Mining topics in documents: standing on the shoulders of big data

By Zhiyuan Chen, B. Liu. 2014

157 citations recorded by the source corpus.

Subfields: Artificial Intelligence, General Social Sciences

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Bing Liu
2. Hu Xu
3. Lei Shu
4. B. Liu
5. Soujanya Poria
6. Erik Cambria
7. Shuai Wang
8. Zixuan Ke

In General Social Sciences

1. Bing Liu
2. Lei Shu
3. Hu Xu
4. Shuai Wang
5. Soujanya Poria
6. Alexander Gelbukh
7. Erik Cambria
8. Annice Kim

Citations in the corpus

• Few-Shot Image Recognition by Predicting Parameters from Activations (2017, neutral): “, an efficient lifelong learning system [3, 4].”
• DOC: Deep Open Classification of Text Documents (2017, background): “(2) 50-class reviews (Chen and Liu, 2014): The dataset has Amazon reviews of 50 classes of products.”
• Lifelong Learning for Sentiment Classification (2015, background): “Existing lifelong learning approaches focused on exploiting invariances (Thrun, 1998) and other types of knowledge (Chen and Liu, 2014b, Chen and Liu, 2014a, Ruvolo and Eaton, 2013) across multiple tasks.”
• Lifelong-RL: Lifelong Relaxation Labeling for Separating Entities and Aspects in Opinion Targets (2016, methodology): “The second set consists of unlabeled review datasets from 100 diverse products or domains (Chen and Liu 2014).”
• Mining Aspect-Specific Opinion using a Holistic Lifelong Topic Model (2016, methodology): “[8] considered the positive word correlation as the knowledge while [7] further utilized the negative word correlation.”
• Life-long learning based on dynamic combination model (2017, background): “Most recently, a life-long learning system is designed for topic modeling, which constantly modifies the learned relationship with new data [15] [16].”
• Aspect extraction for opinion mining with a deep convolutional neural network (2016, methodology): “Topic modeling has been widely used as a basis to perform extraction and grouping of aspects (Hu et al., 2014; Chen and Liu, 2014).”
• Lifelong Machine Learning (2016, background): “Cannot learn by itself automatically WebDB, May 14, 2017 17 Machine learning: ML 2.0 (Thrun, 1996b; Silver et al 2013; Chen and Liu, 2014, 2016-book) Human beings never learn in isolation We learn continuously: Accumulate knowledge learned in the past and use it to learn more knowledge, and we…”
• Incorporating Knowledge Graph Embeddings into Topic Modeling (2017, unknown): “To learn word correlation knowledge automatically, AMC (topic modeling with Automatically generated Must-links and Cannot-links) was proposed (Chen and Liu 2014).”
• Topic Modeling for Short Texts with Auxiliary Word Embeddings (2016, methodology): “Existing works in this line largely rely on either ex-ternal thesauri ( e.g., WordNet) or lexical knowledge derived from documents in a specific domain ( e.g., product comments) [5–7].”