DeepAI AI Chat
Log In Sign Up

SCDV : Sparse Composite Document Vectors using soft clustering over distributional representations

by   Dheeraj Mekala, et al.
Indian Institute of Technology Kanpur

We present a feature vector formation technique for documents - Sparse Composite Document Vector (SCDV) - which overcomes several shortcomings of the current distributional paragraph vector representations that are widely used for text representation. In SCDV, word embedding's are clustered to capture multiple semantic contexts in which words occur. They are then chained together to form document topic-vectors that can express complex, multi-topic documents. Through extensive experiments on multi-class and multi-label classification tasks, we outperform the previous state-of-the-art method, NTSG (Liu et al., 2015a). We also show that SCDV embedding's perform well on heterogeneous tasks like Topic Coherence, context-sensitive Learning and Information Retrieval. Moreover, we achieve significant reduction in training and prediction times compared to other representation methods. SCDV achieves best of both worlds - better performance with lower time and space complexity.


Improving Document Classification with Multi-Sense Embeddings

Efficient representation of text documents is an important building bloc...

Improving Topic Models with Latent Feature Word Representations

Probabilistic topic models are widely used to discover latent topics in ...

Neural Embedding Allocation: Distributed Representations of Topic Models

Word embedding models such as the skip-gram learn vector representations...

Generative Topic Embedding: a Continuous Representation of Documents (Extended Version with Proofs)

Word embedding maps words into a low-dimensional continuous embedding sp...

Document Network Projection in Pretrained Word Embedding Space

We present Regularized Linear Embedding (RLE), a novel method that proje...

Efficient Classification of Long Documents Using Transformers

Several methods have been proposed for classifying long textual document...

Learning Topic Models by Neighborhood Aggregation

Topic models are one of the most frequently used models in machine learn...