Universals of word order reflect optimization of grammars for efficient communication

The universal properties of human languages have been the subject of intense study across the language sciences. We report computational and corpus evidence for the hypothesis that a prominent subset of these universal properties—those related to word order—result from a process of optimization for efficient communication among humans, trading off the need to reduce complexity with the need to reduce ambiguity. We formalize these two pressures with information-theoretic and neural-network models of complexity and ambiguity and simulate grammars with optimized word-order parameters on large-scale data from 51 languages. Evolution of grammars toward efficiency results in word-order patterns that predict a large subset of the major word-order correlations across languages.

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Artificial Neural Network Models for Large-Scale Data

Advances in Data Mining and Database Management - Handbook of Research on Big Data and the IoT ◽

10.4018/978-1-5225-7432-3.ch022 ◽

2019 ◽

pp. 406-439

Author(s):

Vo Ngoc Phu ◽

Vo Thi Ngoc Tran

Keyword(s):

Large Scale ◽

Network Models ◽

Data Sets ◽

Neural Network Models ◽

Large Scale Data ◽

The World ◽

Commercial Applications ◽

Artificial Neural Network Models ◽

Scale Data ◽

Large Scale Data Sets

Artificial intelligence (ARTINT) and information have been famous fields for many years. A reason has been that many different areas have been promoted quickly based on the ARTINT and information, and they have created many significant values for many years. These crucial values have certainly been used more and more for many economies of the countries in the world, other sciences, companies, organizations, etc. Many massive corporations, big organizations, etc. have been established rapidly because these economies have been developed in the strongest way. Unsurprisingly, lots of information and large-scale data sets have been created clearly from these corporations, organizations, etc. This has been the major challenges for many commercial applications, studies, etc. to process and store them successfully. To handle this problem, many algorithms have been proposed for processing these big data sets.

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Artificial Neural Network Models for Large-Scale Data

10.4018/978-1-6684-2408-7.ch006 ◽

2022 ◽

pp. 112-145

Author(s):

Vo Ngoc Phu ◽

Vo Thi Ngoc Tran

Keyword(s):

Large Scale ◽

Network Models ◽

Data Sets ◽

Neural Network Models ◽

Large Scale Data ◽

The World ◽

Commercial Applications ◽

Artificial Neural Network Models ◽

Scale Data ◽

Large Scale Data Sets

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A CLASS OF DATA-CENTER NETWORK MODELS OFFERING SYMMETRY, SCALABILITY, AND RELIABILITY

Parallel Processing Letters ◽

10.1142/s0129626412500132 ◽

2012 ◽

Vol 22 (04) ◽

pp. 1250013 ◽

Cited By ~ 6

Author(s):

WENJUN XIAO ◽

HUOMIN LIANG ◽

BEHROOZ PARHAMI

Keyword(s):

Data Center ◽

Large Scale ◽

Network Models ◽

Performance Criteria ◽

Data Center Network ◽

New Class ◽

Large Scale Data ◽

Communication Algorithms ◽

Efficient Communication ◽

Scale Data

We propose a symmetrical scheme, by drawing results from group theory, and use it to build a new class of data center network models. The results are superior to current network models with respect to a number of performance criteria. Greater symmetry in networks is important, as it leads to simpler structure and more efficient communication algorithms. It also tends to produce better scalability and greater fault tolerance. Our models are general and are expected to find many applications, but they are particularly suitable for large-scale data-center networks.

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Deep Context: A Neural Language Model for Large-scale Networked Documents

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2017/431 ◽

2017 ◽

Cited By ~ 1

Author(s):

Hao Wu ◽

Kristina Lerman

Keyword(s):

Word Order ◽

Link Prediction ◽

Large Scale ◽

Language Model ◽

Distributed Representations ◽

Large Scale Data ◽

Context Vector ◽

Effectiveness And Efficiency ◽

Data Collections ◽

Scale Data

We propose a scalable neural language model that leverages the links between documents to learn the deep context of documents. Our model, Deep Context Vector, takes advantage of distributed representations to exploit the word order in document sentences, as well as the semantic connections among linked documents in a document network. We evaluate our model on large-scale data collections that include Wikipedia pages, and scientific and legal citations networks. We demonstrate its effectiveness and efficiency on document classification and link prediction tasks.

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