most citedUnseen Class Discovery in Open-world Classification

57 citations · 191 across the 6 of their papers we have counts for

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9 papers · 1 filter

cs.CL2018

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-…

cs.CL2018

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…

cs.CL2018

Towards a Continuous Knowledge Learning Engine for Chatbots

Sahisnu Mazumder, Nianzu Ma, Bing Liu

Although chatbots have been very popular in recent years, they still have some serious weaknesses which limit the scope of their applications. One major weakness is that they canno…

cs.CL2018

Disentangling Aspect and Opinion Words in Target-based Sentiment Analysis using Lifelong Learning

Shuai Wang, Mianwei Zhou, Sahisnu Mazumder +2

Given a target name, which can be a product aspect or entity, identifying its aspect words and opinion words in a given corpus is a fine-grained task in target-based sentiment anal…

cs.CL201850 cited

Deep Learning for Sentiment Analysis : A Survey

Lei Zhang, Shuai Wang, Bing Liu

Deep learning has emerged as a powerful machine learning technique that learns multiple layers of representations or features of the data and produces state-of-the-art prediction r…

cs.CL20184 cited

Contextual and Position-Aware Factorization Machines for Sentiment Classification

Shuai Wang, Mianwei Zhou, Geli Fei +2

While existing machine learning models have achieved great success for sentiment classification, they typically do not explicitly capture sentiment-oriented word interaction, which…