Evaluating a rule based strategy to map IMAGACT and T-PAS
Authors: Andrea Amelio Ravelli, Lorenzo Gregori, Anna Feltracco (Università di Firenze, Fondazione Bruno Kessler, Università di Pavia, Università di Bergamo, 2017)
Topic: Lexical and Semantic Resources and Analysis

Abstract
This paper presents the analysis of a mapping between two resources, IMAGACT and T-PAS, made through a rule-based algorithm which converts argument structures in thematic roles. Results are good in terms of Recall, while Precision values are low: an analysis of the causes is proposed.

Introduction
The automatic mapping of information between two resources is not a trivial task, but indeed joining information over specific data can benefit the involved resources. This paper describes the analysis of a mapping between two linguistic resources: IMAGACT and T-PAS. The motivation behind this mapping starts with the observation that both resources deal with Italian verbs disambiguation, are corpus-based and contain pieces of information that can be integrated with each other. IMAGACT is a linguistic ontology of actions, that are grouped in concepts and related to different verb Types. For example, the action ``John takes the cup from the shelf" belongs to the concept ``take an object" and refers to Type 3 of the verb to take. Each Type is also associated to one or more thematic structures (e.g. [AGENT-verb-THEME-SOURCE]) and to videos via a set of captions. T-PAS is a repository of argument typed structures for Italian verbs. Each verb is listed with its structures, which correspond to different senses of the verb. For each structure, the specification of the expected semantic type in every argument position (e.g. for the subject) is provided. In this paper, we describe the results of a first attempt of mapping information between these resources. Specifically, for each of the 248 verbs analysed in both resources, we aim at matching the IMAGACT Types with the corresponding typed argument structures in T-PAS. We operate this mapping by applying a set of rules which convert the information from the argument structure into a thematic-role combination, and find all the Types that match this combination. The linking between argument and thematic structures of a predicate is a debated complex task in linguistic theories AUTHOR. The predictability of thematic roles from argument structure (or viceversa) belongs to the syntax-semantics interface, and a study in this direction is out of the scope of this paper. Our experiment is focused on an empirical analysis of argument and thematic structures in Italian verbs and our aim is to evaluate whether, and to which extent, a rule-based system is able to produce thematic structures. We also intend to verify how these results can be exploited for a mapping purpose. The paper is structured as follows: in Section 2 we present the resources; in Section 3 we describe the mapping procedure; in Section 4 we present and discuss the results of the mapping, tested on a gold standard; in Section 5 we provide direction for future work; in Section 6 we report our conclusions.

Conclusion
In this paper we presented a first attempt of mapping IMAGACT and T-PAS by using a rule-based algorithm for the automatic conversion of T-PAS semantic types into thematic structures. We took advantage of the strong discriminative power of semantic types in their argument position to reduce the possible set of allowed thematic structures. This approach has an intrinsic limit: thematic roles are determined by verb semantics and their difference is not always reflected in the related semantic type. We also found out that the ts AG-v-TH represents the most critical issue, being the most frequent structure, and appearing in more than one Type of the same verb. The results report a good recall and a low precision, confirming that our algorithm is not able to produce an actual mapping between the two resources, but it provides a reliable set of mapping candidates: we believe that it can be fruitfully exploited for a first step of a mapping process, in order to filter a lot of unwanted matching possibilities. We are confident that by exploiting additional linguistic information from the two resources (e.g. captions and occurrences in IMAGACT, lexical information and examples in T-PAS), the precision of this mapping will improve sensibly.