Abstract: The present invention discloses a missing semantics complementing method in the field of natural language processing in the artificial intelligence field, including: obtaining a question statement and a historical dialog statement; resolving a to-be-resolved item in the question statement based on the historical dialog statement and location information of the to-be-resolved item, to obtain a resolved question statement; determining whether a component in the question statement is ellipted, and if a component in the question statement is ellipted, complementing the ellipted component based on the historical dialog statement, to obtain a question statement after ellipsis resolution; merging the resolved question statement and the question statement after ellipsis resolution, to obtain a merged question statement; and determining a target complemented question statement from the resolved question statement, the question statement after ellipsis resolution, and the merged question statement.

Abstract: Provided are systems and methods related to converting unlabeled data into structured and labeled data for answering one or more single-entity-single-relation questions. The systems and methods automates the labeling of data to generate training data for machine learning. The systems and methods identify and import question and answer pairs from an user generated discussion platform and access a knowledge base questions to extract questions by supervised extraction. The extracted questions are further filtered to remove mislabeled questions. When a question is posed, it is parsed for entity and relation, and an answer is identified by searching through the knowledge base.

Abstract: Provided are systems and methods related to converting unlabeled data into structured and labeled data for answering one or more single-entity-single-relation questions. The systems and methods automates the labeling of data to generate training data for machine learning. The systems and methods identify and import question and answer pairs from an user generated discussion platform and access a knowledge base questions to extract questions by supervised extraction. The extracted questions are further filtered to remove mislabeled questions. When a question is posed, it is parsed for entity and relation, and an answer is identified by searching through the knowledge base.

Abstract: A computer-implemented apparatus is provided for generating a response string based at least on a received inquiry string using a recurrent neural network (RNN) encoder-decoder architecture, the apparatus comprising: a first RNN configured to receive the inquiry string as a sequence of vectors x and to encode a sequence of symbols into a fixed length vector representation, vector c; a contextual neural network (CNN) for inferring topic distribution from a training set having a plurality of training questions and a plurality of training labels, the CNN configured to extract word features, compute syntactic features and infer semantic representation based on interconnections derived from the training set to generate a fixed length topic vector representation of a probability distribution in a topic space, the topic space inferred from a concatenated utterance of historical conversation; and a second RNN used as a RNN contextual decoder for estimating a conditional probability distribution of a plurality of resp