To assist people with physical disabilities in text entry, we have studied the contribution of semantic knowledge in the word completion task. We have first constructed a semantic knowledge base (SKB) that stores the semantic association between word pairs. To create the SKB, a novel Lesk-like relatedness filter is employed. On the basis of the SKB, we have proposed an integrated semantics-based word completion model. The model combines the semantic knowledge in the SKB with n-gram probabilities. To deal with potential problems in the model, we propose the strategy of using salient terms and the ad hoc algorithm for the OOV recognition. We tested our model and compared with the model using n-gram probabilities of word and part-of-speech alone and found that our model has achieved significant performance improvement. In addition, test experiments on the algorithm for OOV recognition present a notable enhancement of the system performance.
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