Titlebook: Semantic Web Challenges; Third SemWebEval Cha Harald Sack,Stefan Dietze,Christoph Lange Conference proceedings 2016 Springer International

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slipped-disk

Aspect-Based Sentiment Analysis Using a Two-Step Neural Network Architectureng and analyzing the expressed opinions in customer reviews in a fine-grained way, valuable opportunities and insights for customers and businesses can be gained..We propose a neural network based system to address the task of Aspect-Based Sentiment Analysis to compete in Task 2 of the ESWC-2016 Cha

integrated

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Top-K Shortest Paths in Large Typed RDF Datasets Challenge and SPARQL). Such information corresponds to patterns expressed as SPARQL queries that are matched against the RDF graph. Until recently, patterns had to specify the exact path that would match against the underlying graph. The advent of the SPARQL 1.1 Recommendation introduced property paths as a

Abominate

DWS at the 2016 Open Knowledge Extraction Challenge: A Hearst-Like Pattern-Based Approach to Hypernyy-based Natural Language Processing (NLP) techniques with lexical and semantic knowledge bases to first extract hypernyms from definitional sentences and second select the most suitable class of the extracted hypernyms from those available in the DOLCE foundational ontology.

Herbivorous

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Euthyroid

Enhancing Entity Linking by Combining NER Modelsng a 4-fold cross validation experiment on the OKE 2016 challenge training dataset. We also demonstrate that we achieve better results that in our previous participation on the OKE 2015 test set. We finally report the results of ADEL on the OKE 2016 test set and we present an error analysis highlighting the main difficulties of this challenge.

Basal-Ganglia

App2Check Extension for Sentiment Analysis of Amazon Products Reviewse present an experimental comparison respect to 19 research tools. Then we show App2Check performance when applied to Amazon products reviews. In this experimental evaluation, we show App2Check performance with and without a specific training on Amazon products reviews, and we compare our results with two state-of-the-art research tools.

HUMP