Melita has now been replaced by AKTive Media , please click here to download AKTive Media, Melita is no longer used or available for download
Melita is an annotation interface that uses Adaptive Information Extraction from texts (IE) for reducing the burden of text annotation. In Melita, adaptation starts with the definition of a scenario, including a tag set for annotation (possibly organized as an ontology) and a corpus of texts to be annotated. Annotations are inserted by first selecting a tag from the ontology and then identifying the text area to annotate with the mouse. Melita actively supports corpus annotation using Amilcare, an adaptive Information Extraction tool based on the (LP)2 algorithm [Ciravegna2001].
While users annotate texts, Amilcare runs in the background learning how to reproduce the inserted annotation. Induced rules are silently applied to new texts and their results are compared with the user annotation. When its rules reach a (user-defined) level of accuracy, Melita presents new texts with a preliminary annotation derived by the rule application. In this case users have just to correct mistakes and add missing annotations. User corrections are inputted back to the learner for retraining. This technique focuses the slow and expensive user activity on uncovered cases, avoiding requiring annotating cases where a satisfying effectiveness is already reached. Moreover validating extracted information is a much simpler task than tagging bare texts (and also less error prone), speeding up the process considerably. If the IE based annotation becomes very reliable, the user can decide to let Melita produce further annotation automatically.
Take a look at the videos:
- Introduction video (flash)
- Detailed Tutorial (flash)