Proceedings of the 2nd Workshop on Continuous Vector Space Models and their Compositionality (CVSC)

2014 ◽  
Keyword(s):  
Vector Space ◽  
Continuous Vector ◽  
Vector Space Models

scholarly journals Recent advances in machine translation using comparable corpora

2016 ◽  
Vol 22 (4) ◽  
pp. 501-516 ◽  
Author(s):  
REINHARD RAPP ◽  
SERGE SHAROFF ◽  
PIERRE ZWEIGENBAUM
Keyword(s):  
Neural Networks ◽  
Vector Space ◽  
Machine Translation ◽  
Word Embeddings ◽  
Shared Task ◽  
New Techniques ◽  
Continuous Vector ◽  
Comparable Corpora ◽  
Recent Developments ◽  
Vector Space Models

AbstractThis paper highlights some of the recent developments in the field of machine translation using comparable corpora. We start by updating previous definitions of comparable corpora and then look at bilingual versions of continuous vector space models. Recently, neural networks have been used to obtain latent context representations with only few dimensions which are often called word embeddings. These promising new techniques cannot only be applied to parallel but also to comparable corpora. Subsequent sections of the paper discuss work specifically targeting at machine translation using comparable corpora, as well as work dealing with the extraction of parallel segments from comparable corpora. Finally, we give an overview on the design and the results of a recent shared task on measuring document comparability across languages.

scholarly journals Extractive Summarization using Continuous Vector Space Models

2014 ◽  
Author(s):  
Mikael Kågebäck ◽  
Olof Mogren ◽  
Nina Tahmasebi ◽  
Devdatt Dubhashi
Keyword(s):  
Vector Space ◽  
Extractive Summarization ◽  
Continuous Vector ◽  
Vector Space Models

Unifying Bayesian Inference and Vector Space Models for Improved Decipherment

2015 ◽  
Author(s):  
Qing Dou ◽  
Ashish Vaswani ◽  
Kevin Knight ◽  
Chris Dyer
Keyword(s):  
Bayesian Inference ◽  
Vector Space ◽  
Vector Space Models

scholarly journals From Frequency to Meaning: Vector Space Models of Semantics

2010 ◽  
Vol 37 ◽  
pp. 141-188 ◽  
Author(s):  
P. D. Turney ◽  
P. Pantel
Keyword(s):  
Vector Space ◽  
Open Source ◽  
Semantic Processing ◽  
Human Language ◽  
Open Source Project ◽  
Word Context ◽  
The Matrix ◽  
Vector Space Models ◽  
New Perspective

Computers understand very little of the meaning of human language. This profoundly limits our ability to give instructions to computers, the ability of computers to explain their actions to us, and the ability of computers to analyse and process text. Vector space models (VSMs) of semantics are beginning to address these limits. This paper surveys the use of VSMs for semantic processing of text. We organize the literature on VSMs according to the structure of the matrix in a VSM. There are currently three broad classes of VSMs, based on term-document, word-context, and pair-pattern matrices, yielding three classes of applications. We survey a broad range of applications in these three categories and we take a detailed look at a specific open source project in each category. Our goal in this survey is to show the breadth of applications of VSMs for semantics, to provide a new perspective on VSMs for those who are already familiar with the area, and to provide pointers into the literature for those who are less familiar with the field.

Sign in / Sign up

Export Citation Format

Share Document