2,727 research outputs found
Towards Better Understanding Researcher Strategies in Cross-Lingual Event Analytics
With an increasing amount of information on globally important events, there
is a growing demand for efficient analytics of multilingual event-centric
information. Such analytics is particularly challenging due to the large amount
of content, the event dynamics and the language barrier. Although memory
institutions increasingly collect event-centric Web content in different
languages, very little is known about the strategies of researchers who conduct
analytics of such content. In this paper we present researchers' strategies for
the content, method and feature selection in the context of cross-lingual
event-centric analytics observed in two case studies on multilingual Wikipedia.
We discuss the influence factors for these strategies, the findings enabled by
the adopted methods along with the current limitations and provide
recommendations for services supporting researchers in cross-lingual
event-centric analytics.Comment: In Proceedings of the International Conference on Theory and Practice
of Digital Libraries 201
Web 2.0, language resources and standards to automatically build a multilingual named entity lexicon
This paper proposes to advance in the current state-of-the-art of automatic Language Resource (LR) building by taking into consideration three elements: (i) the knowledge available in existing LRs, (ii) the vast amount of information available from the collaborative paradigm that has emerged from the Web 2.0 and (iii) the use of standards to improve interoperability. We present a case study in which a set of LRs for diïŹerent languages (WordNet for English and Spanish and Parole-Simple-Clips for Italian) are
extended with Named Entities (NE) by exploiting Wikipedia and the aforementioned LRs. The practical result is a multilingual NE lexicon connected to these LRs and to two ontologies: SUMO and SIMPLE. Furthermore, the paper addresses an important problem which aïŹects the Computational Linguistics area in the present, interoperability, by making use of the ISO LMF standard to encode this lexicon. The diïŹerent steps of the procedure (mapping, disambiguation, extraction, NE identiïŹcation and postprocessing) are comprehensively explained and evaluated. The resulting resource contains 974,567, 137,583 and 125,806 NEs for English, Spanish and Italian respectively. Finally, in order to check the usefulness of the constructed resource, we apply it into a state-of-the-art Question Answering system and evaluate its impact; the NE lexicon improves the systemâs accuracy by 28.1%. Compared to previous approaches to build NE repositories, the current proposal represents a step forward in terms of automation, language independence, amount of NEs acquired and richness of the information represented
An automatically built named entity lexicon for Arabic
We have successfully adapted and extended the automatic Multilingual, Interoperable Named Entity Lexicon approach to Arabic, using Arabic WordNet (AWN) and Arabic Wikipedia (AWK). First, we extract AWNâs instantiable nouns and identify the corresponding categories and hyponym subcategories in AWK. Then, we exploit Wikipedia inter-lingual links to locate correspondences between articles in ten different languages in order to identify Named Entities (NEs). We apply keyword search on AWK abstracts to provide for Arabic articles that do not have a correspondence in any of the other languages. In addition, we perform a post-processing step to fetch further NEs from AWK not reachable through AWN. Finally, we investigate diacritization using matching with geonames databases, MADA-TOKAN tools and different heuristics for restoring vowel marks of Arabic NEs. Using this methodology, we have extracted approximately 45,000 Arabic NEs and built, to the best of our knowledge, the largest, most mature and well-structured Arabic NE lexical resource to date. We have stored and organised this lexicon following the Lexical Markup Framework (LMF) ISO standard. We conduct a quantitative and qualitative evaluation of the lexicon against a manually annotated gold standard and achieve precision scores from
95.83% (with 66.13% recall) to 99.31% (with 61.45% recall) according to different values of a threshold
Analysing Timelines of National Histories across Wikipedia Editions: A Comparative Computational Approach
Portrayals of history are never complete, and each description inherently
exhibits a specific viewpoint and emphasis. In this paper, we aim to
automatically identify such differences by computing timelines and detecting
temporal focal points of written history across languages on Wikipedia. In
particular, we study articles related to the history of all UN member states
and compare them in 30 language editions. We develop a computational approach
that allows to identify focal points quantitatively, and find that Wikipedia
narratives about national histories (i) are skewed towards more recent events
(recency bias) and (ii) are distributed unevenly across the continents with
significant focus on the history of European countries (Eurocentric bias). We
also establish that national historical timelines vary across language
editions, although average interlingual consensus is rather high. We hope that
this paper provides a starting point for a broader computational analysis of
written history on Wikipedia and elsewhere
Mining Domain-Specific Thesauri from Wikipedia: A case study
Domain-specific thesauri are high-cost, high-maintenance, high-value knowledge structures. We show how the classic thesaurus structure of terms and links can be mined automatically from Wikipedia. In a comparison with a professional thesaurus for agriculture we find that Wikipedia contains a substantial proportion of its concepts and semantic relations; furthermore it has impressive coverage of contemporary documents in the domain. Thesauri derived using our techniques capitalize on existing public efforts and tend to reflect contemporary language usage better than their costly, painstakingly-constructed manual counterparts
Mining Meaning from Wikipedia
Wikipedia is a goldmine of information; not just for its many readers, but
also for the growing community of researchers who recognize it as a resource of
exceptional scale and utility. It represents a vast investment of manual effort
and judgment: a huge, constantly evolving tapestry of concepts and relations
that is being applied to a host of tasks.
This article provides a comprehensive description of this work. It focuses on
research that extracts and makes use of the concepts, relations, facts and
descriptions found in Wikipedia, and organizes the work into four broad
categories: applying Wikipedia to natural language processing; using it to
facilitate information retrieval and information extraction; and as a resource
for ontology building. The article addresses how Wikipedia is being used as is,
how it is being improved and adapted, and how it is being combined with other
structures to create entirely new resources. We identify the research groups
and individuals involved, and how their work has developed in the last few
years. We provide a comprehensive list of the open-source software they have
produced.Comment: An extensive survey of re-using information in Wikipedia in natural
language processing, information retrieval and extraction and ontology
building. Accepted for publication in International Journal of Human-Computer
Studie
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