I write software that enables computers to automatically extract information from natural language and other data sources using machine learning. I work primarily in Java or Python.
In the past, I have worked primarily on text analysis. This includes semantic analysis, discourse processing, and sentiment analysis.
I am used to building tools from scratch, but I also work with natural language processing and machine learning libraries.
An open source Java tool for detecting quoted speech in text. It provides a greedy algorithm for fast processing and sampling inference for higher accuracy.
A Python implementation of two-class Naive Bayes with additional priors. Inference is done using Gibbs sampling. I used this model for unsupervised sentiment analysis.
A Python tool to automatically predict the strength of chess players. Uses an SVM to perform classification, ranking, and regression.
Sentiment relevance captures whether a piece of text contains an opinion or not. This dataset of movie reviews contains close to 4000 sentences annotated for sentiment relevance.
This corpus consists of annotated chess games that were posted on chess.com. I used this dataset to automatically predict the strength of chess players.
A collection of word analogy problems (e.g., man is to woman as king is to ?) in multiple languages.
(full list available here)
Christian Scheible, Roman Klinger and Sebastian Pado. Model Architectures for Quotation Detection. Proceedings of ACL 2016.
Christian Scheible and Hinrich Schütze. Picking the Amateur’s Mind - Predicting Chess Player Strength from Game Annotations. Proceedings of Coling 2014.
Christian Scheible and Hinrich Schütze. Multi-Domain Sentiment Relevance Classification with Automatic Representation Learning. Proceedings of EACL 2014.
Christian Scheible and Hinrich Schütze. Sentiment Relevance Proceedings of ACL 2013.
Christian Scheible and Hinrich Schütze Cutting Recursive Autoencoder Trees. Proceedings of ICLR 2013.
christian (at) scheibcn.com
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