Abstract: An example method of document cluster labeling comprises: selecting a current document cluster of a plurality of document clusters (e.g., the current document cluster can have documents organized using a DBSCAN or OPTICS algorithm); initializing a label associated with the current document cluster; selecting a term from a list of terms comprised by the document cluster; appending the term to the label associated with the current document cluster; responsive to determining that the label is found in a label dictionary, iteratively selecting a next term from the list of terms comprised by the document cluster and appending the next term to the label associated with the current document cluster; responsive to failing to locate the label in the label dictionary, inserting the label into the label dictionary; and associating the label with the current document cluster.

Abstract: An example method comprises: receiving a natural language text; transforming, by a neural network, the natural language text into a numeric representation comprising a plurality of numeric values; discarding the natural language text; and performing, using the numeric representation of the natural language text, an information extraction task.

Abstract: An example method of method of document cluster labeling comprises: selecting a current document cluster of a plurality of document clusters; initializing a label associated with the current document cluster; selecting a term from a list of terms comprised by the document cluster; appending the term to the label associated with the current document cluster; responsive to determining that the label is found in a label dictionary, iteratively selecting a next term from the list of terms comprised by the document cluster and appending the next term to the label associated with the current document cluster; responsive to failing to locate the label in the label dictionary, inserting the label into the label dictionary; and associating the label with the current document cluster.

Abstract: An example method of document clustering comprises: representing each document of a plurality of documents by a vector comprising a first plurality of real values, wherein each real value of the first plurality of real values reflects a first frequency-based metric of a term comprised by the document; partitioning the plurality of documents into a first set of document clusters based on distances between vectors representing the documents; representing each document cluster of the first set of document clusters by a vector comprising a second plurality of real values, wherein each real value of the second plurality of real values reflects a second frequency-based metric of a term comprised by the document cluster; and partitioning the first set of document clusters into a second set of document clusters based on distances between vectors representing the document clusters of the first set of document clusters.

Abstract: An example method comprises: receiving a natural language text; transforming, by a neural network, the natural language text into a numeric representation comprising a plurality of numeric values; discarding the natural language text; and performing, using the numeric representation of the natural language text, an information extraction task.

Abstract: An example method of document clustering comprises: representing each document of a plurality of documents by a vector comprising a first plurality of real values, wherein each real value of the first plurality of real values reflects a first frequency-based metric of a term comprised by the document; partitioning the plurality of documents into a first set of document clusters based on distances between vectors representing the documents; representing each document cluster of the first set of document clusters by a vector comprising a second plurality of real values, wherein each real value of the second plurality of real values reflects a second frequency-based metric of a term comprised by the document cluster; and partitioning the first set of document clusters into a second set of document clusters based on distances between vectors representing the document clusters of the first set of document clusters.