Sequences in Language and Text

George K. Mikros, Ján Macutek, George K. Mikros, Ján Macutek

• Publisher: De Gruyter Mouton
• Year: 2015
• Topic: Languages & Linguistics
• eBook ISBN:
  9783110394771
• Subtopic: Linguistics
• Index: Languages & Linguistics

The edited volume Sequences in Language and Text is the first collection of original research in the area of the quantitative analysis of sequentially organized linguistic data.

Linguistic sequences are extremely useful textual structures in almost all areas of Language Technology. Character and word n-grams are by far the most successful features in text classification tasks such as authorship identification, text categorization, genre classification, sentiment analysis etc. Furthermore character linguistic sequences are the basis for linguistic modeling and subsequent applications such as speech recognition, language identification etc.

In addition to the above language technology oriented research, the present volume aims to give insight to the theoretical value of linguistic sequences. Sequences in texts can be produced by a number of different factors, either external to the linguistic system or by its own grammatical structure. This volume hosts contributions which will analyze linguistic sequences using quantitative methods under the synergetic theoretical framework that can explain their role in the linguistic system.

Subject Index

accuracy, 1, 2, 3, 4

adjectives, 1, 2, 3

adverbs, 1, 2

affix, 1

ANOVA, 1, 2

arc, 1

– length, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14

associative symmetric sums, 1

author, 1, 2, 3, 4, 5, 6, 7, 8

– conscious control, 1

– subconscious control, 1

authorship attribution, 1, 2, 3, 4

autocorrelation, 1, 2, 3, 4

– function. See function

– index, 1, 2, 3, 4, 5, 6, 7, 8, 9

– negative, 1, 2

– positive, 1, 2, 3

– semantic, 1

– textual, 1, 2, 3, 4, 5, 6, 7, 8

automated similarity judgment program (ASJP), 1, 2, 3, 4, 5, 6, 7, 8, 9

utoregressive integrated moving average (ARIMA) model, 1, 2

autoregressive–moving-average (ARMA) model, 1, 2

axioms, 1, 2, 3

– commutativity, 1

Bayes classifier, 1, 2, 3

binary coding, 1, 2

Bonferroni correction, 1, 2

borderline conditions, 1

boundary conditions, 1, 2, 3

centroi...

Table of contents

• Quantitative Linguistics
• Titel
• Impressum
• Foreword
• Inhaltsverzeichnis
• Introduction
• Linguistic Analysis Based on Fuzzy Similarity Models
• Textual navigation and autocorrelation
• Menzerath-Altmann law versus random model
• Text length and the lambda frequency structure of a text
• Linguistic Motifs
• Linguistic Modelling of Sequential Phenomena - The role of laws
• Menzerath-Altmann Law for Word Length Motifs
• Is the Distribution of L-Motifs Inherited from the Word Length Distribution?
• Sequential Structures in “Dalimil’s Chronicle” - Quantitative analysis of style variation
• Comparative Evaluation of String Similarity Measures for Automatic Language Classification
• Predicting Sales Trends - Can sentiment analysis on social media help?
• Where Alice Meets Little Prince - Another approach to study language relationships
• A Probabilistic Model for the Arc Length in Quantitative Linguistics
• Subject Index
• Authors Index
• Authors’ addresses