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Dialect Identification Using Natural...

Djuve, Kari Oline.

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Dialect Identification Using Natural Language Processing and Machine Learning.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Dialect Identification Using Natural Language Processing and Machine Learning./
Author:	Djuve, Kari Oline.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	95 p.
Notes:	Source: Masters Abstracts International, Volume: 80-05.
Contained By:	Masters Abstracts International80-05.
Subject:	Web Studies. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10979751
ISBN:	9780438667976

Dialect Identification Using Natural Language Processing and Machine Learning.
Djuve, Kari Oline.

Dialect Identification Using Natural Language Processing and Machine Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 95 p.

Source: Masters Abstracts International, Volume: 80-05.

Thesis (M.S.)--Southeastern Louisiana University, 2018.

This item must not be sold to any third party vendors.

Dialectology is the linguist field that focuses on the study of regional variances among speakers of a language. Although linguists primarily use spoken data for dialect studies, written language also shows marked differences between members of different dialectal groups. This thesis study aims to use a supervised decision tree classifier to predict the dialect class of some written texts. Since the context in which the writer is writing in-i.e. formal letter versus journal entry-plays a major role in the style used by writers, a data set of texts pulled from the social media platform Twitter will be used. Tweets, the messages users post on Twitter, are short and informal, thus very likely to be written in the users' dialect. Ten thousand tweets are pulled from two dialectal regions-Southern American English and New England American English dialects. However, these texts are also noisy. This study goes over how to normalize, or clean, these noisy texts so that Natural language processing (natural language processing) tools can be used for feature selection. Common feature selection methods will be applied to the texts to select the most information rich features while reducing the number of features that are redundant or too common to be useful in differentiating between the different dialect classes. Two feature selection pipelines are applied for comparison. The Feature Selection 1 pipeline performs punctuation, non-alphabetic tokens, and stop-words removal. The Feature Selection 2 pipeline extends the Feature Selection 1 pipeline by adding a stemmer. Additionally, TF-IDF weighting will be used for comparison. After the feature selection pipelines, a decision tree classifier is used to make class predictions.

ISBN: 9780438667976Subjects--Topical Terms:

1026830
Web Studies.

Dialect Identification Using Natural Language Processing and Machine Learning.
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520 $a Dialectology is the linguist field that focuses on the study of regional variances among speakers of a language. Although linguists primarily use spoken data for dialect studies, written language also shows marked differences between members of different dialectal groups. This thesis study aims to use a supervised decision tree classifier to predict the dialect class of some written texts. Since the context in which the writer is writing in-i.e. formal letter versus journal entry-plays a major role in the style used by writers, a data set of texts pulled from the social media platform Twitter will be used. Tweets, the messages users post on Twitter, are short and informal, thus very likely to be written in the users' dialect. Ten thousand tweets are pulled from two dialectal regions-Southern American English and New England American English dialects. However, these texts are also noisy. This study goes over how to normalize, or clean, these noisy texts so that Natural language processing (natural language processing) tools can be used for feature selection. Common feature selection methods will be applied to the texts to select the most information rich features while reducing the number of features that are redundant or too common to be useful in differentiating between the different dialect classes. Two feature selection pipelines are applied for comparison. The Feature Selection 1 pipeline performs punctuation, non-alphabetic tokens, and stop-words removal. The Feature Selection 2 pipeline extends the Feature Selection 1 pipeline by adding a stemmer. Additionally, TF-IDF weighting will be used for comparison. After the feature selection pipelines, a decision tree classifier is used to make class predictions.
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