The present disclosure relates to systems and methods for matching electronic activities with record objects based on entity relationships. The method can include accessing a plurality of electronic activities, identifying an electronic activity, identifying a first participant associated with a first entity and a second participant associated with a second entity, determining whether a record object identifier is included in the electronic activity, identifying a first record object of the system of record that includes an instance of the record object identifier, and storing an association between the electronic activity and the first record object. The method can include determining a second record object corresponding to the second entity, identifying, using a matching policy, a third record object linked to the second record object and identifying a third entity, and storing, by the one or more processors, an association between the electronic activity and the third record object.

Techniques for building a machine learning framework with tracking, model building and maintenance, and feedback loop are provided. In one technique, a prediction model is generated based on features of multiple entities. For each entity indicated in a first database, multiple feature values are identified, which include feature values stored in the first database and feature values based on sub-entity data regarding individuals associated with the entity. The feature values are input into the prediction model to generate a score for the entity. Based on the score, a determination is made whether to add, to a second database, a record for that entity. The second database is analyzed to identify other entities. For each such entity, a determination is made whether to generate a training instance; if so, a training instance is generated and added to training data, which is used to generate another prediction model.

Exemplary embodiments relate to a system for recovering a conversation between a user and the system when the system is unable to properly respond to a user's input. The system may process the user input and determine an error condition exists. The system may query one or more storage systems to identify candidate text data based on their semantic similarity to the user input. The storage systems may store data related to past frequently entered inputs and/or user-generated inputs. Alternative text data is selected from the candidate text data, and presented to the user for confirmation.

Systems, devices, and methods of the present invention create a searchable index that includes informative portions of text. In an example, a computer-implemented method creates a discourse tree from a body of text. For each non-terminal node in the discourse tree, the method identifies a rhetorical relationship associated with the non-terminal node. The method labels each terminal node associated with the non-terminal node as either a nucleus or a satellite. The method further accesses a rule associated with the rhetorical relationship, and selects, based on the rule, selects the fragment associated with the nucleus. The method creates a searchable index including the selected fragments.

This disclosure enables various technologies that can (1) learn new synonyms for a given concept without manual curation techniques, (2) relate (e.g., map) some, many, most, or all raw named entity recognition outputs (e.g., “United States”, “United States of America”) to ontological concepts (e.g., ISO-3166 country code: “USA”), (3) account for false positives from a prior named entity recognition process, or (4) aggregate some, many, most, or all named entity recognition results from machine learning or rules based approaches to provide a best of breed hybrid approach (e.g., synergistic effect).

The present disclosure involves computer-implemented method, medium, and system for automatically correlating semantically connected data and metadata. One example method includes identifying a document that is to be analyzed using a semantics based mapping (SBM) infrastructure. A matching process is performed for the identified document using the SBM infrastructure, where the matching process identifies a plurality of matching terms within the document, the plurality of matching terms are assigned to a plurality of semantics identifiers (IDs), and each semantics ID corresponds to one or more terms in the plurality of matching terms. Each of the plurality of matching terms is replaced with a respective term ID to generate an updated document. A request to search for a target term in the document is received. The target term is translated to a target term ID based on the SBM infrastructure. The updated document is searched for one or more matching terms.

An approach is provided for using parsing rules to automatically identify attributes and attribute values from documents and generate metadata that maps attribute values to display labels that may be searched, filtered, and sorted upon within an external storage service. A document processing system maintains parsing rules that define how to identify field labels, which represent attributes, and corresponding field values, which represent attribute values, and metadata mappings that map associations between field values and display labels. The display labels are used within the graphical user interface of the external storage service. The system receives a batch of multiple documents and uses the parsing rules to identify field labels and field values. The system generates metadata using the defined metadata mappings and associates the metadata to the documents processed. The system then sends the documents and their associated metadata to the external storage service for storage.

A computer-implemented method for extracting semantic relations is disclosed. In the method, a plurality of hierarchal structures that originates from a corpus of documents is obtained. Each hierarchal structure includes a plurality of elements having respective recitations included in a corresponding document. In the method, for each predetermined relationship between ancestor and descendant elements in the hierarchal structures, a first keyword list is extracted from the ancestor element and a second keyword list is extracted from the descendant element. A statistical index is calculated for each pair of first and second keywords using the first keyword lists and the second keyword lists. The index indicates a strength of association between the first and second keywords. In the method, a candidate list of keyword pairs having semantic relationships is output using the statistical index calculated for each pair.

A computer system processes a group of inputs. A group of entities that is input for processing is intercepted. The intercepted group is expanded into individual entities. Each of the individual entities is processed to produce results for each individual entity. The results for each individual entity are intercepted and merged to produce results for the group of entities. Embodiments of the present invention further include a method and program product for processing a group of inputs in substantially the same manner described above.

Intent-resolution using a phrase index may include obtaining data expressing a usage intent, the data expressing the usage intent including an unresolved data portion, identifying a phrase fragment based on the data expressing the usage intent and a defined phrase pattern, the phrase fragment including the unresolved data portion, identifying, by a processor, an indexed phrase as a candidate phrase by searching a phrase index based on the phrase fragment, wherein the candidate phrase at least partially matches the phrase fragment in accordance with the defined phrase pattern, and outputting the candidate phrase for presentation to a user as a candidate for resolving the unresolved portion.