Integrate Chinese Semantic Knowledge into Word Sense Disambiguation

Abstract

Word sense disambiguation is important for many applications in natural language

Word sense disambiguation based on stretchable matching of the semantic template

“…Relevant works. For word sense disambiguation, existing technologies are as follows: 1) use the word semantic information and the word itself to better cover similar words when the word context may easily lead to data sparsity [4,25,11,35,36,17]; 2) introduce the syntactic information of the context in order to obtain the correlation of the collocations more efficiently [24,33,36]; 3) adopt the neural network model for the word order instead of an unordered model such as the bag of words [34,9,28,27,10]; 4) apply various embedding methods such as word embedding [7], word sense embedding [3,32] and the context vector [18]; 5) utilize the topic model that identifies the contextual topic so that ambiguous words and inappropriate senses can be filtered out earlier [2] [12][30]; 6) employ a graphbased method with the random walk [19,30,26]; 7) use the probability weighted voting method with dynamic self-adaptation [13]; 8) use the gloss-augmented neural network (GAS) [14]; 9) apply the co-attention model (CAN) and hierarchical co-attention model (HCAN) [15]; 10) utilize the generative adversarial networks (WSD-GAN) to combine both supervised-based and knowledge-based approaches [5]; and 11) use SyntagNet which is a large-scale manually disambiguated lexicalsemantic combination resource [16].…”