Weight Annotation in Information Extraction
Johannes Doleschal, Benny Kimelfeld, Wim Martens, Liat, Peterfreund

TL;DR
This paper extends document spanners to include annotations like confidence and support using semiring-based provenance, exploring weighted automata and their computational properties.
Contribution
It introduces an annotation framework for document spanners using provenance semirings and studies weighted VSet-automata with respect to expressiveness and complexity.
Findings
Weighted VSet-automata can propagate annotations through RA.
Closure properties depend on semiring characteristics.
Enumeration of annotated answers varies with semiring positivity.
Abstract
The framework of document spanners abstracts the task of information extraction from text as a function that maps every document (a string) into a relation over the document's spans (intervals identified by their start and end indices). For instance, the regular spanners are the closure under the Relational Algebra (RA) of the regular expressions with capture variables, and the expressive power of the regular spanners is precisely captured by the class of VSet-automata -- a restricted class of transducers that mark the endpoints of selected spans. In this work, we embark on the investigation of document spanners that can annotate extractions with auxiliary information such as confidence, support, and confidentiality measures. To this end, we adopt the abstraction of provenance semirings by Green et al., where tuples of a relation are annotated with the elements of a commutative…
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