Spark NLP in Action

Open Source

Recognize entities in text
Recognize Persons, Locations, Organizations and Misc entities using out of the box pretrained Deep Learning models based on GloVe (glove_100d) and BERT (ner_dl_bert) word embeddings.
Recognize more entities in text
Recognize over 18 entities such as Countries, People, Organizations, Products, Events, etc. using an out of the box pretrained NerDLApproach trained on the OntoNotes corpus.
Classify documents
Classify open-domain, fact-based questions into one of the following broad semantic categories: Abbreviation, Description, Entities, Human Beings, Locations or Numeric Values.
Analyze sentiment in movie reviews and tweets
Detect the general sentiment expressed in a movie review or tweet by using our pretrained Spark NLP DL classifier.
Detect emotions in tweets
Automatically identify Joy, Surprise, Fear, Sadness in Tweets using out pretrained Spark NLP DL classifier.
Detect cyberbullying in tweets
Identify Racism, Sexism or Neutral tweets using our pretrained emotions detector.
Detect sarcastic tweets
Checkout our sarcasm detection pretrained Spark NLP model. It is able to tell apart normal content from sarcastic content.
Detect toxic comments
Classify comments and tweets into Toxic, Insults, Hate, Obscene, Threat.
Identify Fake news
Determine if news articles are Real of Fake.
Detect Spam messages
Automatically identify messages as being regular messages or Spam.
Find a text in document
Finds a text in document either by keyword or by regex expression.
Grammar analysis & Dependency Parsing
Visualize the syntactic structure of a sentence as a directed labeled graph where nodes are labeled with the part of speech tags and arrows contain the dependency tags.
Split and clean text
Spark NLP pretrained annotators allow an easy and straightforward processing of any type of text documents. This demo showcases our Sentence Detector, Tokenizer, Stemmer, Lemmatizer, Normalizer and Stop Words Removal.
Spell check your text documents
Spark NLP contextual spellchecker allows the quick identification of typos or spell issues within any text document.

Languages

Detect language
Spark NLP Language Detector offers support for 20 different languages: Bulgarian, Czech, German, Greek, English, Spanish, Finnish, French, Croatian, Hungarian, Italy, Norwegian, Polish, Portuguese, Romanian, Russian, Slovak, Swedish, Turkish, and Ukrainian
Recognize entities in English text
Recognize Persons, Locations, Organizations and Misc entities using out of the box pretrained Deep Learning models based on GloVe (glove_100d) and BERT (ner_dl_bert) word embeddings.
Recognize entities in French text
Recognize Persons, Locations, Organizations and Misc entities using an out of the box pretrained Deep Learning model and GloVe word embeddings (glove_100d).
Recognize entities in German text
Recognize Persons, Locations, Organizations and Misc entities using an out of the box pretrained Deep Learning model and GloVe word embeddings (glove_300d).
Recognize entities in Italian text
Recognize Persons, Locations, Organizations and Misc entities using an out of the box pretrained Deep Learning model and GloVe word embeddings (glove_300d).
Recognize entities in Norwegian text
Recognize Persons, Locations, Organizations and Misc entities using 3 different out of the box pretrained Deep Learning models based on different GloVe word embeddings (glove_100d & glove_300d).
Recognize entities in Polish text
Recognize Persons, Locations, Organizations and Misc entities using 3 different out of the box pretrained Deep Learning models based on different GloVe word embeddings (glove_100d & glove_300d).
Recognize entities in Portuguese text
Recognize Persons, Locations, Organizations and Misc entities using 3 different out of the box pretrained Deep Learning models based on different GloVe word embeddings (glove_100d & glove_300d).
Recognize entities in Russian text
Recognize Persons, Locations, Organizations and Misc entities using 3 different out of the box pretrained Deep Learning models based on different GloVe word embeddings (glove_100d & glove_300d).
Recognize entities in Spanish text
Recognize Persons, Locations, Organizations and Misc entities using 3 different out of the box pretrained Deep Learning models based on different GloVe word embeddings (glove_100d & glove_300d).

Healthcare

Detect signs and symptoms
Automatically identify Signs and Symptoms in clinical documents using two of our pretrained Spark NLP clinical models.
Detect diagnosis and procedures
Automatically identify diagnoses and procedures in clinical documents using the pretrained Spark NLP clinical model ner_clinical.
Detect drugs and prescriptions
Automatically identify Drug, Dosage, Duration, Form, Frequency, Route, and Strength details in clinical documents using three of our pretrained Spark NLP clinical models.
Detect risk factors
Automatically identify risk factors such as Coronary artery disease, Diabetes, Family history, Hyperlipidemia, Hypertension, Medications, Obesity, PHI, Smoking habits in clinical documents using our pretrained Spark NLP model.
Detect anatomical references
Automatically identify Anatomical System, Cell, Cellular Component, Anatomical Structure, Immaterial Anatomical Entity, Multi-tissue Structure, Organ, Organism Subdivision, Organism Substance, Pathological Formation in clinical documents using our pretrained Spark NLP model.
Detect demographic information
Automatically identify demographic information such as Date, Doctor, Hospital, ID number, Medical record, Patient, Age, Profession, Organization, State, City, Country, Street, Username, Zip code, Phone number in clinical documents using three of our pretrained Spark NLP models.
Detect clinical events
Automatically identify a variety of clinical events such as Problems, Tests, Treatments, Admissions or Discharges, in clinical documents using two of our pretrained Spark NLP models.
Detect lab results
Automatically identify Lab test names and Lab results from clinical documents using our pretrained Spark NLP model.
Detect tumor characteristics
Automatically identify tumor characteristics such as Anatomical systems, Cancer, Cells, Cellular components, Genes and gene products, Multi-tissue structures, Organs, Organisms, Organism subdivisions, Simple chemicals, Tissues from clinical documents using our pretrained Spark NLP model.
Spell checking for clinical documents
Automatically identify from clinical documents using our pretrained Spark NLP model ner_bionlp.