Learning properties of Noun Phrases: from data to functions

The paper presents two experiments of unsupervised classification of Italian noun phrases. The goal of the experiments is to identify the most prominent contextual properties that allow for a functional classification of noun phrases. For this purpose, we used a Self Organizing Map is trained with syntactically-annotated contexts containing noun phrases. The contexts are defined by means of a set of features representing morpho-syntactic properties of both nouns and their wider contexts. Two types of experiments have been run: one based on noun types and the other based on noun tokens. The results of the type simulation show that when frequency is the most prominent classification factor, the network isolates idiomatic or fixed phrases. The results of the token simulation experiment, instead, show that, of the 36 attributes represented in the original input matrix, only a few of them are prominent in the re-organization of the map. In particular, key features in the emergent macro-cla...

• Type: Conference
• Year: 2008
• Where: LREC
• Authors: Valeria Quochi, Basilio Calderone
• Keywords: Contexts Containing Noun, Education, Italian Noun Phrases, LREC 2008, Noun Phrases