Typesafe Modeling in Text Mining

TL;DR

This paper presents a typesafe, domain-specific language embedded in Scala for defining, executing, and documenting text mining experiments, emphasizing robust annotation modeling and machine learning integration.

Contribution

It introduces a formal notation and tools for typesafe text mining experiments, enhancing reproducibility and generality beyond traditional text processing.

Findings

Framework supports machine learning classification tasks

Annotation-based agents enable flexible experiment design

Type safety improves experiment robustness

Abstract

Based on the concept of annotation-based agents, this report introduces tools and a formal notation for defining and running text mining experiments using a statically typed domain-specific language embedded in Scala. Using machine learning for classification as an example, the framework is used to develop and document text mining experiments, and to show how the concept of generic, typesafe annotation corresponds to a general information model that goes beyond text processing.