scholarly journals Information Extraction Tasks based on BERT and SpaCy on Tourism Domain

2021 ◽  
Vol 15 (1) ◽  
pp. 108-122
Author(s):  
Chantana Chantrapornchai ◽  
Aphisit Tunsakul
Keyword(s):  
Name Entity Recognition ◽  
Text Summarization ◽  
Training Data ◽  
Entity Recognition ◽  
Entity Extraction ◽  
Data Set ◽  
Name Entity ◽  
Sentence Extraction ◽  
Relation Type ◽  
Proper Training

In this paper, we present two methodologies to extract particular information based on the full text returned from the search engine to facilitate the users. The approaches are based three tasks: name entity recognition (NER), text classification and text summarization. The first step is the building training data and data cleansing. We consider tourism domain such as restaurant, hotels, shopping and tourism data set crawling from the websites. First, the tourism data are gathered and the vocabularies are built. Several minor steps include sentence extraction, relation and name entity extraction for tagging purpose. These steps are needed for creating proper training data. Then, the recognition model of a given entity type can be built. From the experiments, given review texts, we demonstrate to build the model to extract the desired entity,i.e, name, location, facility as well as relation type, classify the reviews or summarize the reviews. Two tools, SpaCy and BERT, are used to compare the performance of these tasks.

Developing Name Entity Recognition for Structured and Unstructured Text Formatting Dataset

2020 ◽  
Author(s):  
Nadhia Salsabila Azzahra ◽  
Muhammad Okky Ibrohim ◽  
Junaedi Fahmi ◽  
Bagus Fajar Apriyanto ◽  
Oskar Riandi
Keyword(s):  
Name Entity Recognition ◽  
Entity Recognition ◽  
Unstructured Text ◽  
Name Entity

Referent graph embedding model for name entity recognition of Chinese car reviews

2021 ◽  
pp. 107558
Author(s):  
Zhao Fang ◽  
Qiang Zhang ◽  
Stanley Kok ◽  
Ling Li ◽  
Anning Wang ◽  
...  
Keyword(s):  
Graph Embedding ◽  
Name Entity Recognition ◽  
Entity Recognition ◽  
Name Entity

Exploiting the concept level feature for enhanced name entity recognition in Chinese EMRs

2019 ◽  
Vol 76 (8) ◽  
pp. 6399-6420 ◽  
Author(s):  
Qing Zhao ◽  
Dan Wang ◽  
Jianqiang Li ◽  
Faheem Akhtar
Keyword(s):  
Name Entity Recognition ◽  
Entity Recognition ◽  
Name Entity

scholarly journals ENCADEAr: ENCADEAmento automático de notícias

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Carla Abreu ◽  
Jorge Teixeira ◽  
Eugénio Oliveira
Keyword(s):  
Natural Language Processing ◽  
Natural Language ◽  
Information Extraction ◽  
Supervised Learning ◽  
Language Processing ◽  
Name Entity Recognition ◽  
Entity Recognition ◽  
Name Entity ◽  
Supervised Learning Algorithms ◽  
Processing Information

This work aims at defining and evaluating different techniques to automatically build temporal news sequences. The approach proposed is composed by three steps: (i) near duplicate documents detention; (ii) keywords extraction; (iii) news sequences creation. This approach is based on: Natural Language Processing, Information Extraction, Name Entity Recognition and supervised learning algorithms. The proposed methodology got a precision of 93.1% for news chains sequences creation.

scholarly journals Accurate Name Entity Recognition for Biomedical Literatures: A Combined High-quality Manual Annotation and Deep-learning Natural Language Processing Study

2021 ◽  
Author(s):  
Dao-Ling Huang ◽  
Quanlei Zeng ◽  
Yun Xiong ◽  
Shuixia Liu ◽  
Chaoqun Pang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Language Processing ◽  
Name Entity Recognition ◽  
Entity Recognition ◽  
Manual Annotation ◽  
Gene Variant ◽  
High Quality ◽  
Name Entity ◽  
Entity Annotation

A combined high-quality manual annotation and deep-learning natural language processing study is reported to make accurate name entity recognition (NER) for biomedical literatures. A home-made version of entity annotation guidelines on biomedical literatures was constructed. Our manual annotations have an overall over 92% consistency for all the four entity types such as gene, variant, disease and species with the same publicly available annotated corpora from other experts previously. A total of 400 full biomedical articles from PubMed are annotated based on our home-made entity annotation guidelines. Both a BERT-based large model and a DistilBERT-based simplified model were constructed, trained and optimized for offline and online inference, respectively. The F1-scores of NER of gene, variant, disease and species for the BERT-based model are 97.28%, 93.52%, 92.54% and 95.76%, respectively, while those for the DistilBERT-based model are 95.14%, 86.26%, 91.37% and 89.92%, respectively. The F1 scores of the DistilBERT-based NER model retains 97.8%, 92.2%, 98.7% and 93.9% of those of BERT-based NER for gene, variant, disease and species, respectively. Moreover, the performance for both our BERT-based NER model and DistilBERT-based NER model outperforms that of the state-of-art model,BioBERT, indicating the significance to train an NER model on biomedical-domain literatures jointly with high-quality annotated datasets.

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