bibliometrix: An R-tool for comprehensive science mapping analysis

Deep learning methods employ multiple processing layers to learn hierarchical representations of data, and have produced state-of-the-art results in many domains. Recently, a variety of model designs and methods have blossomed in the context of natural language processing (NLP). In this paper, we review significant deep learning related models and methods that have been employed for numerous NLP tasks and provide a walk-through of their evolution. We also summarize, compare and contrast the various models and put forward a detailed understanding of the past, present and future of deep learning in NLP.

Recent Trends in Deep Learning Based Natural Language Processing [Review Article]

The word-space model is a computational model of word meaning that utilizes the distributional patterns of words collected over large text data to represent semantic similarity between words in ter ...

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The Word-Space Model : Using distributional analysis to represent syntagmatic and paradigmatic relations between words in high-dimensional vector spaces

The frequency distribution of words has been a key object of study in statistical linguistics for the past 70 years. This distribution approximately follows a simple mathematical form known as Zipf’s law. This article first shows that human language has a highly complex, reliable structure in the frequency distribution over and above this classic law, although prior data visualization methods have obscured this fact. A number of empirical phenomena related to word frequencies are then reviewed. These facts are chosen to be informative about the mechanisms giving rise to Zipf’s law and are then used to evaluate many of the theoretical explanations of Zipf’s law in language. No prior account straightforwardly explains all the basic facts or is supported with independent evaluation of its underlying assumptions. To make progress at understanding why language obeys Zipf’s law, studies must seek evidence beyond the law itself, testing assumptions and evaluating novel predictions with new, independent data.

Zipf's word frequency law in natural language: a critical review and future directions.

Our lives revolve around sharing experiences and memories with others. When different people recount the same events, how similar are their underlying neural representations? Participants viewed a 50-min movie, then verbally described the events during functional MRI, producing unguided detailed descriptions lasting up to 40 min. As each person spoke, event-specific spatial patterns were reinstated in default-network, medial-temporal, and high-level visual areas. Individual event patterns were both highly discriminable from one another and similar among people, suggesting consistent spatial organization. In many high-order areas, patterns were more similar between people recalling the same event than between recall and perception, indicating systematic reshaping of percept into memory. These results reveal the existence of a common spatial organization for memories in high-level cortical areas, where encoded information is largely abstracted beyond sensory constraints, and that neural patterns during perception are altered systematically across people into shared memory representations for real-life events.

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Shared memories reveal shared structure in neural activity across individuals

Latent semantic analysis (LSA) is a technique for comparing texts using a vector-based representation that is learned from a corpus This article begins with a description of the history of LSA and its basic functionality LSA enjoys both theoretical support and empirical results that show how it matches human behavior A number of the experiments that compare LSA with humans are described here The article also describes a few of the many successful applications of LSA to text-processing problems and finishes by presenting a number of current research directions

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P. Wiemer-Hastings

Papers

Latent Semantic Analysis