Baidu USA LLC

Chaochun Liu

Hongliang Fei

Wei Fan

Yu Long Cao

Shulong Tan

Yi Zhen

Nan Du

Dawen Zhou

Richard Chun Ching Wang

Ping Li

Yusheng Xie

Erheng Zhong

Yaliang Li

Bocong Liu

Described herein are embodiments for systems and methods to incorporate skip-gram convolution to extract non-consecutive local n-gram patterns for comprehensive information for varying text expressions. In one or more embodiments, one or more recurrent neural networks are employed to extract long-range features from localized level to sequential and global level via a chain-like architecture. Comprehensive experiments on large-scale datasets widely used for the text classification task were conducted to demonstrate the effectiveness of the presented deep skip-gram network embodiments. Performance evaluation on various datasets demonstrates that embodiments of the skip-gram network are powerful for general text classification task set. The skip-gram models are robust and may be generalized well on different datasets, even without tuning the hyper-parameters for specific dataset.

Systems and methods for deep skip-gram network based text classification

Systems and methods are disclosed for question generation to obtain more related medical information based on observed symptoms from a patient. In embodiments, possible diseases associated with the observed symptoms are generated by querying a knowledge graph. In embodiments, candidate symptoms associated with the possible diseases are also identified and are combined with the observed symptoms to obtain combined symptom sets. In embodiments, discriminative scores for the candidate symptom sets are determined and candidate symptoms with top discriminative scores are selected. In embodiments, these selected candidate symptoms may be checked for conflicts with observed symptoms and removed from further consideration if a conflict exists. In embodiments, one or more questions may be generated based on the remaining selected candidate systems to aid in collecting information about the patient. In embodiments, the process may be repeated with the updated observed symptoms.

Question generation systems and methods for automating diagnosis

Presented are relation inference methods and systems that use deep learning techniques for data mining documents to discover a relation between terms of interest in a given field covering a specific topic. For example, in the healthcare domain, various embodiments of the present disclosure provide for a relation inference system that mines large-scale medical documents in a free-text database to extract symptom and disease terms and generates relation information that aids in disease diagnosis. In embodiments, this is accomplished by training and using an RNN, such as an LSTM, a Gated Recurrent Unit (GRU), etc., that takes advantage of a term dictionary to examine co-occurrences of terms of interest within documents to discover correlations between the terms. The correlation may then be used to predict statistically most probable terms (e.g., a disease) related to a given search term (e.g., a symptom).

Systems and methods for relation inference

Systems and methods are disclosed to identify entities that have a similar meaning, and may, in embodiments, be grouped into entity groups for knowledge base construction. In embodiments, the entity relations of similarity or non-similarity for an entity pair are predicted as a binary relationship. In embodiments, the prediction may be based upon similarity score between the entities and the entity features, which features are constructed using an entity feature or representation model. In embodiments, the prediction may be an iterative process involving minimum human checking and existing knowledge update. In embodiments, one or more entity groups are formed using graph search from the predicted entity pairs. In embodiments, a group centroid entity may be selected to represent each group based on one or more factors, such as its generality or popularity.

Systems and methods for homogeneous entity grouping

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San Jose, CA, United States of America

Chaochun Liu