scholarly journals Building a Massive Corpus for Named Entity Recognition Using Free Open Data Sources

2019 ◽  
Author(s):  
Daniel Menezes ◽  
Ruy Milidiu ◽  
Pedro Savarese
Keyword(s):  
Named Entity Recognition ◽  
Open Data ◽  
Entity Recognition ◽  
Data Sources ◽  
Named Entity

scholarly journals Heritage Connector: A Machine Learning Framework for Building Linked Open Data from Museum Collections

2021 ◽  
Author(s):  
Kalyan Dutia ◽  
John Stack
Keyword(s):  
Machine Learning ◽  
Named Entity Recognition ◽  
Open Data ◽  
Linked Open Data ◽  
Entity Recognition ◽  
Digital Museum ◽  
Named Entity ◽  
Learning Framework ◽  
Almost All ◽  
Small Cloud

As with almost all data, museum collection catalogues are largely unstructured, variable in consistency and overwhelmingly composed of thin records. The form of these catalogues means that the potential for new forms of research, access and scholarly enquiry that range across multiple collections and related datasets remains dormant. In the project Heritage Connector: Transforming text into data to extract meaning and make connections, we are applying a battery of digital techniques to connect similar, identical and related items within and across collections and other publications. In this paper we describe a framework to create a Linked Open Data knowledge graph (KG) from digital museum catalogues, connect entities within this graph to Wikidata, and create new connections in this graph from text. We focus on the use of machine learning to create these links at scale with a small amount of labelled data, on a mid-range laptop or a small cloud virtual machine. We publish open-source software providing tools to perform the tasks of KG creation, entity matching and named entity recognition under these constraints.

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.

Identification of Food Quality Descriptors in Customer Chat Conversations using Named Entity Recognition

2020 ◽  
Author(s):  
Aditya Kiran Brahma ◽  
Prathyush Potluri ◽  
Meghana Kanapaneni ◽  
Sumanth Prabhu ◽  
Sundeep Teki
Keyword(s):  
Food Quality ◽  
Named Entity Recognition ◽  
Entity Recognition ◽  
Named Entity
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