Techniques are provided for performing sentiment analysis on words in a first data set. An example embodiment includes generating a word embedding model including a first plurality of features. A value indicating sentiment for the words in the first data set can be determined using a convolutional neural network (CNN). A second plurality of features are generated based on bigrams identified in the data set. The bigrams can be generated using a co-occurrence graph. The model is updated to include the second plurality of features, and sentiment analysis can be performed on a second data set using the updated model.

Techniques are provided for performing sentiment analysis on words in a first data set. An example embodiment includes generating a word embedding model including a first plurality of features. A value indicating sentiment for the words in the first data set can be determined using a convolutional neural network (CNN). A second plurality of features are generated based on bigrams identified in the data set. The bigrams can be generated using a co-occurrence graph. The model is updated to include the second plurality of features, and sentiment analysis can be performed on a second data set using the updated model.

Image processing is performed on an input image generated from scanning a filled-in document form. The input image is evaluated against a blank version of various document forms in order to identify the form type of the filled-in document form. The evaluation results in identifying one of the blank document forms as a match to the filled-in document form. Each document form has a set of keywords. The evaluation uses a vector of keyword matches in the filled-in document form. Once a blank document form is identified to be match, the filled-in document form may be categorized according to that document form and/or data extracted from the filled-in document may be stored in association with keywords of that document form.

Provided is a device including: a display unit configured to display handwritten content based on an analog handwritten input of a user; a user input unit that receives a user input of selecting a portion of the handwritten content displayed on the display unit; and a control unit reproduces a segment of multimedia content, which corresponds to the portion of the handwritten content, from the multimedia content synchronized with the handwritten content.

In one embodiment, a method includes receiving a request to identify k steps for a particular entity to acquire a target attribute in public sentiments, accessing a table of word vector relationships, looking up an entity word vector corresponding to the entity name and a target attribute word vector corresponding to the n-gram representing the target attribute using the table, determining a directional vector in the d-dimensional embedding space that connects from the entity word vector to the target attribute word vector, identifying k points on the directional vector that evenly split the directional vector into k+1 segments, selecting, for each of the k points, a word vector that is closest to the point, identifying, for each of the k selected word vectors, a corresponding n-gram by looking up the word vector in the table, and sending a response message comprising the k identified n-grams.

Provided is a process that includes: receiving a computer generated utterance classified as non-offensive by a machine learning model, wherein the machine learning model is configured to classify input text as offensive or non-offensive; obtaining feedback regarding the computer generated utterance, the feedback being indicative of a reaction by an audience to the computer generated utterance; determining and based on the feedback, whether the computer generated utterance is perceived as offensive by the audience; and causing one or more parameters of the machine learning model to be updated based on the computer generated utterance and a result of the determination of whether the computer generated utterance is perceived as offensive by the audience.

In one embodiment, a method includes receiving a request to identify k steps for a particular entity to acquire a target attribute in public sentiments, accessing a table of word vector relationships, looking up an entity word vector corresponding to the entity name and a target attribute word vector corresponding to the n-gram representing the target attribute using the table, determining a directional vector in the d-dimensional embedding space that connects from the entity word vector to the target attribute word vector, identifying k points on the directional vector that evenly split the directional vector into k+1 segments, selecting, for each of the k points, a word vector that is closest to the point, identifying, for each of the k selected word vectors, a corresponding n-gram by looking up the word vector in the table, and sending a response message comprising the k identified n-grams.

Written text transform relating to a method and system for text recognition and in particular for transforming liquid ink (handwritten text) to digital ink, which subsequently may be analyzed by a processor, comprising segmenting a scan image and vectorizing the segment, analyzing the vectors and building individual strokes, analyzing the strokes and determining a start point and direction of writing for each stroke.

An electronic device is provided. The electronic device includes a memory configured to store a computer executable instructions; and a processor configured to execute the executable instructions to: determine a text corresponding to a received command, provide response information on the command based on a first artificial intelligence model classifying the text as a text corresponding to one of a plurality of pre-stored texts, and provide error information on the command based on the first artificial intelligence model classifying the text as an error, wherein the first artificial intelligence model is configured to classify the text as the error based on the text corresponding to the command being a similar text having one of an entity and an intent different from at least one of the plurality of pre-stored texts.

An object recognition device including an artificial neural network (NN) engine configured to receive learning data and weights, make an object recognition model (ORM) learn by using the received information, and provide selected weight data including weights from the selected portion of the weights, and further configured to receive a feature vector, and apply the feature vector extracted from an object data that constructs the object and the selected weight data to the learned ORM to provide an object recognition result, a nonvolatile memory (NVM) configured to store the learned ORM, and an error correction code (ECC) engine configured to perform an ECC encoding on the selected weight data to generate parity data, provide the selected weight data and the parity data to the NVM, and provide the selected weight data to the NN engine by performing an ECC decoding on the selected weight data based on the parity data.