criteria.
The rest of the paper is organized as follows. We start by describing the high
level architecture of the system. Next, we describe in more details the process of
identification of events from individual news articles. We continue by providing
information about how event information is extracted from a group of articles
mentioning the event. In the end we also describe the main features of the
frontend interface of the Event Registry.
2 Gregor Leban, Blaž Fortuna, Janez Brank, and Marko Grobelnik
2 System architecture
Event Registry consists of a pipeline of components, that each provide unique
and relevant features for the system. In order to identify events we first need
data from which we can extract the information. In Event Registry we use news
articles as the data source. The news articles are collected using the News-
Feed [5] service which collects news articles from more than 75.000 worldwide
news sources. Collected articles are in more than 40 languages, where articles in
English, Spanish, German and Chinese languages amount to about 70% of all
articles. These languages are also the only ones we use in the Event Registry.
Each collected article in the mentioned languages is then analyzed in order
to extract relevant information. One of the key tasks is identification and disam-
biguation of named entities and topics mentioned in the article. We perform this
task using a semantic enrichment service developed in the XLike project. We also
detect date mentions in the text, since they frequently specify the date when the
event occurred. By analyzing the articles we noticed that different news sources
frequently publish almost identical news articles. These duplicated articles don’t
bring any new information, which is why we identify them and ignore them in
the rest of the pipeline.
An important feature of the Event Registry is cross-linguality. To support
it, we identify for each article a set of most similar articles in other languages.
To identify these articles we use canonical correlation analysis[4] which maps
articles from different languages into a common semantic space. The common
space can then be used to identify most similar articles in all other languages.
In order to train the mapping to the common space we used the aligned corpus
of Wikipedia articles.
After extracting relevant features from each individual article we start with
the main task of identifying events from groups of articles. Since this is the main
contribution of the paper we will describe the details of the process in the next
three sections.
3 Identification of events
An assumption that we make in identifying events is that any relevant event
should be reported at least by a few different news publishers. In order to identify
events we therefore apply an online clustering algorithm based on [1] on articles
as they are added into the system. Each article is first transformed into a TF-IDF
weighted feature vector. The features in the vector are the words in the document
as well as the identified named entities and topics. If the cosine similarity of the
closest centroid is above a threshold, the article is added to the closest cluster
and the cluster properties are updated. Otherwise, a new cluster is formed that
contains only the new article.
Each identified cluster of articles is considered to describe an event if it
contains at least a minimum number of articles (the minimum value used in
our system is 5 articles). Once a cluster reaches this limit, we treat it as an
Cross-lingual detection of world events from news articles 3
event and the information about it’s articles are sent to the next components in
the pipeline. Those components are responsible for extracting event information
from the articles and will be described in the next sections.
Since clusters are being constantly updated with new articles we want to
reevaluate each cluster after a few updates in order to determine if it should
be split into two clusters or merged with another cluster. In order to decide if
the cluster should be split we apply a bisecting k-means algorithm (with k = 2)
on the cluster. We then use a variant of the Bayesian Information Criterion to
decide whether to accept the new split or not. Periodically we also identify pairs
of clusters with high similarity and decide if they should be merged or not. The
decision is made using the Lughofer’s ellipsoid criterion [3]. We assume that the
clusters that have not been modified for a few days mention past events and we
therefore remove them from the list of maintained clusters.
3.1 Cross-lingual merging of clusters
Each identified cluster of articles contains only articles in a single language.
Since articles in different languages can describe the same events we want to
identify clusters describing the same event in different languages and represent
them as a single event. In order to determine which cluster pairs to merge we
represent the task as a binary classification problem. Given a cluster pair c1
and c2 in languages l1 and l2 we want to extract a set of features that would
discriminate between cluster pairs that describe the same event and those that
don’t. A very important learning feature that we can extract for each cluster pair
is computed by inspecting individual articles in each cluster. Using canonical
correlation analysis we are able to obtain for each article in c1 a list of 10 most
similar articles in language l2 . Using this information we can check how many
of these articles are in c2 . We can repeat the same computation for articles
in c2 . By normalizing the results by the size of the clusters we can obtain a
score that should by intuition correlate with similarity of the two clusters across
the two languages. Some of the other features that we extract include the time
difference between the average article date of each cluster, cosine similarity of
the annotated concepts and topics, and category similarity.
In order to build a classification model we collected 85 learning examples.
Some cluster pairs were selected randomly and some were selected by users
based on their similarity. The features for the selected learning examples were
extracted automatically, while the correct class was assigned manually. A linear
SVM was then trained on the data which achieved 87% accuracy using 10-fold
cross validation.
4 Event information extraction
Once we obtain one or more new clusters that are believed to describe a single
event, we assign them a new id in the Event Registry. From the associated arti-
cles we then try to extract the relevant information about the event. We try to
4 Gregor Leban, Blaž Fortuna, Janez Brank, and Marko Grobelnik
determine event date by seeing if there is a common date reference frequently
mentioned in the articles. If no date is mentioned frequently enough we use the
average article’s published date as the event date. The location of the event
is determined by locating frequently mentioned named entities that represent
locations. To determine what the event is about we aggregate the named enti-
ties and topics identified in the articles. Each event is also categorized using a
DMoz taxonomy. All extracted information is stored in the Event Registry in a
structured form that provides rich search and visualization capabilities.
5 Event search, visualization options and data
accessibility
Event Registry is available at http://eventregistry.org and currently contains
15.000.000 articles from which we identified about 1 million events. Available
search options include search by relevant named entities, keywords, publishers,
event location, date and category. The resulting events that match the criteria
can then be seen as a list or using one of numerous visualizations. Main vi-
sualizations of search results include location and time aggregates, list of top
named entities and topics, graph of related entities, concept trends, concept ma-
trix, date mentions, clusters of events and event categories. For each individual
event we can provide the list of articles describing it as well as visualizations
of concepts, article timeline, date mentions, article sources and other similar
events. Examples of these visualizations are (due to space limit) available on
http://eventregistry.org/screens. All Event Registry data is also stored using the
Storyline ontology and is available through a SPARQL endpoint available at
http://eventregistry.org/sparql.
6 Acknowledgments
This work was supported by the Slovenian Research Agency and X-Like (ICT-
288342-STREP).
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