Populating a multilingual ontology of proper names from open sources

Savary, Agata; Manicki, Leszek; Baron, Malgorzata

doi:10.15398/jlm.v1i2.63

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…The second version of Prolexbase has been supported by the Hubert Curien Polonium project which brought a good coverage for Polish and English Savary et al (2013). The Serbian part has been significantly improved in the third version of Prolexbase, as a result of a one month visit of Professor Cvetana Krstev which was sponsored by The University of Tours.…”

Section: Motivationmentioning

confidence: 99%

Serbian Language Integration in Prolexbase Multilingual Dictionary

Krstev¹,

Maurel²,

Vitas³

2018

Infotheca

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In this paper we present the multilingual dictionary of proper names Prolexbase, particularly the Serbian volume. We also present the complexity of proper names in the Serbian language, especially those related to their translation: their orthography, derivation, inflection and dialect variations. We describe the model of the Prolexbase, with the emphasize on the solutions that had to be made to include the Serbian language in the database (the use of two alphabets, several levels of derivations, the existence of multiple forms). At the end, we give some figures that corroborate the presence of the Serbian language in the Prolexbase.

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Section: Motivationmentioning

confidence: 99%

Serbian Language Integration in Prolexbase Multilingual Dictionary

Krstev¹,

Maurel²,

Vitas³

2018

Infotheca

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“…In this context, the collaboratively built, semi-structured and multilingual Wikipedia resource appeared as a great relief, and several named entity dictionaries were built out of it [68,57,59]. Prolexbase [38], a manually produced multilingual ontology of proper names built up over many years, recently adopted a semi-automatic enrichment strategy based on Wikipedia [51]. All of these resources are the result of exploiting Wikipedia and, with the excep-tion of [59] which makes use of LMF, they are not interoperable.…”

Section: Related Workmentioning

confidence: 99%

JRC-Names: Multilingual entity name variants and titles as Linked Data

Ehrmann

Jacquet

Steinberger

2016

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Since 2004 the European Commission's Joint Research Centre (JRC) has been analysing the online version of printed media in over twenty languages and has automatically recognised and compiled large amounts of named entities (persons and organisations) and their many name variants. The collected variants not only include standard spellings in various countries, languages and scripts, but also frequently found spelling mistakes or lesser used name forms, all occurring in real-life text (e.g. Benjamin/Binyamin/Bibi/Benyamín/Biniamin/Беньямин/‫ﺒﻨﻴﺎﻤﻴﻥ‬ Netanyahu/Netanjahu/Nétanyahou/Netahny/Нетаньяху/‫.)ﻨﺘﻨﻴﺎﻫﻭ‬ This entity name variant data, known as JRCNames, has been available for public download since 2011. In this article, we report on our efforts to render JRC-Names as Linked Data (LD), using the lexicon model for ontologies lemon. Besides adhering to Semantic Web standards, this new release goes beyond the initial one in that it includes titles found next to the names, as well as date ranges when the titles and the name variants were found. It also establishes links towards existing datasets, such as DBpedia and Talk-Of-Europe. As multilingual linguistic linked dataset, JRC-Names can help bridge the gap between structured data and natural languages, thus supporting large-scale data integration, e.g. cross-lingual mapping, and web-based content processing, e.g. entity linking. JRC-Names is publicly available through the dataset catalogue of the European Union's Open Data Portal.

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“…The task of adaptation of such a base to another language is far from trivial, especially for Slavonic languages with complex NE inflection (Przepiórkowski, 2007). An ontology taking into account Polish inflection (Prolexbase) has been created by Savary et al (2013), but it contains only 40,000 names, grouped into 34 types.…”

Section: Deep Entity Recognitionmentioning

confidence: 99%

Boosting Question Answering by Deep Entity Recognition

Przybyła¹

2016

Preprint

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In this paper an open-domain factoid question answering system for Polish, RAFAEL, is presented. The system goes beyond finding an answering sentence; it also extracts a single string, corresponding to the required entity. Herein the focus is placed on different approaches to entity recognition, essential for retrieving information matching question constraints. Apart from traditional approach, including named entity recognition (NER) solutions, a novel technique, called Deep Entity Recognition (DeepER), is introduced and implemented. It allows a comprehensive search of all forms of entity references matching a given WordNet synset (e.g. an impressionist), based on a previously assembled entity library. It has been created by analysing the first sentences of encyclopaedia entries and disambiguation and redirect pages. DeepER also provides automatic evaluation, which makes possible numerous experiments, including over a thousand questions from a quiz TV show answered on the grounds of Polish Wikipedia. The final results of a manual evaluation on a separate question set show that the strength of DeepER approach lies in its ability to answer questions that demand answers beyond the traditional categories of named entities.The study was supported by research fellowship within "Information technologies: research and their interdisciplinary applications" agreement number POKL. 04.01.01-00-051/10-00.

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Populating a multilingual ontology of proper names from open sources

Cited by 4 publications

References 15 publications

Serbian Language Integration in Prolexbase Multilingual Dictionary

Serbian Language Integration in Prolexbase Multilingual Dictionary

JRC-Names: Multilingual entity name variants and titles as Linked Data

Boosting Question Answering by Deep Entity Recognition

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