Abstract: A method comprising using at least one hardware processor for: receiving (a) a proposition and (b) a plurality of claims; identifying a local claim polarity of each claim of the plurality of claims with respect to the proposition; calculating a pairwise claim polarity agreement score for each pair of claims of the pairs of claims reflecting the likelihood of said each pair of claims to have the same claim polarity, wherein the pairwise claim polarity agreement score is associated with each claim of the pair of claims; and determining a global claim polarity for each claim of the plurality of claims based on the local claim polarity of the claim and pairwise claim polarity agreement scores associated with said each claim.

Abstract: A method and system. Target clusterability is calculated as an average of a respective clusterability of at least one target data item comprised by a target domain. Target-side matchability is calculated as an average of a respective matchability of each target centroid of the target domain to source centroids of a source domain, wherein the source domain comprises at least one source data item. Source-side matchability is calculated as an average of a respective matchability of each source centroid of said source centroids to the target centroids. Source-target pair matchability is calculated as an average of the target-side matchability and the source-side matchability. Cross-domain clusterability between the target domain and the source domain is calculated as a linear combination of the calculated target clusterability and the calculated source-target pair matchability. The cross-domain clusterability is transferred to a device.

Abstract: A method and system for evaluating cross-domain clusterability upon a target domain and a source domain. Target clusterability is calculated as an average of a respective clusterability of at least one target data item comprised by the target domain. Target-side matchability is calculated as an average of a respective matchability of each target centroid of the target domain to source centroids of the source domain, wherein the source domain comprises at least one source data item. Source-side matchability is calculated as an average of a respective matchability of each source centroid of said source centroids to the target centroids. Source-target pair matchability is calculated as an average of the target-side matchability and the source-side matchability. Cross-domain clusterability between the target domain and the source domain is calculated as a linear combination of the calculated target clusterability and the calculated source-target pair matchability.

Abstract: Methods, systems, and computer program products for determining groups of individuals based on multiple items of individual transaction data are provided herein. A method includes initializing a customer segment value for each of multiple purchased items identified in a purchase record based on one or more historical purchasing patterns; updating the customer segment value for each of the multiple purchased items based on the customer segment values for the other purchased items identified in the purchase record; and determining a customer segment composition of a group of individuals associated with the multiple purchased items identified in the purchase record based on the updated customer segment values.

Abstract: A method comprising using at least one hardware processor for: identifying relations between pairs of claims of a set of claims; aggregating the claims of the set of claims into a plurality of clusters based on the identified relations; generating a plurality of arguments from the plurality of clusters, wherein each of the arguments is generated from a cluster of the plurality of clusters, and wherein each of the arguments comprises at least one claim of the set of claims, scoring each possible set of a predefined number of arguments of the plurality of arguments, based on a quality of each argument of the predefined number of arguments and on diversity between the predefined number of arguments; and generating a speech, wherein the speech comprises a top scoring possible set of the possible set of the predefined number of arguments.

Abstract: Systems and associated methods for enhanced concept understanding in large document collections through phrase clustering are described. Embodiments take as input an initial set of phrases and estimate centroids using a clustering process. Embodiments then generate new phrases around each of the current centroids using the current phrases. These new phrases are added to the current set, and the clustering process is iterated. Upon convergence, embodiments finalize clusters based on phrases of any given length.

Abstract: Determining synonyms of words in a set of documents. Particularly, when provided with a word or phrase as input, in exemplary embodiments there is afforded the return of a predetermined number of “top” synonym words (or phrases) for an input word (or phrase) in a specific collection of text documents. Further, a user is able to provide ongoing and iterative positive or negative feedback on the returned synonym words, by manually accepting or rejecting such words as the process is underway.

Abstract: Systems and associated methods for enhanced concept understanding in large document collections through phrase clustering are described. Embodiments take as input an initial set of phrases and estimate centroids using a clustering process. Embodiments then generate new phrases around each of the current centroids using the current phrases. These new phrases are added to the current set, and the clustering process is iterated. Upon convergence, embodiments finalize clusters based on phrases of any given length.

Abstract: A method and system for evaluating cross-domain clusterability upon a target domain and a source domain. Target clusterability is calculated as an average of a respective clusterability of at least one target data item comprised by the target domain. Target-side matchability is calculated as an average of a respective matchability of each target centroid of the target domain to source centroids of the source domain, wherein the source domain comprises at least one source data item. Source-side matchability is calculated as an average of a respective matchability of each source centroid of said source centroids to the target centroids. Source-target pair matchability is calculated as an average of the target-side matchability and the source-side matchability. Cross-domain clusterability between the target domain and the source domain is calculated as a linear combination of the calculated target clusterability and the calculated source-target pair matchability.

Abstract: A process for evaluating cross-domain clusterability upon a target domain and a source domain. The cross-domain clusterability is calculated as a linear combination of a target clusterability and a source-target pair matchability, by use of a trade-off parameter that determines relative contribution of the target clusterability and the source-target pair matchability. The target clusterability quantifies how clusterable the target domain is. The source-target pair matchability is calculated as an average of a target-side matchability and a source-side matchability, which quantifies how well target centroids of the target domain are aligned with the source centroids and how well source centroids of the source domain are aligned with the target centroids, respectively.

Abstract: A computer system for evaluating cross-domain clusterability upon a target domain and a source domain. The cross-domain clusterability is calculated as a linear combination of a target clusterability and a source-target pair matchability, by use of a trade-off parameter that determines relative contribution of the target clusterability and the source-target pair matchability. The target clusterability quantifies how clusterable the target domain is. The source-target pair matchability is calculated as an average of a target-side matchability and a source-side matchability, which quantifies how well target centroids of the target domain are aligned with the source centroids and how well source centroids of the source domain are aligned with the target centroids, respectively.

Abstract: A computer program product evaluating cross-domain clusterability upon a target domain and a source domain. The cross-domain clusterability is calculated as a linear combination of a target clusterability and a source-target pair matchability, by use of a trade-off parameter that determines relative contribution of the target clusterability and the source-target pair matchability. The target clusterability quantifies how clusterable the target domain is. The source-target pair matchability is calculated as an average of a target-side matchability and a source-side matchability, which quantifies how well target centroids of the target domain are aligned with the source centroids and how well source centroids of the source domain are aligned with the target centroids, respectively.

Abstract: A system and associated method for cross-guided data clustering by aligning target clusters in a target domain to source clusters in a source domain. The cross-guided clustering process takes the target domain and the source domain as inputs. A common word attribute shared by both the target domain and the source domain is a pivot vocabulary, and all other words in both domains are a non-pivot vocabulary. The non-pivot vocabulary is projected onto the pivot vocabulary to improve measurement of similarity between data items. Source centroids representing clusters in the source domain are created and projected to the pivot vocabulary. Target centroids representing clusters in the target domain are initially created by conventional clustering method and then repetitively aligned to converge with the source centroids by use of a cross-domain similarity graph that measures a respective similarity of each target centroid to each source centroid.

Abstract: Systems and associated methods for enhanced concept understanding in large document collections through phrase clustering are described. Embodiments take as input an initial set of phrases and estimate centroids using a clustering process. Embodiments then generate new phrases around each of the current centroids using the current phrases. These new phrases are added to the current set, and the clustering process is iterated. Upon convergence, embodiments finalize clusters based on phrases of any given length.

Abstract: Techniques for identifying one or more inconsistencies between an unstructured document and a back-end fact-base are provided. The techniques include automatically parsing a query document and comparing the document with a back-end fact-base comprising facts relevant to the document, identifying one or more inconsistencies between information mentioned in the document and the facts stored in the back-end fact-base, and providing a response to the query document, wherein the response additionally includes the one or more identified inconsistencies.

Abstract: Systems and associated methods provide a cluster-level semi-supervision model for inter-active clustering. Embodiments accept user provided semi-supervision for updating cluster descriptions and assignment of data items to clusters. Assignment feedback re-assigns data items among existing clusters, while cluster description feedback helps to position existing cluster centers more meaningfully. The feedback can continue until the user is satisfied with the clustering achieved or one or more predetermined stopping criteria have been reached.

Abstract: A computer system for evaluating cross-domain clusterability upon a target domain and a source domain. The cross-domain clusterability is calculated as a linear combination of a target clusterability and a source-target pair matchability, by use of a trade-off parameter that determines relative contribution of the target clusterability and the source-target pair matchability. The target clusterability quantifies how clusterable the target domain is. The source-target pair matchability is calculated as an average of a target-side matchability and a source-side matchability, which quantifies how well target centroids of the target domain are aligned with the source centroids and how well source centroids of the source domain are aligned with the target centroids, respectively.

Abstract: A process for evaluating cross-domain clusterability upon a target domain and a source domain. The cross-domain clusterability is calculated as a linear combination of a target clusterability and a source-target pair matchability, by use of a trade-off parameter that determines relative contribution of the target clusterability and the source-target pair matchability. The target clusterability quantifies how clusterable the target domain is. The source- target pair matchability is calculated as an average of a target-side matchability and a source-side matchability, which quantifies how well target centroids of the target domain are aligned with the source centroids and how well source centroids of the source domain are aligned with the target centroids, respectively.

Abstract: A computer program product evaluating cross-domain clusterability upon a target domain and a source domain. The cross-domain clusterability is calculated as a linear combination of a target clusterability and a source-target pair matchability, by use of a trade-off parameter that determines relative contribution of the target clusterability and the source-target pair matchability. The target clusterability quantifies how clusterable the target domain is. The source-target pair matchability is calculated as an average of a target-side matchability and a source-side matchability, which quantifies how well target centroids of the target domain are aligned with the source centroids and how well source centroids of the source domain are aligned with the target centroids, respectively.

Abstract: A system and associated method for evaluating cross-domain clusterability upon a target domain and a source domain. The cross-domain clusterability is calculated as a linear combination of a target clusterability and a source-target pair matchability, by use of a trade-off parameter that determines relative contribution of the target clusterability and the source-target pair matchability. The target clusterability quantifies how clusterable the target domain is. The source-target pair matchability is calculated as an average of a target-side matchability and a source-side matchability, which quantifies how well target centroids of the target domain are aligned with the source centroids and how well source centroids of the source domain are aligned with the target centroids, respectively.