When Lexicon-Grammar Meets Open Information Extraction: a Computational Experiment for Italian Sentences
Authors: Raffaele Guarasci, Emanuele Damiano, Aniello Minutolo, Massimo Esposito (CNR-ICAR, 2019)
Topic: Lexical and Semantic Resources and Analysis

Abstract
In this work we show an experiment on building an Open Information Extraction system (OIE) for Italian language. We propose a system wholly reliant on linguistic structures and on a small set of verbal behavior patterns defined putting together theoretical linguistic knowledge and corpus-based statistical information. Starting from elementary one-verb sentences, the system identifies elementary tuples and then, all their permutations, preserving the overall well-formedness (grammaticality) and trying to preserve semantic coherence (acceptability). Although the work focuses only on the Italian language, it can be proficiently extended also to other languages, since it is essentially based only on linguistic resources and on a representative corpus for the language under consideration.

Introduction
One of the most interesting approach to handle the rapid growth of textual data emerged in the last decade is Open Information Extraction (OIE). Starting from natural language sentences, it allows to extract one or more domain-independent propositions, scaling to the diversity and size of the corpus considered AUTHOR. Each extracted proposition is represented by a verb and its arguments, i.e. “Maria goes to the party” is a proposition with a relation (the verb goes) that links together two arguments (Maria, the party). Arguments (nouns or noun groups) can have different roles (subject, direct object…) and they can be mandatory or optional. In this sentence, both arguments Maria (subject) and the party (direct object) are mandatory, so it is impossible to remove one of them or the sentence becomes unacceptable from a grammatical point of view. Due to the high field of Natural Language Processing (NLP) tasks in which OIE outputs can be used (AUTHOR; AUTHOR; AUTHOR; AUTHOR; AUTHOR), numerous OIE approaches for English have been developed. However, being a language-dependent task, OIE systems cannot be shifted from one language to another, i.e. a system created for English is not compatible with Italian. Moreover, many of the proposed OIE approaches rest on unstable grounds. Some of them use heuristics to manage large quantities of textual data, others lack the support of a theoretical basis, outlining the natural language in a reductive way. Differently from the vast majority of existing OIE approaches, we propose a linguistic-based unsupervised system designed to extract n-ary propositions (not only “relation-argument” triples) from natural language sentences in Italian, ensuring domain independence and scalability. Our system aims to identify the elementary tuple(s) from the input sentence, then all its (their) permutations, by adding progressively arguments composing the sentence. After that – according the behavior patterns of the verb – it generates every possible syntactically valid n-ary proposition, granting grammaticality. To reach this result we have combined two types of resources. To gather information about verb behavior in sentences, we grounded our work on the linguistic basis provided by Lexicon Grammar (LG) AUTHOR. In order to obtain a finegrained characterization of arguments, we combine this theoretical knowledge with distributional corpus-based information extracted from itWaC AUTHOR. From LG tables we extract patterns of verbs behaviors, and from itWaC we enrich these patterns with statistical information. Using complex linguistic structures and dependency parse trees (DPT) we can detect verbal behavior patterns occurring in one-verb sentences and generate from them all the possible well-formed propositions, by adding complements and adverbials. The use of formal patterns derived from a theoretical framework allows to better distinguish between necessary verbal arguments and optional removable adjuncts and to verify syntactic restrictions in verb possible structures. Arguments optionality and syntactic constraints are critical features to grant the grammaticality of the propositions generated, also trying to approximate a first level of semantic acceptability.

Conclusion
In this work we have shown an experiment to perform OIE for Italian language, extracting n-ary propositions from natural language sentences, granting well-formedness of the generations. The system relies on a linguistic resource (LG) and on a representative corpus for Italian (itWaC). While these resources are specific to Italian, they also exist for other languages, so the system can be easily extended. In particular, LG tables exist in digital format also for French AUTHOR, English (AUTHOR; AUTHOR), Portuguese AUTHOR, Romanian AUTHOR. Likewise, the itWaC corpus used in this work is part of the WaCky Wide Web corpora collection AUTHOR, which includes corpora of English (ukWaC), German (deWaC), French (frWac). Concerning performances of the system, although the results are encouraging, we are looking forward to further developments. With regard to methodological progress, we plan to integrate novel methods based on deep learning to increase the performance of the system, trying to reduce DP errors and better handle named entities, frozen and semi-frozen bigrams and multi-word expressions. From an applicative perspective, this work will be experimented in Italian Question Answering system, with the goal to improve the ability in reading complex texts and extracting the correct answers to users' questions. Other possible outcomes can include text summarization or other NLP tasks.