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Context-Dependent Fine-Grained Entity Type Tagging
[article]
2016
arXiv
pre-print
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In the absence of labeled training data, existing fine-grained tagging systems obtain examples automatically, using resolved entities and their types extracted from a knowledge base. ...
We propose the task of context-dependent fine type tagging, where the set of acceptable labels for a mention is restricted to only those deducible from the local context (e.g. sentence or document). ...
We have prepared extensive new resources related to the context-dependent fine type tagging task. ...
arXiv:1412.1820v2
fatcat:pkkmow4tobbhdgowepd63z7t5u
Improving Fine-grained Entity Typing with Entity Linking
[article]
2019
arXiv
pre-print
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Fine-grained entity typing is a challenging problem since it usually involves a relatively large tag set and may require to understand the context of the entity mention. ...
In this paper, we use entity linking to help with the fine-grained entity type classification process. ...
In Proceedings of NAACL-HLT, vol-
dependent fine-grained entity type tagging. arXiv ume 1, pages 16–25.
preprint arXiv:1412.1820. ...
arXiv:1909.12079v1
fatcat:55nrn4t6ybcldaro4ftoaglbxe
A Baseline Fine-Grained Entity Extraction System for TAC-KBP2019
2019
Text Analysis Conference
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For fine-grained entity extraction, we propose a fine-grained entity typing model with a novel attention mechanism and a hybrid type classifier. ...
In addition, we propose a two-step mention-aware attention mechanism to enable the model to focus on important words in mentions and contexts. ...
Fine-grained Name Mention Extraction Fine-grained entity typing is performed on the mention extraction result. ...
dblp:conf/tac/LinPLJ19
fatcat:45qc46o3pjcl7j3edacll76x2e
Fine Grained Classification of Personal Data Entities
[article]
2018
arXiv
pre-print
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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 ...
Entity Type Classification can be defined as the task of assigning category labels to entity mentions in documents. ...
These results support our hypothesis that token level features, specially coarse grained NERs and Type tags from rule based systems, aid fine grained typing of entity mentions with context. ...
arXiv:1811.09368v1
fatcat:hkrm3aphhrcvlnasbl75wctl3y
Improving Fine-grained Entity Typing with Entity Linking
2019
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
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Fine-grained entity typing is a challenging problem since it usually involves a relatively large tag set and may require to understand the context of the entity mention. ...
In this paper, we use entity linking to help with the finegrained entity type classification process. ...
Introduction Given a piece of text and the span of an entity mention in this text, fine-grained entity typing (FET) is the task of assigning fine-grained type labels to the mention (Ling and Weld, 2012 ...
doi:10.18653/v1/d19-1643
dblp:conf/emnlp/DaiDLS19
fatcat:zfthsezodrdrdglp3xtjeasuwi
Entity-aware Image Caption Generation
[article]
2018
arXiv
pre-print
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Current image captioning approaches generate descriptions which lack specific information, such as named entities that are involved in the images. ...
Then we use a knowledge graph based collective inference algorithm to fill in the template with specific named entities retrieved via the hashtags. ...
fine-grained types for a name. ...
arXiv:1804.07889v2
fatcat:nn5vvle3cvgg5pfz4wmmz43gnu
Entity-aware Image Caption Generation
2018
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
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Current image captioning approaches generate descriptions which lack specific information, such as named entities that are involved in the images. ...
Then we use a knowledge graph based collective inference algorithm to fill in the template with specific named entities retrieved via the hashtags. ...
fine-grained types for a name. ...
doi:10.18653/v1/d18-1435
dblp:conf/emnlp/LuWHJC18
fatcat:75hkfoevhffabkxhygrju27hpm
Fine-Grained Named Entity Recognition using ELMo and Wikidata
[article]
2019
arXiv
pre-print
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Furthermore, many named entity systems suffer when considering the categorization of fine grained entity types. ...
Fine-grained Named Entity Recognition is a task whereby we detect and classify entity mentions to a large set of types. These types can span diverse domains such as finance, healthcare, and politics. ...
Gillick et al. (2014) introduced context dependent FgNER and proposed a set of heuristics for pruning labels that might not be relevant given the local context of the entity. ...
arXiv:1904.10503v1
fatcat:7sac2y6gmfbbhgt2znhkp3wzgq
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. ...
embedding entity mentions, context features and entity type hierarchy. ...
doi:10.18653/v1/e17-2119
dblp:conf/eacl/SchutzeWK17
fatcat:frulzitxgrdsvbaohokyxwtxf4
Fine-Grained Named Entity Recognition in Question Answering with DBpedia
2018
Journal of Physics, Conference Series
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On the other hand coarse-grained named entities' types cannot offer enough information for question understanding. ...
Finally, we use the kNN to classification and get fine-gained entity types combining with the entity mentions which can link with DBpedia. ...
First exiting public available QA corpus almost don't have fine-grained entity annotation. Second, QA sentences is too short to use context information. ...
doi:10.1088/1742-6596/1087/3/032003
fatcat:hjozejnyprfyzisml42ahxvfva
GAIA at SM-KBP 2019 - A Multi-media Multi-lingual Knowledge Extraction and Hypothesis Generation System
2019
Text Analysis Conference
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By representing all entity mentions, event triggers, and contexts into this Background KB
Fine-Grained Event Typing FrameNet & Dependency based Fine-Grained Event Typing
Rule based Fine-Grained Event ...
Fine-grained Mention Extraction We develop an attentive classification model that takes a mention with its context sentence and predicts the most possible fine-grained type. ...
dblp:conf/tac/LiLSWPLWZHJZACW19
fatcat:kvjat6c345errlp3ymaqpv66su
Learning from Knowledge Graphs: Neural Fine-Grained Entity Typing with Copy-Generation Networks
2022
Entropy
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Fine-grained entity typing (FET) aims to identify the semantic type of an entity in a plain text, which is a significant task for downstream natural language processing applications. ...
To address this issue, we take advantage of knowledge graphs to improve fine-grained entity typing through the use of a copy mechanism. ...
The fine-grained entity typing task is to infer the type probability distribution in type tags space T given the mention m and context c, i.e., p(t|m, c)=? . Feature Encoder. ...
doi:10.3390/e24070964
pmid:35885187
pmcid:PMC9316539
fatcat:ewuumifbv5d27ccctx76ek6dra
A Neural Architecture for Person Ontology population
[article]
2020
arXiv
pre-print
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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 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. ...
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
Transformer-based Methods for Recognizing Ultra Fine-grained Entities (RUFES)
[article]
2021
arXiv
pre-print
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We observe that our approach has great potential in increasing the performance of fine-grained entity recognition. ...
Our participation relies on two neural-based models, one based on a pre-trained and fine-tuned language model with a stack of Transformer layers for fine-grained entity extraction and one out-of-the-box ...
Introduction Fine-grained entity recognition aims at labeling entity mentions in context with one or more specific types organized in a hierarchy (e.g., Photographer is from a Artist that in turn, is a ...
arXiv:2104.06048v1
fatcat:svpqvf65yzezlbb2cebn6cjzei
Automatically Annotated Turkish Corpus for Named Entity Recognition and Text Categorization using Large-Scale Gazetteers
[article]
2017
arXiv
pre-print
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Moreover, we map fine-grained entity types to the equivalent four coarse-grained types: person, loc, org, misc. ...
The constructed gazetteers contains approximately 300K entities with thousands of fine-grained entity types under 77 different domains. ...
In order to transform FGA to coarse-grained annotation (CGA), we map each fine-grained entity type in each domain to a coarsegrained entity type, i.e. ...
arXiv:1702.02363v2
fatcat:i7zt6gcncjhhxkaqiiyaarg2mi
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