Identifying Grammar Rules for Language Education with Dependency Parsing in German

no code implementations • WS 2019 • Eleni Metheniti, Pomi Park, Kristina Kolesova, G{\"u}nter Neumann

Dependency Parsing

An Interactive Web-Interface for Visualizing the Inner Workings of the Question Answering LSTM

no code implementations • EMNLP 2018 • Ekaterina Loginova, G{\"u}nter Neumann

We present a visualisation tool which aims to illuminate the inner workings of an LSTM model for question answering.

Feature Engineering Machine Translation +1

Code-Mixed Question Answering Challenge: Crowd-sourcing Data and Techniques

no code implementations • WS 2018 • Ch, Khyathi u, Ekaterina Loginova, Vishal Gupta, Josef van Genabith, G{\"u}nter Neumann, Manoj Chinnakotla, Eric Nyberg, Alan W. black

As a first step towards fostering research which supports CM in NLP applications, we systematically crowd-sourced and curated an evaluation dataset for factoid question answering in three CM languages - Hinglish (Hindi+English), Tenglish (Telugu+English) and Tamlish (Tamil+English) which belong to two language families (Indo-Aryan and Dravidian).

Question Answering Sentence

LightRel at SemEval-2018 Task 7: Lightweight and Fast Relation Classification

no code implementations • SEMEVAL 2018 • Tyler Renslow, G{\"u}nter Neumann

We present LightRel, a lightweight and fast relation classifier.

Feature Engineering General Classification +3

An analysis of textual inference in German customer emails

no code implementations • SEMEVAL 2014 • Kathrin Eichler, Aleks Gabryszak, ra, G{\"u}nter Neumann

Natural Language Inference Semantic Textual Similarity

Design and Realization of the EXCITEMENT Open Platform for Textual Entailment

no code implementations • WS 2013 • G{\"u}nter Neumann, Sebastian Pad{\'o}

Natural Language Inference

Parsing Hindi with MDParser

no code implementations • WS 2012 • Alex Volokh, er, G{\"u}nter Neumann

Dependency Parsing Machine Translation +1

An Adaptive Framework for Named Entity Combination

no code implementations • LREC 2012 • Bogdan Sacaleanu, G{\"u}nter Neumann

We have developed a new OSGi-based platform for Named Entity Recognition (NER) which uses a voting strategy to combine the results produced by several existing NER systems (currently OpenNLP, LingPipe and Stanford).

named-entity-recognition Named Entity Recognition +1