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| Дата создания | 2021 |
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Добрый день! Уважаемые студенты, Вашему вниманию представляется дипломная работа на тему: «Оценка и анализ моделей распознавания юмора»
Оригинальность работы 95%
Аннотация
В последнее время в связи с развитием систем общения задача распознавания юмора приобретает всё большую актуальность. Данная работа анализирует модели распознавания юмора, обученные на нескольких наборах данных, с помощью состязательных атак. В первой части работы проведен обзор предметной области и существующих исследований по распознаванию юмора и по состязательным атакам. Вторая часть содержит описание существующих наборов данных и построения моделей распознавания юмора на их основе, а также подробный анализ их работы. В третьей части описаны применяемые состязательные атаки и их влияние на результаты классификации. Наконец,
работа завершается подведением итогов и идеями для дальнейших исследований.
Recently, in connection with the development of communication systems, the task of humor detection is becoming increasingly relevant. This paper analyzes humor recognition models trained on multiple datasets using adversarial attacks. In the first part of the paper, the research area and existing works on humor recognition and adversarial attacks are reviewed. The second part contains description of existing datasets and construction of humor recognition models, as well as detailed analysis of their results. The third part describes adversarial attacks used and their impact on classification results. Finally, the work ends with a summary of obtained results and ideas for further research.
Список ключевых слов – распознавание юмора; модель-классификатор; логистическая регрессия; эмбеддинг; Bag of Words; Word2Vec; Universal Sentence Encoder; ClassifierDL; состязательные атаки
Оглавление
Аннотация ....................................................................................................................
4
Введение.......................................................................................................................
5
Обзор литературы........................................................................................................
7
1.
Распознавание юмора ........................................................................................
7
2.
Состязательные атаки......................................................................................
17
Выводы....................................................................................................................
24
Глава 1. Данные .........................................................................................................
26
1.1
One-Liners ..........................................................................................................
26
1.2
Humicroedit ........................................................................................................
28
1.3 Unfun.me ............................................................................................................
29
1.4 PUNS..................................................................................................................
30
Выводы и результаты.............................................................................................
31
Глава 2. Модели и метрики качества ......................................................................
32
2.1
Метрики качества.............................................................................................
33
2.2
Bag of Words & Logistic Regression ................................................................
35
2.2.1
Train on One-liners ......................................................................................
37
2.2.2
Train on Humicroedit ...................................................................................
38
2.2.3
Train on Unfunme ........................................................................................
39
2.2.4
Train on Puns ...............................................................................................
40
2.3
Word2Vec & Logistic Regression .....................................................................
42
2.3.1
Train on One-Liners .....................................................................................
43
2.3.2
Train on Humicroedit ...................................................................................
44
2.3.3
Train on Unfunme ........................................................................................
45
2.3.4
Train on Puns ...............................................................................................
46
2.4
Universal Sentence Encoder & ClassifierDL ....................................................
47
2.4.1
Train on One-liners ......................................................................................
48
2.4.2
Train on Humicroedit ...................................................................................
49
2.4.3
Train on Unfunme ........................................................................................
50
2.4.4
Train on Puns ...............................................................................................
51
2.5
Сравнение моделей ..........................................................................................
52
2.5.1
One-liners (train, test) ..................................................................................
53
2
2.5.2 Humicroedit (train, test)........................................................................ 54
2.5.3 Unfunme (train, test)............................................................................. 55
2.5.4 Puns (train, test).................................................................................... 56
2.5.5 Сравнение на всех датасетах.............................................................. 57
Выводы и результаты.................................................................................... 58
Глава 3. Состязательные атаки......................................................................... 59
3.1 Метрики качества..................................................................................... 61
3.2 Slang 18+.................................................................................................. 61
3.3 Антонимы................................................................................................. 63
3.4 Рифмы....................................................................................................... 65
3.5 Сравнение................................................................................................. 67
Выводы и результаты.................................................................................... 68
Глава 4. Применение состязательных атак....................................................... 69
4.1 Slang 18+.................................................................................................. 70
4.2 Антонимы................................................................................................. 73
4.3 Рифмы....................................................................................................... 76
Выводы и результаты.................................................................................... 78
Заключение........................................................................................................ 79
Библиографический список.............................................................................. 81
Приложениe 1. Результаты классификации BOW на исходных данных......... 84
Приложениe 2. Результаты классификации Word2Vec на исходных данных. 86
Приложениe 3. Результаты классификации USE на исходных данных.......... 88
Приложениe 4. Процент изменений текстов класса «non-humorous» для каждой
атаки................................................................................................................... 90
Приложениe 5. Процент ошибок для класса «non-humorous» в исходных
результатах........................................................................................................ 91
Приложениe 6. Результаты классификации после применения атаки «Slang 18+»
92
Приложениe 7. Результаты классификации после применения атаки «Антонимы»
94
Приложениe 8. Результаты классификации после применения атаки «Рифмы» 96
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EMNLP.
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82
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NLP.” (2019). EMNLP.
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