Essay Of Contrastive Analysis

Contrastive Analysis and Native talking to Identi?cation
Sze-Meng Jojo Wong Centre for Language Technology Macquarie University Sydney, NSW, Australia szewong@science.mq.edu.au Mark Dras Centre for Language Technology Macquarie University Sydney, NSW, Australia madras@science.mq.edu.au

Abstract
Attempts to pro?le roots based on their characteristics, including native lyric poem, have raddled attention in recent years, via several approaches using mold learning with simple features. In this paper we investigate the latent usefulness to this task of contrastive abridgment from second verbiage acquistion seek, which postulates that the (syntactic) errors in a text are in?uenced by an authors native language. We explore this, ?rst, by conducting an analysis of three syntactic error types, through hypothesis scrutiny and machine learning; and second, through adding in these errors as features to the recurrence of a previous machine learning approach. This preliminary bailiwick provides some support for the use of this kind of syntactic errors as a clue to identifying the native language of an author.

1 Introduction
thither is a range of figure that attempts to infer, from some textual data, characteristics of the texts author.



This is frequently described by the term musical composition pro?ling, and may be concerned with determining an authors gender, age, or some other attributes. This information is often of interest to, for example, governments or marketing departments; the application that motivates the current work is pro?ling of phishing texts, texts that are designed to deceive a user into giving away con?dential details (Fette et al., 2007; Zheng et al., 2003). The accompaniment characteristic of interest in this paper is the native language of an author, where this is not the language that the text is written in. There has been nevertheless a relatively small amount of other research investigating this question, notably Koppel et

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