实例介绍
【实例简介】
这是关于知识图谱的入门论文（国内外经典论文），有助于大家快速的入门知识图谱
【实例截图】
【核心代码】
47a1da42-c7be-4619-a790-9eafb1067ae1
├── 事件抽取
│ ├── 2006-ACL-The stages of event extraction.pdf
│ ├── 2008-ACL-Refining event extraction through cross-document inference.pdf
│ ├── 2011-ACL-Using cross-entity inference to improve event extraction.pdf
│ ├── 2013-ACL-Joint event extraction via structured prediction with global features.pdf
│ ├── 2015-ACL-vent extraction via dynamic multi-pooling convolutional neural networks.pdf
│ ├── 2016-ACL-A language independent neural network for event detection.pdf
│ ├── 2016-NAACL-Joint event extraction via recurrent neural networks.pdf
│ └── 2018-EMNLP-Collective event detection via a hierarchical and bias tagging networks with gated multi-level attention mechanisms.pdf
├── 关系抽取
│ ├── 2012-ACL-Reducing wrong labels in distant supervision for relation extraction.pdf
│ ├── 2014-COLING-Relation Classification via Convolutional Deep Neural Network.pdf
│ ├── 2014-EMNLP-Modeling Joint Entity and Relation Extraction with Table Representation.pdf
│ ├── 2015-EMNLP-Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Paths.pdf
│ ├── 2015-EMNLP-Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks.pdf
│ ├── 2015-EMNLP-Semantic Relation Classification via Convolutional Neural Networks with Simple Negative Sampling.pdf
│ ├── 2016-ACL-Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification.pdf
│ ├── 2016-ACL-Neural Relation Extraction with Selective Attention over Instance.pdf
│ ├── 2017-ACL-Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme.pdf
│ └── 2018-ACL-Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism.pdf
├── 命名实体识别
│ ├── 2013-AAAI-Effective bilingual constraints for semi-supervised learning of named entity recognizers.pdf
│ ├── 2013-NAACL-Named entity recognition with bilingual constraints.pdf
│ ├── 2016-ACL-Combining discrete and neural features for sequence labeling.pdf
│ ├── 2016-ACL-End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF.pdf
│ ├── 2016-ACL-Improving named entity recognition for chinese social media with word segmentation representation learning.pdf
│ ├── 2016-NAACL-Neural Architectures for Named Entity Recognition.pdf
│ ├── 2016-TACL-Named Entity Recognition with Bidirectional LSTM-CNNs.pdf
│ ├── 2017-AAAI-A unified model for cross-domain and semi-supervised named entity recognition in chinese social media.pdf
│ ├── 2017-ACL-Semi-supervised sequence tagging with bidirectional language models.pdf
│ ├── 2017-EACL-F-score driven max margin neural network for named entity recognition in chinese social media.pdf
│ ├── 2017-ICLR-Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks.pdf
│ ├── 2018-AAAI-Empower Sequence Labeling with Task-Aware Neural Language Model.pdf
│ ├── 2018-ACL-Chinese ner using lattice lstm.pdf
│ ├── 2018-ACL-Hybrid semi-Markov CRF for Neural Sequence Labeling.pdf
│ ├── 2018-Adversarial transfer learning for chinese named entity recognition with selfattention mechanism.pdf
│ ├── 2018-COLING-Contextual String Embeddings for Sequence Labeling.pdf
│ ├── 2018-COLING-Robust Lexical Features for Improved Neural Network Named-Entity Recognition.pdf
│ ├── 2019-ACL-Distantly Supervised Named Entity Recognition using Positive-Unlabeled Learning.pdf
│ └── 2019-NAACL-CAN-NER：convolutional attention network for chinese named entity recognition.pdf
├── 实体消歧
│ ├── 2006-EACL-Using Encyclopedic Knowledge for Named entity Disambiguation.pdf
│ ├── 2007-EMNLP-CoNLL-Large-scale named entity disambiguation based on Wikipedia data.pdf
│ ├── 2009-CIKM-Named Entity Disambiguation by Leveraging Wikipedia Semantic Knowledge.pdf
│ ├── 2011-ACL-HLT-Local and Global Algorithms for Disambiguation to Wikipedia.pdf
│ ├── 2011-EMNLP-Robust Disambiguation of Named Entities in Text.pdf
│ ├── 2011-IJCNLP-Cross-Language Entity Linking.pdf
│ ├── 2011-SIGIR-Collective entity linking in web text：a graph-based method.pdf
│ ├── 2014-TACL-Entity linking meets word sense disambiguation：a unified approach.pdf
│ ├── 2016-ACL-Cross-lingual Wikification Using Multilingual Embeddings.pdf
│ ├── 2016-CoNLL-Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation.pdf
│ ├── 2017-EMNLP-Deep Joint Entity Disambiguation with Local Neural Attention.pdf
│ └── 2018-AAAI-DeepType：Multilingual Entity Linking by Neural Type System Evolution.pdf
├── 知识表示
│ ├── 2010-Relational retrieval using a combination of path-contrained random walks.pdf
│ ├── 2013-NIPS-Translating embeddings for modeling multi-relational data.pdf
│ ├── 2013-WWW-AMIE：association rule mining under incomplete evidence in ontological knowledge bases.pdf
│ ├── 2014-EMNLP-Knowledge graph and text jointly embedding.pdf
│ ├── 2015-AAAI-Learning entity and relation embeddings for knowledge graph completion.pdf
│ ├── 2015-ACL-Knowledge graph embedding via dynamic mapping matrix.pdf
│ ├── 2015-CIKM-Learning to represent knowledge graphs with gaussian embedding.pdf
│ ├── 2015-EMNLP-Modeling relation paths for representation learning of knowledge bases.pdf
│ ├── 2015-TransG：A generative mixture model for knowledge graph embedding.pdf
│ └── 2016-AAAI-Knowledge graph completion with adaptive sparse transfer matrix.pdf
├── 综述及报告
│ ├── 2017-Paulheim, H. Knowledge Graph Refinement：A Survey of Approaches and Evaluation Methods.pdf
│ ├── 知识图谱发展报告（2018）-中国中文信息学会.pdf
│ ├── 知识图谱技术综述.pdf
│ ├── 知识图谱研究综述-李涓子.pdf
│ └── 知识图谱研究进展-漆桂林.pdf
└── 问答系统
├── 2005-UAI-Learning to map sentences to logical form：Structured classification with probabilistic categorial grammars.pdf
├── 2006-NAACL-Learning for Semantic Parsing with Statistical Machine Translation.pdf
├── 2009-ACL-Learning a Compositional Semantic Parser using an Existing Syntactic Parser.pdf
├── 2012-WWW-Template-based Question Answering over RDF Data.pdf
├── 2013-ACL-Paraphrase-driven learning for open question answering.pdf
├── 2014-ACL-Information Extraction over Structured Data Question Answering with Freebase.pdf
├── 2014-EMNLP-Question Answering with Subgraph Embeddings.pdf
├── 2015-ACL-Question answering over freebase with multi-column convolutional neural networks.pdf
├── 2016-ACL-Language to Logical Form with Neural Attention.pdf
├── 2016-ACL-Sequence-based Structured Prediction for Semantic Parsing.pdf
├── 2017-ACL-Search-based neural structured learning for sequential question answering.pdf
├── 2017-EMNLP-Neural Semantic Parsing with Typed Constraints for Semi-Structured Tables.pdf
├── 2017-TKDE-Answering Natural Language Questions by Subgraph Matching over Knowledge Graphs.pdf
├── 2018-ACL-Coarse-to-Fine Decoding for Neural Semantic Parsing.pdf
├── 2018-EMNLP-A State-transition Framework to Answer Complex Questions over Knowledge Base.pdf
├── 2018-EMNLP-Knowledge Base Question Answering via Encoding of Complex Query Graphs.pdf
└── 2018-EMNLP-SimpleQuestions Nearly Solved：A New Upperbound and Baseline Approach.pdf
7 directories, 81 files
这是关于知识图谱的入门论文（国内外经典论文），有助于大家快速的入门知识图谱
【实例截图】
【核心代码】
47a1da42-c7be-4619-a790-9eafb1067ae1
├── 事件抽取
│ ├── 2006-ACL-The stages of event extraction.pdf
│ ├── 2008-ACL-Refining event extraction through cross-document inference.pdf
│ ├── 2011-ACL-Using cross-entity inference to improve event extraction.pdf
│ ├── 2013-ACL-Joint event extraction via structured prediction with global features.pdf
│ ├── 2015-ACL-vent extraction via dynamic multi-pooling convolutional neural networks.pdf
│ ├── 2016-ACL-A language independent neural network for event detection.pdf
│ ├── 2016-NAACL-Joint event extraction via recurrent neural networks.pdf
│ └── 2018-EMNLP-Collective event detection via a hierarchical and bias tagging networks with gated multi-level attention mechanisms.pdf
├── 关系抽取
│ ├── 2012-ACL-Reducing wrong labels in distant supervision for relation extraction.pdf
│ ├── 2014-COLING-Relation Classification via Convolutional Deep Neural Network.pdf
│ ├── 2014-EMNLP-Modeling Joint Entity and Relation Extraction with Table Representation.pdf
│ ├── 2015-EMNLP-Classifying Relations via Long Short Term Memory Networks along Shortest Dependency Paths.pdf
│ ├── 2015-EMNLP-Distant Supervision for Relation Extraction via Piecewise Convolutional Neural Networks.pdf
│ ├── 2015-EMNLP-Semantic Relation Classification via Convolutional Neural Networks with Simple Negative Sampling.pdf
│ ├── 2016-ACL-Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification.pdf
│ ├── 2016-ACL-Neural Relation Extraction with Selective Attention over Instance.pdf
│ ├── 2017-ACL-Joint Extraction of Entities and Relations Based on a Novel Tagging Scheme.pdf
│ └── 2018-ACL-Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism.pdf
├── 命名实体识别
│ ├── 2013-AAAI-Effective bilingual constraints for semi-supervised learning of named entity recognizers.pdf
│ ├── 2013-NAACL-Named entity recognition with bilingual constraints.pdf
│ ├── 2016-ACL-Combining discrete and neural features for sequence labeling.pdf
│ ├── 2016-ACL-End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF.pdf
│ ├── 2016-ACL-Improving named entity recognition for chinese social media with word segmentation representation learning.pdf
│ ├── 2016-NAACL-Neural Architectures for Named Entity Recognition.pdf
│ ├── 2016-TACL-Named Entity Recognition with Bidirectional LSTM-CNNs.pdf
│ ├── 2017-AAAI-A unified model for cross-domain and semi-supervised named entity recognition in chinese social media.pdf
│ ├── 2017-ACL-Semi-supervised sequence tagging with bidirectional language models.pdf
│ ├── 2017-EACL-F-score driven max margin neural network for named entity recognition in chinese social media.pdf
│ ├── 2017-ICLR-Transfer Learning for Sequence Tagging with Hierarchical Recurrent Networks.pdf
│ ├── 2018-AAAI-Empower Sequence Labeling with Task-Aware Neural Language Model.pdf
│ ├── 2018-ACL-Chinese ner using lattice lstm.pdf
│ ├── 2018-ACL-Hybrid semi-Markov CRF for Neural Sequence Labeling.pdf
│ ├── 2018-Adversarial transfer learning for chinese named entity recognition with selfattention mechanism.pdf
│ ├── 2018-COLING-Contextual String Embeddings for Sequence Labeling.pdf
│ ├── 2018-COLING-Robust Lexical Features for Improved Neural Network Named-Entity Recognition.pdf
│ ├── 2019-ACL-Distantly Supervised Named Entity Recognition using Positive-Unlabeled Learning.pdf
│ └── 2019-NAACL-CAN-NER：convolutional attention network for chinese named entity recognition.pdf
├── 实体消歧
│ ├── 2006-EACL-Using Encyclopedic Knowledge for Named entity Disambiguation.pdf
│ ├── 2007-EMNLP-CoNLL-Large-scale named entity disambiguation based on Wikipedia data.pdf
│ ├── 2009-CIKM-Named Entity Disambiguation by Leveraging Wikipedia Semantic Knowledge.pdf
│ ├── 2011-ACL-HLT-Local and Global Algorithms for Disambiguation to Wikipedia.pdf
│ ├── 2011-EMNLP-Robust Disambiguation of Named Entities in Text.pdf
│ ├── 2011-IJCNLP-Cross-Language Entity Linking.pdf
│ ├── 2011-SIGIR-Collective entity linking in web text：a graph-based method.pdf
│ ├── 2014-TACL-Entity linking meets word sense disambiguation：a unified approach.pdf
│ ├── 2016-ACL-Cross-lingual Wikification Using Multilingual Embeddings.pdf
│ ├── 2016-CoNLL-Joint Learning of the Embedding of Words and Entities for Named Entity Disambiguation.pdf
│ ├── 2017-EMNLP-Deep Joint Entity Disambiguation with Local Neural Attention.pdf
│ └── 2018-AAAI-DeepType：Multilingual Entity Linking by Neural Type System Evolution.pdf
├── 知识表示
│ ├── 2010-Relational retrieval using a combination of path-contrained random walks.pdf
│ ├── 2013-NIPS-Translating embeddings for modeling multi-relational data.pdf
│ ├── 2013-WWW-AMIE：association rule mining under incomplete evidence in ontological knowledge bases.pdf
│ ├── 2014-EMNLP-Knowledge graph and text jointly embedding.pdf
│ ├── 2015-AAAI-Learning entity and relation embeddings for knowledge graph completion.pdf
│ ├── 2015-ACL-Knowledge graph embedding via dynamic mapping matrix.pdf
│ ├── 2015-CIKM-Learning to represent knowledge graphs with gaussian embedding.pdf
│ ├── 2015-EMNLP-Modeling relation paths for representation learning of knowledge bases.pdf
│ ├── 2015-TransG：A generative mixture model for knowledge graph embedding.pdf
│ └── 2016-AAAI-Knowledge graph completion with adaptive sparse transfer matrix.pdf
├── 综述及报告
│ ├── 2017-Paulheim, H. Knowledge Graph Refinement：A Survey of Approaches and Evaluation Methods.pdf
│ ├── 知识图谱发展报告（2018）-中国中文信息学会.pdf
│ ├── 知识图谱技术综述.pdf
│ ├── 知识图谱研究综述-李涓子.pdf
│ └── 知识图谱研究进展-漆桂林.pdf
└── 问答系统
├── 2005-UAI-Learning to map sentences to logical form：Structured classification with probabilistic categorial grammars.pdf
├── 2006-NAACL-Learning for Semantic Parsing with Statistical Machine Translation.pdf
├── 2009-ACL-Learning a Compositional Semantic Parser using an Existing Syntactic Parser.pdf
├── 2012-WWW-Template-based Question Answering over RDF Data.pdf
├── 2013-ACL-Paraphrase-driven learning for open question answering.pdf
├── 2014-ACL-Information Extraction over Structured Data Question Answering with Freebase.pdf
├── 2014-EMNLP-Question Answering with Subgraph Embeddings.pdf
├── 2015-ACL-Question answering over freebase with multi-column convolutional neural networks.pdf
├── 2016-ACL-Language to Logical Form with Neural Attention.pdf
├── 2016-ACL-Sequence-based Structured Prediction for Semantic Parsing.pdf
├── 2017-ACL-Search-based neural structured learning for sequential question answering.pdf
├── 2017-EMNLP-Neural Semantic Parsing with Typed Constraints for Semi-Structured Tables.pdf
├── 2017-TKDE-Answering Natural Language Questions by Subgraph Matching over Knowledge Graphs.pdf
├── 2018-ACL-Coarse-to-Fine Decoding for Neural Semantic Parsing.pdf
├── 2018-EMNLP-A State-transition Framework to Answer Complex Questions over Knowledge Base.pdf
├── 2018-EMNLP-Knowledge Base Question Answering via Encoding of Complex Query Graphs.pdf
└── 2018-EMNLP-SimpleQuestions Nearly Solved：A New Upperbound and Baseline Approach.pdf
7 directories, 81 files
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