Applying Wikipedia’s Multilingual Knowledge to Cross–Lingual Question Answering

Ferrández, Sergio; Toral, Antonio; Ferrández, Óscar; Ferrández, Antonio; Muñoz, Rafael

doi:10.1007/978-3-540-73351-5_31

Cited by 23 publications

(17 citation statements)

References 11 publications

(14 reference statements)

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“…A number of systems have begun to apply this data for various tasks including question answering [5], thesaurus building [8], disambiguation and named entity extraction [11][16], and topic identification [7]. To our knowledge, Ziggurat is the first attempt at infobox alignment in the multi-lingual Wikipedia.…”

Section: Related Workmentioning

confidence: 99%

Information arbitrage across multi-lingual Wikipedia

Adar

Skinner

Weld

2009

Proceedings of the Second ACM International Conference on Web Search and Data Mining

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The rapid globalization of Wikipedia is generating a parallel, multi-lingual corpus of unprecedented scale. Pages for the same topic in many different languages emerge both as a result of manual translation and independent development. Unfortunately, these pages may appear at different times, vary in size, scope, and quality.Furthermore, differential growth rates cause the conceptual mapping between articles in different languages to be both complex and dynamic. These disparities provide the opportunity for a powerful form of information arbitrageleveraging articles in one or more languages to improve the content in another. Analyzing four large language domains (English, Spanish, French, and German), we present Ziggurat, an automated system for aligning Wikipedia infoboxes, creating new infoboxes as necessary, filling in missing information, and detecting discrepancies between parallel pages. Our method uses self-supervised learning and our experiments demonstrate the method's feasibility, even in the absence of dictionaries.

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Section: Related Workmentioning

confidence: 99%

Information arbitrage across multi-lingual Wikipedia

Adar

Skinner

Weld

2009

Proceedings of the Second ACM International Conference on Web Search and Data Mining

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“…Other potential applications include: (i) multilingual named entity recognition (NER) (Richman and Schone 2008), (ii) text classification (Kumaran and Allan 2004), (iii) uni-or cross-lingual question answering (Ferrández et al 2007), and (iv) proper name normalization (Jijkoun et al 2008). …”

Section: Future Applicationsmentioning

confidence: 99%

Populating a multilingual ontology of proper names from open sources

Savary

Manicki

Baron

2013

JLM

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Even if proper names play a central role in natural language processing (NLP) applications they are still under-represented in lexicons, annotated corpora, and other resources dedicated to text processing. One of the main challenges is both the prevalence and the dynamicity of proper names. At the same time, large and regularly-updated knowledge sources containing partially-structured data, such as Wikipedia or GeoNames, are publicly available and contain large numbers of proper names. We present a method for a semi-automatic enrichment of Prolexbase, an existing multilingual ontology of proper names dedicated to natural language processing, with data extracted from these open sources in three languages: Polish, English and French. Fine-grained data extraction and integration procedures allow the user to enrich previous contents of Prolexbase with new incoming data. All data are manually validated and available under an open licence.

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“…Wikipedia has been exploited for a wide range of tasks such as Monolingual Jijkoun et al, 2005;Buscaldi and Rosso, 2006) and multilingual (Ferrández et al, 2007b) QA, Semantic relatedness (Ponzetto and Strube, 2007;Gabrilovich and Markovitch, 2007;Milne and Witten, 2008), Information Extraction (Wu et al, 2008), NE Disambiguation (Bunescu and Pasca, 2006) or NE Recognition (Nothman et al, 2009). Furthermore, several researchers have used it to build LRs.…”

Section: Wikipedia and Nlpmentioning

confidence: 99%

“…For this propose, we have used the BRILIW (Spanish acronym for "QA using Inter Lingual Index module of EuroWordNet and Wikipedia") system (Ferrández et al, 2007b). It was designed to localise answers from documents, where answers and input questions are written in different languages.…”

Section: Question Answering Applicationmentioning

confidence: 99%

Web 2.0, Language Resources and standards to automatically build a multilingual Named Entity Lexicon

Toral

Ferrández

Monachini

et al. 2011

Lang Resources & Evaluation

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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 different 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 affects the Computational Linguistics area in the present, interoperability, by making use of the ISO LMF standard to encode this lexicon. The different steps of the procedure (mapping, disambiguation, extraction, NE identification 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 system's 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.

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Applying Wikipedia’s Multilingual Knowledge to Cross–Lingual Question Answering

Cited by 23 publications

References 11 publications

Information arbitrage across multi-lingual Wikipedia

Information arbitrage across multi-lingual Wikipedia

Populating a multilingual ontology of proper names from open sources

Web 2.0, Language Resources and standards to automatically build a multilingual Named Entity Lexicon

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