The Multiple Language Question Answering Track at CLEF 2003

Magnini, Bernardo; Romagnoli, Simone; Vallin, Alessandro; Herrera, Jesús; Peñas, Anselmo; Peinado, Víctor; Verdejo, Felisa; Rijke, Maarten de

doi:10.1007/978-3-540-30222-3_46

Cited by 48 publications

(31 citation statements)

References 5 publications

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“…It is the corpus that is used for Dutch in the QA task of CLEF. We selected two question types that are frequent in the CLEF QA question set 3 (Magnini et al, 2003): capital-of an soccer-player-club. These are binary relations, respectively between a location (e.g.…”

Section: Methodsmentioning

confidence: 99%

Relation Extraction for Open and Closed Domain Question Answering

Bouma

Fahmi²,

Mur³

2011

Interactive Multi-Modal Question-Answering

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One of the most accurate methods in Question Answering uses off-line information extraction to find answers for frequently asked questions. It requires automatic extraction from text of all relation instances for relations that users frequently ask for. In this chapter, we present two methods for learning relation instances for relations relevant in a closed and open domain (medical) question answering system. Both methods try to learn automatically dependency paths that typically connect two arguments of a given relation. The first (lightly supervised) method starts from a seed list of argument instances, and extracts dependency paths from all sentences in which a seed pair occurs. This method works well for large text collections and for seeds which are easily identified, such as named entities, and is well-suited for open domain question answering. In a second experiment, we concentrate on medical relation extraction for the question answering module of the IMIX system. The IMIX corpus is relatively small and relation instances may contain complex noun phrases that do not occur frequently in the exact same form in the corpus. In this case, learning from annotated data is necessary. We show that dependency patterns enriched with semantic concept labels give accurate results for relations that are relevant for a medical question answering system. Both methods improve the performance of the Dutch question answering system Joost.

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Section: Methodsmentioning

confidence: 99%

Relation Extraction for Open and Closed Domain Question Answering

Bouma

Fahmi²,

Mur³

2011

Interactive Multi-Modal Question-Answering

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show abstract

“…Thus, for a specific query, RR is the reciprocal of the rank where the first correct/relevant result is given. Although this measure is mostly used in search tasks when there is only one correct answer (Kantor and Voorhees, 2000), others used it for assessing the performance of query suggestions (Meij et al, 2009;Albakour et al, 2011) as well as ranking algorithms in particular (Damljanovic et al, 2010) and IR systems (Voorhees, 1999(Voorhees, , 2003Magnini et al, 2003) in general.…”

Section: R-precision (R-prec)mentioning

confidence: 99%

An overview of semantic search evaluation initiatives

Elbedweihy

Wrigley

Clough

et al. 2015

Journal of Web Semantics

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“…Research in QA has increased as a result of the inclusion of QA evaluations as part of the Text Retrieval Conference (TREC) 1 in 1999, and recently [5] in Multilingual Question Answering as part of the Cross Language Evaluation Forum (CLEF) 2 .…”

Section: Introductionmentioning

confidence: 99%

Toward a Document Model for Question Answering Systems

Pérez-Coutiño

Solorio

Montes-y-Gómez

et al. 2004

Advances in Web Intelligence

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Abstract. The problem of acquiring valuable information from the large amounts available today in electronic media requires automated mechanisms more natural and efficient than those already existing. The trend in the evolution of information retrieval systems goes toward systems capable of answering specific questions formulated by the user in her/his language. The expected answers from such systems are short and accurate sentences, instead of large document lists. On the other hand, the state of the art of these systems is focused -mainly-in the resolution of factual questions, whose answers are named entities (dates, quantities, proper nouns, etc). This paper proposes a model to represent source documents that are then used by question answering systems. The model is based on a representation of a document as a set of named entities (NEs) and their local lexical context. These NEs are extracted and classified automatically by an off-line process. The entities are then taken as instance concepts in an upper ontology and stored as a set of DAML+OIL resources which could be used later by question answering engines. The paper presents a case of study with a news collection in Spanish and some preliminary results.

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The Multiple Language Question Answering Track at CLEF 2003

Cited by 48 publications

References 5 publications

Relation Extraction for Open and Closed Domain Question Answering

Relation Extraction for Open and Closed Domain Question Answering

An overview of semantic search evaluation initiatives

Toward a Document Model for Question Answering Systems

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