Titlebook: Natural Language Processing and Information Systems; 21st International C Elisabeth Métais,Farid Meziane,Sunil Vadera Conference proceeding

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Neutral-Spine

Evaluating Multiple Summaries Without Human Models: A First Experiment with a Trivergent Modelsimultaneously: a summary to evaluate, its source document and a set of other summaries from the same source. We present in this paper, a first pilot experiment using a French corpus from which we obtained promising results.

Vsd168

PatEx: Pattern Oriented RDF Graphs Explorationll as some knowledge of a query language such as SPARQL. Our goal is to facilitate the exploration of these datasets. We present in this paper PatEx, a system designed to explore RDF(S)/OWL data. PatEx provides two exploration strategies: theme-based exploration and keyword search, and users can int

植物茂盛

Multilingual Protest Event Data Collection with GATEtate approach to protest event features collection from short texts (news lead sentences) in several European languages (Bulgarian, French, Polish, Russian, Spanish, Swedish) using the General Architecture for Text Engineering (GATE). The results of the annotation performance evaluation are presente

Employee

A Multi-lingual Approach to Improve Passage Retrieval for Automatic Question Answeringicularly to Question Answering (QA). Indeed, Passage Retrieval (PR) is a longstanding problem in QA, that has been widely studied over the last decades and still requires further efforts in order to enable a user to have a better chance to find a relevant answer to his human natural language questio

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Urologist

Keyword Identification Using Text Graphlet Patternsorization, among others. State-of-the-art methods rely on local features of words (e.g. lexical, syntactic, and presentation features) to assess their candidacy as keywords. In this paper, we propose a novel keyword identification method that relies on representation of text abstracts as word graphs

cogent

Disentangling the Structure of Tables in?Scientific Literature readers. The ability to “understand” the structure of tables is key for information extraction in many domains. However, the complexity and variety of presentation layouts and value formats makes it difficult to automatically extract roles and relationships of table cells. In this paper, we present