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Es-Tacotron2_Multi-Task_Tacotron_2_with_Pre-Traine

attention mechanisms with tensorflow

Recent research has shown great progress on fine-grained entity typing. Most existing methods require pre-defining a set of types and training a multi-class classifier from a large labeled data set based on multi-level linguistic features. They are thus limited to certain domains, genres and languages. In this paper, we pro- pose a novel unsupervised entity typing framework by combin- ing symbolic and distributional semantics. We start from learn- ing general embeddings for each entity mention, compose the em- beddings of specific contexts using linguistic structures, link the mention to knowledge bases and learn its related knowledge rep- resentations. Then we develop a novel joint hierarchical clustering and linking algorithm to type all mentions using these representa- tions. This framework doesn’t rely on any annotated data, prede- fined typing schema, or hand-crafted features, therefore it can be quickly adapted to a new domain, genre and language. Further- more, it has great flexibility at incorporating linguistic structures (e.g., Abstract Meaning Representation (AMR), dependency rela- tions) to improve specific context representation. Experiments on genres (news and discussion forum) show comparable performance with state-of-the-art supervised typing systems trained from a large amount of labeled data. Results on various languages (English, Chinese, Japanese, Hausa, and Yoruba) and domains (general and biomedical) demonstrate the portability of our framework.

电商鹰眼不同版本（1到8期）开发需求说明书 电商鹰眼不同版本（1到8期）开发需求说明书

Supervised Sequence Labelling with Recurrent Neural Networks

大数据+常雷-新一代数据仓库：Apache+HAWQ 大数据+常雷-新一代数据仓库：Apache+HAWQ

大数据+曹永鹏-Mobike大数据平台建设，大数据+曹永鹏-Mobike大数据平台建设

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