Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  consider the problem of unsupervised morphological analysis from a new angle. Past work has endeavored to design unsupervised learning methods which explicitly or implicitly encode inductive biases appropriate to the task at hand.  propose instead to treat morphological analysis as a structured prediction problem, where languages with labeled data serve as training examples for unlabeled languages, without the assumption of parallel data.  define a universal morphological feature space in which every language and its morphological analysis reside.  develop a novel structured nearest neighbor prediction method which seeks to find the morphological analysis for each unlabeled language which lies as close as possible in the feature space to a training language.  apply our model to eight inflecting languages, and induce nominal morphology with substantially higher accuracy than a traditional, MDL-based approach. Our analysis indicates that accuracy continues to improve substantially as the number of training languages increases.
['Young-Bum Kim', 'João Graça', 'Benjamin Snyder']
I am writing a research paper on a idea Universal Morphological Analysis using Structured Nearest Neighbor Prediction
[{"from": "human", "value": "I am writing a research paper on a idea Universal Morphological Analysis using Structured Nearest Neighbor Prediction"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: ,  consider the problem of unsupervised morphological analysis from a new angle. Past work has endeavored to design unsupervised learning methods which explicitly or implicitly encode inductive biases appropriate to the task at hand.  propose instead to treat morphological analysis as a structured prediction problem, where languages with labeled data serve as training examples for unlabeled languages, without the assumption of parallel data.  define a universal morphological feature space in which every language and its morphological analysis reside.  develop a novel structured nearest neighbor prediction method which seeks to find the morphological analysis for each unlabeled language which lies as close as possible in the feature space to a training language.  apply our model to eight inflecting languages, and induce nominal morphology with substantially higher accuracy than a traditional, MDL-based approach. Our analysis indicates that accuracy continues to improve substantially as the number of training languages increases."}]