Improving Classification Accuracy Using Clustering Technique

Product classification is the key issue in e-commerce domains. Many products are released to the market rapidly and to select the correct category in taxonomy for each product has become a challenging task. The application of classification model is useful to precisely classify the products. The study proposed a method to apply clustering prior to classification. This study has used a large-scale real-world data set to identify the efficiency of clustering technique to improve the classification model. The conventional text classification procedures are used in the study such as preprocessing, feature extraction and feature selection before applying the clustering technique. Results show that clustering technique improves the accuracy of the classification model. The best classification model for all three approaches which are classification model only, classification with hierarchical clustering and classification with K-means clustering is K-Nearest Neighbor (KNN) model. Even though the accuracy of the KNN models are the same across different approaches but the KNN model with K-means clustering had the shortest time of execution. Hence, applying K-means clustering prior to KNN model helps in reducing the computation time

Open-world Learning and Application to Product Classification

Classic supervised learning makes the closed-world assumption, meaning that classes seen in testing must have been seen in training. However, in the dynamic world, new or unseen class examples may appear constantly. A model working in such an environment must be able to reject unseen classes (not seen or used in training). If enough data is collected for the unseen classes, the system should incrementally learn to accept/classify them. This learning paradigm is called open-world learning (OWL). Existing OWL methods all need some form of re-training to accept or include the new classes in the overall model. In this paper, we propose a meta-learning approach to the problem. Its key novelty is that it only needs to train a meta-classifier, which can then continually accept new classes when they have enough labeled data for the meta-classifier to use, and also detect/reject future unseen classes. No re-training of the meta-classifier or a new overall classifier covering all old and new classes is needed. In testing, the method only uses the examples of the seen classes (including the newly added classes) on-the-fly for classification and rejection. Experimental results demonstrate the effectiveness of the new approach.Comment: accepted by The Web Conference (WWW 2019) Previous title: Learning to Accept New Classes without Trainin

An exploratory study on utilising the web of linked data for product data mining

The Linked Open Data practice has led to a significant growth of structured data on the Web. While this has created an unprecedented opportunity for research in the field of Natural Language Processing, there is a lack of systematic studies on how such data can be used to support downstream NLP tasks. This work focuses on the e-commerce domain and explores how we can use such structured data to create language resources for product data mining tasks. To do so, we process billions of structured data points in the form of RDF n-quads, to create multi-million words of product-related corpora that are later used in three different ways for creating language resources: training word-embedding models, continued pre-training of BERT-like language models, and training machine translation models that are used as a proxy to generate product-related keywords. These language resources are then evaluated in three downstream tasks, product classification, linking, and fake review detection using an extensive set of benchmarks. Our results show word embeddings to be the most reliable and consistent method to improve the accuracy on all tasks (with up to 6.9% points in macro-average F1 on some datasets). Contrary to some earlier studies that suggest a rather simple but effective approach such as building domain-specific language models by pre-training using in-domain corpora, our work serves a lesson that adapting these methods to new domains may not be as easy as it seems. We further analyse our datasets and reflect on how our findings can inform future research and practice