Lagrangian Duality with ELM for Word Sense Multiprototype Discovery

Homonymy and polysemy are major issues in word sense disambiguation. Combining with multilayer neural network, word sense multiprototyping tackles the issues by defining multiple feature embedding representations for each word which are based on the average feature weight of the word’s different context windows called prototypes. The complexity of parameter estimation of neural network regression as well as the fixed context window size are the restrictions on the implementation of word sense multiprototyping. We propose approximating the least absolute deviation (LAD) between pair-wise word frequency covariance and pair-wise word semantic relatedness by Extreme Machine Learning (ELM) with less-constraint parameter estimation. Lagrangian duality proves the method’s feasibility. An in-cluster closeness calculation is performed to extract a variable context window to contextually identify multiprototypes of word senses based on Kmeans clustering. The higher accuracy of the discovered multiprototypes is verified by our experiments