In this paper we present work in progress for the creation of the Italian Content Annotation Bank (I-CAB), a corpus of Italian news annotated with semantic information at different levels. The first level is represented by temporal expressions, the second level is represented by different types of entities (i.e. person, organizations, locations and geo-political entities), and the third level is represented by relations between entities (e.g. the affiliation relation connecting a person to an organization). So far I-CAB has been manually annotated with temporal expressions, person entities and organization entities. As we intend I-CAB to become a benchmark for various automatic Information Extraction tasks, we followed a policy of reusing already available markup languages. In particular, we adopted the annotation schemes developed for the ACE Entity Detection and Time Expressions Recognition and Normalization tasks. As the ACE guidelines have originally been developed for English, part of the effort consisted in adapting them to the specific morpho-syntactic features of Italian. Finally, we have extended them to include a wider range of entities, such as conjunctions.

We survey the evaluation methodology adopted in Information Extraction (IE), as defined in the MUC conferences and in later independent efforts applying machine learning to IE. We point out a number of problematic issues that may hamper the comparison between results obtained by different researchers. Some of them are common to other NLP tasks: e.g., the difficulty of exactly identifying the effects on performance of the data (sample selection and sample size), of the domain theory (features selected), and of algorithm parameter settings. Issues specific to IE evaluation include: how leniently to assess inexact identification of filler boundaries, the possibility of multiple fillers for a slot, and how the counting is performed. We argue that, when specifying an information extraction task, a number of characteristics should be clearly defined. However, in the papers only a few of them are usually explicitly specified. Our aim is to elaborate a clear and detailed experimental methodology and propose it to the IE community. The goal is to reach a widespread agreement on such proposal so that future IE evaluations will adopt the proposed methodology, making comparisons between algorithms fair and reliable. In order to achieve this goal, we will develop and make available to the community a set of tools and resources that incorporate a standardized IE methodology.