@inproceedings{quochi-calderone-2008-learning,
title = "Learning properties of Noun Phrases: from data to functions",
author = "Quochi, Valeria and
Calderone, Basilio",
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Maegaard, Bente and
Mariani, Joseph and
Odijk, Jan and
Piperidis, Stelios and
Tapias, Daniel",
booktitle = "Proceedings of the Sixth International Conference on Language Resources and Evaluation ({LREC}'08)",
month = may,
year = "2008",
address = "Marrakech, Morocco",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2008/pdf/644_paper.pdf",
abstract = "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-classification are the type of determiner and the grammatical number of the noun. An additional but not less interesting result is an organization into semantic/pragmatic micro-classes. In conclusions, our result confirm the relative prominence of determiner type and grammatical number in the task of noun (phrase)categorization.",
}
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<abstract>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-classification are the type of determiner and the grammatical number of the noun. An additional but not less interesting result is an organization into semantic/pragmatic micro-classes. In conclusions, our result confirm the relative prominence of determiner type and grammatical number in the task of noun (phrase)categorization.</abstract>
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%0 Conference Proceedings
%T Learning properties of Noun Phrases: from data to functions
%A Quochi, Valeria
%A Calderone, Basilio
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Odijk, Jan
%Y Piperidis, Stelios
%Y Tapias, Daniel
%S Proceedings of the Sixth International Conference on Language Resources and Evaluation (LREC’08)
%D 2008
%8 May
%I European Language Resources Association (ELRA)
%C Marrakech, Morocco
%F quochi-calderone-2008-learning
%X 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-classification are the type of determiner and the grammatical number of the noun. An additional but not less interesting result is an organization into semantic/pragmatic micro-classes. In conclusions, our result confirm the relative prominence of determiner type and grammatical number in the task of noun (phrase)categorization.
%U http://www.lrec-conf.org/proceedings/lrec2008/pdf/644_paper.pdf
Markdown (Informal)
[Learning properties of Noun Phrases: from data to functions](http://www.lrec-conf.org/proceedings/lrec2008/pdf/644_paper.pdf) (Quochi & Calderone, LREC 2008)
ACL