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The role of HG in the analysis of temporal iteration and interaural correlation
Word Activation Forces Map Word Networks
Words associate with each other in a manner of intricate clusters^1-3^. Yet the brain capably encodes the complex relations into workable networks^4-7^ such that the onset of a word in the brain automatically and selectively activates its associates, facilitating language understanding and generation^8-10^. One believes that the activation strength from one word to another forges and accounts for the latent structures of the word networks. This implies that mapping the word networks from brains to computers^11,12^, which is necessary for various purposes^1,2,13-15^, may be achieved through modeling the activation strengths. However, although a lot of investigations on word activation effects have been carried out^8-10,16-20^, modeling the activation strengths remains open. Consequently, huge labor is required to do the mappings^11,12^. Here we show that our found word activation forces, statistically defined by a formula in the same form of the universal gravitation, capture essential information on the word networks, leading to a superior approach to the mappings. The approach compatibly encodes syntactical and semantic information into sparse coding directed networks, comprehensively highlights the features of individual words. We find that based on the directed networks, sensible word clusters and hierarchies can be efficiently discovered. Our striking results strongly suggest that the word activation forces might reveal the encoding of word networks in the brain
From Frequency to Meaning: Vector Space Models of Semantics
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
Controlling Risk of Web Question Answering
Web question answering (QA) has become an indispensable component in modern
search systems, which can significantly improve users' search experience by
providing a direct answer to users' information need. This could be achieved by
applying machine reading comprehension (MRC) models over the retrieved passages
to extract answers with respect to the search query. With the development of
deep learning techniques, state-of-the-art MRC performances have been achieved
by recent deep methods. However, existing studies on MRC seldom address the
predictive uncertainty issue, i.e., how likely the prediction of an MRC model
is wrong, leading to uncontrollable risks in real-world Web QA applications. In
this work, we first conduct an in-depth investigation over the risk of Web QA.
We then introduce a novel risk control framework, which consists of a qualify
model for uncertainty estimation using the probe idea, and a decision model for
selectively output. For evaluation, we introduce risk-related metrics, rather
than the traditional EM and F1 in MRC, for the evaluation of risk-aware Web QA.
The empirical results over both the real-world Web QA dataset and the academic
MRC benchmark collection demonstrate the effectiveness of our approach.Comment: 42nd International ACM SIGIR Conference on Research and Development
in Information Retrieva
Neurocognitive Informatics Manifesto.
Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given
Language Recognition Method of Convolutional Neural Network Based on Spectrogram
Language recognition is an important branch of speech technology. As a front-end technology of speech information processing, higher recognition accuracy is required. It is found through research that there are obvious differences between the language maps of different languages, which can be used for language identification. This paper uses a convolutional neural network as a classification model, and compares the language recognition effects of traditional language recognition features and spectrogram features on the five language recognition tasks of Chinese, Japanese, Vietnamese, Russian, and Spanish through experiments. The best effect is the ivector feature, and the spectrogram feature has a higher F value than the low-dimensional ivector feature
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