spaCy/website/docs/usage/_data.json

{
    "sidebar": {
        "Get started": {
            "Installation": "./",
            "Models": "models",
            "spaCy 101": "spacy-101",
            "Lightning tour": "lightning-tour",
            "What's new in v2.0": "v2"
        },
        "Guides": {
            "POS tagging": "pos-tagging",
            "Using the parse": "dependency-parse",
            "Entity recognition": "entity-recognition",
            "Word vectors": "word-vectors-similarities",
            "Custom tokenization": "customizing-tokenizer",
            "Rule-based matching": "rule-based-matching",
            "Adding languages": "adding-languages",
            "Processing pipelines": "language-processing-pipeline",
            "Deep learning": "deep-learning",
            "Production use": "production-use",
            "Training": "training",
            "Training NER": "training-ner",
            "Saving & loading": "saving-loading",
            "Visualizers": "visualizers"
        },
        "Examples": {
            "Tutorials": "tutorials",
            "Showcase": "showcase"
        }
    },

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

    "models": {
        "title": "Models",
        "next": "spacy-101",
        "quickstart": true
    },

    "spacy-101": {
        "title": "spaCy 101",
        "next": "lightning-tour"
    },

    "lightning-tour": {
        "title": "Lightning tour",
        "next": "v2"
    },

    "visualizers": {
        "title": "Visualizers"
    },

    "v2": {
        "title": "What's new in v2.0"
    },

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

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

    "entity-recognition": {
        "title": "Named Entity Recognition",
        "next": "training-ner"
    },

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

    "customizing-tokenizer": {
        "title": "Customising the tokenizer",
        "next": "rule-based-matching"
    },

    "rule-based-matching": {
        "title": "Rule-based matching",
        "next": "adding-languages"
    },

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

    "language-processing-pipeline": {
        "title": "Language processing pipelines",
        "next": "deep-learning"
    },

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

    "production-use": {
        "title": "Production use",
        "next": "training"
    },

    "training": {
        "title": "Training spaCy's statistical models",
        "next": "saving-loading"
    },

    "training-ner": {
        "title": "Training the Named Entity Recognizer",
        "next": "saving-loading"
    },

    "saving-loading": {
        "title": "Saving, loading and data serialization"
    },

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

        "libraries": {
            "spacy_api": {
                "url": "https://github.com/kootenpv/spacy_api",
                "author": "Pascal van Kooten",
                "description": "Server/client to load models in a separate, dedicated process."
            },
            "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."
            },
            "spacy-nlp-zeromq": {
                "url": "https://github.com/pasupulaphani/spacy-nlp-docker",
                "author": "Phaninder Pasupula",
                "description": "Docker image exposing spaCy with ZeroMQ bindings."
            },
            "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."
            },
            "spacyr": {
                "url": "https://github.com/kbenoit/spacyr",
                "author": "Kenneth Benoit",
                "description": "An R wrapper for spaCy."
            }
        },
        "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": {
            "Training a new entity type": {
                "url": "https://github.com/explosion/spaCy/blob/master/examples/training/train_new_entity_type.py",
                "author": "Matthew Honnibal",
                "tags": ["ner", "training"]
            },

            "Training an NER system from scratch": {
                "url": "https://github.com/explosion/spaCy/blob/master/examples/training/train_ner_standalone.py",
                "author": "Matthew Honnibal",
                "tags": ["ner", "training"]
            },

            "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"]
            }
        }
    }
}