Reflexives, Impersonals and Their Kin: a Classification Problem
Authors: Kledia Topciu, Cristiano Chesi (Università di Siena, IUSS, 2019)
Topic: Lexical and Semantic Resources and Analysis

Abstract
Despite the fact that true reflexives always require a local antecedent, attempting an automatic referential resolution is often far from trivial: in many languages, reflexives are morphologically indistinguishable from impersonals and both particles are sensitive to the syntactic structure in a non-trivial sense. Focusing on Italian, we annotated part of the Repubblica Corpus to attempt an automatic classification of the reflexive and impersonal si constructions. In this preliminary study we show that the accuracy of the automatic classification methods that do not use any relevant structural information are rather modest. A thoughtful discussion of the structural analysis required to distinguish among different contexts is provided, in the end suggesting that these structural configurations are not easily recoverable using a purely distributional approach.

Introduction
The non-triviality of reflexive/impersonal constructions in Italian is exemplified in (1): (1) a. Adai sii presentò. A.i SIi introduced3-SG-PAST ‘A. introduced herself.’ b. Sii/j presentò Adai. SIi/j introduced3-SG-PAST A.i ‘A. introduced herself.’ c. Sii/j presentò ad Adai. SIi/j introduced3-SG-PAST to A.i ‘S/He introduced him/herself to A.’ d. proi Sii/j tolse la giaccai. proi SIi/j took3-SG-PAST off the jacket ‘S/He took off the jacket.’ e. Il compagnoj di Adai sii/j presentò. The friendj of A.i SIi/j introduced3-SG-PAST ‘A.’s friend introduced her/him-self.’ f. Riconosciuto il compagnoj di Adai, prok si*i/j/k presentò. Recognized3-SG-P.PART the friendj of A.i, prok SIi/*j/k introduced3-SG-PAST. ‘Once s/he recognized A.’s friend, s/he introduced her/him-self.’ g. Sigeneric pensa sempre a salvarsi la pelle. SIgeneric thinks always to saveINF-REFLthe skin ‘We always think about saving our own skin.’ Expecting the co-referential DP to be always “immediately to the left” of the reflexive form quickly leads to wrong predictions: if this generalization might seem sufficient in (1a) this is bluntly wrong in (1b), where we need to assume an empty referent (pro, Rizzi 1986) before the reflexive (see §1.1). Moreover, we should accept that the coreferential DP can be placed sometimes to the right of the predicate (structurally speaking, pro and post-verbal subject options are related, Belletti 2002); in this case, the (focalized/dislocated) post-verbal subject is a good candidate, (1b). Being “the closest DP” is however not a sufficient condition as suggested by the examples (1c-d). Hence, the null subject hypothesis as well as a structural analysis unravelling the role of each DP surrounding the predicate is requested, for the identification of the correct local binding domain (1e-f). Last but not least, a proper classification of the predicate admitting a reflexive or an impersonal pronoun is needed (1g). Under this perspective, we decided to run a little experiment to verify the consistency of a “usage-based” approach (Tomasello 2003) in this specific context and consider whether the “structural analysis” (Chomsky 1995; 2008) can be proved to be an outdated approach for the classification of the distinct kinds of si. In the remaining part of this introduction we will present the (possibly outdated) structural analyses proposed for reflexive (§1.1) and impersonal (§1.2) clitic si. We will then present our experiment consisting of the annotation of a small fragment of the Repubblica Corpus (Baroni et al. 2004) that we used to train and test a set of Machine Learning classification algorithms (§2). Results presentation (§3) and their discussion (§4) will follow.

Conclusion
In this paper, we discussed the nature of some si constructions in Italian, suggesting that, despite their apparent simplicity, their structural intricacies require a deep syntactic analysis for identifying correctly the typology of the clitic in various contexts and retrieve, when necessary, a proper referent. Also using a simplified set of five classes (I = impersonal; L = local immediately preceding coreferential DP; PV = local, immediately post-verbal coreferential DP; LM = local preceding coreferential DP but with prepositional phrase or relative clause modification; A = absent referent), we demonstrated that, using an annotated sample of the Repubblica corpus, no classifier has exceeded the performance of 61.49% of accuracy. This is well below any human reasonable performance (as suggested by the 99% agreement in classification between annotators). These results, even though still based on a small fragment of the Repubblica Corpus, extend Chesi & Moro (2018) original considerations using a wider dataset and more advanced ML algorithms. These results showed that neither the algorithms used nor the extension of the context (both left and right) helped in classifying correctly the instances of “si” when the referent had to be retrieved non-locally or in impersonal “si” cases. Replacing the words with their POS mildly helped in improving the performance of some classifiers (especially using the coarse tagset), with decision tree classifier (J48) obtaining the best performance (on average) across the tests. Given the poor performance of the classifiers tested, we concluded that the “usage-based” intuition is not sufficient here to account for the acquisition of the discriminative capabilities any Italian native speaker owns and that enable her/him to identify correctly the relevant referent both pre- and post-verbally, even in the case of complex subjects (referent DPs modified by prepositional phrases or relative clauses), as well as its unnecessity (in generic/impersonal readings) or its recovery in case of pro-drop. We might expect then that a richer syntactic annotation could help to boost the automatic classification results in accordance with the structural analysis summarized in §1.1 and §1.2: first, a verbal subcategorization specification properly describing the predicate argument structure could be useful, then a correct analysis of the subject phrase structure, including agreement cues should be used, as well as a richer classification of temporal/modal adverbials/modifiers. As suggested by an anonymous reviewer, information structure, which is largely obliterated in written texts, is expected to disambiguate between reflexive and impersonal constructions: for instance, non-dislocated preverbal subjects (L(M) in our classification) should be ruled out in impersonal constructions (see Raposo & Uriagereka 1996); moreover, non-focalized (or right-dislocated) postverbal subjects (PV in our classification) should be ruled out in reflexive constructions. Then, despite the fact that prosody/information structure cannot be assessed within a corpus-based study, we might expect an improvement of the classifiers performance considering some relevant features associated to these configurations: e.g. post-verbal subject annotation in connection with the verbal class and adverbials placement between the subject and verb indicating a dislocated subject. A follow up of this study should test these predictions and, possibly, extend the study to the whole Repubblica corpus, confirming (or disconfirming) our preliminary results that suggest we cannot avoid a deep structural analysis of these constructions to classify (and interpret) them correctly.