Challenges and Application Models of Natural Language Processing

Weiqi Huang

Business School, GuangZhou Nanfang Collage, 882 Hot Spring Avenue, Conghua District, Guangzhou, China

Keywords: Natural Language Processing, Natural Language Understanding, Natural Language Generation, Natural

Language Processing Model.

Abstract: With the development of artificial intelligence, natural language processing has become an important research

field of human-computer interaction, and its importance has become increasingly prominent. This paper

outlines three major challenges facing natural language processing today: First, there are a large number of

ambiguous words in natural language; Second, natural language processing is highly dependent on contextual

information. Third, differences between different languages introduce additional complexity to processing.

By sorting out the challenges faced, it provides new ideas for the future research direction. Next, it introduces

the classification of natural language applications (natural language understanding and text generation) and

the actual realistic scenarios applied to it, reflecting the application of natural language processing in silence

to help and affect people's lives. Finally, the paper discusses three major models (Transformer model, Bert

model, GPT model) which play an indispensable role in promoting the progress of natural language processing

technology. These models show excellent processing power in a multitude of natural language tasks.

1 INTRODUCTION

In the contemporary digital era, the interaction

between human and machine is becoming more and

more frequent, and natural language processing

(NLP), as a key technology connecting human

language and computer system, is gradually

becoming one of the most dynamic and influential

research directions within the domain of artificial

intelligence. With the popularization of the Internet

and the development of big data technology, massive

text data continues to emerge, which contains rich

information and knowledge, but also brings huge

challenges. How to process, analyze and understand

these text data effectively and transform them into

valuable information has become the focus of

scientific and technological circles and academic

circles. NLP technology is the key to addressing this

challenge. The objective is to enhance the capacity of

computers to understand, interpret, and generate

human language, thereby promoting more natural and

seamless interactions with humans.

The development of natural language processing

is full of challenges and opportunities. From early

rule-following methods to modern deep learning-

https://orcid.org/0009-0006-9968-3658

based models, the range of applications is

increasingly wide. Nowadays, NLP technology has

penetrated into all aspects of our lives, from

intelligent assistants, machine translation, sentiment

analysis, to text mining, automatic summary,

question and answer system, etc. The application of

NLP not only improves the efficiency of information

processing, but also brings great convenience to

people's life and work. However, despite the

remarkable progress, natural language processing still

faces many challenges, such as language ambiguity,

context understanding, and multilingual processing,

which limit the further innovation and utilization of

NLP technology.

As technology evolves and becomes more

advanced, people are generating more and more data,

which provides a huge opportunity to enhance the

training of natural language models. From the early

classic Transformer model (Vaswani, Shazeer,

Parmar et al, 2017), which uses about 100 million

parameters, to the current GPT model (Radford,

Narasimhan, Salimans et al, 2018) with the escalation

in parameters numbers, the model's capabilities are

constantly improved to achieve more natural human-

computer interaction.

Huang, W.

Challenges and Application Models of Natural Language Processing.

DOI: 10.5220/0013704100004670

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 2nd International Conference on Data Science and Engineering (ICDSE 2025), pages 673-678

ISBN: 978-989-758-765-8

673

Through this review, the paper hope to provide

readers with a comprehensive and systematic

overview of natural language processing, and help

readers better understand the current situation and

future development direction of this field.

2 OVERVIEW OF NATURAL

LANGUAGE PROCESSING

NLP serves as a bridge between multiple disciplines,

focusing on how humans and machines can

communicate effectively using natural language. It

aims to equip machines with the ability to

comprehend, decipher, and generate human-like

written or spoken expressions. By leveraging

advanced computational techniques and linguistic

theories, NLP enables machines to analyze,

understand, and interact with human language in a

way that conveys meaningful response and

contextually appropriate (Das, 2024). NLP seeks to

equip with the ability to understand and manipulate

human language through various computational

models that automatically analyze linguistic

structures. In NLP, the foundational elements of

language are typically referred to as atomic terms,

like "bad," "old," or "fantastic." When these atomic

terms are combined, they create compound terms,

such as "very good movie" or "young man." At its

core, an atomic term refers to a single word, whereas

a compound term refers to a multi-word phrase.

Words serve as the basic units of human language,

and their comprehension is essential for any NLP task

(Satpute, 2023). At present, NLP has been applied to

many fields, such as: speech recognition, question

answering system, online translation, text

classification and so on NLP has played an important

role.

2.1 Challenges in natural language

processing

Trying to make machines understand human

language is very challenging. The machine not only

understands the surface meaning of the sentence, but

also understands the underlying information

contained in the sentence. The following are the

difficulties that machines face in NLP.

First, there are a large number of words with

multiple meanings in natural language processing.

For example, in the sentence "I want an apple", the

"apple" that a person wants to express refers to the

"apple phone", but the machine may understand it as

a fruit, that is, the meaning of a word will have

varying interpretations in different contexts. This

illustrates the ambiguity inherent in language

expression. To address this issue, artificial

intelligence techniques, such as machine learning and

deep learning models are employed to identify and

resolve textual ambiguities. Moreover, parameters in

the model can be adjusted to better adapt to different

language environments and contexts, thus improving

the accuracy and effectiveness of ambiguity detection

(Satpute, 2023).

Second, natural language processing relies on

understanding context, but the processing power of

computational models is limited. One reason for this

is the amount and quality of data. Although the data

sets used by modern NLP models are already very

large, they may not perform well when dealing with

rare or domain-specific processes. For example, a

sentiment analysis model may perform well when

dealing with common movie reviews, but poorly

when faced with rare, domain-specific reviews, such

as professional cinematography reviews. The reason

for this long-tail phenomenon is that many words and

expressions appear infrequently in natural language,

and these long-tail data may be ignored in the training

set, resulting in poor performance of NLP models in

dealing with these rare cases. Another reason is

limited computing resources. The NLP model

requires significant memory and computing power

resources to process millions of pieces of text.

Therefore, when we use a computer with insufficient

computing power, it takes a very long time to process

the context (Ardehkhani, 2023). To solve this

problem, the number of Gpus can be increased to

reduce processing time, but this approach will lead to

higher training costs (Strubell, 2019).

Third, there are linguistic differences in natural

languages. In today's Internet society, A robust NLP

model that can handle language diversity is important

(Mitra, 2020). For some small languages, the data set

that can be used for training is very small, which

brings a lot of challenges to model training. Owing to

the scarcity of large-scale training data, models

generally exhibit poorer performance in smaller

languages compared to larger ones. Moreover, the

primary distinctions between languages are

manifested in their grammatical differences. For

example, an adjective in French usually comes after a

noun, while an adjective comes before a noun in

English. In text generation, if the machine does not

understand these grammar rules correctly, the

generated text will be meaningless or difficult to

understand (Nadkarni, 2011).

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2.2 The applied branch of natural

language processing

NLP primarily consists of two key branches. One of

these is Natural Language Understanding (NLU),

which refers to the process of understanding and inter

preting human natural language by computer. This

represents a crucial research avenue within the realm

of artificial intelligence, with the goal of endowing

computer with the capability to understand and

process human language, thereby facilitating natural

and seamless interactions with humans (Guo, 2024).

Another branch is Natural Language Generation

(NLG), whose primary task is to generate human-

readable text from structured and unstructured data to

provide feedback in a way that is easy for humans to

understand (Azhar, 2024). As shown in Figure 1, this

is the main branch module for NLP applications.

Figure 1: The main branch module of NLP applications

(Picture credit : Original)

NLU is mainly used in text segmentation and

sentiment analysis. Text segmentation refers to the

prediction of reliable paragraph boundaries based on

the same topic belonging of the sentences in the same

paragraph for an article containing multiple sentences

(Zhao, 2024). This segmentation process helps

organize data into logically coherent units and is

essential for improving the readability of text.

Emotion analysis is to analyze the words and

expressions in the text to judge the emotional

tendency conveyed by the text, such as positive,

negative or neutral. Businesses leverage sentiment

analysis to discern what user reviews indicate about

their goods or services.

NLG is mainly used for automated text generation

and human-computer interaction. In terms of

automated text generation, NLG helps individuals

efficiently generate various types of text content by

converting structured data into natural language text.

For example, in the field of news reporting, NLG is

able to quickly generate accurate and timely news

content, reducing the workload of human editors. In

terms of human-machine interaction, NLG

technology enables the machine to generate natural

and accurate replies, enhancing the user interaction

experience. For example, artificial intelligence such

as Siri and Google Assistant use NLG technology to

provide users with accurate interactive questions and

answers.

3 APPLICATION MODEL OF

NATURAL LANGUAGE

The development of NLP cannot be separated from

several important models. In 2017, Google

introduced the Transformer model for the first time,

which relies on the self-attention mechanism

(Vaswani, Shazeer, Parmar et al, 2017). has laid an

important foundation for the development of NLP and

exerted a profound influence. Next, in 2018, Google

introduced the Bert model (Devlin, Chang, Lee et al,

2018) and OpenAI also developed the GPT-1 model

(Radford, Narasimhan, Salimans et al, 2018). Both

models incorporate pre-training modules on top of the

Transformer model.

3.1 Transformer model

The Transformer model was initially used in natural

language translation. Compared with previous

mainstream models LSTM and GRU, Transformer

has two obvious advantages: First, Transformer uses

multi-head attention mechanism model and can use

distributed CPU for parallel training to improve

model training efficiency and accuracy (Lei, 2024);

Second, traditional RNNS and LSTMS tend to

perform poorly at capturing long-term dependencies

because these models need to process sequence data

progressively, leading to gradient disappearance or

gradient explosion problems. With its self-attention

mechanism, the Transformer model can directly

calculate the dependencies between any two locations

in the sequence, making it more efficient to capture

long-term dependencies. The Transformer model is

mainly composed of input part, N-layer encoder part,

N-layer decoder part and output part. The complete

flow of Transformer is shown in Figure 2.

Challenges and Application Models of Natural Language Processing

675

Figure 2: Complete process of Transformer model

(Vaswani, Shazeer, Parmar et al, 2017).

Among them, the multi-head attention mechanism

in the decoder is the cornerstone of the Transformer

model (Vaswani, Shazeer, Parmar et al, 2017). The

attention mechanism multiplies the attention weight

by the QK matrix and multiplies the weight value

with V to get the final attention result. Figure 3 shows

the calculation process of attention results. Multi-

head refers to the parallel feature extraction of

multiple single-head self-attention mechanisms. By

searching for parameters in multiple parameter

Spaces, the accuracy of the model will naturally

increase as the parameters extracted increase.

However, the shortcoming of Transformer model is

that it can only be applied to the data analysis of short

sentences, because it cannot capture the position

relationship between words for analysis (Li, 2021).

Figure 3: The process of calculating the outcome of

attention (Picture credit : Original ).

The Transformer model uses A training set : The

standard WMT 2014 English-German dataset

contains 45,000 sentence pairs. The sentence

preprocessing adopts Byte-pair encoding to make the

dictionary of the training set smaller and get a

dictionary composed of 37000 tokens. The devices

used include 8 NVIDIA P100 GPUs. The Evaluation

criteria used are: BLEU (Bilingual Evaluation

Understudy) is a method to evaluate the quality of

machine translation, in particular to measure the

similarity between machine translation output and

human translation. In particular, it evaluates the

overlap of n-grams, which are contiguous sequences

of n items from a given text sample, between the

translated output and the reference translation. Higher

BLEU scores denote a higher level of similarity,

suggesting that the machine-generated translation is

more comparable to human translation quality. This

metric is particularly valuable in evaluating the

fluency and accuracy of translation models, making it

a cornerstone in the field of machine translation

research and development. Specifically, it measures

the overlap of n-grams (contiguous sequences of n

items from a given sample of text) between the

translated output and the reference translation. And

the other training set is: WMT 2014 English-French

dataset, which is a large-scale corpus, comprises 36

million sentences and segments the tokens into

32,000 distinct phrases. The hardware utilized

includes eight NVIDIA P100 GPUs.

3.2 Bert model based on Transformer

model

The Bert model uses the Encoder part of Transformer

(Devlin, Chang, Lee et al, 2018). The biggest

difference from the Transformer Model is the

introduction of two pre-training tasks: Mask

Language Model (MLM) and Next Sentence

Prediction(NSP) are two fundamental tasks in pre-

training language models. MLM involves randomly

concealing certain words in the input text and then

training the model to infer these hidden words based

on the surrounding context. Meanwhile, the NSP task

focuses on training the model to determine if two

sentences are adjacent in a coherent text sequence.

Another difference is the introduction of bidirectional

processing, that is in the pre-training phase. For each

word, the Bert model can evaluate both the preceding

and subsequent context information. Bert model is

widely used in NLU work because of its strong

context understanding ability (Kurt, 2023).

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3.3 GPT model based on Transformer

GPT uses Transformer's encoder architecture

(Radford, 2018) because GPT is primarily used to

generate text, and the decoder is designed for

generation tasks. GPT is capable of predicting the

next word using the previous word, which means the

input sentence is directional. The one-way self-

attention mechanism is used, which only focuses on

the previous words in the sequence, so it has strong

ability to generate and interactive question answering.

In addition, the biggest feature of GPT is the large

amount of parameters, so as to ensure the quality and

accuracy of interactive dialogue. The pre-trained GPT

model is composed of 12 Transformer layers, and the

model dimensions are



=768

(1)

The total number of parameters reached 110 million

(Zheng, 2021).

With the continuous increase of model parameters,

Google has continuously iterated the GPT model.

Launched in 2020, GPT-3 (commonly known as

chatGPT) is the most powerful and extensive

language model to date (Gupta, 2023), and its output

is highly consistent and contextually relevant. GPT-3

ability to learn from a small number of samples is a

major advance, quickly grasping new information

from small samples. This improvement is due to the

increasing number of parameters used in GPT-3

compared to GPT-2 and GPT-1. GPT encompasses

175 billion parameters, and its predecessors GPT-1

and GPT-2 have 117 million and 1.5 billion

parameters, respectively.

4 CONCLUSIONS

As an important branch of artificial intelligence, NLP

has made remarkable progress in theory and

application. NLP has undergone significant evolution,

transitioning from early rule-based methods to

contemporary models powered by deep learning. This

series of advancements has substantially improved

the ability of computers to comprehend and generate

human language, enabling more sophisticated and

natural interactions. Despite these advancements, the

field of NLP continues to confront numerous

challenges that impede its further development and

broader application. In an effort to surmount these

obstacles, researchers are persistently exploring novel

approaches and techniques, including pre-training

language models, multimodal learning, and

reinforcement learning, to boost the performance and

adaptability of the models. In the future, natural

language processing technology will continue to

develop rapidly and deeply integrate with other

technologies such as computer vision, speech

recognition, machine learning, etc., to form a more

intelligent and efficient artificial intelligence system.

These systems will be able to better understand

human language and enable smoother human-

computer interaction.

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