Abstract: A method, computer system, and a computer program product for searching are provided. The method may include receiving a word and a context of the word. The context may include additional words. A first word embedding may be generated by inputting a sequence into a word embedding model that resultantly outputs the first word embedding. The sequence may include the word and the context that are concatenated to each other in the sequence. The first word embedding may be compared with other word embeddings. The other word embeddings may have been generated by inputting respective text portions of other texts into the word embedding model. A candidate match of the other texts may be presented. A respective word embedding of the candidate match may be, of the other word embeddings, most similar to the first word embedding according to the comparing.

Abstract: In several aspects for querying a data source represented by data object embeddings in a vector space, a processor inputs, to a trained embedding generation model, a received query and at least one token for receiving from the trained embedding generation model a set of embeddings of the vector space. The set of embeddings comprises an embedding of the received query and at least one embedding of the at least one token respectively, wherein the embedding of each token is a prediction of an embedding of a supplement of the query. The data object embeddings may be searched for data object embeddings that match the set of embeddings. This may result in search result embeddings of the set of embeddings. Data objects that are represented by the search result embeddings may be determined. At least part of the determined data objects may be provided.

Abstract: A method and related system, comprising receiving a dataset comprising records, where each record of the records comprises information descriptive of an event corresponding to an entity. The records may be clustered resulting in clusters having categories respectively, each cluster category being indicative of an event category of the events. One or more event attributes descriptive of the events may be determined. Records having values of the determined event attributes may be selected from the dataset. The selected records may be grouped according to a grouping criterion, the grouping criterion being based on the values of the determined event attributes. At least one association rule may be determined using the groups and the cluster identifiers, where each association rule indicates a relationship between the event categories of a respective group.

Abstract: A computer assigns a similarity value to a comparison document. The computer receives, reference document contextual word embeddings in first set of topic clusters, each with a representative embedding. The computer receives comparison document contextual word embeddings. The computer determines, using a trained neural network model classifier, for each comparison document contextual word embedding, topic correspondence values relative to the representative embeddings of said first set of clusters. The computer generates a second set of clusters by assigning comparison document embeddings to best matching one of the first clusters, according to the topic correspondence values. The computer determines a second set of representative embeddings and uses a comparison method, to determine a cluster similarity value for second set clusters compared to first set representative embeddings. The computer determines document similarity values based, at least in part, on at least one of cluster similarity values.

Abstract: A method and related system, comprising receiving a dataset comprising records, where each record of the records comprises information descriptive of an event corresponding to an entity. The records may be clustered resulting in clusters having categories respectively, each cluster category being indicative of an event category of the events. One or more event attributes descriptive of the events may be determined. Records having values of the determined event attributes may be selected from the dataset. The selected records may be grouped according to a grouping criterion, the grouping criterion being based on the values of the determined event attributes. At least one association rule may be determined using the groups and the cluster identifiers, where each association rule indicates a relationship between the event categories of a respective group.

Abstract: A computer assigns a similarity value to a comparison document. The computer receives, reference document contextual word embeddings in first set of topic clusters, each with a representative embedding. The computer receives comparison document contextual word embeddings. The computer determines, using a trained neural network model classifier, for each comparison document contextual word embedding, topic correspondence values relative to the representative embeddings of said first set of clusters. The computer generates a second set of clusters by assigning comparison document embeddings to best matching one of the first clusters, according to the topic correspondence values. The computer determines a second set of representative embeddings and uses a comparison method, to determine a cluster similarity value for second set clusters compared to first set representative embeddings. The computer determines document similarity values based, at least in part, on at least one of cluster similarity values.

Abstract: A method, computer system, and a computer program product for searching are provided. The method may include receiving a word and a context of the word. The context may include additional words. A first word embedding may be generated by inputting a sequence into a word embedding model that resultantly outputs the first word embedding. The sequence may include the word and the context that are concatenated to each other in the sequence. The first word embedding may be compared with other word embeddings. The other word embeddings may have been generated by inputting respective text portions of other texts into the word embedding model. A candidate match of the other texts may be presented. A respective word embedding of the candidate match may be, of the other word embeddings, most similar to the first word embedding according to the comparing.