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1. A Parallel Corpus
the Crime Event Extraction for Low
Resource Languages
Abstract
These days, a lot of crime-
them are reported in news portals and social media. Crime
extraction from the published texts can allow monitoring, analysis, and
comparison of police or criminal activities in different countries or regions.
Existing approaches to event extraction mainly suggest processing texts in
English, French, Chinese, and
languages. This paper presents a parallel corpus
a closed-domain event extraction methodology to event extraction from web
news articles in low-resource languages. To identify the even
and the arguments’ roles in the source
an enhanced pattern-based method that involves the multilingual synonyms
dictionary with knowledge about crime
equations. The event extraction from the target
uses a cross-lingual crime
based on supplementary knowledge about the semantic similarity patterns of
A Parallel Corpus-Based Approach to
the Crime Event Extraction for Low
Resource Languages
-related events take place all over the world. Most of
them are reported in news portals and social media. Crime-
extraction from the published texts can allow monitoring, analysis, and
comparison of police or criminal activities in different countries or regions.
Existing approaches to event extraction mainly suggest processing texts in
English, French, Chinese, and some other resource-rich and well
languages. This paper presents a parallel corpus-based approach that follows
domain event extraction methodology to event extraction from web
resource languages. To identify the event, its arguments,
and the arguments’ roles in the source-language part of the corpus we utilize
based method that involves the multilingual synonyms
dictionary with knowledge about crime-related concepts and logic
The event extraction from the target-language part of the corpus
lingual crime-related event extraction transfer technique that is
based on supplementary knowledge about the semantic similarity patterns of
Based Approach to
the Crime Event Extraction for Low-
related events take place all over the world. Most of
-related event
extraction from the published texts can allow monitoring, analysis, and
comparison of police or criminal activities in different countries or regions.
Existing approaches to event extraction mainly suggest processing texts in
rich and well-annotated
based approach that follows
domain event extraction methodology to event extraction from web
t, its arguments,
language part of the corpus we utilize
based method that involves the multilingual synonyms
related concepts and logic-linguistic
language part of the corpus
related event extraction transfer technique that is
based on supplementary knowledge about the semantic similarity patterns of

2. the considered pair of languages. The presented approach does not require a
preliminarily annotated corpus for training making it more attractive to low-
resource languages and allows extracting TRANSFER, CRIME, and POLICE
types of events and their seven subtypes from various topics of news articles
simultaneously. Implementation of our approach for the Russian-Kazakh
parallel corpus of news portals articles allowed obtaining the F1-measure of
crime-related event extraction of over 82% for the source language and 63%
for the target language.