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Embedding Methods for Fine Grained Entity Type Classification

Dani Yogatama, Daniel Gillick, Nevena Lazic
2015 Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)  
We propose a new approach to the task of fine grained entity type classifications based on label embeddings that allows for information sharing among related labels.  ...  We show that it outperforms state-of-the-art methods on two fine grained entity-classification benchmarks and that the model can exploit the finer-grained labels to improve classification of standard coarse  ...  Acknowledgements We thank Andrew McCallum for helpful discussions and anonymous reviewers for feedback on an earlier draft of this paper.  ... 
doi:10.3115/v1/p15-2048 dblp:conf/acl/YogatamaGL15 fatcat:ct6ay6kjqvgxtcxuiot6tzsnw4

Evaluating Word Embeddings in Multi-label Classification Using Fine-grained Name Typing [article]

Yadollah Yaghoobzadeh, Katharina Kann, Hinrich Schütze
2018 arXiv   pre-print
Multi-label classification is an appropriate way to do so. We propose a new evaluation method for word embeddings based on multi-label classification given a word embedding.  ...  The task we use is fine-grained name typing: given a large corpus, find all types that a name can refer to based on the name embedding.  ...  We leave it for the future work. Conclusion We proposed multi-label classification of word embeddings using the task of fine-grained typing of entity names.  ... 
arXiv:1807.07186v1 fatcat:24mg7jlyyfailcs53vbhwvgig4

Evaluating Word Embeddings in Multi-label Classification Using Fine-Grained Name Typing

Yadollah Yaghoobzadeh, Katharina Kann, Hinrich Schütze
2018 Proceedings of The Third Workshop on Representation Learning for NLP  
Multi-label classification is an appropriate way to do so. We propose a new evaluation method for word embeddings based on multi-label classification given a word embedding.  ...  Given the scale of entities in knowledge bases, we can build datasets for this task that are complementary to the current embedding evaluation datasets in: they are very large, contain fine-grained classes  ...  We leave it for the future work. Conclusion We proposed multi-label classification of word embeddings using the task of fine-grained typing of entity names.  ... 
doi:10.18653/v1/w18-3013 dblp:conf/rep4nlp/YaghoobzadehKS18 fatcat:h3vo54nt5nalthii5fbjd7sp2m

Fine Grained Classification of Personal Data Entities [article]

Riddhiman Dasgupta, Balaji Ganesan, Aswin Kannan, Berthold Reinwald, Arun Kumar
2018 arXiv   pre-print
Entity Type Classification can be defined as the task of assigning category labels to entity mentions in documents.  ...  We propose a neural model to expand the class of personal data entities that can be classified at a fine grained level, using the output of existing pattern matching systems as additional contextual features  ...  As can be seen, NER and Type tags have the highest influence on fine-grained entity classification.  ... 
arXiv:1811.09368v1 fatcat:hkrm3aphhrcvlnasbl75wctl3y

An Attentive Neural Architecture for Fine-grained Entity Type Classification [article]

Sonse Shimaoka, Pontus Stenetorp, Kentaro Inui, Sebastian Riedel
2016 arXiv   pre-print
In this work we propose a novel attention-based neural network model for the task of fine-grained entity type classification that unlike previously proposed models recursively composes representations  ...  We also investigate the behavior of the attention mechanism of our model and observe that it can learn contextual linguistic expressions that indicate the fine-grained category memberships of an entity  ...  We would like to thank the anonymous reviewers and Koji Matsuda for their helpful comments and feedback.  ... 
arXiv:1604.05525v1 fatcat:4jgfe7ep6fcjbp4ogkcy7yvyhq

An Attentive Neural Architecture for Fine-grained Entity Type Classification

Sonse Shimaoka, Pontus Stenetorp, Kentaro Inui, Sebastian Riedel
2016 Proceedings of the 5th Workshop on Automated Knowledge Base Construction  
In this work we propose a novel attentionbased neural network model for the task of fine-grained entity type classification that unlike previously proposed models recursively composes representations of  ...  We also investigate the behavior of the attention mechanism of our model and observe that it can learn contextual linguistic expressions that indicate the fine-grained category memberships of an entity  ...  We would like to thank the anonymous reviewers and Koji Matsuda for their helpful comments and feedback.  ... 
doi:10.18653/v1/w16-1313 dblp:conf/akbc/ShimaokaSIR16 fatcat:qt6nloof2fdhlfm7txwmwvqgoe

Entity Type Prediction in Knowledge Graphs using Embeddings [article]

Russa Biswas, Radina Sofronova, Mehwish Alam, Harald Sack
2020 arXiv   pre-print
To deal with this problem a multi-label classification approach is proposed in this work for entity typing using KG embeddings.  ...  We compare our approach with the current state-of-the-art type prediction method and report on experiments with the KGs.  ...  In this paper, a multi-label classification approach is proposed for fine grained entity typing.  ... 
arXiv:2004.13702v1 fatcat:34uxw253l5fhpnyf7tmi7y2yqi

Neural Architectures for Fine-grained Entity Type Classification [article]

Sonse Shimaoka, Pontus Stenetorp, Kentaro Inui, Sebastian Riedel
2017 arXiv   pre-print
In this work, we investigate several neural network architectures for fine-grained entity type classification.  ...  We enable parameter sharing through a hierarchical label encoding method, that in low-dimensional projections show clear clusters for each type hierarchy.  ...  We would like to thank Dan Gillick for answering several questions related to his 2014 paper.  ... 
arXiv:1606.01341v2 fatcat:jovudwkaffculodkd73jnaafcm

A Neural Architecture for Person Ontology population [article]

Balaji Ganesan, Riddhiman Dasgupta, Akshay Parekh, Hima Patel, and Berthold Reinwald
2020 arXiv   pre-print
In this work, we present a system for automatically populating a person ontology graph from unstructured data using neural models for Entity Classification and Relation Extraction.  ...  While artificial neural networks have led to improvements in Entity Recognition, Entity Classification, and Relation Extraction, creating an ontology largely remains a manual process, because it requires  ...  In recent years, a number of Neural Fine Grained Entity Classification (NFGEC) models have been proposed, which assign fine grained labels to entities based on context.  ... 
arXiv:2001.08013v1 fatcat:buyltvqwyvf6vpgq2nvxoiqqqa

Discovering Fine-Grained Semantics in Knowledge Graph Relations [article]

Nitisha Jain, Ralf Krestel
2022 arXiv   pre-print
For numerous use cases, such as entity type classification, question answering and knowledge graph completion, the correct semantic interpretation of these relations is necessary.  ...  In this work, we provide a strategy for discovering the different semantics associated with abstract relations and deriving many sub-relations with fine-grained meaning.  ...  This indicates the efficacy of the method for finding optimal fine-grained sub-relations. Discussion.  ... 
arXiv:2202.08917v1 fatcat:l5qxck7tzje3zegkrujyjpnm7y

On the Complementary Nature of Knowledge Graph Embedding, Fine Grain Entity Types, and Language Modeling [article]

Rajat Patel, Francis Ferraro
2020 arXiv   pre-print
We demonstrate the complementary natures of neural knowledge graph embedding, fine-grain entity type prediction, and neural language modeling.  ...  We show that a language model-inspired knowledge graph embedding approach yields both improved knowledge graph embeddings and fine-grain entity type representations.  ...  Methods Strict F1 Macro F1 Micro F1 AFET Table 4 : 4 We compare previous techniques on Wiki-MAN dataset for fine-grain entity type classification.  ... 
arXiv:2010.05732v1 fatcat:at2mbe2pafdvdouco4ozyp5uv4

End-to-End Trainable Attentive Decoder for Hierarchical Entity Classification

Sanjeev Karn, Ulli Waltinger, Hinrich Schütze
2017 Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers  
We address fine-grained entity classification and propose a novel attention-based recurrent neural network (RNN) encoderdecoder that generates paths in the type hierarchy and can be trained end-to-end.  ...  We show that our model performs better on fine-grained entity classification than prior work that relies on flat or local classifiers that do not directly model hierarchical structure.  ...  We thank Stephan Baier, Siemens CT members and the anonymous reviewers for valuable feedback. This research was supported by Bundeswirtschaftsministerium (bmwi. de), grant 01MD15010A (Smart Data Web).  ... 
doi:10.18653/v1/e17-2119 dblp:conf/eacl/SchutzeWK17 fatcat:frulzitxgrdsvbaohokyxwtxf4

Neural Architectures for Fine-grained Entity Type Classification

Sonse Shimaoka, Pontus Stenetorp, Kentaro Inui, Sebastian Riedel
2017 Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers  
In this work, we investigate several neural network architectures for fine-grained entity type classification and make three key contributions.  ...  :on Men:on Representa:on /organiza)on, /organiza)on/sports_team Figure 1: An illustration of the attentive encoder neural model predicting fine-grained semantic types for the mention "New Zealand" in the  ...  We would like to thank Dan Gillick for answering several questions related to his 2014 paper and the anonymous reviewers for their helpful feedback and encouragement.  ... 
doi:10.18653/v1/e17-1119 dblp:conf/eacl/InuiRSS17 fatcat:gfie2ocblzb4doytfd35vhg36i

Multi-Task Transfer Learning for Fine-Grained Named Entity Recognition

Masato Hagiwara, Ryuji Tamaki, Ikuya Yamada
2019 Text Analysis Conference  
This paper describes Studio Ousia's participation to the EDL track of TAC KBP 2019-(ultra) fine-grained named entity recognition (NER).  ...  The proposed system first trains a YAGO-based ultra fine-grained NER model in a multi-label, multi-task fashion.  ...  ., 2015) has been focused on fine-grained entity typing (vs entity detection), while few systems have proposed to solve entity detection and classification jointly.  ... 
dblp:conf/tac/HagiwaraTY19 fatcat:27mwd5amunaqrcvfhn3mmxenrm

Attributed and Predictive Entity Embedding for Fine-Grained Entity Typing in Knowledge Bases

Hailong Jin, Lei Hou, Juanzi Li, Tiansi Dong
2018 International Conference on Computational Linguistics  
Fine-grained entity typing aims at identifying the semantic type of an entity in KB.  ...  To address these issues, we propose an attributed and predictive entity embedding method, which can fully utilize various kinds of information comprehensively.  ...  Conclusion In this paper, we propose an attributed and predictive entity embedding method to address the task of fine-grained entity typing in KB.  ... 
dblp:conf/coling/JinHLD18 fatcat:t2q25r527vfx5b4nwa2zpbstf4
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