Christian Scheible

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We present a new method, based on graph theory, for bilingual lexicon extraction without relying on resources with limited availability like parallel corpora. The graphs we use represent linguistic relations between words such as adjectival modification. We experiment with a number of ways of combining different linguistic relations and present a novel(More)
Supervised classification needs large amounts of annotated training data that is expensive to create. Two approaches that reduce the cost of annotation are active learning and crowd-sourcing. However, these two approaches have not been combined successfully to date. We evaluate the utility of active learning in crowdsourcing on two tasks, named entity(More)
This paper presents an innovative, complex approach to semantic verb classification that relies on selectional preferences as verb properties. The probabilistic verb class model underlying the semantic classes is trained by a combination of the EM algorithm and the MDL principle, providing soft clusters with two dimensions (verb senses and(More)
"While continuous word vector representations enjoy increasing popularity, it is still poorly understood (i) how reliable they are for other languages than English, and (ii) to what extent they encode deep semantic relatedness such as paradigmatic relations. In this talk I will present experiments with continuous word vectors for English and German."
This paper presents a graph-theoretic approach to the identification of yet-unknown word translations. The proposed algorithm is based on the recursive Sim-Rank algorithm and relies on the intuition that two words are similar if they establish similar grammatical relationships with similar other words. We also present a formulation of SimRank in matrix form(More)
Sentiment analysis systems can benefit from the translation of sentiment information. We present a novel, graph-based approach using SimRank, a well-established graph-theoretic algorithm, to transfer sentiment information from a source language to a target language. We evaluate this method in comparison with semantic orientation using pointwise mutual(More)
We present a novel graph-theoretic method for the initial annotation of high-confidence training data for bootstrapping sentiment classifiers. We estimate polarity using topic-specific PageRank. Sentiment information is propagated from an initial seed lexicon through a joint graph representation of words and documents. We report improved classification(More)