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1.发明授权公开(公告)号：US11630953B2
公开(公告)日：2023-04-18
申请号：US16960014
申请日：2019-07-25
发明人： Hongliang Fei , Xu Li , Dingcheng Li , Ping Li
摘要： Described herein are embodiments for end-to-end reinforcement learning based coreference resolution models to directly optimize coreference evaluation metrics. Embodiments of a reinforced policy gradient model are disclosed to incorporate reward associated with a sequence of coreference linking actions. Furthermore, maximum entropy regularization may be used for adequate exploration to prevent a model embodiment from prematurely converging to a bad local optimum. Experiments on datasets compared with state-of-the-art methods verified the effectiveness of embodiments.
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公开(公告)号：US10650305B2
公开(公告)日：2020-05-12
申请号：US15205798
申请日：2016-07-08
申请人： Baidu USA, LLC
发明人： Chaochun Liu , Nan Du , Shulong Tan , Hongliang Fei , Wei Fan
摘要： 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).
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公开(公告)号：US11580415B2
公开(公告)日：2023-02-14
申请号：US16506291
申请日：2019-07-09
申请人： Baidu USA, LLC
发明人： Hongliang Fei , Shulong Tan , Ping Li
IPC分类号： G06F17/00 , G06N5/02 , G06N7/00 , G06F40/247
摘要： Due to the high language use variability in real-life, manual construction of semantic resources to cover all synonyms is prohibitively expensive and may result in limited coverage. Described herein are systems and methods that automate the process of synonymy resource development, including both formal entities and noisy descriptions from end-users. Embodiments of a multi-task model with hierarchical task relationship are presented that learn more representative entity/term embeddings and apply them to synonym prediction. In model embodiments, a skip-gram word embedding model is extended by introducing an auxiliary task “neighboring word/term semantic type prediction” and hierarchically organize them based on the task complexity. In one or more embodiments, existing term-term synonymous knowledge is integrated into the word embedding learning framework. Embeddings trained from the multi-task model embodiments yield significant improvement for entity semantic relatedness evaluation, neighboring word/term semantic type prediction, and synonym prediction compared with baselines.
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公开(公告)号：US11194860B2
公开(公告)日：2021-12-07
申请号：US15207445
申请日：2016-07-11
申请人： Baidu USA, LLC
发明人： Erheng Zhong , Chaochun Liu , Yusheng Xie , Nan Du , Hongliang Fei , Yi Zhen , Yu Cao , Richard Chun Ching Wang , Dawen Zhou , Wei Fan
IPC分类号： G06F16/901 , G16H50/70 , G16H10/20 , G16H50/30 , G16H50/20
摘要： 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.
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公开(公告)号：US11494615B2
公开(公告)日：2022-11-08
申请号：US16368440
申请日：2019-03-28
申请人： Baidu USA, LLC
发明人： Hongliang Fei , Chaochun Liu , Yaliang Li , Ping Li
摘要： 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.
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公开(公告)号：US20180039735A1
公开(公告)日：2018-02-08
申请号：US15226249
申请日：2016-08-02
申请人： Baidu USA, LLC
发明人： Yi Zhen , Hongliang Fei , Shulong Tan , Wei Fan
CPC分类号： G16H10/60 , G06F19/328 , G06Q10/06315 , G16H15/00 , G16H40/20 , G16H50/20
摘要： Presented are systems and methods that allow healthcare providers and governments to infer demand for healthcare resources to ensure effective and timely healthcare services to patients by reducing healthcare supply shortages, emergencies, and healthcare costs. In embodiments, this is accomplished by gathering data from a number of sources to generate labeled records from which entity features and relationships between entities are extracted, correlates, and/or combined with other external healthcare data. In embodiments, this information is used to train a model that predicts healthcare resource demands given a set of input conditions or factors.
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公开(公告)号：US20180025121A1
公开(公告)日：2018-01-25
申请号：US15215393
申请日：2016-07-20
申请人： Baidu USA, LLC
发明人： Hongliang Fei , Shulong Tan , Yi Zhen , Erheng Zhong , Chaochun Liu , Dawen Zhou , Wei Fan
IPC分类号： G06F19/00
摘要： Systems and methods are disclosed provide improved automated extraction of medical-related information. In embodiments, finer-grained medical-related data, such as medical entities, including symptoms, diseases, dimensions, and temporal information, can be extracted. In embodiments, by extracted finer level medical-related information from an input statement and generating visual displays of that information, a medical professional can readily see relevant medical information that provides medical entities and associated dimension information, as well as evolving history.
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公开(公告)号：US11195128B2
公开(公告)日：2021-12-07
申请号：US15226249
申请日：2016-08-02
申请人： Baidu USA, LLC
发明人： Yi Zhen , Hongliang Fei , Shulong Tan , Wei Fan
摘要： Presented are systems and methods that allow healthcare providers and governments to infer demand for healthcare resources to ensure effective and timely healthcare services to patients by reducing healthcare supply shortages, emergencies, and healthcare costs. In embodiments, this is accomplished by gathering data from a number of sources to generate labeled records from which entity features and relationships between entities are extracted, correlates, and/or combined with other external healthcare data. In embodiments, this information is used to train a model that predicts healthcare resource demands given a set of input conditions or factors.
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公开(公告)号：US10372743B2
公开(公告)日：2019-08-06
申请号：US15215492
申请日：2016-07-20
申请人： Baidu USA, LLC
发明人： Shulong Tan , Hongliang Fei , Yi Zhen , Yu Cao , Bocong Liu , Chaochun Liu , Richard Chun Ching Wang , Dawen Zhou , Wei Fan
摘要： 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.
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公开(公告)号：US20180012121A1
公开(公告)日：2018-01-11
申请号：US15205798
申请日：2016-07-08
申请人： Baidu USA, LLC
发明人： Chaochun Liu , Nan Du , Shulong Tan , Hongliang Fei , Wei Fan
CPC分类号： G06N3/0445 , G06F16/332 , G06F16/36 , G06F16/93 , G06N3/08 , G06N5/022
摘要： 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).
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