• Corpus ID: 221739266

Cascaded Models for Better Fine-Grained Named Entity Recognition

@article{Awasthy2020CascadedMF,
  title={Cascaded Models for Better Fine-Grained Named Entity Recognition},
  author={Parul Awasthy and Taesun Moon and Jian Ni and Radu Florian},
  journal={ArXiv},
  year={2020},
  volume={abs/2009.07317}
}
Named Entity Recognition (NER) is an essential precursor task for many natural language applications, such as relation extraction or event extraction. Much of the NER research has been done on datasets with few classes of entity types (e.g. PER, LOC, ORG, MISC), but many real world applications (disaster relief, complex event extraction, law enforcement) can benefit from a larger NER typeset. More recently, datasets were created that have hundreds to thousands of types of entities, sparking new… 

Figures and Tables from this paper

Fine-grained Named Entity Recognition using only Coarse-grained labels

TLDR
Fine-grained NER is necessary to get a better understanding of the entities and should further help with the information extraction, given only the entities’ coarse- grained labels.

References

SHOWING 1-10 OF 32 REFERENCES

An Empirical Study on Fine-Grained Named Entity Recognition

TLDR
An empirical comparison of FG-NER models for English and Japanese is presented and it is demonstrated that LSTM+CNN+CRF (Ma and Hovy, 2016), one of the state-of-the-art methods for English NER, also works well for EnglishFG-NER but does not work well for Japanese, a language that has a large number of character types.

Fine-Grained Entity Recognition

TLDR
A fine-grained set of 112 tags is defined, the tagging problem is formulates as multi-class, multi-label classification, an unsupervised method for collecting training data is described, and the FIGER implementation is presented.

Context-Dependent Fine-Grained Entity Type Tagging

TLDR
This work proposes 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).

Overview of TAC-KBP 2019 Fine-grained Entity Extraction

TLDR
An outline of the Ultra-Fine-Grained Name Tagging for Entity Types task at the Knowledge Base Population (KBP) track at TAC 2019 is given and remaining challenges and future research directions are sketched out.

Towards Lingua Franca Named Entity Recognition with BERT

TLDR
This paper investigates a single Named Entity Recognition model, based on a multilingual BERT, that is trained jointly on many languages simultaneously, and is able to decode these languages with better accuracy than models trained only on one language.

End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF

TLDR
A novel neutral network architecture is introduced that benefits from both word- and character-level representations automatically, by using combination of bidirectional LSTM, CNN and CRF, thus making it applicable to a wide range of sequence labeling tasks.

Extended Named Entity Ontology with Attribute Information

TLDR
The design of a set of attributes for ENE categories is reported on using a bottom up approach to creating the knowledge using a Japanese encyclopedia, which contains abundant descriptions of ENE instances.

A framework for named entity recognition in the open domain

In this paper, a system for Named Entity Recognition in the Open domain (NERO) is described. It is concerned with recognition of various types of entity, types that will be appropriate for

Towards large-scale, open-domain and ontology-based named entity classification

TLDR
The main contribution of the paper is a systematic analysis of the impact of varying certain parameters on such a context-based approach exploiting similarities in vector space for the disambiguation of named entities.

A survey of named entity recognition and classification

TLDR
Observations about languages, named entity types, domains and textual genres studied in the literature, along with other critical aspects of NERC such as features and evaluation methods, are reported.