spacy-lookup | Named Entity Recognition based on dictionaries | Natural Language Processing library

by mpuig Python Version: 0.1.0 License: MIT

X-Ray Key Features Code Snippets Community Discussions(3)Vulnerabilities Install Support

kandi X-RAY | spacy-lookup Summary

spacy-lookup is a Python library typically used in Artificial Intelligence, Natural Language Processing applications. spacy-lookup has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install spacy-lookup' or download it from GitHub, PyPI.

Named Entity Recognition based on dictionaries

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Support

spacy-lookup has a low active ecosystem.

It has 217 star(s) with 38 fork(s). There are 9 watchers for this library.

It had no major release in the last 12 months.

There are 4 open issues and 10 have been closed. On average issues are closed in 75 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of spacy-lookup is 0.1.0

Quality

spacy-lookup has 0 bugs and 0 code smells.

Security

spacy-lookup has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

spacy-lookup code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

spacy-lookup is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

spacy-lookup releases are available to install and integrate.

Deployable package is available in PyPI.

Build file is available. You can build the component from source.

spacy-lookup saves you 64 person hours of effort in developing the same functionality from scratch.

It has 167 lines of code, 17 functions and 5 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed spacy-lookup and discovered the below as its top functions. This is intended to give you an instant insight into spacy-lookup implemented functionality, and help decide if they suit your requirements.

Process the given text .
Setup spacy package .
Initialize the class .
Returns an iterable of entities .
Return True if the tokens has entities in the given list .
Get entity description .

Get all kandi verified functions for this library.

spacy-lookup Key Features

No Key Features are available at this moment for spacy-lookup.

spacy-lookup Examples and Code Snippets

No Code Snippets are available at this moment for spacy-lookup.

Community Discussions

Trending Discussions on spacy-lookup

Adding tagger to blank English spacy pipeline

SpaCy can't find table(s) lexeme_norm for language 'en' in spacy-lookups-data

spacy lemmatizing inconsistency with lemma_lookup table

QUESTION

Adding tagger to blank English spacy pipeline

Asked 2021-Aug-02 at 15:18

I am having a hard time figuring out how to assemble spacy pipelines bit by bit from built in models in spacy V3. I have downloaded the en_core_web_sm model and can load it with nlp = spacy.load("en_core_web_sm"). Processing of sample text works just fine like this.

Now what I want though is to build an English pipeline from blank and add components bit by bit. I do NOT want to load the entire en_core_web_sm pipeline and exclude components. For the sake of concreteness let's say I only want the spacy default tagger in the pipeline. The documentation suggests to me that

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ANSWER

Answered 2021-Aug-02 at 14:09

nlp.add_pipe("tagger") adds a new blank/uninitialized tagger, not the tagger from en_core_web_sm or any other pretrained pipeline. If you add the tagger this way, you need to initialize and train it before you can use it.

You can add a component from an existing pipeline using the source option:

Source https://stackoverflow.com/questions/68618759

QUESTION

SpaCy can't find table(s) lexeme_norm for language 'en' in spacy-lookups-data

Asked 2021-Feb-25 at 13:12

I am trying to train a text categorization pipe in SpaCy:

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ANSWER

Answered 2021-Feb-25 at 13:12

It isn't allowed to call nlp.begin_training() on pretrained models. If you want to train a new model, just use: nlp = spacy.blank('en') instead of nlp = spacy.load("en_core_web_sm")

However, if you want to continue training on an existing model call optimizer = nlp.create_optimizer() instead of begin_training()

Source https://stackoverflow.com/questions/66367447

QUESTION

spacy lemmatizing inconsistency with lemma_lookup table

Asked 2020-Apr-09 at 12:10

There seems to be an inconsistency when iterating over a spacy document and lemmatizing the tokens compared to looking up the lemma of the word in the Vocab lemma_lookup table.

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ANSWER

Answered 2020-Apr-09 at 12:10

With a model like en_core_web_lg that includes a tagger and rules for a rule-based lemmatizer, it provides the rule-based lemmas rather than the lookup lemmas when POS tags are available to use with the rules. The lookup lemmas aren't great overall and are only used as a backup if the model/pipeline doesn't have enough information to provide the rule-based lemmas.

With faster, the POS tag is ADV, which is left as-is by the rules. If it had been tagged as ADJ, the lemma would be fast with the current rules.

The lemmatizer tries to provide the best lemmas it can without requiring the user to manage any settings, but it's also not very configurable right now (v2.2). If you want to run the tagger but have lookup lemmas, you'll have to replace the lemmas after running the tagger.

Source https://stackoverflow.com/questions/61118213

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install spacy-lookup

You can install using 'pip install spacy-lookup' or download it from GitHub, PyPI.
You can use spacy-lookup like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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