Extreme Multi-Label Classification with Label Masking for Product Attribute Value Extraction

2022-05-01 · ECNLP (ACL) 2022 5 · Wei-Te Chen, Yandi Xia, Keiji Shinzato

Although most studies have treated attribute value extraction (AVE) as named entity recognition, these approaches are not practical in real-world e-commerce platforms because they perform poorly, and require canonicalization of extracted values. Furthermore, since values needed for actual services is static in many attributes, extraction of new values is not always necessary. Given the above, we formalize AVE as extreme multi-label classification (XMC). A major problem in solving AVE as XMC is that the distribution between positive and negative labels for products is heavily imbalanced. To mitigate the negative impact derived from such biased distribution, we propose label masking, a simple and effective method to reduce the number of negative labels in training. We exploit attribute taxonomy designed for e-commerce platforms to determine which labels are negative for products. Experimental results using a dataset collected from a Japanese e-commerce platform demonstrate that the label masking improves micro and macro F_1 scores by 3.38 and 23.20 points, respectively.

📄 PDF Abstract BibTeX

Code (0)

등록된 구현이 없습니다.

Tasks

AttributeAttribute Value ExtractionExtreme Multi-Label ClassificationMulti-Label Classification MUlTI-LABEL-ClASSIFICATIONnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)

PLM: Partial Label Masking for Imbalanced Multi-label Classification

2021-05-22 · Kevin Duarte, Yogesh S. Rawat, Mubarak Shah

Neural networks trained on real-world datasets with long-tailed label distributions are biased towards frequent classes and perform poorly on infrequent classes. The imbalance in the ratio of positive and negative sample…

Classification image-classification Image Classification Multi-Label Classification+1

Adopting the Multi-answer Questioning Task with an Auxiliary Metric for Extreme Multi-label Text Classification Utilizing the Label Hierarchy

2023-03-02 · Li Wang, Ying Wah Teh, Mohammed Ali Al-Garadi

Extreme multi-label text classification utilizes the label hierarchy to partition extreme labels into multiple label groups, turning the task into simple multi-group multi-label classification tasks. Current research enc…

Classification Extreme Multi-Label Classification Multi-Label Classification MUlTI-LABEL-ClASSIFICATION+5

Review of Extreme Multilabel Classification

2023-02-12 · Arpan Dasgupta, Preeti Lamba, Ankita Kushwaha, Kiran Ravish 외

Extreme multi-label classification or XMLC, is an active area of interest in machine learning. Compared to traditional multi-label classification, here the number of labels is extremely large, hence, the name extreme mul…

Classification compressed sensing Extreme Multi-Label Classification Multi-Label Classification+1

Multi-Label Classification Method Based on Extreme Learning Machines

2016-08-30 · Rajasekar Venkatesan, Meng Joo Er

In this paper, an Extreme Learning Machine (ELM) based technique for Multi-label classification problems is proposed and discussed. In multi-label classification, each of the input data samples belongs to one or more tha…

Classification General Classification Multi-class Classification Multi-Label Classification+1

Deep Extreme Multi-label Learning

2017-04-12 · Wenjie Zhang, Junchi Yan, Xiangfeng Wang, Hongyuan Zha

Extreme multi-label learning (XML) or classification has been a practical and important problem since the boom of big data. The main challenge lies in the exponential label space which involves $2^L$ possible label sets …

Classification Extreme Multi-Label Classification General Classification Multi-Label Classification+2