A computer-implemented method, system and computer program product for improving understandability of text by a user. A final word vector for each word in a sentence of a document is computed, such as by averaging a first word vector and a second word vector for that word. Furthermore, elements of a user portrait are vectorized. A distance is then computed between a vector for each word in the sentence and a vectorized element in the user’s portrait which is summed to form an evaluation result for the element. An evaluation result is also formed for every other element in the user’s portrait by performing such a computation step. A “final evaluation result” is then generated corresponding to the evaluation results for every element in the user’s portrait. The document is then transformed in response to the final evaluation result indicating a lack of understanding of the sentence by the user.

A computing device generates a first token for first data content that is associated with a first relationship and a second relationship, and a second token for second data content that is associated with the first relationship and a third relationship, such that the first token and second token are generated based on a frequency of use of data values included in the first and the second data content. The computing device calculates a first similarity score of data values from third data content that is associated with the second relationship and a fourth relationship with data values from fourth data content that is associated with the third relationship and the fourth relationship in response to the first and second token matching. The computing device then performs, in response to the first similarity score satisfying a similarity threshold, a first modification to any of the data content.

A method of executing a sequence of tasks includes receiving a natural language input indicative of the sequence of tasks. The natural language input may include a first task and a plurality of possible next tasks for the first task. The tasks may each be associated with a playback performance skeleton, indicative of a series of actions to be carried out on a web page. The series of action may have been generated, ahead of time, from a recorded performance of a similar task. The first task may be arranged to be performed. Then, based on a result of the performance of the first task, a successive task from among a plurality of possible next tasks associated with the result of performance of the first task may be selected. The successive task may then be arranged to be performed.

A method performed by a computing device includes determining a set of identigens for each word of a query of a topic to produce sets of identigens. Each set of identigens represents one or more different meanings of a word of the query. The method further includes interpreting, using identigen pairing rules, the sets of identigens to determine a most likely meaning interpretation of the query and produce an excluding query entigen group with an excluding entigen. The method further includes recovering a response entigen group for the query from a knowledge database utilizing the excluding query entigen group. The response entigen group provides a response to the query.

A method of generating an instruction performance skeleton employs an instruction unit configured to receive a natural language instruction. From the natural language instruction, a sequence of clauses may be extracted. The instruction unit then determines a target website or websites on which to perform the task. The object models of the target website are generated. A comparison of the sequence of actions to the object model and its labelling hierarchical class structure is performed. Based on this comparison, an instruction performance skeleton is generated. In future, on the basis of a further natural language instruction that is similar to the previous natural language instruction, the instruction performance skeleton may be modified to generate a playback performance skeleton to arrange performance of a task.

A method of generating an instruction performance skeleton employs an instruction unit configured to receive a natural language instruction. From the natural language instruction, a sequence of clauses may be extracted. The instruction unit then determines a target website or websites on which to perform the task. The object models of the target website are generated. A comparison of the sequence of actions to the object model and its labelling hierarchical class structure is performed. Based on this comparison, an instruction performance skeleton is generated. In future, on the basis of a further natural language instruction that is similar to the previous natural language instruction, the instruction performance skeleton may be modified to generate a playback performance skeleton to arrange performance of a task.

Techniques for generating conversational responses for a conversational user interface are disclosed. In one embodiment, a method is disclosed comprising obtaining user input from a user via a conversational user interface, using the user input to obtain a user emotion and a user intent, obtaining candidate probabilities for a fragment of a response to the user input using the obtained user emotion, the obtained user intent and the user input, generating the response to the user input using the candidate probabilities obtained for the fragment to select a candidate for the fragment of the response, and communicating the response to the user via the conversational user interface.

A method for facilitating a multiparty conversation is disclosed. An electronic device using the method may facilitate a multiparty conversation by identifying participants of a conversation, localizing relative positions of the participants, detecting speeches of the conversation, matching one of the participants to each of the detected speeches according to the relative positions of the participants, counting participations of the matched participant in the conversation, identifying a passive subject from all the participants according to the participations of all the participants in the conversation, finding a topic of the conversation between the participants, and engaging the passive subject by addressing the passive subject and speaking a sentence related to the topic.

Text is intelligently annotated by first creating a topic map summarizing topics of interest of the user. A data structure is created. The topic map is used to create two linked user dictionaries, a topic dictionary reflecting topic names and a traversal dictionary reflecting the knowledge structure of a topic. Actions may be linked with topic types. When the text to be annotated is being read, the topic data structure of the topics found in the text are automatically instantiated using the dictionaries and any actions previously linked to topic types. Instantiated topic data structures are automatically attached to the text being annotated. A user GUI may be created to allow the user to access and interact with the text annotations.

Text is intelligently annotated by first creating a topic map summarizing topics of interest of the user. A data structure is created. The topic map is used to create two linked user dictionaries, a topic dictionary reflecting topic names and a traversal dictionary reflecting the knowledge structure of a topic. Actions may be linked with topic types. When the text to be annotated is being read, the topic data structure of the topics found in the text are automatically instantiated using the dictionaries and any actions previously linked to topic types. Instantiated topic data structures are automatically attached to the text being annotated. A user GUI may be created to allow the user to access and interact with the text annotations.