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Neural network techniques are widely applied to obtain high-qualitydistributed representations of words, i.e., word embeddings, to address textmining, information retrieval, and natural language processing tasks. Recently,efficient methods have been proposed to learn word embeddings from context thatcaptures both semantic and syntactic relationships between words. However, itis challenging to handle unseen words or rare words with insufficient context.In this paper, inspired by the study on word recognition process in cognitivepsychology, we propose to take advantage of seemingly less obvious butessentially important morphological knowledge to address these challenges. Inparticular, we introduce a novel neural network architecture called KNET thatleverages both contextual information and morphological word similarity builtbased on morphological knowledge to learn word embeddings. Meanwhile, thelearning architecture is also able to refine the pre-defined morphologicalknowledge and obtain more accurate word similarity. Experiments on ananalogical reasoning task and a word similarity task both demonstrate that theproposed KNET framework can greatly enhance the effectiveness of wordembeddings.

KNET: A General Framework for Learning Word Embedding using Morphological Knowledge