BACKGROUND

The present invention relates, generally, to the field of computing, and more particularly to question answering (QA) in a natural language.

QA may be a computer system that combines information retrieval and natural language processing (NLP) capabilities and enables the location of answers to a specific question from an unstructured collection of natural language documents. Typically, a QA system extracts pertinent information from the question and generates a set of candidate answers while each set has a confidence score. QA may determine the best answer using a correlation between the confidence score and the candidate answer. For example, answers with high associated confidence scores may be rated a better quality.

SUMMARY

According to one embodiment, a method, computer system, and computer program product for determining a precise answer to a question using a latent token representation technique is provided. The present invention may receive the question, a plurality of the candidate answers, and a plurality of documents associated with the plurality of candidate answers in the natural language. Then the present invention may tokenize the question, the plurality of the candidate answers, and the plurality of the documents into a corresponding n-gram sequence. The present invention may map n-gram elements from the tokenized question to the n-gram elements of the plurality of the tokenized candidate answers and the plurality of the tokenized documents using the latent token representation technique. The present invention may score the plurality of tokenized candidate answers based on the latent token representation technique. Then, the present invention may determine the precise answer based on the plurality of scored candidate answers.

