E2EET: From Pipeline to End-to-end Entity Typing via Transformer-Based
Embeddings
- URL: http://arxiv.org/abs/2003.10097v1
- Date: Mon, 23 Mar 2020 06:46:28 GMT
- Title: E2EET: From Pipeline to End-to-end Entity Typing via Transformer-Based
Embeddings
- Authors: Michael Stewart and Wei Liu
- Abstract summary: We propose a new type of entity typing called Entity Typing (ET)
ET involves labelling each entity mention with one or more class labels.
We propose to incorporate context using transformer-based embeddings for a mention-level model, and an end-to-end model using a Bi-GRU.
An extensive ablative study demonstrates the effectiveness of contextualised embeddings for mention-level models and the competitiveness of our end-to-end model for entity typing.
- Score: 7.431445082017672
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Entity Typing (ET) is the process of identifying the semantic types of every
entity within a corpus. In contrast to Named Entity Recognition, where each
token in a sentence is labelled with zero or one class label, ET involves
labelling each entity mention with one or more class labels. Existing entity
typing models, which operate at the mention level, are limited by two key
factors: they do not make use of recently-proposed context-dependent
embeddings, and are trained on fixed context windows. They are therefore
sensitive to window size selection and are unable to incorporate the context of
the entire document. In light of these drawbacks we propose to incorporate
context using transformer-based embeddings for a mention-level model, and an
end-to-end model using a Bi-GRU to remove the dependency on window size. An
extensive ablative study demonstrates the effectiveness of contextualised
embeddings for mention-level models and the competitiveness of our end-to-end
model for entity typing.
Related papers
- Entity Disambiguation via Fusion Entity Decoding [68.77265315142296]
We propose an encoder-decoder model to disambiguate entities with more detailed entity descriptions.
We observe +1.5% improvements in end-to-end entity linking in the GERBIL benchmark compared with EntQA.
arXiv Detail & Related papers (2024-04-02T04:27:54Z) - Seed-Guided Fine-Grained Entity Typing in Science and Engineering
Domains [51.02035914828596]
We study the task of seed-guided fine-grained entity typing in science and engineering domains.
We propose SEType which first enriches the weak supervision by finding more entities for each seen type from an unlabeled corpus.
It then matches the enriched entities to unlabeled text to get pseudo-labeled samples and trains a textual entailment model that can make inferences for both seen and unseen types.
arXiv Detail & Related papers (2024-01-23T22:36:03Z) - Entity Type Prediction Leveraging Graph Walks and Entity Descriptions [4.147346416230273]
textitGRAND is a novel approach for entity typing leveraging different graph walk strategies in RDF2vec together with textual entity descriptions.
The proposed approach outperforms the baseline approaches on the benchmark datasets DBpedia and FIGER for entity typing in KGs for both fine-grained and coarse-grained classes.
arXiv Detail & Related papers (2022-07-28T13:56:55Z) - Few-Shot Fine-Grained Entity Typing with Automatic Label Interpretation
and Instance Generation [36.541309948222306]
We study the problem of few-shot Fine-grained Entity Typing (FET), where only a few annotated entity mentions with contexts are given for each entity type.
We propose a novel framework for few-shot FET consisting of two modules: (1) an entity type label interpretation module automatically learns to relate type labels to the vocabulary by jointly leveraging few-shot instances and the label hierarchy, and (2) a type-based contextualized instance generator produces new instances based on given instances to enlarge the training set for better generalization.
arXiv Detail & Related papers (2022-06-28T04:05:40Z) - UniRE: A Unified Label Space for Entity Relation Extraction [67.53850477281058]
Joint entity relation extraction models setup two separated label spaces for the two sub-tasks.
We argue that this setting may hinder the information interaction between entities and relations.
In this work, we propose to eliminate the different treatment on the two sub-tasks' label spaces.
arXiv Detail & Related papers (2021-07-09T08:09:37Z) - Improving Entity Linking through Semantic Reinforced Entity Embeddings [16.868791358905916]
We propose a method to inject fine-grained semantic information into entity embeddings to reduce the distinctiveness and facilitate the learning of contextual commonality.
Based on our entity embeddings, we achieved new sate-of-the-art performance on entity linking.
arXiv Detail & Related papers (2021-06-16T00:27:56Z) - Autoregressive Entity Retrieval [55.38027440347138]
Entities are at the center of how we represent and aggregate knowledge.
The ability to retrieve such entities given a query is fundamental for knowledge-intensive tasks such as entity linking and open-domain question answering.
We propose GENRE, the first system that retrieves entities by generating their unique names, left to right, token-by-token in an autoregressive fashion.
arXiv Detail & Related papers (2020-10-02T10:13:31Z) - GRIT: Generative Role-filler Transformers for Document-level Event
Entity Extraction [134.5580003327839]
We introduce a generative transformer-based encoder-decoder framework (GRIT) to model context at the document level.
We evaluate our approach on the MUC-4 dataset, and show that our model performs substantially better than prior work.
arXiv Detail & Related papers (2020-08-21T01:07:36Z) - Interpretable Entity Representations through Large-Scale Typing [61.4277527871572]
We present an approach to creating entity representations that are human readable and achieve high performance out of the box.
Our representations are vectors whose values correspond to posterior probabilities over fine-grained entity types.
We show that it is possible to reduce the size of our type set in a learning-based way for particular domains.
arXiv Detail & Related papers (2020-04-30T23:58:03Z) - Fine-Grained Named Entity Typing over Distantly Supervised Data Based on
Refined Representations [16.30478830298353]
Fine-Grained Named Entity Typing (FG-NET) is a key component in Natural Language Processing (NLP)
We propose an edge-weighted attentive graph convolution network that refines the noisy mention representations by attending over corpus-level contextual clues prior to the end classification.
Experimental evaluation shows that the proposed model outperforms the existing research by a relative score of upto 10.2% and 8.3% for macro f1 and micro f1 respectively.
arXiv Detail & Related papers (2020-04-07T17:26:36Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.