Named Entity Recognition (NER) is the process of automatic extraction of Named Entities (NEs) by means of recognizing (finding the entities in a given text) and their classification (assigning a type). A lot of work has been done in English and other languages for NER but very little ...
You can adjust the threshold for confidence score your system uses to tune your system. If it is more important to identify all potential instances of PII, you can use a lower threshold. This means that you may get more false positives (non- PII data being recognized as PII ...
You can adjust the threshold for confidence score your system uses to tune your system. If it is more important to identify all potential instances of PII, you can use a lower threshold. This means that you may get more false positives (non- PII data being recognized as PII ...
Only the spacy package is required for the named recognition application. Explore the application environment You'll use Docker to run the application in a container. Docker lets you containerize the application, providing a consistent and isolated environment for running it. This means the applicatio...
-1 means all the dataset # The parameters for the training optimizer, including learning rate, lr schedule, etc. optim: name: adam lr: 5e-5 weight_decay: 0.00 # scheduler setup sched: name: WarmupAnnealing # Scheduler params warmup_steps: null warmup_ratio: 0.1 last_epoch: -1 # pytor...
Catastrophic forgetting means that the NER model gets retrained on only the new text that I take for the entity ruler. My new input text would be just one sentence with a date. Then, it would be easy to find out whether catastrophic forgetting happens at all. I can ...
Chinese electronic medical record Named entity recognition Dynamic embedding Domain-specific knowledge 1. Introduction With the accumulation of medical data, many Clinical Decision Support Systems (CDSS) use Electronic Medical Records (EMRs) as an important source of knowledge. Accurately identifying entitie...
This could be an issue, as named entities rarely consist of a single token, which means that the results can be inaccurate if some tokens of the named entity are not correctly identified. When it comes to the Arabic language specifically, the literature, up to the authors’ knowledge, lacks...
vector and the text feature vector to obtain a composite feature vector of each character in the voice signal (S204); and processing the composite feature vector of each character in the voice signal by means of a depth learning model, so as to obtain a named-entity recognition result (S205...
Table 3: Results of Chinese named entity recognition. BS means boundary smoothing. In five out of the above eight datasets, integrating boundary smoothing significantly increases the precision rate with a slight drop in the recall, resulting in a better overall F1 score. This is consistent with ...