Lifelong Learning of Topics and Domain-Specific Word Embeddings
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Lifelong Domain Word Embedding via Meta-Learning
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-optimal for domain-specific applications…
Lifelong learningMeta-LearningWord EmbeddingsLifelong Word Embedding via Meta-Learning
Learning high-quality word embeddings is of significant importance in achieving better performance in many down-stream learning tasks. On one hand, traditional word embeddings are trained on a large scale corpus for gene…
Lifelong learningMeta-LearningWord EmbeddingsA Neural Generative Model for Joint Learning Topics and Topic-Specific Word Embeddings
We propose a novel generative model to explore both local and global context for joint learning topics and topic-specific word embeddings. In particular, we assume that global latent topics are shared across documents, a…
Sentiment AnalysisSentiment ClassificationTopic ModelsWord Embeddings+2Jointly Learning Word Embeddings and Latent Topics
Word embedding models such as Skip-gram learn a vector-space representation for each word, based on the local word collocation patterns that are observed in a text corpus. Latent topic models, on the other hand, take a m…
Learning Word EmbeddingsTopic ModelsWord EmbeddingsMulti-source Multi-view Transfer Learning in Neural Topic Modeling with Pretrained Topic and Word Embeddings
Though word embeddings and topics are complementary representations, several past works have only used pretrained word embeddings in (neural) topic modeling to address data sparsity problem in short text or small collect…
Information RetrievalRetrievalTransfer LearningWord Embeddings