Developing a large scale FrameNet for Italian: the IFrameNet experience
Authors: Roberto Basili, Silvia Brambilla, Danilo Croce, Fabio Tamburini (Università di Roma Tor Vergata, Università di Bologna, 2017)
Topic: Lexical and Semantic Resources and Analysis

Abstract
This paper presents work in progress for the development of IFrameNet, a large-scale, computationally oriented, lexical resource based on Fillmore’s frame semantics for Italian. For the development of IFrameNet linguistic analysis, corpusprocessing and machine learning techniques are combined in order to support the semiautomatic development and annotation of the resource.

Introduction
Firstly developed at the University of Berkeley (California) in 1997, FrameNet adopts theories from Frame Semantics AUTHOR to NLP and explains words’ meanings according to the semantic frames they evoke. It illustrates semantic frames (i.e. schematizations of prototypical events, relations or entities in the reality), through the involved participants (called frame elements, FEs) and the evoking words (or, better, the lexical units, LUs). Moreover, FrameNet aims to give a valence representation of the lexical units and underline the relations between frames and between frame elements AUTHOR. The initial American project has since been extended to other languages: French, Chinese, Brazilian Portuguese, German, Spanish, Japanese, Swedish and Korean. All these projects are based on the idea that most of the Frames are the same among languages and that, thanks to this, it is possible to adopt Berkeley’s Frames and FEs and their relations, with few changes, once all the language-specific information has been cut away (AUTHOR, AUTHOR). With regard to Italian, over the past ten years several research projects have been carried out at different universities and Research Centres. In particular, the ILC-CNR in Pisa (e.g. AUTHOR; AUTHOR), FBK in Trento (e.g. AUTHOR, AUTHOR) and the University of Rome, Tor Vergata (e.g. AUTHOR, AUTHOR) proposed automatic or semiautomatic methods to develop an Italian FrameNet. However, as of today, a resource even remotely equivalent to Berkeley’s FrameNet (BFN) is still missing. As a lexical resource of this kind is useful in many computational applications (such as HumanRobot interaction), a new effort is currently being jointly made at the universities of Bologna and Roma, Tor Vergata. The IFrameNet project aims to develop a large-coverage FrameNet-like resource for Italian, relying on robust and scalable methods, in which the automatic corpus processing is consistently integrated with manual lexical analysis. It builds upon the achievements of previous projects that automatically harvested FrameNet LUs exploiting both distributional and WordNet based models (AUTHOR). Since the LUs induction is a noisy process, the data thus obtained need to be manually refined and validated. The aim is also to provide Sample Sentences for LUs with the highest corpus frequency. On the one side, they will be derived from already existing resources such as the HuRIC corpus AUTHOR or the EvalIta2011 FLaIT task data: FBK set AUTHOR and ILC set AUTHOR. On the other side, candidate sentences will also be extracted through semi-automatic distributional analysis of a large corpus i.e. CORIS AUTHOR and refined through linguistic analysis and manual validation of data thus obtained.

Conclusion
In order to evaluate the proposed supervised classification method for the stage “First Lexical Analysis and Validation” we run and experimental evaluation over a set of 3261 frames, the ones with more than 5 lexical units in the initial lexicon. In this way, we selected 1,095 different LUs, represented as an embedding vector in the wordspace. On average, we have 12 LU per frame, and every individual lexical entry l appears in about 1.88 frames. The baseline of a classification task that maps a sentence s including a lexical unit into its own frame is about 35%, as for the ambiguity characterizing most frequent entries. We asked three annotators to evaluate individual triples ⟨l, s, f⟩ validating the system proposal. Four main cases where possible: • MISSING FRAME. The sentence s is not manifesting any of the frames f evoked by the lexical unit l, but corresponds to a frame not yet present in the lexicon for l. In this case the algorithm cannot provide the suitable frame, as it cannot generate a novel frame. • NOT APPLICABLE. The sentence s does not contain an occurrence of the lexical unit l in one of its proper senses: this case is typical for phraseological uses of a verb such as morire di freddo, andare di fretta, … that do not directly correspond to lexical predicates and thus cannot be treated through the lexical embedding vectors. • CORRECT/INCORRECT, when the outcome argmaxf’ { h(l,s,f’) } is correct (or incorrect) as the frame evoked by l in s is exactly (or not) f. According to the above method annotators validated 667 sentences for 113 frames and 212 different verbal lexical units. The analysis resulted into a precision (i.e. the number of correct candidate frames emitted by the algorithm w.r.t. the number of valid cases, that is all but the MISSING FRAME or NOT APPLICABLE cases) is 75,2%, well beyond the 35% baseline. The method could be applied onto the 74,5% of the sentences, including CORRECT cases and MISSING FRAME cases. We neglected in this coverage score the NOT APPLICABLE cases that amount to 44 sentences, i.e. about 6,4%. Examples of the correct assignment of the algorithm on quite ambiguous verbs, such as finire (i.e. to end, in frames ACTIVITY_FINISH, CAUSE_TO_END and KILLING) or rivelare (i.e. to reveal, in frames REVEAL_SECRET, OMEN, EVIDENCE) are the following: La vicenda avrebbe potuto [finire]ACTIVITY_FINISH lì , ma il prefetto di Nuoro fece presentare ... In prova si è [rivelato]EVIDENCE ad altissimo livello sia sull' asciutto sia sul … An example of Missing Frame is BEAT_OPPONENT for the verb battere in ... impegnato a fornire quante più informazioni possibili, anche per [battere]BEAT_OPPONENT la concorrenza dei siti Ipsoa e il ... as the lexicon of the verb battere only includes the frames CAUSE_HARM, CORPORAL_PUNISHMENT and EXPERIENCE_BODI-LY_HARM. The experiments only run over verbal lexical units will be extended soon to nouns and adjectives. However, the encouraging precision reached by the method allows for direct use it in an iterative active learning schema, where the more ambiguous sentences found and annotated within a specific training stage are used to train the system at the next stage. We expect this to speed up the lexicon development process and to allow bootstrapping with fewer resources. The lexicon will be made available for crowdsourcing further annotations and delivered incrementally in the next few months.