Hub
Pricing About
  • Software
  • Blog
  • Forum
  • Events
  • Documentation
  • About KNIME
  • KNIME Community Hub
  • Nodes
  • StanfordNLP NE Learner
NodeNode / Learner

StanfordNLP NE Learner

Other Data Types Text Processing Enrichment
Drag & drop
Like
Copy short link

The StanfordNLP NE Learner creates a conditional random field model based on documents and a dictionary with entities that occur in the documents. The chosen tag and the used dictionary will be saved internally, so they can be used by the StanfordNLP NE tagger to tag new documents and validate the model. If you want to use the model externally, the model file can be found at your workflow directory:

/%KNIMEWORKSPACE%/%WORKFLOW%/StanfordNLP NE Learner(##)/port_1/object/portobject.zip

You can select the document column and the dictionary column to train your model with. It is possible to use multi-term entities within the dictionary. There is also a tab in the dialog to specify the learner properties. Currently, there are only a few options, since the number of parameters is pretty huge. So please contact us, if there are important/highly used parameters, we should integrate!

NOTE : If you are interested in the StanfordNLP toolkit, please visit http://nlp.stanford.edu/software/ . Some of the following property descriptions are taken from the NERFeatureFactory class from StanfordNLP. Please look into it for further information.

Node details

Input ports
  1. Type: Table
    Documents input table
    The input table containing the documents to train the model with.
  2. Type: Table
    Dictionary input table
    The input dictionary containing known single- and/or multi-term entities to train the model.
Output ports
  1. Type: StanfordNERModelPortObject
    Model output
    The StanfordNLP NE model.

Extension

The StanfordNLP NE Learner node is part of this extension:

  1. Go to item

Related workflows & nodes

  1. Go to item
    NER Tagger Model Training
    NLP Natural Language Processing NER
    +1
    This workflows shows how to train a model for named-entity recognition. The workflow star…
    knime > Examples > 08_Other_Analytics_Types > 01_Text_Processing > 14_NER_Tagger_Model_Training
    knime
  2. Go to item
    NER Tagger Model Training
    Education
    This workflows shows how to train a model for named-entity recognition. The model can be …
    knime > Education > Courses > L4-TP Introduction to Text Processing > Supplementary Workflows > NER_Tagger_Model_Training
    knime
  3. Go to item
    Train a NER Model
    Preprocessing Filter Model training
    +3
    This workflow describes the model training process. The first part reads the text corpus …
    knime > Examples > 08_Other_Analytics_Types > 02_Chemistry_and_Life_Sciences > 04_Prediction_Of_Drug_Purpose > 02_Train_A_NER_Model
    knime
  4. Go to item
    Train a NER Model
    Preprocessing Filter Model training
    +3
    This workflow describes the model training process. The first part reads the text corpus …
    b_eslami > Public > 02_Chemistry_and_Life_Sciences > 04_Prediction_Of_Drug_Purpose > 02_Train_A_NER_Model
    b_eslami
  5. Go to item
    Tagging of Disease Names in Biomedical Literature
    Text mining Network mining Practicing Data Science
    Automated access to disease information is an important goal of information extraction an…
    knime > Examples > 08_Other_Analytics_Types > 02_Chemistry_and_Life_Sciences > 03_Fun_with_Tags
    knime
  6. Go to item
    Fun with Tags
    Automated access to disease information is an important goal of information extraction an…
    b_eslami > Public > 02_Chemistry_and_Life_Sciences > 03_Fun_with_Tags
    b_eslami
  7. Go to item
    Fun with Tags
    Biology
    Automated access to disease information is an important goal of information extraction an…
    b_eslami > Public > 02_Chemistry_and_Life_Sciences > 02_Fetch_And_Transform_PubChem_Data > 03_Fun_with_Tags
    b_eslami
  1. Go to item
  2. Go to item
  3. Go to item

KNIME
Open for Innovation

KNIME AG
Talacker 50
8001 Zurich, Switzerland
  • Software
  • Getting started
  • Documentation
  • E-Learning course
  • Solutions
  • KNIME Hub
  • KNIME Forum
  • Blog
  • Events
  • Partner
  • Developers
  • KNIME Home
  • KNIME Open Source Story
  • Careers
  • Contact us
Download KNIME Analytics Platform Read more on KNIME Business Hub
© 2023 KNIME AG. All rights reserved.
  • Trademarks
  • Imprint
  • Privacy
  • Terms & Conditions
  • Credits