Integrating Crowdsourcing and Active Learning for Classification of Work-Life Events from Tweets

• URL: http://arxiv.org/abs/2003.12139v2
• Date: Thu, 2 Apr 2020 15:30:35 GMT
• Title: Integrating Crowdsourcing and Active Learning for Classification of Work-Life Events from Tweets
• Authors: Yunpeng Zhao, Mattia Prosperi, Tianchen Lyu, Yi Guo, Jiang Bian
• Abstract summary: Social media data are unstructured and must undergo complex manipulation for research use. We devised a crowdsourcing pipeline combined with active learning strategies. Results show that crowdsourcing is useful to create high-quality annotations and active learning helps in reducing the number of required tweets.
• Score: 9.137917522951277
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Social media, especially Twitter, is being increasingly used for research with predictive analytics. In social media studies, natural language processing (NLP) techniques are used in conjunction with expert-based, manual and qualitative analyses. However, social media data are unstructured and must undergo complex manipulation for research use. The manual annotation is the most resource and time-consuming process that multiple expert raters have to reach consensus on every item, but is essential to create gold-standard datasets for training NLP-based machine learning classifiers. To reduce the burden of the manual annotation, yet maintaining its reliability, we devised a crowdsourcing pipeline combined with active learning strategies. We demonstrated its effectiveness through a case study that identifies job loss events from individual tweets. We used Amazon Mechanical Turk platform to recruit annotators from the Internet and designed a number of quality control measures to assure annotation accuracy. We evaluated 4 different active learning strategies (i.e., least confident, entropy, vote entropy, and Kullback-Leibler divergence). The active learning strategies aim at reducing the number of tweets needed to reach a desired performance of automated classification. Results show that crowdsourcing is useful to create high-quality annotations and active learning helps in reducing the number of required tweets, although there was no substantial difference among the strategies tested.

Related papers

• Unsupervised Sentiment Analysis of Plastic Surgery Social Media Posts [91.3755431537592]
  The massive collection of user posts across social media platforms is primarily untapped for artificial intelligence (AI) use cases. Natural language processing (NLP) is a subfield of AI that leverages bodies of documents, known as corpora, to train computers in human-like language understanding. This study demonstrates that the applied results of unsupervised analysis allow a computer to predict either negative, positive, or neutral user sentiment towards plastic surgery.
  arXiv  Detail & Related papers  (2023-07-05T20:16:20Z)
• Active Learning for Abstractive Text Summarization [50.79416783266641]
  We propose the first effective query strategy for Active Learning in abstractive text summarization. We show that using our strategy in AL annotation helps to improve the model performance in terms of ROUGE and consistency scores.
  arXiv  Detail & Related papers  (2023-01-09T10:33:14Z)
• Design of Negative Sampling Strategies for Distantly Supervised Skill Extraction [19.43668931500507]
  We propose an end-to-end system for skill extraction, based on distant supervision through literal matching. We observe that using the ESCO taxonomy to select negative examples from related skills yields the biggest improvements. We release the benchmark dataset for research purposes to stimulate further research on the task.
  arXiv  Detail & Related papers  (2022-09-13T13:37:06Z)
• Active Learning of Ordinal Embeddings: A User Study on Football Data [4.856635699699126]
  Humans innately measure distance between instances in an unlabeled dataset using an unknown similarity function. This work uses deep metric learning to learn these user-defined similarity functions from few annotations for a large football trajectory dataset.
  arXiv  Detail & Related papers  (2022-07-26T07:55:23Z)
• Online Continual Learning with Natural Distribution Shifts: An Empirical Study with Visual Data [101.6195176510611]
  "Online" continual learning enables evaluating both information retention and online learning efficacy. In online continual learning, each incoming small batch of data is first used for testing and then added to the training set, making the problem truly online. We introduce a new benchmark for online continual visual learning that exhibits large scale and natural distribution shifts.
  arXiv  Detail & Related papers  (2021-08-20T06:17:20Z)
• Combining Feature and Instance Attribution to Detect Artifacts [62.63504976810927]
  We propose methods to facilitate identification of training data artifacts. We show that this proposed training-feature attribution approach can be used to uncover artifacts in training data. We execute a small user study to evaluate whether these methods are useful to NLP researchers in practice.
  arXiv  Detail & Related papers  (2021-07-01T09:26:13Z)
• Bayesian active learning for production, a systematic study and a reusable library [85.32971950095742]
  In this paper, we analyse the main drawbacks of current active learning techniques. We do a systematic study on the effects of the most common issues of real-world datasets on the deep active learning process. We derive two techniques that can speed up the active learning loop such as partial uncertainty sampling and larger query size.
  arXiv  Detail & Related papers  (2020-06-17T14:51:11Z)
• Mining Implicit Relevance Feedback from User Behavior for Web Question Answering [92.45607094299181]
  We make the first study to explore the correlation between user behavior and passage relevance. Our approach significantly improves the accuracy of passage ranking without extra human labeled data. In practice, this work has proved effective to substantially reduce the human labeling cost for the QA service in a global commercial search engine.
  arXiv  Detail & Related papers  (2020-06-13T07:02:08Z)

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.