We present a feature vector formation technique for documents-Sparse Composite
Document Vector (SCDV)-which overcomes several shortcomings of the current …

Efficient representation of text documents is an important building block in many NLP tasks.
Research on long text categorization has shown that simple weighted averaging of word …

In this paper, we propose a novel representation for text documents based on aggregating
word embedding vectors into document embeddings. Our approach is inspired by the Vector …

We present an efficient document representation learning framework, Document Vector
through Corruption (Doc2VecC). Doc2VecC represents each document as a simple average …

Recently, there has been a lot of activity in learning distributed representations of words in
vector spaces. Although there are models capable of learning high-quality distributed …

Paragraph Vectors has been recently proposed as an unsupervised method for learning
distributed representations for pieces of texts. In their work, the authors showed that the …

We examine a number of methods to compute a dense vector embedding for a document in
a corpus, given a set of word vectors such as those from word2vec or GloVe. We describe …

Word embedding is a feature learning technique which aims at mapping words from a
vocabulary into vectors of real numbers in a low-dimensional space. By leveraging large …

Document processing in natural language includes retrieval, sentiment analysis, theme
extraction, etc. Classical methods for handling these tasks are based on models of …

Document representation with outlier tokens exacerbates the classification performance due
to the uncertain orientation of such tokens. Most existing document representation methods …