A method of operating a communication system includes generating a transcript of at least a portion of a conversation between a plurality of users. The transcript includes a plurality of subsets of characters. The method further includes displaying the transcript on a plurality of communication devices, identifying an occurrence of at least one selected subset of characters from the plurality of subsets of characters, and querying a definition source for at least one definition for the selected subset of characters. The definition for the selected subset of characters is displayed on the plurality of communication devices.

Automated analysis of text documents is used to scan text documents in order to find phrases or text fragments from other documents, or modifying the existing ones. A comparatively fast and universally applicable method finds phrases, sentences or even text fragments from other documents. The method includes: all electronic files containing model documents are converted to a given format; meaningful fragments, called “clauses”, are extracted from them; the converted files containing model documents are stored in the database; each electronic file containing a document to be analyzed is converted to the given format; clauses extracted from analyzed documents are compared with clauses extracted from model documents; fractions of clauses from an analyzed document matching clauses from each model document are calculated; fractions found are then compared with a pre-set threshold value in order to find out whether there are text fragments from a model document in the analyzed one.

Methods and systems are provided for generating automatic program recommendations based on user interactions. In some embodiments, control circuitry processes verbal data received during an interaction between a user of a user device and a person with whom the user is interacting. The control circuitry analyzes the verbal data to automatically identify a media asset referred to during the interaction by at least one of the user and the person with whom the user is interacting. The control circuitry adds the identified media asset to a list of media assets associated with the user of the user device. The list of media assets is transmitted to a second user device of the user.

A quitting-intention estimating system facilitates detecting an entity's intention to quit an organization. During operation, the system obtains recent electronic-communication information associated with the entity under investigation, such as an employee of a corporation, or a volunteer of a non-profit organization. The system can determine the entity's quitting intention by determining a plurality of recent communication features from the electronic-communication information, for example, by using a variety of feature-extracting modules that extract the features from the entity's electronic communications. The system then computes a quitting-intention value for the entity under investigation, using a quitting-intention estimating function that takes the recent communication features as input. The quitting-intention estimating function detects a change in the entity's behavior from previous communication features, and generates a quitting-intention value which indicates a likelihood that the change in the entity's behavior corresponds to an intention to quit the organization.

A method for controlling a mobile terminal is provided. The method includes receiving content data including video data; determining whether first caption data including a first language caption is included in the content data; determining, if the first caption data is included in the content data, whether a high-difficulty word is included in the first language caption; generating explanation data corresponding to the high-difficulty word if the high-difficulty word is included in the first language caption; and converting the first caption data into second caption data by adding the explanation data to the first caption data.

A computer implemented method, system and a computer program product is provided for summarizing a document which includes receiving a reading speed of the reader, determining a summary length of a summary of the document based on the received reading speed of the reader, and generating a summary of the document having the determined summary length.

Systems and methods described herein utilize supervised machine learning to generate a figure-of-speech prediction model for classify content words in a running text as either being figurative (e.g., as a metaphor, simile, etc.) or non-figurative (i.e., literal). The prediction model may extract and analyze any number of features in making its prediction, including a topic model feature, unigram feature, part-of-speech feature, concreteness feature, concreteness difference feature, literal context feature, non-literal context feature, and off-topic feature, each of which are described in detail herein. Since uses of figure of speech in writings may signal content sophistication, the figure-of-speech prediction model allows scoring engines to further take into consideration a text's use of figure of speech when generating a score.

A method comprising: receiving a multiplicity of videos from a source; for each video: receiving meta data related to the video; extracting from the video a video frame containing computer code; identifying a region of interest (ROI) within the video frame; performing OCR of the ROI to extract a code segment; analyzing the code segment by: semantically analyzing the code segment to obtain a first rank, structurally analyzing the code segment to obtain a second rank, and analyzing the meta data to obtain a third rank; and combining the first rank, second rank and third rank into a total rank associated with the code segment; receiving a query; matching the query to each code segment to identify matching code segments and associated videos; and providing the associated videos in accordance with total ranks associated with the matching code segments.

Computer-implemented techniques can include receiving a selected word in a source language, obtaining one or more parts of speech for the selected word, and for each of the one or more parts-of-speech, obtaining candidate translations of the selected word to a different target language, each candidate translation corresponding to a particular semantic meaning of the selected word. The techniques can include for each semantic meaning of the selected word: obtaining an image corresponding to the semantic meaning of the selected word, and compiling translation information including (i) the semantic meaning, (ii) a corresponding part-of-speech, (iii) the image, and (iv) at least one corresponding candidate translation. The techniques can also include outputting the translation information.

In one aspect, an example method involves accessing data representing a program schedule of a media program, wherein the program schedule comprises first text. The method also includes selecting second text from among the first text. The method further includes transmitting, via a communication network, an instruction configured to cause the selected second-text to be added to an electronic dictionary of a closed-captioning generator.