scholarly journals Named-Entity Recognition in Sports Field Based on a Character-Level Graph Convolutional Network

Information ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 30
Author(s):  
Xieraili Seti ◽  
Aishan Wumaier ◽  
Turgen Yibulayin ◽  
Diliyaer Paerhati ◽  
Lulu Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Conditional Random Fields ◽  
Structural Information ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Convolutional Network ◽  
Named Entities ◽  
Attention Model ◽  
Named Entity

Traditional methods for identifying naming ignore the correlation between named entities and lose hierarchical structural information between the named entities in a given text. Although traditional named-entity methods are effective for conventional datasets that have simple structures, they are not as effective for sports texts. This paper proposes a Chinese sports text named-entity recognition method based on a character graph convolutional neural network (Char GCN) with a self-attention mechanism model. In this method, each Chinese character in the sports text is regarded as a node. The edge between the nodes is constructed using a similar character position and the character feature of the named-entity in the sports text. The internal structural information of the entity is extracted using a character map convolutional neural network. The hierarchical semantic information of the sports text is captured by the self-attention model to enhance the relationship between the named entities and capture the relevance and dependency between the characters. The conditional random fields classification function can accurately identify the named entities in the Chinese sports text. The results conducted on four datasets demonstrate that the proposed method improves the F-Score values significantly to 92.51%, 91.91%, 93.98%, and 95.01%, respectively, in comparison to the traditional naming methods.

An Experimental Study of Hybrid Machine Learning Models for Extracting Named Entities

10.29007/dp5m ◽  
2019 ◽  
Author(s):  
Lei Jiang ◽  
Elena Bolshakova
Keyword(s):  
Neural Network ◽  
Conditional Random Fields ◽  
Named Entity Recognition ◽  
Network Models ◽  
Entity Recognition ◽  
Neural Network Models ◽  
Named Entities ◽  
Hybrid Neural Network ◽  
Named Entity ◽  
Two Hybrid

The paper describes two hybrid neural network models for named entity recognition (NER) in texts, as well as results of experiments with them. The first model, namely Bi-LSTM-CRF, is known and used for NER, while the other model named Gated-CNN- CRF is proposed in this work. It combines convolutional neural network (CNN), gated linear units, and conditional random fields (CRF). Both models were tested for NER on three different language datasets, for English, Russian, and Chinese. All resulted scores of precision, recall and F1-measure for both models are close to the state-of-the-art for NER, and for the English dataset CoNLL-2003, Gated-CNN-CRF model achieves 92.66 of F1-measure, outperforming the known result.

scholarly journals Klasifikasi jenis kejadian menggunakan kombinasi NeuroNER dan Recurrent Convolutional Neural Network pada data Twitter

2018 ◽  
Vol 4 (2) ◽  
pp. 81
Author(s):  
Fatra Nonggala Putra ◽  
Chastine Fatichah
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real Time ◽  
Detection System ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Experimental Result ◽  
Incident Detection ◽  
Named Entity ◽  
F Measure

Sistem deteksi kejadian dari data Twitter bertujuan untuk mendapatkan data secara real-time sebagai alternatif sistem deteksi kejadian yang murah. Penelitian tentang sistem deteksi kejadian telah dilakukan sebelumnya. Salah satu modul utama dari sistem deteksi kejadian adalah modul klasifikasi jenis kejadian. Informasi dapat diklasifikasikan sebagai kejadian penting jika memiliki entitas yang merepresentasikan di mana lokasi kejadian terjadi. Beberapa penelitian sebelumnya masih memanfaatkan fitur ‘buatan tangan’, maupun fitur model berbasis pipeline seperti n-gram sebagai penentuan fitur kunci klasifikasi yang tidak efektif dengan performa kurang optimal. Oleh karena itu, diusulkan penggabungan metode Neuro Named Entity Recognition (NeuroNER) dan klasifier Recurrent Convolutional Neural Network (RCNN) yang diharapkan dapat melakukan deteksi kejadian secara efektif dan optimal. Pertama, sistem melakukan pengenalan entitas bernama pada data tweet untuk mengenali entitas lokasi yang terdapat dalam teks tweet, karena informasi kejadian haruslah memiliki minimal satu entitas lokasi. Kedua, jika tweet terdeteksi memiliki entitas lokasi maka akan dilakukan proses klasifikasi kejadian menggunakan klasifier RCNN. Berdasarkan hasil uji coba, disimpulkan bahwa sistem deteksi kejadian menggunakan penggabungan NeuroNER dan RCNN bekerja dengan sangat baik dengan nilai rata-rata precision, recall, dan f-measure masing-masing 94,87%, 92,73%, dan 93,73%.    The incident detection system from Twitter data aims to obtain real-time information as an alternative low-cost incident detection system. One of the main modules in the incident detection system is the classification module. Information is classified as important incident if it has an entity that represents where the incident occurred. Some previous studies still use 'handmade' features as well as feature-based pipeline models such as n-grams as the key features for classification which are deemed as ineffective. Therefore, this research propose a combination of Neuro Named Entity Recognition (NeuroNER) and Recurrent Convolutional Neural Network (RCNN) as an effective classification method for incident detection. First, the system perform named entity recognition to identify the location contained in the tweet text because the event information should have at least one location entity. Then, if the location is successfully identified, the incident will be classified using RCNN. Experimental result shows that the incident detection system using combination  of NeuroNER and RCNN works very well with the average value of precision, recall, and f-measure 92.44%, 94.76%, and 93.53% respectively.

scholarly journals Statistical Method for Named Entity Recognition in Telugu, an Indian Language

2019 ◽  
Vol 8 (2) ◽  
pp. 4211-4216
Keyword(s):  
Language Processing ◽  
Conditional Random Fields ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Semantic Features ◽  
Indian Language ◽  
Named Entities ◽  
Maximum Entropy Models ◽  
Named Entity ◽  
Proper Nouns

One of the important tasks of Natural Language Processing (NLP) is Named Entity Recognition (NER). The primary operation of NER is to identify proper nouns i.e. to locate all the named entities in the text and tag them as certain named entity categories such as Entity, Time expression and Numeric expression. In the previous works, NER for Telugu language is addressed with Conditional Random Fields (CRF) and Maximum Entropy models however they failed to handle ambiguous named entity tags for the same named entity. This paper presents a hybrid statistical system for Named Entity Recognition in Telugu language in which named entities are identified by both dictionary-based approach and statistical Hidden Markov Model (HMM). The proposed method uses Lexicon-lookup dictionary and contexts based on semantic features for predicting named entity tags. Further HMM is used to resolve the named entity ambiguities in predicted named entity tags. The present work reports an average accuracy of 86.3% for finding the named entities

scholarly journals Conditional Random Fields for Biomedical Named Entity Recognition Revisited

2020 ◽  
Author(s):  
Xie-Yuan Xie
Keyword(s):  
Random Fields ◽  
Conditional Random Fields ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Biomedical Domain ◽  
Minimal Set ◽  
Named Entities ◽  
Named Entity ◽  
Biomedical Texts ◽  
Biomedical Named Entity Recognition

Abstract Named Entity Recognition (NER) is a key task which automatically extracts Named Entities (NE) from the text. Names of persons, places, date and time are examples of NEs. We are applying Conditional Random Fields (CRFs) for NER in biomedical domain. Examples of NEs in biomedical texts are gene, proteins. We used a minimal set of features to train CRF algorithm and obtained a good results for biomedical texts.

scholarly journals Tagging Named Entities in Croatian Tweets

2017 ◽  
Vol 4 (1) ◽  
pp. 20-41
Author(s):  
Krešimir Baksa ◽  
Dino Golović ◽  
Goran Glavaš ◽  
Jan Šnajder
Keyword(s):  
Conditional Random Fields ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Entity Extraction ◽  
Named Entities ◽  
News Stories ◽  
Named Entity ◽  
Set Size ◽  
Named Entity Extraction ◽  
Textual Content

Named entity extraction tools designed for recognizing named entities in texts written in standard language (e.g., news stories or legal texts) have been shown to be inadequate for user-generated textual content (e.g., tweets, forum posts). In this work, we propose a supervised approach to named entity recognition and classification for Croatian tweets. We compare two sequence labelling models: a hidden Markov model (HMM) and conditional random fields (CRF). Our experiments reveal that CRF is the best model for the task, achieving a very good performance of over 87% micro-averaged F1 score. We analyse the contributions of different feature groups and influence of the training set size on the performance of the CRF model.

Machine-learning methods for text named entity recognition

2016 ◽  
pp. 150-157
Author(s):  
O.O. Marchenko ◽  
Keyword(s):  
Machine Learning ◽  
Random Fields ◽  
Conditional Random Fields ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Learning Methods ◽  
Named Entities ◽  
Named Entity ◽  
Machine Learning Methods ◽  
Multi Classification

The article describes machine learning methods for the named entity recognition. To build named entity classifiers two basic models of machine learning, The Naїve Bayes and Conditional Random Fields, were used. A model for multi-classification of named entities using Error Correcting Output Codes was also researched. The paper describes a method for classifiers' training and the results of test experiments. Conditional Random Fields overcome other models in precision and recall evaluations.

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