Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that facilitate identifying entity-attribute relationships in text corpora. Methods include determining whether an attribute in a candidate entity-attribute pair is an actual attribute of the entity in the entity-attribute candidate pair. This includes generating embeddings for words in the set of sentences that include the entity and the attribute and generating, using known entity-attribute pairs. This also includes generating an attribute distributional embedding for the entity based on other attributes associated with the entity from the known entity-attribute pairs, and generating an attribute distributional embedding for the attribute based on known attributes associated with known entities of the attribute in the known entity-attribute pairs.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that facilitate identifying entity-attribute relationships in text corpora. Methods include determining whether an attribute in a candidate entity-attribute pair is an actual attribute of the entity in the entity-attribute candidate pair. This includes generating embeddings for words in the set of sentences that include the entity and the attribute and generating, using known entity-attribute pairs. This also includes generating an attribute distributional embedding for the entity based on other attributes associated with the entity from the known entity-attribute pairs, and generating an attribute distributional embedding for the attribute based on known attributes associated with known entities of the attribute in the known entity-attribute pairs.

Abstract: A computer-implemented method can include receiving, at a computing device having one or more processors, questions and answers, each question having one or more answers, and each question and each answer being associated with a particular user. The method can include receiving, at the computing device, evaluations of the answers from users. The method can include identifying, at the computing device, at least one of textual and contextual features for each answer to obtain sets of features. The method can include generating, at the computing device, a user preference graph indicating relationships between users associated with at least one of the questions, the answers, and the evaluations. The method can also include detecting, at the computing device, whether each specific answer is a deceptive answer based on its set of features and the user preference graph.

Abstract: A computer-implemented method can include receiving, at a computing device having one or more processors, questions and answers, each question having one or more answers, and each question and each answer being associated with a particular user. The method can include receiving, at the computing device, evaluations of the answers from users. The method can include identifying, at the computing device, at least one of textual and contextual features for each answer to obtain sets of features. The method can include generating, at the computing device, a user preference graph indicating relationships between users associated with at least one of the questions, the answers, and the evaluations. The method can also include detecting, at the computing device, whether each specific answer is a deceptive answer based on its set of features and the user preference graph.