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This paper provides an introduction to the Text Encoding Initia-tive (TEI), focused at bringing in newcomers who have to deal with a digital document project and are looking at the capacity that the TEI environment may have to fulfil his needs. To this end, we avoid a strictly technical presentation of the TEI and concentrate on the actual issues that such projects face, with parallel made on the situation within two institutions. While a quick walkthrough the TEI technical framework is provided, the papers ends up by showing the essential role of the community in the actual technical contributions that are being brought to the TEI.
Generating syntactically and semantically valid and relevant questions from paragraphs is useful with many applications. Manual generation is a labour-intensive task, as it requires the reading, parsing and understanding of long passages of text. A n
Automatic question generation (AQG) has broad applicability in domains such as tutoring systems, conversational agents, healthcare literacy, and information retrieval. Existing efforts at AQG have been limited to short answer lengths of up to two or
In this paper we present a question answering system using a neural network to interpret questions learned from the DBpedia repository. We train a sequence-to-sequence neural network model with n-triples extracted from the DBpedia Infobox Properties.
Matching question-answer relations between two turns in conversations is not only the first step in analyzing dialogue structures, but also valuable for training dialogue systems. This paper presents a QA matching model considering both distance info
We investigate the problem of recovering jointly $r$-rank and $s$-bisparse matrices from as few linear measurements as possible, considering arbitrary measurements as well as rank-one measurements. In both cases, we show that $m asymp r s ln(en/s)$ m