Dieke Oele


Simple Embedding-Based Word Sense Disambiguation
Dieke Oele | Gertjan van Noord
Proceedings of the 9th Global Wordnet Conference

We present a simple knowledge-based WSD method that uses word and sense embeddings to compute the similarity between the gloss of a sense and the context of the word. Our method is inspired by the Lesk algorithm as it exploits both the context of the words and the definitions of the senses. It only requires large unlabeled corpora and a sense inventory such as WordNet, and therefore does not rely on annotated data. We explore whether additional extensions to Lesk are compatible with our method. The results of our experiments show that by lexically extending the amount of words in the gloss and context, although it works well for other implementations of Lesk, harms our method. Using a lexical selection method on the context words, on the other hand, improves it. The combination of our method with lexical selection enables our method to outperform state-of the art knowledge-based systems.


Distributional Lesk: Effective Knowledge-Based Word Sense Disambiguation
Dieke Oele | Gertjan van Noord
IWCS 2017 — 12th International Conference on Computational Semantics — Short papers

BuzzSaw at SemEval-2017 Task 7: Global vs. Local Context for Interpreting and Locating Homographic English Puns with Sense Embeddings
Dieke Oele | Kilian Evang
Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017)

This paper describes our system participating in the SemEval-2017 Task 7, for the subtasks of homographic pun location and homographic pun interpretation. For pun interpretation, we use a knowledge-based Word Sense Disambiguation (WSD) method based on sense embeddings. Pun-based jokes can be divided into two parts, each containing information about the two distinct senses of the pun. To exploit this structure we split the context that is input to the WSD system into two local contexts and find the best sense for each of them. We use the output of pun interpretation for pun location. As we expect the two meanings of a pun to be very dissimilar, we compute sense embedding cosine distances for each sense-pair and select the word that has the highest distance. We describe experiments on different methods of splitting the context and compare our method to several baselines. We find evidence supporting our hypotheses and obtain competitive results for pun interpretation.


SMT and Hybrid systems of the QTLeap project in the WMT16 IT-task
Rosa Gaudio | Gorka Labaka | Eneko Agirre | Petya Osenova | Kiril Simov | Martin Popel | Dieke Oele | Gertjan van Noord | Luís Gomes | João António Rodrigues | Steven Neale | João Silva | Andreia Querido | Nuno Rendeiro | António Branco
Proceedings of the First Conference on Machine Translation: Volume 2, Shared Task Papers


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Comparison of Coreference Resolvers for Deep Syntax Translation
Michal Novák | Dieke Oele | Gertjan van Noord
Proceedings of the Second Workshop on Discourse in Machine Translation

Lexical choice in Abstract Dependency Trees
Dieke Oele | Gertjan van Noord
Proceedings of the 1st Deep Machine Translation Workshop