Automated question generation using semantics and deep learning
AssigneeSap SE
InventorsJayananda Appanna Kotri, Abinaya S, Yashwanth Dasari, Sharad Kejriwal, Tarun Sharma
Life of this patent
Estimating the term…
Abstract
In an example embodiment, factual question generation from freeform content is achieved through semantic role labeling and recurrent neural networks (RNNs). Specifically, semantic role labeling is used to identify an answer phrase so that it can be replaced with an appropriate question word. RNNs are then used to extract triples (Subject-Object-Predicate) from the sentence, and each of these triples can be used as an answer phrase/word. An RNN is then fed with training data to generate the questions more efficiently.
Claims20
A system comprising:
- a memory;
- and
- a computer-readable medium having instructions stored thereon, which, when executed by a processor, cause the system to perform operations comprising:
- obtain computerized textual input;
- tokenize the computerized textual input into a plurality of sentences;
- identify keywords in the plurality of sentences;
- generate a summary of the computerized textual input by automatically selecting a predetermined percentage of sentences from the plurality of sentences using the identified keywords;
- for each sentence in the summary:
- for each keyword in the sentence:
- replace the keyword with a gap;
- use a semantic-based approach to transform the gap-filled sentence into a question;
- and
- use a recurrent neural network approach to transform the gap-filled sentence into a question;
- compute an intersection between questions generated using the semantic-based approach and questions generated using the recurrent neural network approach;
- and
- cause one or more of the questions in the intersection to be presented in a graphical user interface.
A method comprising:
- obtaining computerized textual input;
- tokenizing the computerized textual input into a plurality of sentences;
- identifying keywords in the plurality of sentences;
- generating a summary of the computerized textual input by automatically selecting a predetermined percentage of sentences from the plurality of sentences using the identified keywords;
- for each sentence in the summary:
- for each keyword in the sentence:
- replacing the keyword with a gap;
- using a semantic-based approach to transform the gap-filled sentence into a question;
- and
- using a recurrent neural network approach to transform the gap-filled sentence into a question;
- computing an intersection between questions generated using the semantic-based approach and questions generated using the recurrent neural network approach;
- and
- causing one or more of the questions in the intersection to be presented in a graphical user interface.
A non-transitory machine-readable storage medium comprising instructions which, when implemented by one or more machines, cause the one or more machines to perform operations comprising:
- obtaining computerized textual input;
- tokenizing the computerized textual input into a plurality of sentences;
- identifying keywords in the plurality of sentences;
- generating a summary of the computerized textual input by automatically selecting a predetermined percentage of sentences from the plurality of sentences using the identified keywords;
- for each sentence in the summary:
- for each keyword in the sentence:
- replacing the keyword with a gap;
- using a semantic-based approach to transform the gap-filled sentence into a question;
- and
- using a recurrent neural network approach to transform the gap-filled sentence into a question;
- computing an intersection between questions generated using the semantic-based approach and questions generated using the recurrent neural network approach;
- and
- causing one or more of the questions in the intersection to be presented in a graphical user interface.
Dependencies are inferred from the claim text.