Institute

Studying

Research


 

Christian Scheible

Mister  Christian Scheible

Christian Scheible
Phone +49 711 685-81386
Room2.023
E-Mail
Address
Universität Stuttgart
Institut für Maschinelle Sprachverarbeitung
Pfaffenwaldring 5b
70569 Stuttgart
Deutschland

Note: As I left the IMS in May 2017, this page will not be updated. Please visit my personal homepage at http://www.scheibcn.com/

I was a post-doc in the theoretical CL group working on machine learning for natural language processing.


Teaching

WS15/16

  • Software-Projekt statistische maschinelle Sprachverarbeitung
  • Foundations of programming for computational linguists (Java)

SS16

  • Sentiment Analysis

WS15/16

  • Software-Projekt statistische maschinelle Sprachverarbeitung
  • Foundations of programming for computational linguists (Java)

SS15

  • Grundlagen der maschinellen Sprachverarbeitung

WS14/15

WS13/14

SS11

SS10

SS09

SS07

Publications
  • Christian Scheible, Roman Klinger and Sebastian Pado
    Model Architectures for Quotation Detection. 
    Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL), 2016.
    [pdf] [bib]
  • Maximilian Koper, Christian Scheible, Sabine Schulte im Walde
    Multilingual Reliability and "Semantic" Structure of Continuous Word Spaces. 
    Proceedings of the 11th International Conference on Computational Semantics 2015.
    [pdf] [bib]
  • Christian Scheible and Hinrich Schütze
    Picking the Amateur's Mind - Predicting Chess Player Strength from Game Annotations. 
    Proceedings of the 25th International Conference on Computational Linguistics (Coling), 2014.
    [pdf] [bib]
  • Christian Scheible and Hinrich Schütze
    Multi-Domain Sentiment Relevance Classification with Automatic Representation Learning.
    Proceedings of the 14th Conference of the European Chapter of the Association for Computational Linguistics (EACL), 2014.
    [pdf] [bib]
  • Jimmy Dubuisson, Jean-Pierre Eckmann, Christian Scheible and Hinrich Schütze
    The Topology of Semantic Knowledge.
    Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2013.
    [pdf] [bib]
  • Christian Scheible and Hinrich Schütze
    Sentiment Relevance.
    Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL), 2013.
    [pdf] [bib][data]
  • Christian Scheible and Hinrich Schütze
    Cutting Recursive Autoencoder Trees.
    Proceedings of the 1st International Conference on Learning Representations (ICLR), 2013.
    [arxiv]
  • Hinrich Schütze and Christian Scheible
    Two SVDs produce more focal deep learning representations.
    Proceedings of the 1st International Conference on Learning Representations (ICLR), 2013.
    [arxiv]
  • Christian Scheible and Hinrich Schütze
    Unsupervised Sentiment Analysis with a Simple and Fast Bayesian Model using Part-of-Speech Feature Selection.
    Proceedings of the 1st Workshop on Practice and Theory of Opinion Mining and Sentiment Analysis, 2012.
    [pdf] [bib]
  • Christian Scheible and Hinrich Schütze
    Bootstrapping Sentiment Labels For Unannotated Documents With Polarity PageRank.
    Proceedings of the Eigth conference on International Language Resources and Evaluation (LREC), 2012.
    [pdf] [bib]
  • Florian Laws, Christian Scheible and Hinrich Schütze
    Active Learning with Amazon Mechanical Turk.
    Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2011.
    [pdf] [bib]
  • Sabine Schulte im Walde, Helmut Schmid, Wiebke Wagner, Christian Hying and Christian Scheible
    A Clustering Approach to Automatic Verb Classification incorporating Selectional Preferences: Model, Implementation, and User Manual.
    SinSpeC: Working Papers of the SFB 732 "Incremental Specification in Context", Volume 7, December 2010.
    [view]
  • Christian Scheible, Florian Laws, Lukas Michelbacher and Hinrich Schütze
    Sentiment Translation through Multi-Edge Graphs.
    Proceedings of the 23rd International Conference on Computational Linguistics (Coling), 1104-1112, 2010.
    [pdf] [bib]
  • Florian Laws, Lukas Michelbacher, Beate Dorow, Christian Scheible, Ulrich Heid and Hinrich Schütze
    A Linguistically Grounded Graph Model for Bilingual Lexicon Extraction.
    Proceedings of the 23rd International Conference on Computational Linguistics (Coling), 614-622, 2010.
    [pdf] [bib]
  • Christian Scheible
    Sentiment Translation through Lexicon Induction.
    Proceedings of the ACL Student Research Workshop (ACL SRW), 25-30, 2010.
    [pdf] [bib]
  • Christian Scheible
    An Evaluation of Predicate Argument Clustering using Pseudo-Disambiguation.
    Proceedings of the Seventh conference on International Language Resources and Evaluation (LREC), 2010.
    [pdf] [bib]
  • Beate Dorow, Florian Laws, Lukas Michelbacher, Christian Scheible and Jason Utt
    A Graph-Theoretic Algorithm for Automatic Extension of Translation Lexicons.
    Proceedings of the EACL Workshop on Geometrical Methods for Natural Language Semantics (GEMS), 2009.
    [pdf] [bib]
  • Sabine Schulte im Walde, Christian Hying, Christian Scheible and Helmut Schmid
    Combining EM Training and the MDL Principle for an Automatic Verb Classification incorporating Selectional Preferences.
    Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics (ACL), 2008.
    [pdf] [bib]

Undergraduate/Graduate Theses

  • Christian Scheible:
    Graph-Based Sentiment Translation,
    Diplomarbeit, 2010.
  • Christian Scheible:
    Evaluating a Verb Clustering Model Using Pseudo-Disambiguation,
    Studienarbeit, 2009.

 

Resources

Data

Sentiment Relevance

The sentiment relevance dataset is available here. This is the data from our ACL 2013 paper.

Chess

The chess dataset, a collection of player-annotated chess games, is available here. This is from our COLING 2014 paper.

Software

QSample

A Java tool for detecting quotations (direct, indirect, and mixed) in text. Please see the project page or thegithub page for more information. This is the implementation of our ACL 2016 paper.

Unsupervised Bayesian Naive Bayes

Python implementation

A Python implementation of unsupervised Bayesian Naive Bayes with Gibbs sampling. Available on github (zip) (tgz). Used in our PATHOS 2012 paper.

Hierarchical Bayes Compiler models

The experiments in the paper were conducted using models compiled with HBC: