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.

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

The non-psychopath's guide to managing an open source project

2021 ◽  
Vol 64 (4) ◽  
pp. 25-27
Author(s):  
George V. Neville-Neil
Keyword(s):  
Open Source ◽  
Open Source Project

Respect your staff, learn from others, and know when to let go.

Language and life history: A new perspective on the development and evolution of human language

2006 ◽  
Vol 29 (3) ◽  
pp. 259-280 ◽  
Author(s):  
John L. Locke ◽  
Barry Bogin
Keyword(s):  
Life History ◽  
Adult Development ◽  
Homo Sapiens ◽  
Human Life ◽  
Modern Human ◽  
Linguistic Knowledge ◽  
Human Language ◽  
Juvenile Period ◽  
Development Of Language ◽  
New Perspective

It has long been claimed that Homo sapiens is the only species that has language, but only recently has it been recognized that humans also have an unusual pattern of growth and development. Social mammals have two stages of pre-adult development: infancy and juvenility. Humans have two additional prolonged and pronounced life history stages: childhood, an interval of four years extending between infancy and the juvenile period that follows, and adolescence, a stage of about eight years that stretches from juvenility to adulthood. We begin by reviewing the primary biological and linguistic changes occurring in each of the four pre-adult ontogenetic stages in human life history. Then we attempt to trace the evolution of childhood and juvenility in our hominin ancestors. We propose that several different forms of selection applied in infancy and childhood; and that, in adolescence, elaborated vocal behaviors played a role in courtship and intrasexual competition, enhancing fitness and ultimately integrating performative and pragmatic skills with linguistic knowledge in a broad faculty of language. A theoretical consequence of our proposal is that fossil evidence of the uniquely human stages may be used, with other findings, to date the emergence of language. If important aspects of language cannot appear until sexual maturity, as we propose, then a second consequence is that the development of language requires the whole of modern human ontogeny. Our life history model thus offers new ways of investigating, and thinking about, the evolution, development, and ultimately the nature of human language.

scholarly journals Jupyter: Thinking and Storytelling with Code and Data

2021 ◽  
Author(s):  
Brian Granger ◽  
Fernando Pérez
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Community Of Practice ◽  
Scientific Computing ◽  
Data Science ◽  
Climate Science ◽  
Three Dimensions ◽  
Open Source Project ◽  
Interactive Computing ◽  
The Impact

Project Jupyter is an open-source project for interactive computing widely used in data science, machine learning, and scientific computing. We argue that even though Jupyter helps users perform complex, technical work, Jupyter itself solves problems that are fundamentally human in nature. Namely, Jupyter helps humans to think and tell stories with code and data. We illustrate this by describing three dimensions of Jupyter: interactive computing, computational narratives, and  the idea that Jupyter is more than software. We illustrate the impact of these dimensions on a community of practice in Earth and climate science.

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