Application of Deep Learning PDF

Running Head: PROPOSAL
Application of Deep Learning (DL) to Natural Language Processing (NLP)
[Name of Institute]
[Name of Student]
[Date]

Proposal 2
TABLE OF CONTENTS
Introduction......................................................................................................................................3
Problem Statement.......................................................................................................................4
Aim and Objectives......................................................................................................................4
Literature Review............................................................................................................................4
Advantages and Challenges.........................................................................................................5
Advantages...............................................................................................................................5
Challenges................................................................................................................................5
Motivation for Deep Learning in NLP.........................................................................................6
Dialogue Systems.........................................................................................................................9
Methods...........................................................................................................................................9
Linguistic Phenomena................................................................................................................10
Neural Network Components.....................................................................................................10
REFERENCES..............................................................................................................................11

Proposal 3
Application of Deep Learning (DL) to Natural Language Processing (NLP)
Introduction
Deep Learning (DL) refers to the advances made by artificial intelligence in learning and
using neural networks such as “Deep Neural Networks (DNN), Convolutional Neural Networks
(CNN), and Recurrent Neural Networks (RNN)” (Otter, et al., 2020). Recently, DL has been
effectively applied to “Natural Language Processing (NLP)” and noteworthy progress has been
made (Hasan and Farri, 2019). This article will summarize the recent advances made by DL in
NLP and discusses its benefits and challenges (Rao and McMahan, 2019). There are 5 major
tasks for NLP, including “classification, matching, translation, structured prediction, and
sequential decision-making processes” (Huang, et al., 2019).
The first four tasks can be traced back to DL methods that have overtaken the traditional
methods (Landolt, et al., 2021). Training of system and result-oriented learning from start to
finish are important features of DL, making it a useful resource for NLP. It may not be enough
just to argue and make decisions on certain grounds of language processing, which is essential
for complex issues such as multiple interchange cycles (Zhang, et al., 2020). Moreover,
integrating translation processing and neural processing and dealing with the long-tail
phenomenon is also challenging for DL in NLP (Bacchi, et al., 2019).
“Natural Language Processing (NLP)” is a software engineering subcommand that
provides support between natural language and the PC. It enables machines to acquire, process
and parse human language (Rao and McMahan, 2019). The importance of NLP as a device that
supports the perception of human-generated information is a reasonable consequence of the
contextuality of information. By better understanding the context, information becomes more
important, making it suitable for inspection and text mining (through AI) (Sorin, et al., 2020).
NLP offers this through projects and examples of human communication. The advancement of
NLP technology increasingly relies on information-driven methodologies that can help create
more remarkable and powerful models (Young, et al., 2018).
The constant advancement of computing power, such as the increased accessibility of
vast information, offers opportunities for DL, which is probably the most attractive method in
NLP, especially considering that DL is used in computer vision and speech recognition (Torfi, et
al., 2020). These advancements have led people to move from traditional information-based

Proposal 4
methods to new information-based methods of promoting NLP. The goal of this transformation
is that the new method is more efficient in terms of results and the design is simpler (Deng and
Liu, 2018).
Problem Statement
DL uses “Deep Neural Networks” to measure a large amount of information and
familiarize with the problem of information processing. It can solve any problem as long as user
have access to a large and relevant dataset(s). It also examines the important features of problem
definition. In the field of NLP, there are many important applications that are widely used by
individuals, similar to machine translation, speech recognition, and information retrieval. As DL
progressed, more and more NLP tasks were solved well with the application of DL methods.
Accordingly, a unique and meaningful AI methodology is provided to make the operation
of DL models in NLP tasks such as sentiment inspection easier and more meaningful. Syntax
entity detection is a fundamental task in NLP and is also used in many applications such as web
indexing and online advertising. However, as a fundamental and important venture in NLP, there
is much work in using deep neural networks to solve “Named Entity Recognition (NER)”
problems, whilst very little work is in applying rational methods of DL.
Aim and Objectives
The aim of this study is to explore multiple deep learning methods for NLP. The
objectives of this study are stated below:
Implement and test the explainable DL methods
To explore the latest practical implication practices of DL in NLP
Literature Review
According to the studies, most of the NLP problems can be formalized in these five tasks
(as mentioned above). In these tasks, words, phrases, sentences, parts and even files are generally
considered to be a series of tags (strings) and although they can handle several complicated facts
in comparison (Sorin, et al., 2020). In addition, sentences are the most common unit of
processing. Studies recently discovered that DL can be updated and viewed in the first four
assignments and is becoming a groundbreaking assignment innovation (Zhang, et al., 2020). Of