Бадрызлова Юлия Геннадьевна Автоматические методы распознавания метафоры в текстах на русском языке

Краткое описание

Бадрызлова Юлия Геннадьевна Автоматические методы распознавания метафоры в текстах на русском языке
ОГЛАВЛЕНИЕ ДИССЕРТАЦИИ
кандидат наук Бадрызлова Юлия Геннадьевна
Table of Contents

Introduction

Chapter I. Metaphor as a computational problem

1. Annotated corpora and databases of metaphor

1.1. Top-down and bottom-up approaches to metaphor identification in discourse

1.2. MIPVU: a procedure for linguistic metaphor identification

1.3. VUAMC: the VU Amsterdam Metaphor Corpus

2. Computational approaches to metaphor identification: state-of-the-art

2.1. Klebanov, Leong, Gutierrez, Shutova, and Flor (2016)

2.2. Klebanov, Leong, Heilman, and Flor (2014)

2.3. Mu, Yannakoudakis, and Shutova (2019)

2.4. Bulat, Clark, and Shutova (2017)

2.5. Shutova, Kiela, and Maillard (2016)

2.6. Stemle and Onysko (2018)

2.7. Wu et al. (2018)

2.8. Turney, Neuman, Assaf, and Cohen (2011)

2.9. Hovy et al. (2013)

3. Computational metaphor identification systems for Russian

3.1. Strzalkowski et al. (2013)

3.2. Tsvetkov, Mukomel, and Gershman (2013)

3.3. Tsvetkov et al. (2014)

Summary of Chapter

Chapter II. Experimental corpus

4. Corpus design

4.1. Selection of data

4.2. Selection of target verbs

5. Corpus annotation

5.1. Non-metaphoric class

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5.2. Metaphoric class

5.3. Distribution of metaphoric subclasses in the corpus

6. Annotation reliability test

6.1. Selection of sentences

6.2. Annotator instructions

6.3. Binarization of categorical annotation

6.4. Annotation results and analysis

Summary of Chapter II

Chapter III. Automated metaphor identification experiment

7. Motivation behind the choice of features

7.1. Motivation behind the use of distributional semantic feature

7.2. Motivation behind the use of lexical co-occurrence feature

7.3. Motivation behind the use of morphosyntactic co-occurrence feature

8. Data preprocessing and the context windows

9. The feature set

9.1. Distributional semantic features

9.2. Lexical co-occurrence features

9.3. Morphosyntactic co-occurrence feature

9.4 . Concreteness / abstractness feature

9.5. Flag words and quotation marks features

10. Experimental setup

11. Results

11.1. Evaluation of alternative parameters of the features

11.2. Window sensitivity

11.3. Inefficient features

11.4. Classification results

Summary of Chapter III

Chapter IV. Linguistic analysis of experimental results

12. Discussion: results of the lexical classifier and their implications

12.1. Correlation between lexical diversity of MET and NONMET subcorpora and performance of the lexical classifier

12.2. Feature importance

12.3. Detecting possible lexical predictors

12.4. Correlation between metaphor association and concreteness

13. Discussion: results of the distributional semantic classifier

13.1. Linguistic interpretation of the performance across datasets

13.2. Correlation between metaphoricity, semantic similarity, and accuracy

14. Discussion: results of the morphosyntactic classifier

14.1. Correlation between metaphor association of grammatical categories and the performance of the morphological classifier

14.2. Feature importance

Summary of Chapter IV

Thesis summary

List of References

List of tables

List of figures

Appendix 1. Annotator guidelines for the inter-annotator reliability test (Chapter II. Section 3.2)

Appendix 2. Concrete ('thingness') paradigm words (Chapter III. Section

Appendix 3. Abstract paradigm words (Chapter III. Section 9.4)