Named Entity Recognition using Conditional Random Fields

Patil, Nita; Patil, Ajay S.; Pawar, B. V.

doi:10.1016/j.procs.2020.03.431

Cited by 49 publications

(31 citation statements)

References 6 publications

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“…Estratégias de REN e ER podem ser divididas em dois tipos de abordagem: tokenbased e span-based. A primeira classifica cada palavra ou token do texto em um dos tipos de entidade, adicionalmente identificando se a palavra pertence ao início, meio ou fim da entidade identificada [Finkel et al 2005, Patil et al 2020. Já as estratégias span-based primeiro enumeram todos os spans (sequências de tokens) que sejam menores que um dado limite e, em seguida, classificam cada um dos spans enumerados [Eberts & Ulges 2020, Fu et al 2021.…”

Section: Trabalhos Relacionadosunclassified

“…Um exemplo de método token-based tradicionalmente utilizado em REN são os baseados em Conditional Random Fields (CRFs) [Finkel et al 2005, Patil et al 2020. Os CRFs são modelos probabilísticos que inferem a categoria de cada token t de um texto explorando atributos de t (e.g., padrão de letras maiúsculas e minúsculas) e atributos dos tokens adjacentes a t, bem como as inferências realizadas para os mesmos.…”

Section: Trabalhos Relacionadosunclassified

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Reforço e Delimitação Contextual para Reconhecimento de Entidades e Relações em Documentos Oficiais

Belém

Ganem

França

et al. 2022

Anais Do XXXVII Simpósio Brasileiro De Banco De Dados (SBBD 2022)

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Arquiteturas neurais baseadas em transformers tornaram-se o principal componente de vários métodos do estado-da-arte em tarefas de processamento de linguagem natural, tais como Reconhecimento de Entidades Nomeadas e Extração de Relações (REN+ER). Como essas arquiteturas baseiam-se em aspectos semânticos de sequências de palavras, elas podem não funcionar na identificação e delimitação de entidades nomeadas quando há pouco contexto semântico associado, tais como entidades compostas por dígitos e pontuações apenas (e.g., números de CPF) e entidades com nomes compostos. Neste artigo, são propostas novas técnicas de reforço contextual e delimitação de entidades baseadas em pré- e pós-processamento de dados para enriquecer o contexto semântico, melhorando assim um método do estado-da-arte para REN+RE, o SpERT (Span-Based Entity and Relation Transformer). Tais técnicas foram avaliadas usando dados reais de diários oficiais e de processos judiciais. Os resultados mostram que, quando aplicadas em conjunto, as estratégias de pré- e pós-processamento levam a ganhos significativos na efetividade de REN+ER.

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Section: Trabalhos Relacionadosunclassified

Reforço e Delimitação Contextual para Reconhecimento de Entidades e Relações em Documentos Oficiais

Belém

Ganem

França

et al. 2022

Anais Do XXXVII Simpósio Brasileiro De Banco De Dados (SBBD 2022)

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“…Information extraction is an important pre-processing approach that greatly improves text mining https://www.indjst.org/ potential. (1,2). Named Entity Recognition (NER) is one of the most important aspects of data extraction.…”

Section: Introductionmentioning

confidence: 99%

“…With the advancement of electronic media, more and more regional language text is now being produced (1) . Mining relevant information from unstructured electronic data has become a difficult task for organizations such as newspapers, medical institutions, educational institutions, and others, and text information cannot be used directly by natural language processing tasks to achieve predictable results.…”

Section: Introductionmentioning

confidence: 99%

Named Entity Recognition for Sheko Language Using Bidirectional LSTM

Ashebir¹,

Gantela²

2022

IJST

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Objectives: This study aims to advance Sheko language name entity Recognition first of its kind. Named Entity Recognition (NER) is one of the most important text processing in machine translation, text summarization, and information retrieval. Sheko language named entity recognition concerns in addressing the usage of the bidirectional Long Short-Term Memory (LSTM) model in recognizing tokens into predefined classes. Methods: A bidirectional long shortterm memory is used to model the NER for sheko language to identify words into seven predefined classes: Person, Organization, Geography, Natural Phenomenon, Geopolitical Entity, time, and other classes. As feature selection plays a vital role in long short-term memory framework, the experiment is conducted to discover the most suitable features for Sheko NER tagging task by using 63,813 words to train and test our model. Out of which is 70% for training and 30% for testing. Datasets were collected from Sheko Mizan Aman Radio Station (MARS), Sheko southern region mass media, Language, and Literature Department. Findings: Through several conducted experiments, Sheko NER has successfully achieved a performance of 97% test accuracy. From the experimental result, it is possible to determine that tag context is a significant feature in named entity recognition and classification for Sheko language. Finally, we have contributed a new architecture for Sheko NER which uses automatically features for Sheko named entity recognition which is not dependent on other NLP tasks, and added some preprocessing steps. We provide a comprehensive Comparison with other traditional NER algorithms.

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“…The conditional random fields method has also been discussed in several themes such as extracting causal relations from emergency [32], named entity recognition [33], and biomedical text [24]. Moharasan and Ho [34] which conduct research on clinis texts with semi-supervised conditional random fields, they proposed and evaluated a two-stage semi-supervised novel for the temporal event extraction, in this work they prove that the influence of undocumented clinical texts helps to significantly increase the accuracy of temporal event extraction.…”

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confidence: 99%

Bigram feature extraction and conditional random fields model to improve text classification clinical trial document

et al. 2021

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In the field of health and medicine, there is a very important term known as clinical trials. Clinical trials are a type of activity that studies how the safest way to treat patients is. These clinical trials are usually written in unstructured free text which requires translation from a computer. The aim of this paper is to classify the texts of cancer clinical trial documents consisting of unstructured free texts taken from cancer clinical trial protocols. The proposed algorithm is conditional random Fields and bigram features. A new classification model from the cancer clinical trial document text is proposed to compete with other methods in terms of precision, recall, and f-1 score. The results of this study are better than the previous results, namely 88.07 precision, 88.05 recall and f-1 score 88.06.

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Named Entity Recognition using Conditional Random Fields

Cited by 49 publications

References 6 publications

Reforço e Delimitação Contextual para Reconhecimento de Entidades e Relações em Documentos Oficiais

Reforço e Delimitação Contextual para Reconhecimento de Entidades e Relações em Documentos Oficiais

Named Entity Recognition for Sheko Language Using Bidirectional LSTM

Bigram feature extraction and conditional random fields model to improve text classification clinical trial document

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