Skip to search formSkip to main contentSkip to account menu

Word embedding

Known as: Word vector space, Thought vectors, Word vectors 
Word embedding is the collective name for a set of language modeling and feature learning techniques in natural language processing (NLP) where words… 
Wikipedia (opens in a new tab)

Related topics

Related topics

18 relations
BioinformaticsBrown clusteringCo-occurrence matrixDeep learning

Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
2020
2020
FrameAxis: Characterizing Framing Bias and Intensity with Word Embedding
We propose FrameAxis, a method of characterizing the framing of a given text by identifying the most relevant semantic axes… 
2017
2017
Turkish document classification based on Word2Vec and SVM classifier
In this study, Turkish texts belonging to different categories were classified by using word2vec word vectors. Firstly, vectors… 
2017
2017
ShotgunWSD: An unsupervised algorithm for global word sense disambiguation inspired by DNA sequencing
In this paper, we present a novel unsupervised algorithm for word sense disambiguation (WSD) at the document level. Our algorithm… 
2017
2017
CIST@CLSciSumm-17: Multiple Features Based Citation Linkage, Classification and Summarization
This paper describes our methods and experiments applied for CLSciSumm-17. We try Convolutional Neural Network, word vectors and… 
2017
2017
Convolutional neural networks and multimodal fusion for text aided image classification
With the exponential growth of web meta-data, exploiting multimodal online sources via standard search engine has become a trend… 
Review
2016
Review
2016
NileTMRG at SemEval-2016 Task 5: Deep Convolutional Neural Networks for Aspect Category and Sentiment Extraction
This paper describes our participation in the SemEval-2016 task 5, Aspect Based Sentiment Analysis (ABSA). We participated in two… 
Highly Cited
2015
Highly Cited
2015
Character-based Neural Machine Translation
We introduce a neural machine translation model that views the input and output sentences as sequences of characters rather than… 
2014
2014
Text Classification with Document Embeddings
Distributed representations have gained a lot of interests in natural language processing community. In this paper, we propose a… 
2008
2008
Multiple Document Summarization Using Principal Component Analysis Incorporating Semantic Vector Space Model
Text Summarization is very effective in relevant assessment tasks. The Multiple Document Summarizer presents a novel approach to… 
2001
2001
Context Space
Tile representation of documents and queries as vectors in space is a well-known information retrieval paradigm (Salton and… 
By clicking accept or continuing to use the site, you agree to the terms outlined in our Privacy Policy (opens in a new tab), Terms of Service (opens in a new tab), and Dataset License (opens in a new tab)