Hasso-Plattner-Institut
  
 

Vision of the tele-TASK Project

In the center of the research work of Prof. Dr. Christoph Meinel and his team in the field of Knowledge Engineering and Web University is the tele-TASK project (tele-Teaching Anywhere Solution Kit). It was started more than 15 years ago when we began to research how Internet and web technologies can be used for supporting teaching (> tele-teaching) and learning (> e-learning). Our vision was to design an easy to use mobile system for recording and broadcasting university lectures and presentations over the Internet in order to develop and test on the one hand new tele-teaching and e-learning concepts and on the other hand to innovative portal and navigation techniques.

Demonstration des tele-TASK Systems

Our Research Directions

Over the years tele-TASK turned out to be a very fruitful project which helped us, on one side, to gain valuable experiences and a deeper understanding of e-learning and tele-teaching. On the other side, it inspires us to try our upcoming techniques in the are of Web3.0 - semantic, social, service Web -, and to make them accessible for Web-university (details). 

Some Links to the tele-TASK Portal

Here are some links to the tele-TASK portal which provides meanwhile more than 5.000 recorded telelectures: 

Buy, lease or rent tele-TASK

If you like to record and transmit your presentations over the Internet online and offline you can work with our tele-TASK recording system. Simply buy or rent tele-TASK ... 

The tele-TASK Team

  • Prof. Dr. Christoph Meinel (Head)
  • Dipl-Inf. Matthias Bauer
  • Dipl-Ing. Haojin Yang
  • Franka Grünewald, MSc.
  • Dipl.-Inf. Frank Priester (Technical Support)
  • Former Members: Volker Schillings, Tongbo Chen, Mingchao Ma, Mathias Kutzner, Bert Baumann, Long Wang, Andreas Groß, Maria Siebert, ...

tele-TASK Symposia

Scientific Publication about tele-TASK

Punctuation Prediction for Unsegmented Transcript Based on Word Vector

Xiaoyin Che, Cheng Wang, Haojin Yang, Christoph Meinel
In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016), pages 654-658, Portorož, Slovenia, 5 2016

Abstract:

In this paper we propose an approach to predict punctuation marks for unsegmented speech transcript. The approach is purely lexical, with pre-trained Word Vectors as the only input. A training model of Deep Neural Network (DNN) or Convolutional Neural Network (CNN) is applied to classify whether a punctuation mark should be inserted after the third word of a 5-words sequence and which kind of punctuation mark the inserted one should be. TED talks within IWSLT dataset are used in both training and evaluation phases. The proposed approach shows its effectiveness by achieving better result than the state-of-the-art lexical solution which works with same type of data, especially when predicting puncuation position only.

Keywords:

Punctuation Prediction, Word Vector, Deep Learning, Convolutional Neural Network

BibTeX file

@inproceedings{2016_Che_LREC,
author = { Xiaoyin Che, Cheng Wang, Haojin Yang, Christoph Meinel },
title = { Punctuation Prediction for Unsegmented Transcript Based on Word Vector },
year = { 2016 },
pages = { 654-658 },
month = { 5 },
abstract = { In this paper we propose an approach to predict punctuation marks for unsegmented speech transcript. The approach is purely lexical, with pre-trained Word Vectors as the only input. A training model of Deep Neural Network (DNN) or Convolutional Neural Network (CNN) is applied to classify whether a punctuation mark should be inserted after the third word of a 5-words sequence and which kind of punctuation mark the inserted one should be. TED talks within IWSLT dataset are used in both training and evaluation phases. The proposed approach shows its effectiveness by achieving better result than the state-of-the-art lexical solution which works with same type of data, especially when predicting puncuation position only. },
keywords = { Punctuation Prediction, Word Vector, Deep Learning, Convolutional Neural Network },
address = { Portorož, Slovenia },
booktitle = { Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC 2016) },
isbn = { 978-2-9517408-9-1 },
language = { English },
priority = { 0 }
}

Copyright Notice

last change: Fri, 14 Oct 2016 12:35:03 +0200

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