Gradual machine learning

Introduction

GML begins with some easy instances in a task, which can be automatically labeled by the machine with high accuracy, and then gradually reasons about the labels of the more challenging instances based on the observations provided by the labeled instances. The following two properties of GML make it fundamentally different from the existing learning paradigms:

  1. Distribution misalignment between easy and hard instances in a task.

    2. The scenario of gradual machine learning does not satisfy the i.i.d (independent and identically distributed) assumption underlying most existing machine learning models: the labeled easy instances are not representative of the unlabeled hard instances. The distribution misalignment between the labeled and unlabeled instances renders most existing learning models unfit for gradual machine learning.

  2. Gradual learning by small stages in a task.

    3. Gradual machine learning proceeds in small stages. At each stage, it typically labels only one instance based on the evidential certainty provided by the labeled easier instances. The process of iterative labeling can be performed in an unsupervised manner without requiring any human intervention.

Gradual machine learning is a new and promising research direction supposed to complement deep learning. We have successfully applied gradual machine learning to the classification tasks of entity resolution and sentiment analysis. As a general paradigm, GML can be generalized to various classification tasks. We have also initiated an open-source project, at https://github.com/gml-explore/numbskull, to support GML application and implementation.

Selected Publications

Gradual Machine Learning for Entity Resolution. WWW 2019.
Boyi Hou, Qun Chen, Jiquan Shen, Xin Liu, Ping Zhong, Yanyan Wang, Zhaoqiang Chen,Zhanhuai Li
[Abstract]  [Bibtex]  [PDF]  [Homepage]

Usually considered as a classification problem, entity resolution can be very challenging on real data due to the prevalence of dirty values. The state-of-the-art solutions for ER were built on a variety of learning models (most notably deep neural networks), which require lots of accurately labeled training data. Unfortunately, high quality labeled data usually require expensive manual work, and are therefore not readily available in many real scenarios. In this demo, we propose a novel learning paradigm for ER, called gradual machine learning, which aims to enable effective machine label ing without the requirement for manual labeling effort. It begins with some easy instances in a task, which can be automatically labeled by the machine with high accuracy, and then gradually labels more challenging instances based on iterative factor graph inference. In gradual machine learning, the hard instances in a task are gradually labeled in small stages based on the estimated evidential certainty provided by the labeled easier instances. Our extensive experiments on real data have shown that the proposed approach performs considerably better than its unsupervised alter natives, and its performance is also highly competitive compared to the state-of-the-art supervised techniques. Using ER as a test case, we demonstrate that gradual machine learning is a promising paradigm potentially applicable to other challenging classification tasks requiring extensive labeling effort.

@inproceedings{hou2019gradual,
title={Gradual machine learning for entity resolution},
author={Hou, Boyi and Chen, Qun and Shen, Jiquan and Liu, Xin and Zhong, Ping and Wang, Yanyan and Chen, Zhaoqiang and Li, Zhanhuai},
booktitle={The World Wide Web Conference},
pages={3526--3530},
year={2019},
organization={ACM}
}

Joint Inference for Aspect-Level Sentiment Analysis by Deep Neural Networks and Linguistic Hints. IEEE Transactions on Knowledge and Data Engineering (TKDE), 2019.
Yanyan Wang, Qun Chen, Murtadha Ahmed, Zhanhuai Li, Wei Pan, and Hailong Liu
[Abstract]  [Bibtex]  [PDF]  [Homepage]

The state-of-the-art techniques for aspect-level sentiment analysis focused on feature modeling using a variety of deep neural networks (DNN). Unfortunately, their performance may still fall short of expectation in real scenarios due to the semantic complexity of natural languages. Motivated by the observation that many linguistic hints (e.g., sentiment words and shift words) are reliable polarity indicators, we propose a joint framework, SenHint, which can seamlessly integrate the output of deep neural networks and the implications of linguistic hints in a unified model based on Markov logic network (MLN). SenHint leverages the linguistic hints for multiple purposes: (1) to identify the easy instances, whose polarities can be automatically determined by the machine with high accuracy; (2) to capture the influence of sentiment words on aspect polarities; (3) to capture the implicit relations between aspect polarities. We present the required techniques for extracting linguistic hints, encoding their implications as well as the output of DNN into the unified model, and joint inference. Finally, we have empirically evaluated the performance of SenHint on both English and Chinese benchmark datasets. Our extensive experiments have shown that compared to the state-of-the-art DNN techniques, SenHint can effectively improve polarity detection accuracy by considerable margins.

@article{wang2019joint,
title={Joint Inference for Aspect-level Sentiment Analysis by Deep Neural Networks and Linguistic Hints},
author={Wang, Yanyan and Chen, Qun and Ahmed, Murtadha and Li, Zhanhua and Pan, Wei and Liu, Hailong},
journal={IEEE Transactions on Knowledge and Data Engineering},
year={2019},
publisher={IEEE}

}


GML Framework