360 citations · 690 across the 24 of their papers we have counts for
5 papers · 1 filter
Open-world Learning and Application to Product Classification
Hu Xu, Bing Liu, Lei Shu +1
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 cl…
Lifelong Domain Word Embedding via Meta-Learning
Hu Xu, Bing Liu, Lei Shu +1
Learning high-quality domain word embeddings is important for achieving good performance in many NLP tasks. General-purpose embeddings trained on large-scale corpora are often sub-…
Double Embeddings and CNN-based Sequence Labeling for Aspect Extraction
Hu Xu, Bing Liu, Lei Shu +1
One key task of fine-grained sentiment analysis of product reviews is to extract product aspects or features that users have expressed opinions on. This paper focuses on supervised…
Generative Stock Question Answering
Zhaopeng Tu, Yong Jiang, Xiaojiang Liu +2
We study the problem of stock related question answering (StockQA): automatically generating answers to stock related questions, just like professional stock analysts providing act…
Unseen Class Discovery in Open-world Classification
Lei Shu, Hu Xu, Bing Liu
This paper concerns open-world classification, where the classifier not only needs to classify test examples into seen classes that have appeared in training but also reject exampl…