57 citations · 449 across the 18 of their papers we have counts for
11 papers · 1 filter
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…
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…
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…
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…
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…
Lifelong Learning for Sentiment Classification
Zhiyuan Chen, Nianzu Ma, Bing Liu
This paper proposes a novel lifelong learning (LL) approach to sentiment classification. LL mimics the human continuous learning process, i.e., retaining the knowledge learned from…
Context-aware Path Ranking for Knowledge Base Completion
Sahisnu Mazumder, Bing Liu
Knowledge base (KB) completion aims to infer missing facts from existing ones in a KB. Among various approaches, path ranking (PR) algorithms have received increasing attention in…
DOC: Deep Open Classification of Text Documents
Lei Shu, Hu Xu, Bing Liu
Traditional supervised learning makes the closed-world assumption that the classes appeared in the test data must have appeared in training. This also applies to text learning or t…
Lifelong Learning CRF for Supervised Aspect Extraction
Lei Shu, Hu Xu, Bing Liu
This paper makes a focused contribution to supervised aspect extraction. It shows that if the system has performed aspect extraction from many past domains and retained their resul…
Supervised Opinion Aspect Extraction by Exploiting Past Extraction Results
Lei Shu, Bing Liu, Hu Xu +1
One of the key tasks of sentiment analysis of product reviews is to extract product aspects or features that users have expressed opinions on. In this work, we focus on using super…