Mikael Kågebäck


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Word Sense Disambiguation using a Bidirectional LSTM
Mikael Kågebäck | Hans Salomonsson
Proceedings of the 5th Workshop on Cognitive Aspects of the Lexicon (CogALex - V)

In this paper we present a clean, yet effective, model for word sense disambiguation. Our approach leverage a bidirectional long short-term memory network which is shared between all words. This enables the model to share statistical strength and to scale well with vocabulary size. The model is trained end-to-end, directly from the raw text to sense labels, and makes effective use of word order. We evaluate our approach on two standard datasets, using identical hyperparameter settings, which are in turn tuned on a third set of held out data. We employ no external resources (e.g. knowledge graphs, part-of-speech tagging, etc), language specific features, or hand crafted rules, but still achieve statistically equivalent results to the best state-of-the-art systems, that employ no such limitations.


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Neural context embeddings for automatic discovery of word senses
Mikael Kågebäck | Fredrik Johansson | Richard Johansson | Devdatt Dubhashi
Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing

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Extractive Summarization by Aggregating Multiple Similarities
Olof Mogren | Mikael Kågebäck | Devdatt Dubhashi
Proceedings of the International Conference Recent Advances in Natural Language Processing


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Extractive Summarization using Continuous Vector Space Models
Mikael Kågebäck | Olof Mogren | Nina Tahmasebi | Devdatt Dubhashi
Proceedings of the 2nd Workshop on Continuous Vector Space Models and their Compositionality (CVSC)