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A probabilistic genre-independent model of pronominalization

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TLDR
Results based on the annotation of twelve texts from four genres show that only a few factors have a strong influence on pronominalization across genres, i.e. distance from last mention, agreement, and form of the antecedent.
Abstract
Our aim in this paper is to identify genreindependent factors that influence the decision to pronominalize. Results based on the annotation of twelve texts from four genres show that only a few factors have a strong influence on pronominalization across genres, i.e. distance from last mention, agreement, and form of the antecedent. Finally, we describe a probabilistic model of pronominalization derived from our data.

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References
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A Coefficient of agreement for nominal Scales

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Book

Classification and regression trees

Leo Breiman
TL;DR: The methodology used to construct tree structured rules is the focus of a monograph as mentioned in this paper, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.
Journal ArticleDOI

WordNet : an electronic lexical database

Christiane Fellbaum
- 01 Sep 2000 - 
TL;DR: The lexical database: nouns in WordNet, Katherine J. Miller a semantic network of English verbs, and applications of WordNet: building semantic concordances are presented.