Borrowing wisdom from world: modeling rich external knowledge for Chinese named entity recognition

2022 ◽  
Author(s):  
Yu Nie ◽  
Yilai Zhang ◽  
Yongkang Peng ◽  
Lisha Yang
Keyword(s):  
Named Entity Recognition ◽  
Entity Recognition ◽  
External Knowledge ◽  
Named Entity ◽  
World Modeling

When External Knowledge Does Not Aggregate in Named Entity Recognition

2021 ◽  
pp. 616-627
Author(s):  
Pedro Ivo Monteiro Privatto ◽  
Ivan Rizzo Guilherme
Keyword(s):  
Named Entity Recognition ◽  
Entity Recognition ◽  
External Knowledge ◽  
Named Entity

scholarly journals DEXTER: Deep Encoding of External Knowledge for Named Entity Recognition in Virtual Assistants

2021 ◽  
Author(s):  
Deepak Muralidharan ◽  
Joel Ruben Antony Moniz ◽  
Weicheng Zhang ◽  
Stephen Pulman ◽  
Lin Li ◽  
...  
Keyword(s):  
Named Entity Recognition ◽  
Entity Recognition ◽  
External Knowledge ◽  
Named Entity

scholarly journals A Study of the Importance of External Knowledge in the Named Entity Recognition Task

2018 ◽  
Author(s):  
Dominic Seyler ◽  
Tatiana Dembelova ◽  
Luciano Del Corro ◽  
Johannes Hoffart ◽  
Gerhard Weikum
Keyword(s):  
Named Entity Recognition ◽  
Recognition Task ◽  
Entity Recognition ◽  
External Knowledge ◽  
Named Entity

Arabic named entity recognition using optimized feature sets

2008 ◽  
Author(s):  
Yassine Benajiba ◽  
Mona Diab ◽  
Paolo Rosso
Keyword(s):  
Named Entity Recognition ◽  
Entity Recognition ◽  
Feature Sets ◽  
Named Entity

Developing a RadLex-based Named Entity Recognition Tool for Mining Textual Radiology Reports (Preprint)

2020 ◽  
Author(s):  
Shintaro Tsuji ◽  
Andrew Wen ◽  
Naoki Takahashi ◽  
Hongjian Zhang ◽  
Katsuhiko Ogasawara ◽  
...  
Keyword(s):  
Named Entity Recognition ◽  
Noun Phrases ◽  
General Purpose ◽  
Entity Recognition ◽  
Free Text ◽  
Clinical Text ◽  
Named Entity ◽  
Radiology Reports ◽  
Two Measures ◽  
F Measure

BACKGROUND Named entity recognition (NER) plays an important role in extracting the features of descriptions for mining free-text radiology reports. However, the performance of existing NER tools is limited because the number of entities depends on its dictionary lookup. Especially, the recognition of compound terms is very complicated because there are a variety of patterns. OBJECTIVE The objective of the study is to develop and evaluate a NER tool concerned with compound terms using the RadLex for mining free-text radiology reports. METHODS We leveraged the clinical Text Analysis and Knowledge Extraction System (cTAKES) to develop customized pipelines using both RadLex and SentiWordNet (a general-purpose dictionary, GPD). We manually annotated 400 of radiology reports for compound terms (Cts) in noun phrases and used them as the gold standard for the performance evaluation (precision, recall, and F-measure). Additionally, we also created a compound-term-enhanced dictionary (CtED) by analyzing false negatives (FNs) and false positives (FPs), and applied it for another 100 radiology reports for validation. We also evaluated the stem terms of compound terms, through defining two measures: an occurrence ratio (OR) and a matching ratio (MR). RESULTS The F-measure of the cTAKES+RadLex+GPD was 32.2% (Precision 92.1%, Recall 19.6%) and that of combined the CtED was 67.1% (Precision 98.1%, Recall 51.0%). The OR indicated that stem terms of “effusion”, "node", "tube", and "disease" were used frequently, but it still lacks capturing Cts. The MR showed that 71.9% of stem terms matched with that of ontologies and RadLex improved about 22% of the MR from the cTAKES default dictionary. The OR and MR revealed that the characteristics of stem terms would have the potential to help generate synonymous phrases using ontologies. CONCLUSIONS We developed a RadLex-based customized pipeline for parsing radiology reports and demonstrated that CtED and stem term analysis has the potential to improve dictionary-based NER performance toward expanding vocabularies.

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