Trill: A reusable Front-End for QA systems

In this paper, we (i) introduce SPARQL T and its user-friendly inter- face for expressing powerful structured queries on the history of RDF knowledge bases, and (ii) overview

The prototype includes all classical components of a QA system: (a) question classification, (b) passage retrieval, (c) answer extraction and (d) natural language processing

Like many state of the art QA systems base their performance on Named Entity Recognition (NER) [10, 3] as well as on precise linguistic information [2, 4], in order to build QA@L 2 F

(1) incorporation of syntactic knowledge in the IR-engine, (2) incorporation of lexical equivalences, (3) incorporation of coreference resolution for off-line answer extraction,

For the first implementation of the BRILI system, we have used the indexation phase of documents and the answer extraction module of our monolingual Spanish QA system (AliQAn)

We hope to receive feedback on this study design from other participants at the HUMANIZE workshop and spark conversations about ways to address present bias in recommender

The paper serves as an overview of the European Open Science Cloud in supporting the FAIR data approach in general, and as an overview of the Lithuanian

Then two distributed structure learning algorithm are illustrated: SEM- PRE (“distributed Structure lEarning by MaPREduce”) and LEAP MR (“LEArning Probabilistic description logics