Abstract: A method may include receiving an electronic message from a sender. The method may further include parsing the electronic message into a set of sections, the set of sections including structured sections and an unstructured section. The method may further include detecting etiquette errors in the structured sections of the electronic message, wherein the etiquette errors include at least one of a missing word, a redundant word, an incorrect usage of a word, a style error, an emotional punctuation error, or a punctuation error. The method may further include generating an etiquette score based on the etiquette errors.

Abstract: A method includes receiving uncompilable code from a candidate. The method further includes extracting features from the uncompilable code. The method further includes outputting, with a coding machine-learning model, compilable code based on the uncompilable code and the extracted features. The method further includes generating a coding score based on the uncompilable code and the compilable code. The method further includes receiving first media of one or more answers to questions provided by the candidate during an interview. The method further includes outputting, with a media machine-learning model, one or more corresponding ratings for the one or more answers. The method further includes generating a media score based on the one or more corresponding ratings. The method further includes generating a total score based on the coding score and the media score.

Abstract: A method may include receiving an electronic message from a sender. The method may further include parsing the electronic message into a set of sections, the set of sections including structured sections and an unstructured section. The method may further include detecting etiquette errors in the structured sections of the electronic message, wherein the etiquette errors include at least one of a missing word, a redundant word, an incorrect usage of a word, a style error, an emotional punctuation error, or a punctuation error. The method may further include generating an etiquette score based on the etiquette errors.

Abstract: A method may include receiving an electronic message from a sender. The method may further include parsing the electronic message into a set of sections, the set of sections including structured sections and an unstructured section. The method may further include detecting etiquette errors in the structured sections of the electronic message, wherein the etiquette errors include at least one of a missing word, a redundant word, an incorrect usage of a word, a style error, an emotional punctuation error, or a punctuation error. The method may further include generating an etiquette score based on the etiquette errors.

Abstract: A method includes receiving uncompilable code from a candidate. The method further includes extracting features from the uncompilable code. The method further includes outputting, with a coding machine-learning model, compilable code based on the uncompilable code and the extracted features. The method further includes generating a coding score based on the uncompilable code and the compilable code. The method further includes receiving first media of one or more answers to questions provided by the candidate during an interview. The method further includes outputting, with a media machine-learning model, one or more corresponding ratings for the one or more answers. The method further includes generating a media score based on the one or more corresponding ratings. The method further includes generating a total score based on the coding score and the media score.

Abstract: A method to generate compilable code from uncompilable code. The method includes receiving uncompilable code. The method further includes generating compilable code by one or more of (1) identifying and correcting syntax errors in the uncompilable code and (2) using an n-gram based token prediction model. The method further includes extracting features from the compilable code. The method further includes generating a machine learning model that recognizes patterns from extracted features in the compilable code.

Abstract: A method may include receiving an electronic message from a sender. The method may further include parsing the electronic message into a set of sections, the set of sections including structured sections and an unstructured section. The method may further include detecting etiquette errors in the structured sections of the electronic message, wherein the etiquette errors include at least one of a missing word, a redundant word, an incorrect usage of a word, a style error, an emotional punctuation error, or a punctuation error. The method may further include generating an etiquette score based on the etiquette errors.

Abstract: A method to generate compilable code from uncompilable code. The method includes receiving uncompilable code. The method further includes generating compilable code by one or more of (1) identifying and correcting syntax errors in the uncompilable code and (2) using an n-gram based token prediction model. The method further includes extracting features from the compilable code. The method further includes generating a machine learning model that recognizes patterns from extracted features in the compilable code.