I’m quite new to data analysis (and Python in general), and I’m currently a bit stuck in my project. For my NLP-task I need to create training data, i.e. find specific entities in sentences and label them. I have multiple csv files containing the entities I am trying to find, many of them consisting of multiple words. I have tokenized
Tag: named-entity-recognition
Create a NER dictionary from a given text
I have the following variable data[1][‘entities’][0] = (48, 54, ‘Category 1’) stands for (start_offset, end_offset, entity). I want to read each word of data[0] and tag it according to data[1] entities. I am expecting to have as final output, Here, ‘O’ stands for ‘OutOfEntity’, ‘S’ stands for ‘Start’, ‘B’ stands for ‘Between’, and ‘E’ stands for ‘End’ and are unique
Name Entity Recognition (NER) for multiple languages
I am writing some code to perform Named Entity Recognition (NER), which is coming along quite nicely for English texts. However, I would like to be able to apply NER to any language. To do this, I would like to 1) identify the language of a text, and then 2) apply the NER for the identified language. For step 2,
Convert from Prodigy’s JSONL format for labeled NER to spaCy’s training format?
I am new to Prodigy and spaCy as well as CLI coding. I’d like to use Prodigy to label my data for an NER model, and then use spaCy in python to create models. Prodigy outputs in SQLite format. SpaCy takes in this other kind of format, not sure what to call it: How can I convert from one to
How to use spacy to do Name Entity recognition on CSV file
I have tried so many things to do name entity recognition on a column in my csv file, i tried ne_chunk but i am unable to get the result of my ne_chunk in columns like so Instead after using this code, i got this error So, i am wondering if i could do this using spaCy which is another thing