Beijing Baidu Netcom Science and Technology Co., Ltd.

Baidu USA LLC

Other

Petrochina Company Limited

Jingyuan Zhang

Di Wu

Jia Zhao

Jingbo Fan

Yuan Ye

Ruili Qiao

Ping Li

Dingcheng Li

Xin Wang

Ning Zhang

Mingming Sun

Qian Chen

Sihan Wang

Gang Qiao

Yue Feng

Yongjun Zhang

Hongxia Chen

Baojun Zhang

Guizhi Wang

Deshun Zhang

Shukun Sun

Jianzhong Li

Yunguang Han

Kefeng Meng

Yongcheng Sun

Yulong Li

Huimin Yuan

Xiao Huang

Siamak Zamani Dadaneh

Qiaofei Wang

Dongting Kuang

Yingnan Ning

Lihua Xing

Defu He

Rui Meng

Xiwu Zhao

Shulan Wang

Shuqing Liu

Yingjie Feng

Yingzi Guo

Chunfeng He

Hongbo Ren

Mobile phone display screen with graphical user interface

Described herein are embodiments for a deep level-wise extreme multi-label learning and classification (XMLC) framework to facilitate the semantic indexing of literatures. In one or more embodiments, the Deep Level-wise XMLC framework comprises two sequential modules, a deep level-wise multi-label learning module and a hierarchical pointer generation module. In one or more embodiments, the first module decomposes terms of domain ontology into multiple levels and builds a special convolutional neural network for each level with category-dependent dynamic max-pooling and macro F-measure based weights tuning. In one or more embodiments, the second module merges the level-wise outputs into a final summarized semantic indexing. The effectiveness of Deep Level-wise XMLC framework embodiments is demonstrated by comparing it with several state-of-the-art methods of automatic labeling on various datasets.

Systems and methods for large scale semantic indexing with deep level-wise extreme multi-label learning

Described herein are embodiments for question answering over knowledge graph using a Knowledge Embedding based Question Answering (KEQA) framework. Instead of inferring an input questions' head entity and predicate directly, KEQA embodiments target jointly recovering the question's head entity, predicate, and tail entity representations in the KG embedding spaces. In embodiments, a joint distance metric incorporating various loss terms is used to measure distances of a predicated fact to all candidate facts. In embodiments, the fact with the minimum distance is returned as the answer. Embodiments of a joint training strategy are also disclosed for better performance. Performance evaluation on various datasets demonstrates the effectiveness of the disclosed systems and methods using the KEQA framework.

Knowledge-graph-embedding-based question answering

Leveraging domain knowledge is an effective strategy for enhancing the quality of inferred low-dimensional representations of documents by topic models. Presented herein are embodiments of a Bayesian nonparametric model that employ knowledge graph (KG) embedding in the context of topic modeling for extracting more coherent topics; embodiments of the model may be referred to as topic modeling with knowledge graph embedding (TMKGE). TMKGE embodiments are hierarchical Dirichlet process (HDP)-based models that flexibly borrow information from a KG to improve the interpretability of topics. Also, embodiments of a new, efficient online variational inference method based on a stick-breaking construction of HDP were developed for TMKGE models, making TMKGE suitable for large document corpora and KGs. Experiments on datasets illustrate the superior performance of TMKGE in terms of topic coherence and document classification accuracy, compared to state-of-the-art topic modeling methods.

Integration of knowledge graph embedding into topic modeling with hierarchical Dirichlet process

Described herein are embodiments of a unified neural network framework to integrate Topic modeling, Word embedding and Entity Embedding (TWEE) for representation learning of inputs. In one or more embodiments, a novel topic sparse autoencoder is introduced to incorporate discriminative topics into the representation learning of the input. Topic distributions of inputs are generated from a global viewpoint and are utilized to enable autoencoder to learn topical representations. A sparsity constraint may be added to ensure that the most discriminative representations are related to topics. In addition, both words and entity related information may be embedded into the network to help learn a more comprehensive input representation. Extensive empirical experiments show that embodiments of the TWEE framework outperform the state-of-the-art methods on different datasets.

Representation learning for input classification via topic sparse autoencoder and entity embedding

Described herein are embodiments for systems and methods for mutual machine learning with global topic discovery and local word embedding. Both topic modeling and word embedding map documents onto a low-dimensional space, with the former clustering words into a global topic space and the latter mapping word into a local continuous embedding space. Embodiments of Topic Modeling and Sparse Autoencoder (TMSA) framework unify these two complementary patterns by constructing a mutual learning mechanism between word co-occurrence based topic modeling and autoencoder. In embodiments, word topics generated with topic modeling are passed into auto-encoder to impose topic sparsity for the autoencoder to learn topic-relevant word representations. In return, word embedding learned by autoencoder is sent back to topic modeling to improve the quality of topic generations. Performance evaluation on various datasets demonstrates the effectiveness of the disclosed TMSA framework in discovering topics and embedding words.

Systems and methods for mutual learning for topic discovery and word embedding

Mobile phone or portion thereof with graphical user interface

Growing community of inventors

Beijing, China

Jingyuan Zhang