MACHINE LEARNING TECHNIQUES FOR SEMANTIC PROCESSING OF STRUCTURED NATURAL LANGUAGE DOCUMENTS TO DETECT ACTION ITEMS
Various embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured to accurately and concisely generate one or more action item logs of one or more document data objects. For example, certain embodiments of the present invention provide methods, apparatuses, systems, computing devices, and/or the like that are configured to generate an action item log of a document data object comprising one or more semantically complete or incomplete units of text data, by generating content segmentation units, determining action item presence predictions, generating action item sets from each content segmentation unit within a candidate action item subset, aggregating the action item sets to create an action item log, and storing the action item log.
1 - 20 . (canceled)
21 . An apparatus for generating an action item log user interface element for a webpage that displays content data associated with a document data object, the apparatus comprising at least one processor and at least one memory including program code, the at least one memory and the program code configured to, with the at least one processor, cause the apparatus to at least:
parse the content data to identify a plurality of content segmentation units;
apply an action item classification machine learning model to each content segmentation unit of the plurality of content segmentation units to determine an action item presence prediction for each content segmentation unit, wherein the action item classification machine learning model comprises a Bidirectional Encoder Representations from Transformers (BERT) model;
determine, based on each action item presence prediction for each respective content segmentation unit, a candidate action item subset of the plurality of content segmentation units;
apply an action item extraction machine learning model to the candidate action item subset to generate an action item set; and
generate one or more action item log user interface elements configured for rendering to a computing device display based on the action item set.
22 . The apparatus of claim 21 , wherein the plurality of content segmentation units comprise one or more sentences of the document data object.
23 . The apparatus of claim 21 , wherein the document data object is a structured document data object that is associated with a structural scheme, and wherein the plurality of content segmentation units comprise one or more predefined structural elements of the document data object that are determined based on the structural scheme.
24 . The apparatus of claim 21 , wherein the action item extraction machine learning model comprises a BERT model.
25 . The apparatus of claim 21 , wherein the action item extraction machine learning model comprises a part-of-speech tagger model that is configured to generate a part-of-speech tag sequence for each content segmentation unit, and (ii) a sequence processing model that is configured to generate the action item set based on the part-of-speech tag sequence.
26 . The apparatus of claim 25 , wherein the sequence processing model is a BERT model.
27 . The apparatus of claim 25 , wherein the sequence processing model is characterized by one or more action item detection regular expression rules.
28 . The apparatus of claim 21 , wherein the action item classification machine learning model is trained based on a plurality of training document data objects stored to a storage subsystem, wherein each training document data object of the plurality of training document data objects is a structured document data object that is associated with a structural scheme.
29 . A computer-implemented method for generating an action item log user interface element for a webpage that displays content data associated with a document data object, the computer-implemented method comprising:
parsing the content data to identify a plurality of content segmentation units;
applying an action item classification machine learning model to each content segmentation unit of the plurality of content segmentation units to determine an action item presence prediction for each content segmentation unit, wherein the action item classification machine learning model comprises a Bidirectional Encoder Representations from Transformers (BERT) model;
determining, based on each action item presence prediction for each respective content segmentation unit, a candidate action item subset of the plurality of content segmentation units;
applying an action item extraction machine learning model to the candidate action item subset to generate an action item set; and
generating one or more action item log user interface elements configured for rendering to a computing device display based on the action item set.
30 . The computer-implemented method of claim 29 , wherein the plurality of content segmentation units comprise one or more sentences of the document data object.
31 . The computer-implemented method of claim 29 , wherein the document data object is a structured document data object that is associated with a structural scheme, and wherein the plurality of content segmentation units comprise one or more predefined structural elements of the document data object that are determined based on the structural scheme.
32 . The computer-implemented method of claim 29 , wherein the action item extraction machine learning model comprises a BERT model.
33 . The computer-implemented method of claim 29 , wherein the action item extraction machine learning model comprises a part-of-speech tagger model that is configured to generate a part-of-speech tag sequence for each content segmentation unit, and (ii) a sequence processing model that is configured to generate the action item set based on the part-of-speech tag sequence.
34 . The computer-implemented method of claim 33 , wherein the sequence processing model is a BERT model.
35 . The computer-implemented method of claim 33 , wherein the sequence processing model is characterized by one or more action item detection regular expression rules.
36 . The computer-implemented method of claim 29 , wherein the action item classification machine learning model is trained based on a plurality of training document data objects stored to a storage subsystem, wherein each training document data object of the plurality of training document data objects is a structured document data object that is associated with a structural scheme.
37 . A computer program product for generating action item log user interface element for a webpage that displays content data associated with a document data object, the computer program product comprising at least one non-transitory computer-readable storage medium having computer-readable program code portions stored therein, the computer-readable program code portions configured to:
parse the content data to identify a plurality of content segmentation units;
apply an action item classification machine learning model to each content segmentation unit of the plurality of content segmentation units to determine an action item presence prediction for each content segmentation unit, wherein the action item classification machine learning model comprises a Bidirectional Encoder Representations from Transformers (BERT) model;
determine, based on each action item presence prediction for each respective content segmentation unit, a candidate action item subset of the plurality of content segmentation units;
apply an action item extraction machine learning model to the candidate action item subset to generate an action item set; and
generate one or more action item log user interface elements configured for rendering to a computing device display based on the action item set.
38 . The computer program product of claim 37 , wherein the plurality of content segmentation units comprise one or more sentences of the document data object.
39 . The computer program product of claim 37 , wherein the document data object is a structured document data object that is associated with a structural scheme, and wherein the plurality of content segmentation units comprise one or more predefined structural elements of the document data object that are determined based on the structural scheme.
40 . The computer program product of claim 37 , wherein the action item classification machine learning model is trained based on a plurality of training document data objects stored to a storage subsystem, wherein each training document data object of the plurality of training document data objects is a structured document data object that is associated with a structural scheme.