Python-LanguageCrunchNLP服务器docker镜像

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  • 2022-06-09 02:49
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LanguageCrunch NLP服务器docker镜像
Python-LanguageCrunchNLP服务器docker镜像.zip
  • languagecrunch-master
  • build.sh
    56B
  • src
  • main.py
    9.9KB
  • __init__.py
    0B
  • countries_tagger.py
    4KB
  • sentence_classifier.py
    5.2KB
  • Dockerfile
    828B
  • requirements.txt
    451B
  • README.md
    4KB
  • contributors.md
    60B
内容介绍
# LanguageCrunch NLP Service docker image Docker image: https://hub.docker.com/r/artpar/languagecrunch/ - **[Quickstart](#quickstart)** - **[Endpoints](#endpoints)** - **[Model Details](#model-details)** - [Sentiment](#sentiment) - [Entity extraction](#entity-extraction) - [Sentence type detection](#sentence-type-detection) - [Relation extraction](#relation-extraction) - [Word look up](#word-look-up) ## Quickstart Pull and run the Docker image, listening on port 8080: ``` $ docker run -it -p 8080:8080 artpar/languagecrunch ``` Example API call: ```bash $ curl http://localhost:8080/nlp/parse?`echo -n "The new twitter is so weird. Seriously. Why is there a new twitter? What was wrong with the old one? Fix it now." | python -c "import urllib, sys; print(urllib.urlencode({'sentence': sys.stdin.read()}))"` ``` ## Endpoints ### Sentence parse [Spacy] `GET http://localhost:8080/nlp/parse?sentence=<URL-encoded sentences>` ### Word lookup [Wordnet] `GET http://localhost:8080/nlp/word?word=ask&pos=v` ### Coreference resolution [neuralcoref] `GET http://localhost:8080/nlp/coref?sentence=<URL-encoded sentences>` ## Model Details ### Sentiment `sentence: The new twitter is so weird. Seriously. Why is there a new twitter? What was wrong with the old one? Fix it now.` ```json { "relations": [], "sentences": [ { "sentence": "The new twitter is so weird. ", "sentence_type": "assertive", "sentiment": { "polarity": -0.18181818181818182, "subjectivity": 0.7272727272727273 }, "root": { "text": "is ", "orth": 2 }, "pos": [ { "text": "The new twitter", "lemma": "the", "pos": "DET", "tag": "DT", "dep": "nsubj", . . . ``` ### Entity extraction - PERSON - NORP - FACILITY - ORG - GPE - LOC - PRODUCT - EVENT - WORK_OF_ART - LAW - LANGUAGE - DATE - TIME - PERCENT - MONEY - QUANTITY - ORDINAL - CARDINAL `Eg: Bill Gates, the founder of Microsoft, hosted a party last night` ```json "entities": [ { "text": "Bill Gates", "label": "PERSON" }, { "text": "Microsoft", "label": "ORG" }, { "text": "last night", "label": "TIME" } ] } ``` ### Sentence type detection - assertive - interrogative - exclamatory - negative ### Relation extraction `Eg: Bill Gates, the founder of Microsoft, hosted a party last night` ``` "relations": [ { "subject": "the founder", "object": "Microsoft", "relation": "ORG" } ], ``` `Eg: Apple is looking at buying U.K. startup for $1 billion` ```[ { subject: "N/A", object: "U.K. startup", relation: "GPE" }, { subject: "buying", object: "$1 billion", relation: "MONEY" } ], ``` ### Word look up - Category of word - Hypernyms - **colour** is a hypernym of **red**. - Specific words of a category - Holonyms - **red** is a holonym of **color** - Synonyms to match - Examples - Word frames ( how the word is used ) - Coreference resolution - Pronouns/references to nouns `Eg: startle, verb` ```json "results": [ { "definition": "to stimulate to action", "examples": [ "..startled him awake", "galvanized into action" ], "lemma_names": [ "startle", "galvanize", "galvanise" ], "hypernyms": [ { "definition": "surprise greatly; knock someone's socks off", "examples": [ "I was floored when I heard that I was promoted" ], "lemma_names": [ "shock", "floor", "ball_over", "blow_out_of_the_water", "take_aback" ] } ], "lemmas": [ { "frame_strings": [ "Somebody startle somebody", "Something startle somebody", "Somebody startle somebody into V-ing something" ], ``` ## Contributors [List of contributors](contributors.md)
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