Search Results for author: Siegfried schuh

Found 15 papers, 1 papers with code

DisSim: A Discourse-Aware Syntactic Text Simplification Framework for English and German

no code implementations WS 2019 Christina Niklaus, Matthias Cetto, Andr{\'e} Freitas, H, Siegfried schuh

We introduce DisSim, a discourse-aware sentence splitting framework for English and German whose goal is to transform syntactically complex sentences into an intermediate representation that presents a simple and more regular structure which is easier to process for downstream semantic applications.

Sentence Text Simplification

SemEval-2017 Task 11: End-User Development using Natural Language

no code implementations SEMEVAL 2017 Juliano Sales, H, Siegfried schuh, Andr{\'e} Freitas

This task proposes a challenge to support the interaction between users and applications, micro-services and software APIs using natural language.

Semantic Parsing

SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs and News

no code implementations SEMEVAL 2017 Keith Cortis, Andr{\'e} Freitas, Tobias Daudert, Manuela Huerlimann, Manel Zarrouk, H, Siegfried schuh, Brian Davis

This paper discusses the {``}Fine-Grained Sentiment Analysis on Financial Microblogs and News{''} task as part of SemEval-2017, specifically under the {``}Detecting sentiment, humour, and truth{''} theme.

Sentiment Analysis

Categorization of Semantic Roles for Dictionary Definitions

no code implementations WS 2016 Vivian Silva, H, Siegfried schuh, Andr{\'e} Freitas

Understanding the semantic relationships between terms is a fundamental task in natural language processing applications.

Question Answering

NNBlocks: A Deep Learning Framework for Computational Linguistics Neural Network Models

no code implementations LREC 2016 Frederico Tommasi Caroli, Andr{\'e} Freitas, Jo{\~a}o Carlos Pereira da Silva, H, Siegfried schuh

Lately, with the success of Deep Learning techniques in some computational linguistics tasks, many researchers want to explore new models for their linguistics applications.

Evaluation of Technology Term Recognition with Random Indexing

no code implementations LREC 2014 Behrang Zadeh, H, Siegfried schuh

Moreover, the accomplished experiments suggest that the obtained results, to a great extent, are independent of the value of k.

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