FIELD

The field relates generally to information processing, and more particularly to techniques for managing data.

BACKGROUND

In many information processing systems, data stored electronically is in an unstructured format, with documents comprising a large portion of unstructured data. Collection and analysis, however, may be limited to highly structured data, as unstructured text data requires special treatment. For example, unstructured text data may require manual screening in which a corpus of unstructured text data is reviewed and sampled by service personnel. Alternatively, the unstructured text data may require manual customization and maintenance of a large set of rules that can be used to determine correspondence with predefined themes of interest. Such processing is unduly tedious and time-consuming, particularly for large volumes of unstructured text data.

SUMMARY

Illustrative embodiments of the present invention provide techniques for iterative application of a machine learning-based information extraction model to documents having unstructured text data.

In one embodiment, an apparatus comprises at least one processing device comprising a processor coupled to a memory. The at least one processing device is configured to perform the steps of receiving a query to extract information from a document, the document comprising unstructured text data, and performing two or more iterations of utilizing a machine learning-based information extraction model to extract portions of unstructured text data from the document. In each of the two or more iterations, the machine learning-based information extraction model provides as output a portion of the unstructured text data extracted from the document and a relevance score associated with the portion of the unstructured text data extracted from the document in that iteration. In a first one of the two or more iterations, the machine learning-based information extraction model takes as input the query and the document. In subsequent ones of the two or more iterations, the machine learning-based information extraction model takes as input the query and a modified version of the document with one or more portions of the unstructured text data of the document extracted in one or more previous ones of the two or more iterations removed therefrom. The at least one processing device is also configured to perform the steps of determining whether the portions of the unstructured text data extracted from the document in the two or more iterations have an associated relevance score exceeding a threshold relevance score and at least a threshold level of similarity to the query and generating a response to the query, the response to the query comprising a subset of the portions of the unstructured text data extracted from the document in the two or more iterations determined to have associated relevance scores exceeding the threshold relevance score and at least the threshold level of similarity to the query.

These and other illustrative embodiments include, without limitation, methods, apparatus, networks, systems and processor-readable storage media.

