Extracts relations triplets contained in sentences of a document by investigating relations of tagged named-entities.
The node can be used in two different ways by either checking the Apply preprocessing option or not. If the option is selected, the node takes care of part-of-speech (POS) and named-entity (NE) tagging as well as lemmatizing. Stanford CoreNLP standard settings are used in this case. However, tags are not applied to the documents, since the preprocessing is only applied internally. If the option is unchecked, it is necessary to provide a column with tagged documents (POS and NE) as well as a column containing lemmatized documents. Lemmatized documents consist of terms that were converted to their canonical, dictionary or citation form.
Note: Creating the same pipeline by using KNIME's Stanford nodes with default settings will not necessarily lead to the same results as using the Apply preprocessing option, since KNIME is using the Penn-Treebank (PTB) tag set. This tag set uses the
SYM tag for
any kind of punctuation and quotation marks. However, Stanford CoreNLP uses a modified version of the
PTB tag set to distinguish these symbols, since they are important for dependency parsing and
natural logic annotation.
The node creates four new columns: two object columns containing named-entities, one column containing the type of relation with the highest confidence and a column containing the confidence for this relation. The node handles classic named-entities like
Relation types that can be extracted are
_NR specifies no relation between two entities. A detailed explanation of StanfordNLP's approach for relation extraction can be found in this article.
Note: Relation Extraction is a computationally expensive operation. For the usage of this node it is recommended to run KNIME with at least 4GB of heap space. To increase the heap space, change the -Xmx setting in the knime.ini file.
This node is based on Stanford CoreNLP 3.9.1.
For more information about StanfordNLP and Relation Extraction, click here.
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