Automatic Induction of FrameNet lexical units in Italian
Authors: Silvia Brambilla, Danilo Croce, Fabio Tamburini, Roberto Basili (Università di Roma Tor Vergata, Università di Bologna, 2020)
Topic: Distributional Semantics

Abstract
In this paper we investigate the applicability of automatic methods for frame induction to improve the coverage of IFrameNet, a novel lexical resource based on Frame Semantics in Italian. The experimental evaluations show that the adopted methods based on neural word embeddings pave the way for the assisted development of a large scale lexical resource for our language.

Introduction
When dealing with large-scale lexical resources, such as FrameNet AUTHOR, PropBank AUTHOR, VerbNet AUTHOR or VerbAtlas AUTHOR, the semi-automatic association between predicates and lexical items (also known as Lexical Units or LUs) is crucial to improve the coverage of a resource while limiting the costs of its manual annotation. Several approaches to this semi-supervised task exist, as discussed in AUTHOR. In particular, AUTHOR exploited distributional models of lexical meaning AUTHOR to induce new LUs consistently with the Frame Semantics theory AUTHOR, representing words meaning and semantic frames through geometrical word spaces. As a result, this approach allows to induce new LUs when applied to the English version of FrameNet. However, this is a quite consolidated resource with many existing LUs connected to each semantic predicate, i.e., each frame. The applicability of this method in scenarios where only one or two LUs are available for each frame is still an open issue. At the same time, since the work of AUTHOR, the application of neural approaches to the acquisition of word embeddings AUTHOR significantly improved in terms both of representation capability and scalability of geometrical models of lexical semantics. In this paper we thus investigate the applicability of the method proposed in AUTHOR to boost the coverage of a novel and still limited lexical resource based on Frame Semantics in Italian. This resource has been developed within the IFrameNet (IFN) project AUTHOR, which aims at creating a large coverage FrameNet-like resource for Italian and to come up with a complete dictionary in which every lexical entry we denote a lemma, with its Part of Speech tag, that activates at least one LU.} is linked to all the frames it can evoke (i.e., the frames for which it is a LU). At this moment, while the resource counts more than 7,700 lexical items associated to more than 1,048 frames, each lexical item is connected, on average, to only 1.3 frames, and it is problematic if considering the high polysemy of Italian words AUTHOR. The experimental evaluation shows that neural word embeddings enable the effective application of the distributional approach from AUTHOR to improve the coverage of IFN. Moreover, the adopted distributional framework allowed to develop a graphical semantic browser to support annotators while assigning new LUs to frames. This study paves the way to the semi-automatic development of IFN and investigates about the applicability of neural word embeddings to the incremental semi-automatic LU induction process.

Conclusion
In this work, we presented the actual state of the IFrameNet project, which aims at developing a large-scale lexical resource based on Frame Semantics in Italian. Moreover, we investigated the applicability of a method for the automatic Induction of FrameNet Lexical Units to improve the coverage of the actual resource, in terms of number of frames assigned to the almost 8,000 existing lexical entries. With respect to previous work, i.e., Pennacchiotti et al. (2008) we empirically demonstrate the beneficial impact of neural word embeddings in the overall workflow in Italian. The robustness of the adopted model is confirmed also when applied to a resource with a limited average number of frames associated to Lexical Units. The experimental evaluations in many cases showed the valuable support of the method in discovering new Lexical Units by suggesting novel evoked frames. Moreover, the error analysis suggested that most of the “discarded” frames still entertain various kinds of relationships with the “correct” ones as defined in FrameNet, such as Inheritance or Usage. In some cases, it also highlighted metaphorical meanings that the lexical entries could assume. As a future work, we will certainly exploit the produced IFrameNet Navigator to extend the current LU Italian dictionary, support the annotation of novel sentences and introduce frame-to-frame relations in Italian. Another path that might worth investigating is the exploitation of dependencybased word embeddings for the distributional representation of LUs and frames. This may beneficial since dependency-based contexts highlight more functional similarities (Levy and Goldberg, 2014). Finally, we plan to use the derived frame distributions to augment existing contextualized embeddings in support of Frame Induction (Sikos and Padó, 2019) or Semantic Role Labeling (Shi and Lin, 2019) tasks.