A method and system for providing replacement text segment suggestions for an unoriginal text segment in a document may include examining a portion of the document to determine if the portion includes a text segment containing unoriginal content. Upon determining that the portion includes the unoriginal text segment, the method may enable display of a notification that the text segment contains unoriginal content and receive a request, via a network, to provide the replacement text segment for the unoriginal text segment. Upon receiving the request, the method may identify the replacement text segment for the text unoriginal segment, based at least in part a guideline relating to the use of content that is included in a source. The replacement text segment may include a citation for the source.

A method and system for providing replacement text segment suggestions for an unoriginal text segment in a document may include examining a portion of the document to determine if the portion includes a text segment containing unoriginal content. Upon determining that the portion includes the unoriginal text segment, the method may enable display of a notification that the text segment contains unoriginal content and receive a request, via a network, to provide the replacement text segment for the unoriginal text segment. Upon receiving the request, the method may identify the replacement text segment for the text unoriginal segment, based at least in part a guideline relating to the use of content that is included in a source. The replacement text segment may include a citation for the source.

Approaches for the translation of structured text include an embedding module for encoding and embedding source text in a first language, an encoder for encoding output of the embedding module, a decoder for iteratively decoding output of the encoder based on generated tokens in translated text from previous iterations, a beam module for constraining output of the decoder with respect to possible embedded tags to include in the translated text for a current iteration using a beam search, and a layer for selecting a token to be included in the translated text for the current iteration. The translated text is in a second language different from the first language. In some embodiments, the approach further includes scoring and pointer modules for selecting the token based on the output of the beam module or copied from the source text or reference text from a training pair best matching the source text.

A speech recognition module receives training data of speech and creates a representation for individual words, non-words, phonemes, and any combination. A set of speech processing detectors analyze the training data of speech from humans communicating. The set of speech processing detectors detect speech parameters that are indicative of paralinguistic effects on top of enunciated words, phonemes, and non-words in the audio stream. One or more machine learning models undergo supervised machine learning on their neural network to train on how to associate one or more mark-up markers with a textual representation, for each individual word, individual non-word, individual phoneme, and any combinations of these, that was enunciated with a particular paralinguistic effect. Each mark-up marker can correspond to its own paralinguistic effect.

A speech recognition module receives training data of speech and creates a representation for individual words, non-words, phonemes, and any combination. A set of speech processing detectors analyze the training data of speech from humans communicating. The set of speech processing detectors detect speech parameters that are indicative of paralinguistic effects on top of enunciated words, phonemes, and non-words in the audio stream. One or more machine learning models undergo supervised machine learning on their neural network to train on how to associate one or more mark-up markers with a textual representation, for each individual word, individual non-word, individual phoneme, and any combinations of these, that was enunciated with a particular paralinguistic effect. Each mark-up marker can correspond to its own paralinguistic effect.

This document provides systems and methods for altering text presentation to increase reading accuracy, efficiency, and retention. This can include identification text specific attributes from machine readable text (through parsing of the text), varying the text presentation in accordance with the attributes, and creating an enhanced visual product for enhancing the reading experience. For example, a computer system can extract attributes such as parts of speech from an input sentence and display that sentence in cascading text segments down and across a display screen. The system can further use domain-specific dictionaries derived from domain-specific texts to identify domain-specific compound noun phrases and verb phrases that require specific linguistic tagging to be usable in other linguistic analysis steps.

This document provides systems and methods for altering text presentation to increase reading accuracy, efficiency, and retention. This can include identification text specific attributes from machine readable text (through parsing of the text), varying the text presentation in accordance with the attributes, and creating an enhanced visual product for enhancing the reading experience. For example, a computer system can extract attributes such as parts of speech from an input sentence and display that sentence in cascading text segments down and across a display screen. The system can further use domain-specific dictionaries derived from domain-specific texts to identify domain-specific compound noun phrases and verb phrases that require specific linguistic tagging to be usable in other linguistic analysis steps.

Provided is a technique for generating a tagging model for attaching a tag in consideration of a phrase based on dependency between words. A tagging model generation apparatus includes a learning section 2 which generates, by using inputted learning data, a tagging model including probability-related information serving as information related to the probability that each tag is associated with each word-related information, and joint probability-related information serving as information related to a joint probability which serves as the probability of appearance of each tag in which appearance frequencies of a plurality of consecutive tags associated with pieces of word-related information of a plurality of consecutive words in each text are taken into consideration, and a storage section 3 which stores the generated tagging model.

Provided is a technique for generating a tagging model for attaching a tag in consideration of a phrase based on dependency between words. A tagging model generation apparatus includes a learning section 2 which generates, by using inputted learning data, a tagging model including probability-related information serving as information related to the probability that each tag is associated with each word-related information, and joint probability-related information serving as information related to a joint probability which serves as the probability of appearance of each tag in which appearance frequencies of a plurality of consecutive tags associated with pieces of word-related information of a plurality of consecutive words in each text are taken into consideration, and a storage section 3 which stores the generated tagging model.

A system and methods are disclosed for enabling users to attach a tag and highlight in a single action or activity to a select piece or portion of text in digital content (e.g., in a digital book or other content provided for viewing on an electronic device). In some implementations, the teacher may associate a tag with a particular text in the assignment, push the assignment embedding the tag to one or more students, and the tag becomes automatically visible to the one or more students through their highlight menu when they open up the assignment for completion. In some implementations, with this single activity, users may easily share tasks, comments etc., with ease. One or more other activities are also used among teachers and students to create and execute assignments with efficiency.