Priberam’s Question Answering System in QA@CLEF 2008

Amaral, Carlos; Cassan, Adán; Figueira, Helena; Martins, André F. T.; Mendes, Afonso; Mendes, Pedro; Pina, José Antonio López; Pinto, C.

doi:10.1007/978-3-642-04447-2_39

Cited by 2 publications

(2 citation statements)

References 5 publications

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“…Besides these text fields and document timestamps, no other metadata was used. To detect named entities, we used Priberam's Text Analysis (Amaral et al, 2008) for English and Spanish, and Turbo Parser (Martins et al, 2013) for German. The extracted entities consist of people, organizations, places and other types.…”

Section: Monolingual Representationmentioning

confidence: 99%

Multilingual Clustering of Streaming News

Miranda¹,

Znotiņš²,

Cohen³

et al. 2018

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

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Clustering news across languages enables efficient media monitoring by aggregating articles from multilingual sources into coherent stories. Doing so in an online setting allows scalable processing of massive news streams. To this end, we describe a novel method for clustering an incoming stream of multilingual documents into monolingual and crosslingual story clusters. Unlike typical clustering approaches that consider a small and known number of labels, we tackle the problem of discovering an ever growing number of cluster labels in an online fashion, using real news datasets in multiple languages. Our method is simple to implement, computationally efficient and produces state-of-the-art results on datasets in German, English and Spanish.

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Section: Monolingual Representationmentioning

confidence: 99%

Multilingual Clustering of Streaming News

Miranda¹,

Znotiņš²,

Cohen³

et al. 2018

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

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show abstract

“…It must determine what are the foci of interest for the user, which information is sought on them, and still detect possible restrictions to consider, as occurs in typical QA question analysis phase [5,[12][13][14].…”

Section: Proposed Approachmentioning

confidence: 99%

BINLI: An Ontology-Based Natural Language Interface for Multidimensional Data Analysis

Saias

Quaresma

Salgueiro³

et al. 2012

IIM

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Current technology facilitates access to the vast amount of information that is produced every day. Both individuals and companies are active consumers of data from the Web and other sources, and these data guide decision making. Due to the huge volume of data to be processed in a business context, managers rely on decision support systems to facilitate data analysis. OLAP tools are Business Intelligence solutions for multidimensional analysis of data, allowing the user to control the perspective and the degree of detail in each dimension of the analysis. A conventional OLAP system is configured to a set of analysis scenarios associated with multidimensional data cubes in the repository. To handle a more spontaneous query, not supported in these provided scenarios, one must have specialized technical skills in data analytics. This makes it very difficult for average users to be autonomous in analyzing their data, as they will always need the assistance of specialists. This article describes an ontology-based natural language interface whose goal is to simplify and make more flexible and intuitive the interaction between users and OLAP solutions. Instead of programming an MDX query, the user can freely write a question in his own human language. The system interprets this question by combining the requested information elements, and generates an answer from the OLAP repository.

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Priberam’s Question Answering System in QA@CLEF 2008

Cited by 2 publications

References 5 publications

Multilingual Clustering of Streaming News

Multilingual Clustering of Streaming News

BINLI: An Ontology-Based Natural Language Interface for Multidimensional Data Analysis

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