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The initial version of the Socket Python SDK is now on PyPI, enabling developers to more easily interact with the Socket REST API in Python projects.
LingPatLab is a robust API designed to perform advanced Natural Language Processing (NLP) tasks, utilizing the capabilities of the spaCy library. This tool is expertly crafted to convert raw textual data into structured, analyzable forms. It is ideal for developers, researchers, and linguists who require comprehensive processing capabilities, from tokenization to sophisticated text summarization.
To get started with LingPatLab, you can set up the API as follows:
from spacy_core.api import SpacyCoreAPI
api = LingPatLab()
To tokenize and parse input text into structured sentences:
parsed_sentence: Sentence = api.parse_input_text("Your input text here.")
print(parsed_sentence.to_string())
To extract phrases from a structured Sentences object:
phrases: List[str] = api.extract_topics(parsed_sentences)
for phrase in phrases:
print(phrase)
To generate a summary of the input text:
summary: str = api.generate_summary("Your input text here.")
print(summary)
LingPatLab utilizes several custom data classes to structure the data throughout the NLP process:
Sentence
: Represents a single sentence, containing a list of tokens (SpacyResult
objects).Sentences
: Represents a collection of sentences, useful for processing paragraphs or multiple lines of text.SpacyResult
: Encapsulates the detailed analysis of a single token, including part of speech, dependency relations, and additional linguistic features.OtherInfo
: Contains additional information about a token, particularly in relation to its syntactic head.FAQs
Linguistic Pattern Lab using spaCy
We found that lingpatlab demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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