spaCy/website/docs/usage/_data.json

{
    "sidebar": {
        "Get started": {
            "Installation": "./",
            "Models": "models",
            "Lightning tour": "lightning-tour",
            "Resources": "resources"
        },
        "Workflows": {
            "Loading the pipeline": "language-processing-pipeline",
            "Processing text": "processing-text",
            "spaCy's data model": "data-model",
            "POS tagging": "pos-tagging",
            "Using the parse": "dependency-parse",
            "Entity recognition": "entity-recognition",
            "Custom pipelines": "customizing-pipeline",
            "Rule-based matching": "rule-based-matching",
            "Word vectors": "word-vectors-similarities",
            "Deep learning": "deep-learning",
            "Custom tokenization": "customizing-tokenizer",
            "Training": "training",
            "Adding languages": "adding-languages"
        },
        "Examples": {
            "Tutorials": "tutorials",
            "Showcase": "showcase"
        }
    },

    "index": {
        "title": "Install spaCy",
        "next": "models"
    },

    "models": {
        "title": "Models",
        "next": "lightning-tour"
    },

    "lightning-tour": {
        "title": "Lightning tour",
        "next": "resources"
    },

    "resources": {
        "title": "Resources"
    },

    "language-processing-pipeline": {
        "title": "Loading a language processing pipeline",
        "next": "processing-text"
    },

    "customizing-pipeline": {
        "title": "Customizing the pipeline",
        "next": "customizing-tokenizer"
    },

    "processing-text": {
        "title": "Processing text",
        "next": "data-model"
    },

    "data-model": {
        "title": "Understanding spaCy's data model"
    },

    "dependency-parse": {
        "title": "Using the dependency parse",
        "next": "entity-recognition"
    },

    "entity-recognition": {
        "title": "Entity recognition",
        "next": "rule-based-matching"
    },

    "rule-based-matching": {
        "title": "Rule-based matching"
    },

    "word-vectors-similarities": {
        "title": "Using word vectors and semantic similarities"
    },

    "deep-learning": {
        "title": "Hooking a deep learning model into spaCy"
    },

    "customizing-tokenizer": {
        "title": "Customizing the tokenizer",
        "next": "training"
    },

    "training": {
        "title": "Training the tagger, parser and entity recognizer"
    },

    "pos-tagging": {
        "title": "Part-of-speech tagging",
        "next": "dependency-parse"
    },

    "adding-languages": {
        "title": "Adding languages",
        "next": "training"
    },

    "showcase": {
        "title": "Showcase",

        "libraries": {
            "spacy-nlp": {
                "url": "https://github.com/kengz/spacy-nlp",
                "author": "Wah Loon Keng",
                "description": "Expose spaCy NLP text parsing to Node.js (and other languages) via Socket.IO."
            },
            "spacy-api-docker": {
                "url": "https://github.com/jgontrum/spacy-api-docker",
                "author": "Johannes Gontrum",
                "description": "spaCy accessed by a REST API, wrapped in a Docker container."
            },
            "textacy": {
                "url": "https://github.com/chartbeat-labs/textacy",
                "author": " Burton DeWilde (Chartbeat)",
                "description": "Higher-level NLP built on spaCy."
            },
            "visual-qa": {
                "url": "https://github.com/avisingh599/visual-qa",
                "author": "Avi Singh",
                "description": "Keras-based LSTM/CNN models for Visual Question Answering."
            },
            "rasa_nlu": {
                "url": "https://github.com/golastmile/rasa_nlu",
                "author": "LASTMILE",
                "description": "High level APIs for building your own language parser using existing NLP and ML libraries."
            }
        },
        "visualizations": {
            "displaCy": {
                "url": "https://demos.explosion.ai/displacy",
                "author": "Ines Montani",
                "description": "An open-source NLP visualiser for the modern web.",
                "image": "displacy.jpg"
            },
            "displaCy ENT": {
                "url": "https://demos.explosion.ai/displacy-ent",
                "author": "Ines Montani",
                "description": "An open-source named entity visualiser for the modern web.",
                "image": "displacy-ent.jpg"
            }
        },
        "products": {
            "sense2vec": {
                "url": "https://demos.explosion.ai/sense2vec",
                "author": "Matthew Honnibal and Ines Montani",
                "description": "Semantic analysis of the Reddit hivemind.",
                "image": "sense2vec.jpg"
            },
            "TruthBot": {
                "url": "http://summerscope.github.io/govhack/2016/truthbot/",
                "author": "Team Truthbot",
                "description": "The world's first artificially intelligent fact checking robot.",
                "image": "truthbot.jpg"
            },
            "Laice": {
                "url": "https://github.com/kendricktan/laice",
                "author": "Kendrick Tan",
                "description": "Train your own Natural Language Processor from a browser.",
                "image": "laice.jpg"
            },
            "FoxType": {
                "url": "https://foxtype.com",
                "description": "Smart tools for writers.",
                "image": "foxtype.jpg"
            },
            "Kip": {
                "url": "https://kipthis.com",
                "description": "An AI chat assistant for group shopping.",
                "image": "kip.jpg"
            },
            "Indico": {
                "url": "https://indico.io",
                "description": "Text and image analysis powered by Machine Learning.",
                "image": "indico.jpg"
            },
            "TextAnalysisOnline": {
                "url": "http://textanalysisonline.com",
                "description": "Online tool for spaCy's tokenizer, parser, NER and more.",
                "image": "textanalysis.jpg"
            }
        },
        "books": {
            "Introduction to Machine Learning with Python: A Guide for Data Scientists": {
                "url": "https://books.google.de/books?id=vbQlDQAAQBAJ",
                "author": "Andreas C. Müller and Sarah Guido (O'Reilly, 2016)",
                "description": "Andreas is a lead developer of Scikit-Learn, and Sarah is a lead data scientist at Mashable. We're proud to get a mention."
            },

            "Text Analytics with Python: A Practical Real-World Approach to Gaining Actionable Insights from your Data": {
                "url": "https://www.amazon.com/Text-Analytics-Python-Real-World-Actionable/dp/148422387X",
                "author": "Dipanjan Sarkar (Apress / Springer, 2016)",
                "description": "Derive useful insights from your data using Python. Learn the techniques related to natural language processing and text analytics, and gain the skills to know which technique is best suited to solve a particular problem."
            }
        },
        "research": {
            "Distributional semantics for understanding spoken meal descriptions": {
                "url": "https://www.semanticscholar.org/paper/Distributional-semantics-for-understanding-spoken-Korpusik-Huang/5f55c5535e80d3e5ed7f1f0b89531e32725faff5",
                "author": "Mandy Korpusik et al. (2016)"
            },

            "Refactoring the Genia Event Extraction Shared Task Toward a General Framework for IE-Driven KB Development": {
                "url": "https://www.semanticscholar.org/paper/Refactoring-the-Genia-Event-Extraction-Shared-Task-Kim-Wang/06d94b64a7bd2d3433f57caddad5084435d6a91f",
                "author": "Jin-Dong Kim et al. (2016)"
            },
            "Mixing Dirichlet Topic Models and Word Embeddings to Make lda2vec": {
                "url": "https://www.semanticscholar.org/paper/Mixing-Dirichlet-Topic-Models-and-Word-Embeddings-Moody/bf8116e06f7b498c6abfbf97aeb67d0838c08609",
                "author": "Christopher E. Moody (2016)"
            },
            "Predicting Pre-click Quality for Native Advertisements": {
                "url": "https://www.semanticscholar.org/paper/Predicting-Pre-click-Quality-for-Native-Zhou-Redi/564985430ff2fbc3a9daa9c2af8997b7f5046da8",
                "author": "Ke Zhou et al. (2016)"
            },
            "Threat detection in online discussions": {
                "url": "https://www.semanticscholar.org/paper/Threat-detection-in-online-discussions-Wester-%C3%98vrelid/f4150e2fb4d8646ebc2ea84f1a86afa1b593239b",
                "author": "Aksel Wester et al. (2016)"
            },
            "The language of mental health problems in social media": {
                "url": "https://www.semanticscholar.org/paper/The-language-of-mental-health-problems-in-social-Gkotsis-Oellrich/537db6c2984514d92a754a591841e2e20845985a",
                "author": "George Gkotsis et al. (2016)"
            }
        }
    },

    "tutorials": {
        "title": "Tutorials",
        "next": "showcase",

        "first_steps": {
            "Setting up an NLP environment with Python": {
                "url": "https://shirishkadam.com/2016/10/06/setting-up-natural-language-processing-environment-with-python/",
                "author": "Shirish Kadam"
            },
            "NLP with spaCy in 10 lines of code": {
                "url": "https://github.com/cytora/pycon-nlp-in-10-lines",
                "author": "Andraz Hribernik et al. (Cytora)",
                "tags": ["jupyter"]
            },
            "Intro to NLP with spaCy": {
                "url": "https://nicschrading.com/project/Intro-to-NLP-with-spaCy/",
                "author": "J Nicolas Schrading"
            },
            "NLP with spaCy and IPython Notebook": {
                "url": "http://blog.sharepointexperience.com/2016/01/nlp-and-sharepoint-part-1/",
                "author": "Dustin Miller (SharePoint)",
                "tags": ["jupyter"]
            },
            "Getting Started with spaCy": {
                "url": "http://textminingonline.com/getting-started-with-spacy",
                "author": "TextMiner"
            },
            "spaCy – A fast natural language processing library": {
                "url": "https://bjoernkw.com/2015/11/22/spacy-a-fast-natural-language-processing-library/",
                "author": "Björn Wilmsmann"
            },
            "NLP (almost) From Scratch - POS Network with spaCy": {
                "url": "http://sujitpal.blogspot.de/2016/07/nlp-almost-from-scratch-implementing.html",
                "author": "Sujit Pal",
                "tags": ["gensim", "keras"]
            },
            "NLP tasks with various libraries": {
                "url": "http://clarkgrubb.com/nlp",
                "author": "Clark Grubb"
            },
            "A very (very) short primer on spacy.io": {
                "url": "http://blog.milonimrod.com/2015/10/a-very-very-short-primer-on-spacyio.html",
                "author": "Nimrod Milo  "
            }
        },

        "deep_dives": {
            "Modern NLP in Python – What you can learn about food by analyzing a million Yelp reviews": {
                "url": "http://nbviewer.jupyter.org/github/skipgram/modern-nlp-in-python/blob/master/executable/Modern_NLP_in_Python.ipynb",
                "author": "Patrick Harrison (S&P Global)",
                "tags": ["jupyter", "gensim"]
            },
            "Deep Learning with custom pipelines and Keras": {
                "url": "https://explosion.ai/blog/spacy-deep-learning-keras",
                "author": "Matthew Honnibal",
                "tags": ["keras", "sentiment"]
            },
            "A decomposable attention model for Natural Language Inference": {
                "url": "https://github.com/explosion/spaCy/tree/master/examples/keras_parikh_entailment",
                "author": "Matthew Honnibal",
                "tags": ["keras", "similarity"]
            },

            "Using the German model": {
                "url": "https://explosion.ai/blog/german-model",
                "author": "Wolfgang Seeker",
                "tags": ["multi-lingual"]
            },
            "Sense2vec with spaCy and Gensim": {
                "url": "https://explosion.ai/blog/sense2vec-with-spacy",
                "author": "Matthew Honnibal",
                "tags": ["big data", "gensim"]
            },
            "Building your bot's brain with Node.js and spaCy": {
                "url": "https://explosion.ai/blog/chatbot-node-js-spacy",
                "author": "Wah Loon Keng",
                "tags": ["bots", "node.js"]
            },
            "An intent classifier with spaCy": {
                "url": "http://blog.themusio.com/2016/07/18/musios-intent-classifier-2/",
                "author": "Musio",
                "tags": ["bots", "keras"]
            },
            "Visual Question Answering with spaCy": {
                "url": "http://iamaaditya.github.io/2016/04/visual_question_answering_demo_notebook",
                "author": "Aaditya Prakash",
                "tags": ["vqa", "keras"]
            },
            "Extracting time suggestions from emails with spaCy": {
                "url": "https://medium.com/redsift-outbox/what-time-cc9ce0c2aed2",
                "author": "Chris Savvopoulos",
                "tags": ["ner"]
            },

            "Advanced text analysis with spaCy and Scikit-Learn": {
                "url": "https://github.com/JonathanReeve/advanced-text-analysis-workshop-2017/blob/master/advanced-text-analysis.ipynb",
                "author": "Jonathan Reeve",
                "tags": ["jupyter", "scikit-learn"]
            }
        },

        "code": {
            "Information extraction": {
                "url": "https://github.com/explosion/spaCy/blob/master/examples/information_extraction.py",
                "author": "Matthew Honnibal",
                "tags": ["snippet"]
            },
            "Neural bag of words": {
                "url": "https://github.com/explosion/spaCy/blob/master/examples/nn_text_class.py",
                "author": "Matthew Honnibal",
                "tags": ["sentiment"]
            },
            "Part-of-speech tagging": {
                "url": "https://github.com/explosion/spaCy/blob/master/examples/pos_tag.py",
                "author": "Matthew Honnibal",
                "tags": ["pos"]
            },
            "Parallel parse": {
                "url": "https://github.com/explosion/spaCy/blob/master/examples/parallel_parse.py",
                "author": "Matthew Honnibal",
                "tags": ["big data"]
            },
            "Inventory count": {
                "url": "https://github.com/explosion/spaCy/tree/master/examples/inventory_count",
                "author": "Oleg Zd"
            },
            "Multi-word matches": {
                "url": "https://github.com/explosion/spaCy/blob/master/examples/multi_word_matches.py",
                "author": "Matthew Honnibal",
                "tags": ["matcher", "out of date"]
            }
        }
    }
}