StanfordNLP NE Tagger0 ×
StreamableKNIME Textprocessing Plug-in version 3.6.0.v201807031132 by KNIME AG, Zurich, Switzerland
This node assigns a named entity tag to each term of a document.
It is applicable for English, German and Spanish texts. The
built-in tagger models are models created by the Stanford NLP group:
http://nlp.stanford.edu/software/.
You can use the StanfordNLP NE Learner to create your own model based on untagged documents and a dictionary
and forward the model to the second input port of this node.
If there is no input model, the "use model from input port" option will be deactivated.
The other way around, if there is a model at the input port and the optionis activated, the StanfordNLP
model selection will be disabled.
Note: The provided tagger models vary in memory consumption and processing speed. Especially the distsim models have an increased runtime, but mostly a better performance as well. There are also models without distributional similarity features. For the usage of these models it is recommended to run KNIME with at least 2GB of heap space. To increase the head space, change the -Xmx setting in the knime.ini file.
Options
General options
- Document column
- The column containing the documents to tag.
- Replace documents
- If checked, the documents will be replaced by the new tagged documents. Otherwise the tagged documents will be appended as new column.
- Append column
- The name of the new appended column, containing the tagged documents.
- Word tokenizer
- Select the tokenizer used for word tokenization. Go to Preferences -> KNIME -> Textprocessing to read the description for each tokenizer.
- Number of maximal parallel tagging processes
- Defines the maximal number of parallel threads that are used for tagging. Please note, that for each thread a tagging model will be loaded into memory. If this value is set to a number greater than 1, make sure that enough heap space is available, in order to be able to load the models. If you are not sure how much heap is available for KNIME, leave the number to 1.
Tagger Options
- Unmodifiable flag
- The unmodifiable flag.
- Use model from input port
- If checked, the model from second input port will be included.
- Combine multi-words
- If checked, consecutive words with the same tag will be combined.
- Built-in tagger model
- Built-in StanfordNLP tagger models. Choose one, if you do not have an external model. For more information visit the
StanfordNLP model description):
- English all 3 class (distsim): Location, Person, Organization
- English conll 4 class (distsim): Location, Person, Organization, Misc
- English muc 7 class (distsim): Location, Person, Organization, Money, Percent, Date, Time
- English nowiki 3 class caseless (distsim): Location, Person, Organization
- English nowiki 3 class (no distsim): Location, Person, Organization
- English all 3 class (no distsim): Location, Person, Organization
- English conll 4 class (no distsim): Location, Person, Organization, Misc
- English muc 7 class (no distsim): Location, Person, Organization, Money, Percent, Date, Time
- English all 3 class caseless (distsim): Location, Person, Organization
- English conll 4 class caseless (distsim): Location, Person, Organization, Misc
- English muc 7 class caseless (distsim): Location, Person, Organization, Money, Percent, Date, Time
- German Conll GermEval 2014 hgc (created by Faruqui and Pado)
- German dewac (created by Faruqui and Pado)
- German hgc (created by Faruqui and Pado)
- Spanish ancora
- Spanish KBP ancora
Input Ports
- The input table containing the documents to tag.
- The input port object containing the StanfordNLP NE model, the used dictionary and the used tag.
Output Ports
Workflows
Update Site
To use this node in KNIME, install KNIME Textprocessing Plug-in from the following update site: