scholarly journals FoodIE: A Rule-based Named-entity Recognition Method for Food Information Extraction

2019 ◽  
Author(s):  
Gorjan Popovski ◽  
Stefan Kochev ◽  
Barbara Seljak ◽  
Tome Eftimov
Keyword(s):  
Information Extraction ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Recognition Method ◽  
Rule Based ◽  
Named Entity

Person Named Entity Recognition in Balinese

2021 ◽  
Vol 10 (1) ◽  
pp. 99
Author(s):  
Kenny Kurniadi ◽  
Ngurah Agus Sanjaya ER
Keyword(s):  
Information Extraction ◽  
Named Entity Recognition ◽  
Morphological Structure ◽  
Entity Recognition ◽  
Linguistic Meaning ◽  
Rule Based ◽  
Named Entity

Named Entity Recognition (NER) is part of information extraction whose task is to classify text which is categorized into several classes such as names of people (figures), organizations, and locations. In this study, the authors propose making a NER identify the names of characters in Balinese language documents. This study will use a rule-based method (rule-based). Rules are build based on the morphological structure and linguistic meaning of Balinese names. The research conducted, that the system has an accuracy of 67.41%, precision of 83.42%, recall of 77.83%, and F-Score of 80.53%.

scholarly journals ChemTok: A New Rule Based Tokenizer for Chemical Named Entity Recognition

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Abbas Akkasi ◽  
Ekrem Varoğlu ◽  
Nazife Dimililer
Keyword(s):  
Conditional Random Fields ◽  
Named Entity Recognition ◽  
Classification Performance ◽  
Entity Recognition ◽  
Support Vector ◽  
Learning Approaches ◽  
Data Set ◽  
Rule Based ◽  
Named Entity ◽  
Vector Machines

Named Entity Recognition (NER) from text constitutes the first step in many text mining applications. The most important preliminary step for NER systems using machine learning approaches is tokenization where raw text is segmented into tokens. This study proposes an enhanced rule based tokenizer, ChemTok, which utilizes rules extracted mainly from the train data set. The main novelty of ChemTok is the use of the extracted rules in order to merge the tokens split in the previous steps, thus producing longer and more discriminative tokens. ChemTok is compared to the tokenization methods utilized by ChemSpot and tmChem. Support Vector Machines and Conditional Random Fields are employed as the learning algorithms. The experimental results show that the classifiers trained on the output of ChemTok outperforms all classifiers trained on the output of the other two tokenizers in terms of classification performance, and the number of incorrectly segmented entities.

Chinese named entity recognition method based on BERT

2021 ◽  
Author(s):  
Yuan Chang ◽  
Lei Kong ◽  
Kejia Jia ◽  
Qinglei Meng
Keyword(s):  
Named Entity Recognition ◽  
Entity Recognition ◽  
Recognition Method ◽  
Named Entity
Sign in / Sign up

Export Citation Format

Share Document