Relation Classification via Convolutional Deep Neural Network
In this paper, we exploit a convolutional deep neural network (DNN) to extract lexical and sentence level features for relation classification. Expand Save
Alert
Cite
Research Feed
Recurrent Convolutional Neural Networks for Text Classification
We introduce a recurrent convolutional neural network for text classification without human-designed features. Expand Save
Alert
Cite
Research Feed
How to Generate a Good Word Embedding
The authors analyze three critical components in training word embeddings: model, corpus, and training parameters. Expand Save
Alert
Cite
Research Feed
Ontology Matching with Word Embeddings
We introduce word embeddings to the field of ontology matching. Expand Save
Alert
Cite
Research Feed
Topic-sensitive probabilistic model for expert finding in question answer communities
We propose a topic-sensitive probabilistic model to find experts in CQA by taking into account both the link structure and the topical similarity among users. Expand Save
Alert
Cite
Research Feed
Mining Opinion Words and Opinion Targets in a Two-Stage Framework
This paper proposes a novel two-stage method for mining opinion words and opinion targets. Expand Save
Alert
Cite
Research Feed
Hybrid Recommendation Models for Binary User Preference Prediction Problem
This paper presents detailed information of our solutions to the task 2 of KDD Cup 2011. Expand Save
Alert
Cite
Research Feed
Walk and learn: a two-stage approach for opinion words and opinion targets co-extraction
This paper proposes a novel two-stage method for opinion words and opinion targets co-extraction. Expand Save
Alert
Cite
Research Feed
Product Feature Mining: Semantic Clues versus Syntactic Constituents
This paper proposes a novel product feature mining method which leverages lexical and contextual semantic clues to extract product features from online reviews. Expand Save
Alert
Cite
Research Feed
Word and Document Embeddings based on Neural Network Approaches
Siwei Lai Computer Science ArXiv 18 November 2016 We make comprehensive comparisons among existing word embeddings and document representation models. Expand Save
Alert
Cite
Research Feed