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Embedding Methods for Fine Grained Entity Type Classification
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)
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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]
2018
arXiv
pre-print
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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
2018
Proceedings of The Third Workshop on Representation Learning for NLP
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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]
2018
arXiv
pre-print
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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]
2016
arXiv
pre-print
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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
2016
Proceedings of the 5th Workshop on Automated Knowledge Base Construction
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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]
2020
arXiv
pre-print
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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]
2017
arXiv
pre-print
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2016 pre-print arXiv:1606.01341v1 fatcat:2nvz43v6hbd7hfenfvibadyh6a
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]
2020
arXiv
pre-print
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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]
2022
arXiv
pre-print
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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]
2020
arXiv
pre-print
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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
2017
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers
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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
2017
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers
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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
2019
Text Analysis Conference
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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
2018
International Conference on Computational Linguistics
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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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