Pattern Learning for Detecting Defect Reports and Improvement Requests in App Reviews

• URL: http://arxiv.org/abs/2004.08793v1
• Date: Sun, 19 Apr 2020 08:13:13 GMT
• Title: Pattern Learning for Detecting Defect Reports and Improvement Requests in App Reviews
• Authors: Gino V.H. Mangnoesing, Maria Mihaela Trusca, Flavius Frasincar
• Abstract summary: In this study, we follow novel approaches that target this absence of actionable insights by classifying reviews as defect reports and requests for improvement. We employ a supervised system that is capable of learning lexico-semantic patterns through genetic programming. We show that the automatically learned patterns outperform the manually created ones, to be generated.
• Score: 4.460358746823561
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Online reviews are an important source of feedback for understanding customers. In this study, we follow novel approaches that target this absence of actionable insights by classifying reviews as defect reports and requests for improvement. Unlike traditional classification methods based on expert rules, we reduce the manual labour by employing a supervised system that is capable of learning lexico-semantic patterns through genetic programming. Additionally, we experiment with a distantly-supervised SVM that makes use of noisy labels generated by patterns. Using a real-world dataset of app reviews, we show that the automatically learned patterns outperform the manually created ones, to be generated. Also the distantly-supervised SVM models are not far behind the pattern-based solutions, showing the usefulness of this approach when the amount of annotated data is limited.

Related papers

• VL4AD: Vision-Language Models Improve Pixel-wise Anomaly Detection [5.66050466694651]
  We propose Vision-Language (VL) encoders into existing anomaly detectors to leverage the semantically broad VL pre-training for improved outlier awareness. We also propose a new scoring function that enables data- and training-free outlier supervision via textual prompts. The resulting VL4AD model achieves competitive performance on widely used benchmark datasets.
  arXiv  Detail & Related papers  (2024-09-25T20:12:10Z)
• Position: Quo Vadis, Unsupervised Time Series Anomaly Detection? [11.269007806012931]
  The current state of machine learning scholarship in Timeseries Anomaly Detection (TAD) is plagued by the persistent use of flawed evaluation metrics. Our paper presents a critical analysis of the status quo in TAD, revealing the misleading track of current research.
  arXiv  Detail & Related papers  (2024-05-04T14:43:31Z)
• An Information Theoretic Approach to Machine Unlearning [45.600917449314444]
  Key challenge in unlearning is forgetting the necessary data in a timely manner, while preserving model performance. In this work, we address the zero-shot unlearning scenario, whereby an unlearning algorithm must be able to remove data given only a trained model and the data to be forgotten. We derive a simple but principled zero-shot unlearning method based on the geometry of the model.
  arXiv  Detail & Related papers  (2024-02-02T13:33:30Z)
• XAL: EXplainable Active Learning Makes Classifiers Better Low-resource Learners [71.8257151788923]
  We propose a novel Explainable Active Learning framework (XAL) for low-resource text classification. XAL encourages classifiers to justify their inferences and delve into unlabeled data for which they cannot provide reasonable explanations. Experiments on six datasets show that XAL achieves consistent improvement over 9 strong baselines.
  arXiv  Detail & Related papers  (2023-10-09T08:07:04Z)
• Bring Your Own Data! Self-Supervised Evaluation for Large Language Models [52.15056231665816]
  We propose a framework for self-supervised evaluation of Large Language Models (LLMs) We demonstrate self-supervised evaluation strategies for measuring closed-book knowledge, toxicity, and long-range context dependence. We find strong correlations between self-supervised and human-supervised evaluations.
  arXiv  Detail & Related papers  (2023-06-23T17:59:09Z)
• Automated Labeling of German Chest X-Ray Radiology Reports using Deep Learning [50.591267188664666]
  We propose a deep learning-based CheXpert label prediction model, pre-trained on reports labeled by a rule-based German CheXpert model. Our results demonstrate the effectiveness of our approach, which significantly outperformed the rule-based model on all three tasks.
  arXiv  Detail & Related papers  (2023-06-09T16:08:35Z)
• Annotation Error Detection: Analyzing the Past and Present for a More Coherent Future [63.99570204416711]
  We reimplement 18 methods for detecting potential annotation errors and evaluate them on 9 English datasets. We define a uniform evaluation setup including a new formalization of the annotation error detection task. We release our datasets and implementations in an easy-to-use and open source software package.
  arXiv  Detail & Related papers  (2022-06-05T22:31:45Z)
• Simulating Bandit Learning from User Feedback for Extractive Question Answering [51.97943858898579]
  We study learning from user feedback for extractive question answering by simulating feedback using supervised data. We show that systems initially trained on a small number of examples can dramatically improve given feedback from users on model-predicted answers.
  arXiv  Detail & Related papers  (2022-03-18T17:47:58Z)
• Automatic Classification of Error Types in Solutions to Programming Assignments at Online Learning Platform [4.028503203417233]
  We apply machine learning methods to improve the feedback of automated verification systems for programming assignments. We detect frequent error types by clustering previously submitted incorrect solutions, label these clusters and use this labeled dataset to identify the type of an error in a new submission.
  arXiv  Detail & Related papers  (2021-07-13T11:59:57Z)
• Adversarial Examples for Unsupervised Machine Learning Models [71.81480647638529]
  Adrial examples causing evasive predictions are widely used to evaluate and improve the robustness of machine learning models. We propose a framework of generating adversarial examples for unsupervised models and demonstrate novel applications to data augmentation.
  arXiv  Detail & Related papers  (2021-03-02T17:47:58Z)
• Visually Analyzing and Steering Zero Shot Learning [2.802183323381949]
  We propose a visual analytics system to help a user analyze and steer zero-shot learning models. Through usage scenarios, we highlight how our system can help a user improve performance in zero-shot learning.
  arXiv  Detail & Related papers  (2020-09-11T06:58:13Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.