Abstract: The present teaching relates to automatic formality classification and transformation of online text items. In one example, a request is received for determining a formality level of a text item in an online communication. One or more linguistic features are extracted from the text item. Contextual information with respect to the online communication is extracted. A formality level of the text item is determined based on the one or more linguistic features and the contextual information. The formality level represents a degree of formality of the text item. The formality level is provided as a response to the request.

Abstract: Software on a website hosting an online forum extracts a plurality of sub-dialogues from each thread in a corpus from the online forum. The software obtains one or more sub-dialogue annotations associated with each sub-dialogue. The sub-dialogue annotations include an annotation as to whether the sub-dialogue is constructive. The software extracts a plurality of features from each sub-dialogue uses them and the sub-dialogue annotations associated with the sub-dialogue to train a classifier that determines whether a particular sub-dialogue is constructive. Then the software obtains a new sub-dialogue from a thread currently displayed in the online forum and extracts the plurality of features from the new sub-dialogue. The software inputs the features extracted from the new sub-dialogue into the classifier and obtains a determination as to whether the new sub-dialogue is constructive.

Abstract: In embodiments of the present invention improved capabilities are described for a sender-receiver interface for artificial intelligence communication assistance for augmenting a communication after it has been transmitted.

Abstract: The present teaching relates to automatic formality classification and transformation of online text items. In one example, a request is received for transforming a formality level of a text item in an online communication. A current formality level of the text item is obtained. The current formality level represents a current degree of formality of the text item. A target formality level is determined for the text item based on the request. The target formality level represents a targeted degree of formality for the text item. The text item having the current formality level is transformed to a transformed text item having the target formality level. The transformed text item has a same literal meaning as the text item. The transformed text item is provided as a response to the request.

Abstract: Disclosure includes an electronic message composition support system, method and architecture. Techniques including machine learning and natural language processing techniques are used to extend message composition capability and support and to provide feedback to a user regarding an error, condition, etc. detected in the user's message before the user sends the message, e.g., while the user is composing the message using a messaging application's user interface.

Abstract: An electronic message composition support system, method and architecture is provided. Techniques including machine learning and natural language processing techniques are used to extend message composition capability and support and to provide feedback to a user regarding an error, condition, etc. detected in the user's message before the user sends the message, e.g., while the user is composing the message using a messaging application's user interface.

Abstract: Software on a website hosting an online forum extracts a plurality of sub-dialogues from each thread in a corpus from the online forum. The software obtains one or more sub-dialogue annotations associated with each sub-dialogue. The sub-dialogue annotations include an annotation as to whether the sub-dialogue is constructive. The software extracts a plurality of features from each sub-dialogue uses them and the sub-dialogue annotations associated with the sub-dialogue to train a classifier that determines whether a particular sub-dialogue is constructive. Then the software obtains a new sub-dialogue from a thread currently displayed in the online forum and extracts the plurality of features from the new sub-dialogue. The software inputs the features extracted from the new sub-dialogue into the classifier and obtains a determination as to whether the new sub-dialogue is constructive.

Abstract: Systems and methods are provided for measuring a user's English language proficiency. A constructed response generated by a user is received, the constructed response being based on a picture. The constructed response is processed to determine a first numerical measure indicative of a presence of one or more grammar errors in the constructed response. The constructed response is processed to determine a second numerical measure indicative of a degree to which the constructed response describes a subject matter of the picture. The constructed response is processed to determine a third numerical measure indicative of a degree of awkward word usage in the constructed response. A model is applied to the first, second, and third numerical measures to determine a score for the constructed response indicative of the user's English language proficiency. The model includes first, second, and third variables with associated first, second, and third weighting factors, respectively.

Abstract: Methods and apparatus for detecting abusive language are disclosed. In one embodiment, a set of character N-grams is ascertained for a set of text. Feature values for a plurality of features of the set of text are determined, based, at least in part, on the set of character N-grams. A computer-generated model is applied to the feature values for the plurality of features to generate a score for the set of text, where the model includes a plurality of weights, each of the weights corresponding to one of the features. It may then be determined whether the set of text includes abusive language based, at least in part, on the score.

Abstract: Systems and methods are provided for measuring a user's English language proficiency. A constructed response generated by a user is received, the constructed response being based on a picture. The constructed response is processed to determine a first numerical measure indicative of a presence of one or more grammar errors in the constructed response. The constructed response is processed to determine a second numerical measure indicative of a degree to which the constructed response describes a subject matter of the picture. The constructed response is processed to determine a third numerical measure indicative of a degree of awkward word usage in the constructed response. A model is applied to the first, second, and third numerical measures to determine a score for the constructed response indicative of the user's English language proficiency. The model includes first, second, and third variables with associated first, second, and third weighting factors, respectively.

Abstract: Disclosure includes an electronic message composition support system, method and architecture. Techniques including machine learning and natural language processing techniques are used to extend message composition capability and support and to provide feedback to a user regarding an error, condition, etc. detected in the user's message before the user sends the message, e.g., while the user is composing the message using a messaging application's user interface.

Abstract: The present teaching relates to automatic formality classification and transformation of online text items. In one example, a request is received for transforming a formality level of a text item in an online communication. A current formality level of the text item is obtained. The current formality level represents a current degree of formality of the text item. A target formality level is determined for the text item based on the request. The target formality level represents a targeted degree of formality for the text item. The text item having the current formality level is transformed to a transformed text item having the target formality level. The transformed text item has a same literal meaning as the text item. The transformed text item is provided as a response to the request.

Abstract: The present teaching relates to automatic formality classification and transformation of online text items. In one example, a request is received for determining a formality level of a text item in an online communication. One or more linguistic features are extracted from the text item. Contextual information with respect to the online communication is extracted. A formality level of the text item is determined based on the one or more linguistic features and the contextual information. The formality level represents a degree of formality of the text item. The formality level is provided as a response to the request.

Abstract: Systems and methods are provided for detecting punctuation errors in a text including one or more sentences. A sentence including a plurality of words is received, the sentence including one or more preexisting punctuation marks. One or more punctuation marks are determined with a statistical classifier based on a set of rules, to be inserted in the sentence. The determined punctuation marks are compared with the preexisting punctuation marks. A report of punctuation errors is output based on the comparison.

Abstract: Systems and methods are provided for correcting a grammatical error in a text sequence. A first text sequence in a first language is received. The first text sequence is translated to a second language to provide a first translated text. The first text sequence is translated to a third language to provide a second translated text. The third language is different from the second language. The first translated text is translated to the first language to provide a first back translation. The second translated text is translated to the first language to provide a second back translation. A plurality of candidate text sequences that include features of the first back translation and the second back translation are determined. The plurality of candidate text sequences include alternative grammatical options for the first text sequence. The plurality of candidate text sequences are scored with the processing system.

Abstract: In accordance with the teachings described herein, systems and methods are provided for identifying organizational elements in argumentative or persuasive discourse. A text that has been annotated is received. The annotated text includes argumentative or persuasive discourse that includes claims and evidence and organizational elements configured to organize the claims and evidence. Annotations of the annotated text distinguish the organizational elements from the claims and evidence. A rule set or a feature set is identified from the annotated text, where the rule set or the feature set includes textual patterns or word frequency features related to the organizational elements of the annotated text. A model is built based on the annotations and on the rule set or the feature set. The model is configured to identify organizational elements in a new text. The model is applied to the new text.

Abstract: Systems and methods are provided for measuring a user's English language proficiency. A constructed response generated by a user is received, the constructed response being based on a picture. The constructed response is processed to determine a first numerical measure indicative of a presence of one or more grammar errors in the constructed response. The constructed response is processed to determine a second numerical measure indicative of a degree to which the constructed response describes a subject matter of the picture. The constructed response is processed to determine a third numerical measure indicative of a degree of awkward word usage in the constructed response. A model is applied to the first, second, and third numerical measures to determine a score for the constructed response indicative of the user's English language proficiency. The model includes first, second, and third variables with associated first, second, and third weighting factors, respectively.

Abstract: Systems and methods are provided for correcting a grammatical error in a text sequence. A first text sequence in a first language is received. The first text sequence is translated to a second language to provide a first translated text. The first text sequence is translated to a third language to provide a second translated text. The third language is different from the second language. The first translated text is translated to the first language to provide a first back translation. The second translated text is translated to the first language to provide a second back translation. A plurality of candidate text sequences that include features of the first back translation and the second back translation are determined. The plurality of candidate text sequences include alternative grammatical options for the first text sequence. The plurality of candidate text sequences are scored with the processing system.

Abstract: A food slicer has a deck defining a food prep surface and a blade oriented laterally across the deck. The blade has a cutting edge exposed on the food prep surface. The blade can divide the deck into a pre-slice deck and a post-slice deck. An elevation of the pre-slice deck can be user adjustable relative to the position of the cutting edge to selectively adjust a slice thickness produced by the food slicer. The food slicer can also have a safety mechanism with a blade guard positioned adjacent the cutting edge. The blade guard is movable between a blocking position blocking the cutting edge and a slicing position exposing the cutting edge.