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Custom entity ruler with SpaCy did not return a match


Custom entity ruler with SpaCy did not return a match

By : user3100671
Date : January 12 2021, 09:11 PM
this will help The problem is an interaction between the NER component provided in the English model and your EntityRuler component. The NER component finds 2 as a number (CARDINAL) and there's a restriction that entities aren't allowed to overlap, so the EntityRuler component doesn't find any matches.
You can either add your EntityRuler before the NER component:
code :
nlp.add_pipe(ruler, before='ner')
ruler = EntityRuler(nlp, overwrite_ents=True)


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Why does my custom spaCy entity type get detected?

Why does my custom spaCy entity type get detected?


By : anirudh
Date : March 29 2020, 07:55 AM
wish of those help This is unexpected behavior. I opened it as spaCy issue 1697: Custom Entity Labels Are Erroneously Detected.
spaCy coreference resolution - named entity recognition (NER) to return unique entity ID's?

spaCy coreference resolution - named entity recognition (NER) to return unique entity ID's?


By : user2183094
Date : November 15 2020, 04:01 AM
To fix this issue As @gdaras comments, coreference resolution is not currently supported in spaCy.
Here is the Stanford coreNLP implementation.
Token extension versus matcher versus phrase matcher vs entity ruler in spaCy

Token extension versus matcher versus phrase matcher vs entity ruler in spaCy


By : Franklin.Q
Date : March 29 2020, 07:55 AM
may help you . I think ultimately, it all comes down to finding the optimal tradeoff between speed, maintainability of the code and the way this piece of logic fits into the larger picture of your application. Finding a few strings in a text is unlikely to be the end goal of what you're trying to do – otherwise, you probably wouldn't be using spaCy and would stick to regular expressions. How your application needs to "consume" the result of the matching and what the matches mean in the larger context should motivate the approach you choose.
As you mention in the conclusion, if your matches are "named entities" by definition, adding them to the doc.ents makes a lot of sense and will even give you an easy way to combine your logic with statistical predictions. Even if it adds slightly more overhead, it'll likely still outperform any scaffolding you'd otherwise have to write around it yourself.
How to prepare data for spacy's custom named entity recognition?

How to prepare data for spacy's custom named entity recognition?


By : user3072507
Date : December 25 2020, 09:19 AM
hope this fix your issue No, spaCy will need exact start & end indices for your entity strings, since the string by itself may not always be uniquely identified and resolved in the source text. Examples:
Apple is usually an ORG, but can be a PERSON. Ann is a PERSON, but not in Annotation tools are best for this purpose.
code :
>>> import re
>>> [m.span() for m in re.finditer('Amazon', 'The Amazon is a river in South America.  Amazon Inc is a company.')]
[(4, 10), (41, 47)]

Unable to create a custom entity type/label using Matcher in Spacy 2

Unable to create a custom entity type/label using Matcher in Spacy 2


By : Tushar Goel
Date : March 29 2020, 07:55 AM
it should still fix some issue Oh okay, I think I found a solution. The label has to be added to nlp.vocab.strings if it is not there:
code :
nlp.vocab.strings.add('FRUIT') 
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