ITGETARUNS A Linguistic Rule-Based System for Pragmatic Text Processing

English. We present results obtained by our system ITGetaruns for all tasks. It is a linguistic rule-based system in its bottom-up version that computes a complete parser of the input text. On top of that it produces semantics at different levels which is then used by the algorithm for sentiment and polarity detection. Our results are not remarkable apart from the ones related to Irony detection, where we ranked fourth over eight participants. The results were characterized by our intention to favour Recall over Precision and this is also testified by Recall values for Polarity which in one case rank highest of all. Italiano. Presentiamo i risultati ottenuti dal nostro sistema ITGetaruns per tutti i task. Si tratta di un sistema basato su regole linguistiche nella sua versione bottom-up, che produce un parse complete del testo in ingresso. Al di sopra di questo produce semantica a diversi livelli, che viene poi usata dall'algoritmo per l'analisi della polarita e della soggettivita. I nostri risultati non sono notevoli a parte quelli relativi alla individuazione dell'Ironia, nella quale ci siamo classificati quarti su sette partecipanti. I risultati sono caratterizzati dalla nostra intenzione di favorire il Recall sulla Precision and questo e anche documentato dai valori della Recall per la polarita che in un caso sono i piu alti in assoluto. 1 Description of the System The system we called ITGetaruns shares its backbone with the companion English system which has been used – and documented for a number of international challenges on Semantic and Pragmatic computing in English texts. It is organized around a manually checked subcategorized lexicon, a sequence of rules organized according to theoretical linguistics criteria and combines data-driven (bottom-up) and grammar-driven (top-down) techniques. Technically speaking, it is based on a shallow parser which in turn is based on a chunker and NER and multiword recognizer. On top of this parser, there is constituent or phrase structure parser which sketches sentence structure. This is then passed to a deep dependency parser which combines constituent level information, lexical information, and a Deep Island Parser. The aim of this third parser is that of producing semantically viable Predicate-Argument Structures. Finally, on top of this level of representation, the Pragmatic System is built. Conceptually speaking, the deep island parser (hence DIP) is very simple to define, but hard to implement. A semantic island is made up by a set of A/As which are dependent on a verb complex (hence VCX). Arguments and Adjuncts may occur in any order and in any position: before or after the verb complex, or be simply empty or null. Their existence is determined by constituents surrounding the VCX. The VCX itself can be composed of all main and minor constituents occuring with the verb and contributing to characterize its semantics. We are here referring to: proclitcs, negation and other adverbials, modals, restructuring verbs (lasciare/let, fare/make, etc.), and all auxiliaries. Tensed morphology can then appear on the main lexical verb or on the auxiliary/ modal/ restructuring verb. Gender can appear on the past participle when the verb takes auxiliary ESSERE, or when a complement is duplicated by Clitic Left Dislocation. The DIP is preceded by a tagger which is accompanied by a multiword expression labeler. Tagged input is passed to an augmented contextfree parser that works on top of a chunker. The chunker collects main constituents on the basis of a Recursive Transition Network of Italian and then passes the output to a cascaded sentence level parser. Constituents are labeled with usual grammatical relations on the basis of syntactic subcategorization contained in our verb lexicon of Italian counting some 17,000 entries. There are some 270 different syntactic classes which differentiates also the most common prepositions associated to oblique arguments. Linear position and precedence in the input string is assumed at first as a valid criterion for distinguishing SUBJects from OBJects. Adjustments will be executed by the semantic parser, which will be responsible for the final relabeling of the output. The DIP receives the output of the surface parser, a list of Referring Expressions and a list of VCX. Referring expressions are all nominal heads accompanied by semantic class information collected in a previous recursive run through the list of the now lemmatized and morphologically analyzed input sentence. It also receives the output of the context-free parser. The DIP searches for SUBJects at first and assumes it is positioned before the verb and close to it. In case there is none such chunk available the search is widened if intermediate chunks are detected: they can be Prepositional Phrases, Adverbials or simply Parentheticals. If this search fails, the DIP looks for OBJects close after the verb then and again possibly separated by some intermediate chunk. They will be relabeled as Subjects. Conditions on the A/As boundaries are formulated in these terms: between current VCX and prospective argument there cannot be any other VCX. Additional constraints regard presence of relative or complement clauses which are detected from the output chunked structure. The prospective argument is deleted from the list of Referring Expressions and the same happens with the VCX. The same applies for the OBJect, OBJect1 and OBLique. When arguments are completed, the parser searches recursively for ADJuncts which are PPs, using the same boundary constraint formulation above. Special provisions are given to copulative constructions which can often be reversed in Italian: the predicate coming first and then the subject NP. The choice is governed by looking at referring attributes, which include definiteness, quantification, distinction between proper/common noun. It assigns the most referring nominal to the SUBJect and the less referring nominal to the predicate. In this phase, whenever a SUBJect is not found from available referring expressions, it is created as little_pro and morphological features are added from the ones belonging to the verb complex. After updating of the Referring Expressions with the new Grammatical Relations, the parser searches the most adequate Semantic Role to be associated to it. This is again taken from a lexicon of corresponding verb predicates and works according to the type of overall Predicate-Argument Structure (hence PAS). The SUBJect is in fact strictly depending on the semantics associated to the verb, but in case of ambiguity the system delays the assignment of semantic role until a complete PAS is obtained. In this phase, passive diathesis is checked in order to apply a lexical rule from LFG, that assigns OBJect semantic role to the SUBJect of the corresponding passive form of the verb predicate.