That’s So Annoying!!!: A Lexical and Frame-Semantic Embedding Based Data Augmentation Approach to Automatic Categorization of Annoying Behaviors using #petpeeve Tweets

By William Yang Wang, Diyi Yang. 2015

317 citations recorded by the source corpus.

We propose a novel data augmentation approach to enhance computational behavioral analysis using social media text. In particular, we collect a Twitter corpus of the descriptions of annoying behaviors using the #petpeeve hashtags. In the qualitative analysis, we study the language use in these tweets, with a special focus on the fine-grained categories and the geographic variation of the language. In quantitative analysis, we show that lexical and syntactic features are useful for automatic categorization of annoying behaviors, and frame-semantic features further boost the performance; that leveraging large lexical embeddings to create additional training instances significantly improves the lexical model; and incorporating frame-semantic embedding achieves the best overall performance.

Subfields: Artificial Intelligence, Statistical and Nonlinear Physics, Natural Language Processing

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Citations in the corpus

Few-shot Mining of Naturally Occurring Inputs and Outputs (2022, methodology): “Data augmentation Data augmentation is a popular technique with a large body of work (Wang and Yang, 2015; Jia and Liang, 2016) et al., 2019), common-sense reasoning (Yang et al., 2020), and machine translation (Sennrich et al., 2016).”
Data Recombination for Neural Semantic Parsing (2016, methodology): “Wang and Yang (2015) use a similar strategy, but identify similar words and phrases based on cosine distance be-tween vector space embeddings.”
Automatically Learning Data Augmentation Policies for Dialogue Tasks (2019, background): “…Sato et al., 2015; DeVries and Taylor, 2017; Tran et al., 2017; Lemley et al., 2017) and NLP (F¨urstenau and Lapata, 2009; Sen-nrich et al., 2016; Wang and Yang, 2015; Zhang et al., 2015; Jia and Liang, 2016; Kim and Rush, 2016; Hu et al., 2017; Xu et al., 2017; Xia et al., 2017; Silfverberg et…”
“Liar, Liar Pants on Fire”: A New Benchmark Dataset for Fake News Detection (2017, methodology): “For LR and SVM, we used the L IB S HORT T EXT toolkit 6 , which was shown to provide very strong performances on short text classiﬁcation problems (Wang and Yang, 2015).”
EDA: Easy Data Augmentation Techniques for Boosting Performance on Text Classification Tasks (2019, methodology): “We note that synonym replacement has been used previously (Kolomiyets et al., 2011; Zhang et al., 2015; Wang & Yang, 2015), but to our knowledge, random insertions, swaps, and deletions have not been studied.”
Conditional BERT Contextual Augmentation (2018, methodology): “Wang [30] proposed the use of neighboring words in continuous representations to create new instances for every word in a tweet to augment the training dataset.”
G-DAug: Generative Data Augmentation for Commonsense Reasoning (2020, background): “, 2015; Wei and Zou, 2019) and word embeddings (Wang and Yang, 2015; Fadaee et al., 2017; Kobayashi, 2018; Wu et al., 2019), and very recently, generative models for synthesizing”
SSMBA: Self-Supervised Manifold Based Data Augmentation for Improving Out-of-Domain Robustness (2020, methodology): “More recent work has used word embeddings (Wang and Yang, 2015) and LSTM language models (Fadaee et al., 2017) to perform word replacement.”
A Study of Various Text Augmentation Techniques for Relation Classification in Free Text (2019, methodology): “Wang and Yang (Wang and Yang, 2015) used word embeddings of sentences to generate augmented data for the purpose of increasing data size and trained a multi-class classifier on tweet data.”
Quantifying the Evaluation of Heuristic Methods for Textual Data Augmentation (2020, methodology): “…proposing many different approaches for text data augmentation; for example, (Zhang et al., 2015; Wei and Zou, 2019) used thesaurus-based and (Wang and Yang, 2015; Kobayashi, 2018; Jiao et al., 2019) used embedding-based lexical substitution approach, (Wei and Zou, 2019; Xie et al., 2019)…”
Submodular Optimization-based Diverse Paraphrasing and its Effectiveness in Data Augmentation (2019, background): “, 2013; Bernhard and Gurevych, 2008), data augmentation (Zhang et al., 2015; Wang and Yang, 2015), conversational agents (Li et al.”
Substructure Substitution: Structured Data Augmentation for NLP (2021, background): “They typically create new examples by substituting (word) tokens with ones having the same desired features, such as synonym substitution (Zhang et al., 2015; Wang and Yang, 2015; Fadaee et al., 2017; Kobayashi, 2018) or substitution with words having the same morphological features (Silfverberg et ”
Augmenting NLP models using Latent Feature Interpolations (2020, methodology): “(Wang and Yang, 2015) used k-NN and Cosine Similarity metrics to find a similar word for replacement.”
Sequence-to-Sequence Data Augmentation for Dialogue Language Understanding (2018, methodology): “Paraphrasing techniques including word-level substitution (Zhang et al., 2015; Wang and Yang, 2015), hand-crafted rules generation (Fader et al.”
An Empirical Survey of Data Augmentation for Limited Data Learning in NLP (2021, methodology): “A simple approach is to fetch synonyms as words for substitutions (Kolomiyets et al., 2011; Yang, 2015; Zhang et al., 2015a; Wei and Zou, 2019; Miao et al., 2020).”
Synthetic Data for Deep Learning (2019, background): “The work [629], which concentrated on studying tweets, proposed to use embedding-based data augmentation, using neighboring words in the word vector space as synonyms.”
A Survey of Deep Learning for Scientific Discovery (2020, background): “This latter strategy is usually guided by word embeddings [239] or contextualized word embeddings [110].”