REPENTINO – A Wide-Scope Gazetteer for Entity Recognition in Portuguese

Sarmento, Luís; Pinto, Ana Sofia; Cabral, Luís Miguel

doi:10.1007/11751984_4

Cited by 14 publications

(13 citation statements)

References 4 publications

(5 reference statements)

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“…REPENTINO (REPositório para reconhecimento de ENTidades com NOme) (Sarmento et al, 2006) is a repository of monolingual (Portuguese) NEs. This resource contains 450,129 entities, which are organised according to a taxonomy made up of several top categories (abstract, art and media, nature, event, legal, localisation, organisation, product, being and substance) which in turn are subdivided into subcategories.…”

Section: Onomastica Acquisition and Creationmentioning

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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Section: Onomastica Acquisition and Creationmentioning

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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“…for the HAREM and MiniHAREM corpora, we apply a BLS that makes use of gazetteers only. We use the gazetteers presented in [20], as well as some sections of the REPENTINO gazetteer [30]. [16] shows the best result reported so far.…”

Section: Modelingmentioning

confidence: 99%

Portuguese corpus-based learning using ETL

2008

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We present Entropy Guided Transformation Learning models for three Portuguese Language Processing tasks: Part-of-Speech Tagging, Noun Phrase Chunking and Named Entity Recognition. For Part-of-Speech Tagging, we separately use the Mac-Morpho Corpus and the Tycho Brahe Corpus. For Noun Phrase Chunking, we use the SNR-CLIC Corpus. For Named Entity Recognition, we separately use three corpora: HAREM, MiniHAREM and LearnNEC06. For each one of the tasks, the ETL modeling phase is quick and simple. ETL only requires the training set and no handcrafted templates. ETL also simplifies the incorporation of new input features, such as capitalization information, which are sucessfully used in the ETL based systems. Using the ETL approach, we obtain state-of-the-art competitive performance in all six corpora-based tasks. These results indicate that ETL is a suitable approach for the construction of Portuguese corpus-based systems.

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“…A importância do Reconhecimento de Entidades Nomeadas (REN) tem crescido com a propagação de sistemas de extração de informação (Sarmento et al, 2006), uma vez que o pré-processamento da maioria das atividades desses sistemas tem como principal responsabilidade o REN (Nothman et al, 2013). O REN pode ser considerado uma sub-tarefa dos processos de extração de informação e envolve processar um texto e identificar as ocorrências de palavras ou expressões pertencentesàs categorias de entidades nomeadas (Mikheev et al, 1999).…”

Section: Reconhecimento De Entidades Nomeadasunclassified

“…As entidades nomeadas incluem todas as entidades que podem ser identificadas por um nome próprio, como pessoas, organizações, lugares, marcas, produtos entre outros (Sarmento et al, 2006;Kozareva, 2006). Além dessas entidades tambémé possível reconhecer expressões temporais e expressões numéricas.…”

Section: Reconhecimento De Entidades Nomeadasunclassified

“…Segundo Sarmento et al (2006), o objetivo do Reconhecimento de Entidades Nomeadasé identificar e classificar todas as entidades nomeadas que existem em um texto de acordo com uma hierarquia ou ontologia. De acordo com Ceci et al (2010), existem muitas técnicas automáticas que auxiliam no processo de reconhecimento de entidades nomeadas, como aplicação de: expressões regulares, thesauros, heurísticas, gazetteers, modelos estatísticos e ontologias.…”

Section: Reconhecimento De Entidades Nomeadasunclassified

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Extração de informação contextual utilizando mineração de textos para sistemas de recomendação sensíveis ao contexto

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REPENTINO – A Wide-Scope Gazetteer for Entity Recognition in Portuguese

Cited by 14 publications

References 4 publications

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

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

Portuguese corpus-based learning using ETL

Extração de informação contextual utilizando mineração de textos para sistemas de recomendação sensíveis ao contexto

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