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.
Options
Learner options
Learner Properties
Popular Predecessors
- Tagged Document Viewer5 %
Column Rename5 %- Term to String5 %
Row Filter (deprecated)4 %Stanford tagger3 %- Bag Of Words Creator3 %
- OpenNLP NE Tagger3 %
- Rule-based Row Filter3 %
- Duplicate Row Filter3 %
- Table Creator2 %
- Column Filter2 %
Excel Reader (XLS)2 %- PDF Parser2 %
- GroupBy2 %
- Partitioning2 %
Excel Reader (XLS)2 %- Strings to Document2 %
StanfordNLP NE tagger2 %- StanfordNLP NE Tagger2 %
- Excel Reader2 %
- Sorter2 %
File Reader1 %- Concatenate1 %
- String Manipulation1 %
- Punctuation Erasure1 %
- Dictionary Tagger1 %
Strings To Document1 %- Column Expressions (legacy)1 %
- IDF1 %
- Reference Row Filter1 %
- POS Tagger1 %
- Column Splitter1 %
- Math Formula (Variable)1 %
- Case Converter1 %
- Stanford Tagger1 %
Row Splitter (deprecated)1 %- Joiner1 %
- Domain Calculator< 1 %
- Row Sampling< 1 %
- Stanford Lemmatizer< 1 %
- Document Grabber< 1 %
- Tika Parser< 1 %
- RSS Feed Reader< 1 %
- Term Co-Occurrence Counter< 1 %
- Snowball Stemmer< 1 %
- CSV Reader< 1 %
- Reference Row Splitter< 1 %
- ICF< 1 %
- String to Term< 1 %
CSV Reader< 1 %Database Reader< 1 %Table Reader< 1 %- Java Snippet< 1 %
- Nominal Value Row Filter< 1 %
- Chi-Square Keyword Extractor< 1 %
Bag of Words Creator< 1 %- Document Vector< 1 %
- Inject Variables (Data)< 1 %
- ARFF Reader< 1 %
Loop End< 1 %- PNN Learner (DDA)< 1 %
- Column Resorter< 1 %
- String Replacer< 1 %
- DB Reader< 1 %
Excel Reader (XLS)< 1 %- Keygraph Keyword Extractor< 1 %
- Tika Language Detector< 1 %
- Tika Parser URL Input< 1 %
- Dictionary Filter< 1 %
- Stop Word Filter< 1 %
- Dml Document Parser< 1 %
- Flat File Document Parser< 1 %
Dictionary tagger< 1 %- Dictionary Tagger (Multi Column)< 1 %
OpenNLP NE tagger< 1 %- StanfordNLP NE Scorer< 1 %
- Document Data Extractor< 1 %
- Column Filter (legacy)< 1 %
- String Input (legacy)< 1 %
- Case Converter< 1 %
- Number to String< 1 %
- Empty Table Creator< 1 %
- Porter Stemmer< 1 %
- Abner Tagger< 1 %
- Sentence Extractor< 1 %
- Table Editor (JavaScript)< 1 %
Views
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Workflows
02_Train_A_NER_ModelKNIME Hub- 02_Train_A_NER_ModelKNIME Hub
- 02_Train_A_NER_ModelKNIME Hub
- 02_Train_A_NER_ModelKNIME Hub
- 03_Fun_with_TagsKNIME Hub
- Show all 16 workflows
Links
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Developers
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