In some examples, a machine learning system may use morphological knowledge to enhance a deep learning framework for learning word embedding. The system may consider, among other things, morphological similarities between and among words in a learning process so as to handle new or rare words, edit distances, longest common substring similarities, morpheme similarities, and syllable similarities as morphological knowledge to build a relation matrix between or among words. The system may apply the deep learning framework to query classification, web search, text mining, information retrieval, and natural language processing tasks, for example. The system may accomplish such tasks with relatively high efficiency and speed, while utilizing less computing resources as compared to other systems.
Please help me write a proper abstract based on the patent claims. CLAIM: 1. A system comprising: one or more processors; and memory storing instructions that, when executed by the one or more processors, configure the one or more processors to perform operations comprising: receiving a text-based object that includes an input word; searching in a vocabulary space to find one or more words that correspond morphologically to the input word; receiving contextual information associated with the input word and the one or more words that correspond morphologically to the input word; and based, at least in part, on the contextual information associated with the input word and the one or more words that correspond morphologically to the input word, modifying a morphological knowledge base during a machine learning process. 2. The system of claim 1, wherein the operations further comprise: searching in a vocabulary space; and calculating morpheme similarity between or among the input word and the one or more words that correspond morphologically to the input word. 3. The system of claim 1, wherein the operations further comprise: searching in a vocabulary space; and calculating syllable similarity between or among the input word and the one or more words that correspond morphologically to the input word. 4. The system of claim 1, wherein the operations further comprise predicting a target word or phrase having a meaning based, at least in part, on the contextual information and the one or more words that correspond morphologically to the input word. 5. The system of claim 1, wherein the operations further comprise determining the contextual information associated with the input word in a process that is parallel with and independent from determining the one or more words that correspond morphologically to the input word. 6. The system of claim 5, wherein the operations further comprise calculating a weighted sum of a representation of the contextual information and a representation of the one or more words that correspond morphologically to the input word. 7. The system of claim 1, wherein the input word and the one or more words that correspond morphologically to the input word are each represented by a one-hot vector. 8. The system of claim 1, wherein the one or more words that correspond morphologically to the input word are collectively represented by a matrix. 9. The system of claim 1, wherein the operations further comprise extracting the one or more words that correspond morphologically to the input word from one or more natural language text sentences. 10. The system of claim 1, wherein the input word comprises a misspelled word. 11. A computing device comprising: an input port to receive an input word; and a processor communicatively coupled to the input port to: represent the input word as an n-dimensional representation; find a group of words that are morphologically similar to the input word; extract embedding of individual words among the group of words from an embedding matrix based, at least in part, on contextual information associated with the input word; and generate a target word having a meaning based, at least in part, on the group of words and the contextual information. 12. The computing device of claim 11, wherein the n-dimensional representation comprises a bag-of-words or a one-hot vector. 13. The computing device of claim 11, wherein the n-dimensional representation has a size equal to a size of a vocabulary that includes the input word. 14. The computing device of claim 13, wherein the input word comprises two or more words of the vocabulary that are combined to form a single word. 15. The computing device of claim 11, wherein the group of words that are morphologically similar to the input word is represented as a matrix. 16. The computing device of claim 11, wherein the processor is configured to extract embedding of the individual words using deep neural networks. 17. A method comprising: receiving an input word via an input port of a neural network; determining contextual information associated with the input word in a first branch of a neural network structure; determining morphological information associated with the input word in a second branch of the neural network structure; and generating a target word based, at least in part, on a weighted combination of the contextual information and the morphological information. 18. The method as paragraph Q recites, wherein the morphological information comprises a group of words morphologically associated with the input word. 19. The method of claim 18, wherein the input word and each word of the group of words are one-hot vectors based, at least in part, on a language vocabulary. 20. The method as paragraph Q recites, wherein the first branch of the neural network structure is parallel with the second branch of the neural network structure.