TEXT SEQUENCE PREDICTION USING RECURRENT NEURAL NETWORK

被引：0

作者：

Khare, Akash ^{[1
]}

Gupta, Anjali ^{[1
]}

Mittal, Anirudh ^{[1
]}

Jyoti, Amrita ^{[1
]}

机构：

[1] ABES Engn Coll, Ghaziabad, Uttar Pradesh, India

来源：

ADVANCES AND APPLICATIONS IN MATHEMATICAL SCIENCES | 2021年 / 20卷 / 03期

关键词：

Convolutional Neural Network; Deep Learning; Long Short-term memory (LSTM); Machine Learning; Next word prediction; Recurrent Neural Network; Natural language processing;

D O I：

暂无

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

This paper tries to show how long-term memory-recursive neural networks can be used to generate text sequences in real-time conditions, by predicting a single data point at a time. Next word prediction is an intensive problem in the field of NLP (Natural language processing). Word prediction is the problem of calculating which words are likely to carry forward a given primary text piece. The resulting system is capable of generating the next real-time word in a wide variety of styles.

引用

页码：377 / 382

页数：6

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