In this paper, we present an efficient method for including new words from a specialized corpus, containing new words, into pre-trained generic word embeddings.

To explore more about our method, we choose some typical domain-specific words and observe their nearest neighbors (in both extended and base vocabulary) after ...

This paper builds on the established view of word embeddings as matrix factorizations to present a spectral algorithm for this task, and demonstrates that ...

Continuous Word Embedding Fusion via Spectral Decomposition. January 2018. DOI ... Word embeddings encode semantic meanings of words into low-dimension word ...

Bibliographic details on Continuous Word Embedding Fusion via Spectral Decomposition.

Word embeddings have become a mainstream tool in statistical natural language processing. Practitioners often use pre-trained word vectors, ...

In this paper, we present an efficient method for including new words from a specialized corpus, containing new words, into pre-trained generic word embeddings.

It has many useful applications in signal processing and statistics. Formally, the singular value decomposition of an m × n real or complex matrix M is a ...

Continuous Word Embedding Fusion via Spectral Decomposition. Tianfan Fu | Cheng Zhang | Stephan Mandt |. Paper Details: Month: October Year: 2018

[word embedding] Word embedding is the collective name for a set of language modeling and feature learning techniques in natural language processing where ...