Applying Deep Belief Networks to Word Sense Disambiguation
This work addresses word sense disambiguation for natural language processing, but it is incremental as it applies an existing method to a known problem.
The authors tackled word sense disambiguation by applying Deep Belief Networks, achieving superior performance compared to state-of-the-art methods like SVM and MaxEnt on the SENSEVAL-2 dataset.
In this paper, we applied a novel learning algorithm, namely, Deep Belief Networks (DBN) to word sense disambiguation (WSD). DBN is a probabilistic generative model composed of multiple layers of hidden units. DBN uses Restricted Boltzmann Machine (RBM) to greedily train layer by layer as a pretraining. Then, a separate fine tuning step is employed to improve the discriminative power. We compared DBN with various state-of-the-art supervised learning algorithms in WSD such as Support Vector Machine (SVM), Maximum Entropy model (MaxEnt), Naive Bayes classifier (NB) and Kernel Principal Component Analysis (KPCA). We used all words in the given paragraph, surrounding context words and part-of-speech of surrounding words as our knowledge sources. We conducted our experiment on the SENSEVAL-2 data set. We observed that DBN outperformed all other learning algorithms.