Abstract: The invention provides a method and system for automatically detecting semantic errors in a text. In order to detect the semantic errors in the text, the method generates a first set of features and a second set of features corresponding to each word in the text. A first set of features corresponds to statistical features of a word and a second set of features for a word corresponds to the features generated for a plurality of words surrounding the word in the text using matrix factorization techniques. The method, then, combines a first set of features and a second set of features to generate a third set of features corresponding to each word in the text. Thereafter, the method utilizes an Artificial Neural Network (ANN) model to detect semantic validity of each word based on a corresponding third set of features.

Abstract: The invention provides a method and system for automatically detecting semantic errors in a text. In order to detect the semantic errors in the text, the method generates a first set of features and a second set of features corresponding to each word in the text. A first set of features corresponds to statistical features of a word and a second set of features for a word corresponds to the features generated for a plurality of words surrounding the word in the text using matrix factorization techniques. The method, then, combines a first set of features and a second set of features to generate a third set of features corresponding to each word in the text. Thereafter, the method utilizes an Artificial Neural Network (ANN) model to detect semantic validity of each word based on a corresponding third set of features.

Abstract: A method for an automated text-evaluation service, and more particularly a method and apparatus for automatically evaluating text and returning a score which represents a degree of inappropriate language. The method is implemented in a computer infrastructure having computer executable code tangibly embodied in a computer readable storage medium having programming instructions. The programming instructions are configured to: receive an input text which comprises an unstructured message at a first computing device; process the input text according to a string-structure similarity measure which compares each word of the input text to a predefined dictionary to indicate whether there is similarity in meaning, and generate an evaluation score for each word of the input text and send the evaluation score to another computing device. The evaluation score for each input message is based on the string-structure similarity measure between each word of the input text and the predefined dictionary.

Abstract: A method for an automated text-evaluation service, and more particularly a method and apparatus for automatically evaluating text and returning a score which represents a degree of inappropriate language. The method is implemented in a computer infrastructure having computer executable code tangibly embodied in a computer readable storage medium having programming instructions. The programming instructions are configured to: receive an input text which comprises an unstructured message at a first computing device; process the input text according to a string-structure similarity measure which compares each word of the input text to a predefined dictionary to indicate whether there is similarity in meaning, and generate an evaluation score for each word of the input text and send the evaluation score to another computing device. The evaluation score for each input message is based on the string-structure similarity measure between each word of the input text and the predefined dictionary.