Maria Pelevina

Maria Pelevina, Nikolay Arefyev, Chris Biemann et al.

We present a simple yet effective approach for learning word sense embeddings. In contrast to existing techniques, which either directly learn sense representations from corpora or rely on sense inventories from lexical resources, our approach can induce a sense inventory from existing word embeddings via clustering of ego-networks of related words. An integrated WSD mechanism enables labeling of words in context with learned sense vectors, which gives rise to downstream applications. Experiments show that the performance of our method is comparable to state-of-the-art unsupervised WSD systems.

Maria Pelevina

For engineering software with formal correctness proofs it is crucial that proofs can be efficiently reused in case the software or its specification is changed. Unfortunately, in reality even slight changes in the code or its specification often result in disproportionate waste of verification effort: For instance, whenever a method's specification is modified and as a consequence the proof of its correctness breaks, all other proofs based on this specification break too. Abstract method calls is a recently proposed verification rule for method calls that allows for efficient systematic reuse of proofs. In this thesis, we implement, extend and evaluate this approach within the KeY verification system.