Devices and methods for determining the content of a first segment of text in a first language, using a second segment of text in a second language. The second segment of text is a translation of the first segment of text.

Provided are a text processing method, a device and a storage medium, relating to a field of computer technology, and especially to a field of artificial intelligence, such as natural language processing and deep learning. The specific implementation scheme includes: performing text processing on first text, by using a text processing acceleration operator; and processing, in parallel and faster, content after the text processing, by using the text processing acceleration operator. Text processing and parallel acceleration are carried out by the text processing acceleration operator, which can improve the speed of text processing.

Electronic communication methods and systems for determining sentiment associated with a communication, conveniently displaying indicia of the sentiment information, and scoring the sentiment information are disclosed. The methods and systems can include associating an emoji with a sentiment and annotating information, for example, a highlight reel or a waveform, with the emoji.

Electronic communication methods and systems for determining sentiment associated with a communication, conveniently displaying indicia of the sentiment information, and scoring the sentiment information are disclosed. The methods and systems can include associating an emoji with a sentiment and annotating information, for example, a highlight reel or a waveform, with the emoji.

Described herein is a method and model system to improve the quality of search recall in the social media communication posts that often contains the local language words written in Romanized English script. A Deep Learning Transformer based model architecture and algorithm improves the search recall for a given English query. The model is trained to find named entities from post and queries, and these entities are compared to find the matching score using a specially designed model that takes into account the post's recency and its cleanliness score. The cleanliness score is obtained from a trained LSTM based model. The input English query is expanded to a set of equivalent queries by including contextually nearest words. The number of nearest words can be controlled using a slider mechanism.

Various embodiments for real-time text layout conversion control on a mobile electronic device, by a processor device, are provided. On a mobile electronic device having a touch-screen display, a set of text layout conversion control signals are defined and associated with at least one of a plurality of contextual attributes, for text layout conversion management. Upon recognition of the at least one of the plurality of contextual attributes, a text layout is dynamically converted and re-rendered for presentation on the touch-screen display.

The embodiments herein achieve a picture based communication system. The system allows users option to select one or more pictures, and any associated attributes. The selection of one or more pictures, and any associated attributes is taken as input. The selected words and attributes are converted to a graph representation, and subsequently the graph representation is converted to a sentence in target language. The method further involves predicting new relations, words, and attributes for further selection by user.

A first searcher searches a dictionary with a first character string that is input, and acquires a second character string corresponding to the first character string. The second searcher performs a web search with a search character string based on the first character string that is input, and acquires a keyword extracted from a search result as a third character string. A search switching step switches between the first searcher and the second searcher. An outputter outputs the second character string acquired by the first searcher or the third character string acquired by the second searcher.

A first searcher searches a dictionary with a first character string that is input, and acquires a second character string corresponding to the first character string. The second searcher performs a web search with a search character string based on the first character string that is input, and acquires a keyword extracted from a search result as a third character string. A search switching step switches between the first searcher and the second searcher. An outputter outputs the second character string acquired by the first searcher or the third character string acquired by the second searcher.

A translation device includes a speech recognition unit, a storage, a translation processor, and an information acquisition unit. The speech recognition unit recognizes a voice to generate a spoken sentence in a first language. The storage stores a plurality of example sentences each including a parameter representing a category corresponding to a plurality of terms. The translation processor searches the plurality of example sentences stored in the storage for an example sentence on the basis of the spoken sentence as a search result example sentence, and generates a converted sentence based on the search result example sentence. The information acquisition unit acquires specific information representing a specific term which corresponds to a specific parameter. If the search result example sentence includes the specific parameter, the translation processor generates the converted sentence based on the specific term represented by the specific information.