Technical Realization and Future Prospects of Natural Language

Processing (NLP) in Multi-Domain Applications

Hongsheng Li

College of Engineering, Virginia Polytechnic Institute and State University, Blacksburg, 24060, U.S.A.

Keywords: Natural Language Processing, Deep Learning. Multimodal Fusion, Privacy Preservation, Cross-Domain

Applications.

Abstract: In the context of the intelligent era, natural language processing (NLP), as the core technology of human-

computer interaction and knowledge mining, is continuously driving technological innovation in the field

with its multi-disciplinary application demands. This paper systematically explores the evolution law of NLP

technology from traditional statistical learning to deep learning and pre-training models through the

comparative analysis method and typical technology case validation and proposes key technology innovation

paths based on the differentiated scenarios in three major fields, namely, business, healthcare, and education.

This paper concludes that the technology iteration significantly improves the semantic understanding ability

of the model through multi-scale feature fusion, but the cross-domain application still has problems such as

performance attenuation due to data noise, and the conflict between the trade-off between privacy protection

and model utility. By integrating federated learning and multimodal semantic alignment strategies, the study

proposes a solution that balances technical performance and ethical constraints. The results provide a

quantifiable evaluation framework for the cross-domain deployment of NLP technology, and its methodology

has been validated for utility in financial risk control, intelligent diagnosis, and treatment scenarios, which is

a reference for the subsequent low-resource scenarios application and the integration of multimodal

technology.

1 INTRODUCTION

In the intelligent era of accelerated digital

transformation, Natural Language Processing (NLP)

has become the core technology for human-computer

interaction and knowledge mining. Nowadays, the

global NLP technology market continues to expand,

and the industry is driven by the differentiated

demand for unstructured text processing in the

commercial, medical, and educational fields. In the

commercial field, NLP can help enterprises

dynamically adjust their marketing strategies by

identifying consumer behavioral patterns through

sentiment analysis models (Reddy et al., 2021); in the

healthcare field, NLP technology can strengthen the

information structuring capability of electronic health

records (EHRs) and improve the efficiency of clinical

decision support (Sett & Singh, 2024); in the

education field, online learning platforms are faced

with a huge amount of data, which is the most

important factor in the development of the industry.

https://orcid.org/0009-0005-7293-4536

In the medical field, NLP technology strengthens the

information structuring capability of electronic health

records (EHR) and improves the efficiency of clinical

decision support (Sett & Singh, 2024); in the

education field, online learning platforms are faced

with the need to process massive amounts of

unstructured text, and automatic essay grading

systems based on deep learning can effectively

improve the timeliness of text evaluation (Wang et al.,

2022)-these real-world needs are driving the in-depth

application of NLP technology and technological

innovation in multiple fields.

Currently, numerous scholars explore the

performance of NLP algorithms in related fields.

Traditional machine learning has gained fundamental

results in the field of feature engineering, and the

improved Bayesian algorithm developed by Liu et al.

(2018) achieved a 95% recall rate in the Chinese spam

filtering task, verifying the effectiveness of the

statistical method. The deep learning field, on the

other hand, breaks through bottlenecks through

564

Li, H.

Technical Realization and Future Prospects of Natural Language Processing (NLP) in Multi-Domain Applications.

DOI: 10.5220/0013702100004670

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 564-572

ISBN: 978-989-758-765-8

architectural innovations, and the Transformer

architecture proposed by Vaswani's team (2017)

improves the BLEU value to 28.4 in the WMT14

English-German translation task through the self-

attention mechanism and compresses the training

time to 3.5 GPU days. The breakthroughs in pre-

trained models are reflected in the parametric fusion

of cross-domain knowledge, such as the Clinically-

T5 model developed by Croxford et al. (2025), which

is enhanced by the UMLS knowledge graph and

improves the ROUGE-L score by 0.12 over the

generalized GPT-3 in the medical summary

generation task. In terms of application innovations,

the multiscale BERT developed by Wang et al. (2022 )

developed a multi-scale BERT model to improve the

QWK value of automated essay scoring to 0.791; a

hybrid medical text classification system constructed

by Sett & Singh (2024) reduces the inference latency

by 87% through TF-IDF + logistic regression; and a

federated learning scheme in privacy-preserving

technology balances data security and model utility

with 89% accuracy in intent recognition.

This paper systematically analyzes the evolution

of NLP technologies, cross-domain application

innovations and their core challenges, aiming to build

a multi-dimensional technology evaluation

framework. This paper reveals the core advantages

and effectiveness boundaries of different technology

schools through comparative analysis; verifies the

feasible paths of technology realization based on

typical cases in the commercial, medical, and

educational fields; and explores the future

development models in the context of privacy

protection (Sousa & Kern, 2023) and ethical

constraints (Bolukbasi et al., 2016). Chapters 2 to 5

sequentially discuss the three iterations of

breakthroughs in the NLP technology system

(statistical learning → deep learning → pre-trained

models), the technological realization of the three

core domains, the existing challenges and their

responses, while Chapters 6 and 7 propose the

direction of technological development, such as

multimodal fusion.

2 CURRENT STATUS OF NLP

DEVELOPMENT AND

TECHNICAL APPROACHES

2.1 Evolution of the main technical

approaches to natural language

processing

The evolution of natural language processing

technology needs to be measured by some core

evaluation metrics: e.g., precision (the proportion of

correctly recognized samples predicted to be positive)

and recall (the proportion of correctly recognized

samples in the true positive category) from the base

performance metric, F1-score evaluates the

classification quality through the summed average

synthesis, and the BLEU value is based on the n-gram

matching metrics to quantify the text generation

effect. These metrics provide an objective evaluation

benchmark for technology iteration.

The current NLP technology system covers three

main phases: (1) traditional machine learning relying

on statistical feature engineering; (2) deep learning

enabling end-to-end feature learning; and (3) pre-

trained models completing parametric encoding of

knowledge. The evolution of each stage reflects the

need for advancement in the evaluation dimension.

The traditional machine learning stage is

dominated by probabilistic models and kernel

methods. Studies have shown that an improved

scheme based on the Bayesian algorithm (GWO_GA

architecture) achieves 95% recall in Chinese spam

filtering tasks (Liu et al., 2018). The hybrid support

vector machine (HSVM) achieves a precision of

82.12% and recall of 90.82% on a noisy sentiment

classification dataset (Kumar et al., 2024), which

validates the effectiveness of the traditional approach

in specific scenarios.

The deep learning stage, on the other hand, breaks

through the feature engineering limitations but faces

the problem of computational efficiency bottleneck.

RNN architectures face efficiency bottlenecks in

long-sequence tasks due to sequential computation,

whereas the Transformer’s self-attention reduces

dependency length to a constant. With the self-

attention mechanism, the Transformer architecture

improves the BLEU value of the WMT14 English-

German translation task to 28.4, and the training time

is compressed to 3.5 days (8 P100 GPU environments)

significantly optimizes the training efficiency

compared to LSTM-like models (Vaswani et al.,

2017).

Of the pre-trained language models, BERT

achieved an average performance of 80.5 points on

the GLUE benchmark (18.7% improvement

compared to ELMo) by masking the language task. In

the medical scenario, the Clinically-T5 model

developed by Croxford et al. (2025) optimized for

UMLS knowledge graphs achieves ROUGE-L scores

and manual scores of 0.58 and 4.2/5, respectively,

which significantly outperforms the performance of

the general-purpose GPT-3 model in the summary

generation task. The generative model GPT-4

achieves a text readability score of 4.2/5 in financial

Technical Realization and Future Prospects of Natural Language Processing (NLP) in Multi-Domain Applications

565

news writing scenarios, which is 39% less confusing

than its predecessor. Meanwhile, privacy-preserving

techniques have also evolved, and deep learning-

based privacy-preserving methods have successfully

kept the model performance loss after text

desensitization to less than 8% (Sousa & Kern, 2023).

2.2 Wide Application of Natural

Language Processing

Now NLP has deeply penetrated the three core fields

of business, healthcare, and education, showing

significant technology-enabling value. In the business

field, social media analytics realizes real-time

monitoring of platform users' emotions through

machine learning algorithms (e.g., logistic regression)

and NLP technologies (e.g., sentiment analysis).

Topic clustering based on the BERT model achieved

an F1-score of 0.90 in the consumer sentiment

recognition task (Reddy et al., 2021), while the dialog

model based on the Transformer architecture can

improve customer service response efficiency to 2.3

times of the traditional system. Key breakthroughs in

the healthcare domain are reflected in the processing

of electronic health records (EHRs), where a clinical

text classification model developed by Sett and Singh

(2024) combined with TF-IDF and multi-category

logistic regression achieves an accuracy of 67% and

further reduces misclassification by 23% by merging

related disease categories. In the emergency triage

scenario, the inference latency of the TF-IDF +

logistic regression scheme was reduced by 87%

compared to the BERT-base model (Sett & Singh,

2024), and models such as BioBERT also support

multi-language medical text processing, which

significantly improves the efficiency of cross-cultural

doctor-patient communication (Francis & Subha,

2024).

Advances in the field of education have focused

on automatic essay scoring (AES), where Wang et al.

(2022) proposed a multi-scale BERT model (BERT-

DOC-TOK-SEG) that improves the QWK value by

3.5% and achieves a scoring accuracy of 0.791 on the

ASAP dataset by jointly learning document-level,

word-level, and paragraph-level features. An NLP-

driven virtual teaching assistant system also

transforms complex medical terminology into

concise language, helping non-native English-

speaking healthcare professionals reduce professional

communication errors by more than 40% (Francis &

Subha, 2024).

3 ANALYSIS OF THE MAIN

APPLICATION AREAS OF NLP

3.1 Limitations of traditional methods

Traditional natural language processing methods

include the following: rule-based systems (e.g.,

regular expression matching), statistical learning

methods (e.g., TF-IDF weighted logistic regression),

and probabilistic graphical models (e.g., plain Bayes

and conditional random fields), which exhibit three

core shortcomings in complex language tasks.

First, there is a bottleneck in the adaptability of

statistical methods in specialized domains. Studies

have shown that traditional TF-IDF methods are

overly sensitive to terminological morphological

variants (e.g., differences between technical terms

and colloquial expressions) and spelling errors in

medical texts, leading to the problem of unstable

feature space construction (Sett & Singh, 2024). In

the field of educational assessment, the logistic

regression-based essay scoring model has a quadratic

weighted kappa coefficient (QWK) of 0.705 on the

ASAP dataset, which is significantly lower than the

benchmark value of 0.791 for the deep learning model

(Wang et al., 2022), which reflects structural

deficiencies of the traditional approach in higher-

order semantic capture.

Second, there are fundamental constraints on

contextual modeling capabilities. In the task of

disambiguating medical texts, traditional conditional

random field (CRF) models have an error rate of 28%

(Sousa & Kern, 2023), which is fundamentally due to

the feature independence assumptions of the plain

Bayesian approach - for example, the inability to

differentiate between "cold" in the descriptions of

respiratory symptoms and temperatures with different

meanings (Liu et al., 2018). In contrast, BiLSTM

improves entity recognition accuracy by 17%

(absolute F1-score) through bidirectional context

modeling, while BERT models based on the self-

attention mechanism reach the current optimal level

of denotational disambiguation (Vaswani et al., 2017).

Third, the multimodal processing capability is

severely limited. The analysis of e-commerce

platform data shows that the text model using TF-IDF

alone leads to the problem of customer complaint

omission due to ignoring the semantic association of

the graphic and text, and when multimodal BERT is

used for cross-modal modeling, the F1-score

improves from 0.62 to 0.83 in the baseline model, and

this improvement passes the test of statistical

significance (Reddy et al., 2021), which strongly

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confirms that the traditional limitations of the

unimodal approach.

3.2 Breakthroughs in deep learning

methods

This section focuses on analyzing two key

technological breakthroughs in deep learning for

natural language processing: the improvement of

BERT architecture based on multi-scale feature

fusion and the innovative application of

Transformer's self-attention mechanism and its

limitations.

3.2.1 Multiscale Characterization Capability

of BERT

Among the breakthroughs in deep learning methods,

the BERT framework based on multiscale semantic

feature fusion demonstrates significant technical

advantages. Wang et al. (2022) proposed an

innovative solution for the automatic essay scoring

task, i.e., to improve the model performance through

the joint learning of semantic representations at three

levels. Firstly, the [CLS] vectors of BERT are utilized

to capture document-level global semantic features,

secondly, a global max-pooling operation is

implemented on the 768-dimensional hidden state

sequences (based on the bert-base-uncased model)

output from BERT to extract the key semantic signals

at the word level, and lastly, the text is cut into 10-

190 word segment-level semantic units through a

dynamic segmentation strategy, and each segment is

processed by After independent BERT processing,

LSTM combined with attention mechanism is used to

generate structured representations. These three

levels of representation are integrated into the final

prediction model through the weight fusion

mechanism.

Experimental validation shows that the method

makes two breakthroughs on the ASAP dataset: the

average QWK value of its multi-scale fusion model

(BERT-DOC-TOK-SEG) reaches 0.782, which is 2.9%

and 2.3% higher than that of the single-document-

feature (BERT-DOC) and word-level-feature

(BERT-TOK) models, respectively, and the

difference is statistically significant (p< 0.0001);

meanwhile, by constraining the scoring distribution

through the similarity loss function (SIM), the

standard deviation of the prediction results for long

text (500 words or more) is effectively reduced by

41.4%, which significantly mitigates the scoring bias

caused by the fluctuation of text length in the

traditional scheme (Wang et al., 2022). The

architecture confirms the necessity of multi-scale

feature fusion strategy to enhance semantic

understanding, especially in processing long text

tasks showing advantages over conventional pre-

trained models.

3.2.2 Advantages of Transformer

Architecture for Self-Supervised

Learning and Its Efficacy Boundaries

Transformer breaks through the sequence modeling

limitations of traditional architectures through the

self-attention mechanism. In the WMT14 English-

German translation task, the base Transformer model

achieves 27.3 BLEU with 0.4 seconds per step on 8

P100 GPUs (total of 12 hours), while the larger

Transformer-big variant attains 28.4 BLEU after 3.5

days of training(Vaswani et al., 2017). This

breakthrough stems from the design of parallel

computing with multiple heads of attention, which

compresses the longest dependency paths between

sequence elements to O(1) complexity, enabling true

global context modeling.

Although the performance of Transformer is

very impressive, traditional methods are still

irreplaceable in specific scenarios. The medical

text processing system developed by Sett and

Singh (2024) shows that when processing

emergency triage text with fewer than 50

characters, the inference latency of the TF-

IDF+PCA dimensionality reduction scheme is only

3.2ms, which is 87% lower than that of the BERT-

base model. 87%. The scheme maintains 98%

accuracy in real-time demanding scenarios by

controlling the feature dimensionality to less than

300, together with a multi-category logistic

regression classifier. As we can see, there are pros

and cons to the attention mechanism, and the

inherent architectural flaws of Transformer mainly

come from this: firstly, the O(n²-d) computational

complexity, which consumes up to 7.8 times the

memory of RNN when processing 4096-word-long

text; secondly, sinusoidal positional encodings

were chosen over learned embeddings to enable

sequence length extrapolation, though their

performance on out-of-distribution lengths is not

explicitly tested; and attention weight

visualizations demonstrate that some heads

specialize in syntactic or semantic relationships,

though computational resource allocation per head

is not quantified (Vaswani et al., 2017), leading to

a 22% decrease in convergence speed in low-

resource domains.

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567

4 APPLICATION-SPECIFIC

CASE STUDIES

4.1 Business Sector: Social Media Data

Analytics

Natural Language Processing technology is

redefining the business analytics model of social

media through two core functions: semantic

understanding and pattern mining. Its core value is

embodied in three dimensions: real-time data flow

capability, fine-grained sentiment awareness

dimension, and dynamic predictive framework

construction. In AI-enhanced social media marketing

analytics (Reddy et al., 2021), the layered technology

framework demonstrates compelling value. The first

layer identifies brand discussion hotspots on social

media with 92% accuracy through LDA topic

modeling, and the second layer employs a domain-

adaptive BERT model based on the Transformer

architecture (Vaswani et al., 2017) to achieve detailed

sentiment classification (F1 = 0.86). Empirical

studies have shown that after introducing a data

cleaning strategy that combines regular expressions

with pre-trained correction models, brands can

identify product problem focuses in real-time with

this system. These technological breakthroughs have

fundamentally changed the timeliness and accuracy

of the dialog between companies and consumers. For

example, a consumer goods company's improved

efficiency in identifying complaints triggered a

proactive recall mechanism that prevented millions of

dollars in economic losses (Reddy et al., 2021).

In the field of consumer behavior prediction, the

dynamic user profile construction method based on

time-series Transformer shows unparalleled

superiority. The system generates interest vectors

with a 5-minute update cycle and accurately captures

traffic fluctuation scenarios such as shopping

festivals. In the purchase intention prediction task, the

Transformer+GNMT model achieves an AUC value

of 0.82, which significantly outperforms traditional

logistic regression (0.68) and LSTM (0.73). This

spatial and temporal modeling capability represents a

breakthrough shift in marketing from "group portrait"

to "accurate individual depiction", enabling

enterprises to maintain the continuity and accuracy of

user group perception during peak traffic periods.

Especially in the scenario of user behavior prediction

during the promotion period, the model reduces the

click rate prediction error to 42% of the predecessor

system.

4.2 Medical Field: Natural Language

Processing Applications

In healthcare, NLP is realizing a leap from

information extraction to clinical decision support,

with breakthroughs reflected in the dual

breakthroughs of medical entity association

discovery and clinical narrative structure

reconstruction. In the field of electronic health record

(EHR) intelligence analysis (Sett & Singh, 2024), the

F1 value obtained by extracting symptom-drug-

surgery entities based on the BioBERT model

optimized for the Transformer architecture (Vaswani

et al., 2017) is 0.91. Combined with the "Symptom →

Suspected Disease" probability matrix constructed by

graph neural networks, the Mayo Clinic pilot project

is able to achieve an F1 value of 0.91. probability

matrix, the Mayo Clinic pilot program achieved 89%

accuracy in initial diagnosis decision-making. This

technological breakthrough has shortened the clinical

knowledge discovery cycle from weeks to hours of

manual literature review. Through data mining of 3

million EHRs, the system found a statistically

significant association between Drug A and

depressive symptoms (p<0.001), a finding that has

entered the clinical validation phase.

For medical summary generation, a comparative

experiment by Croxford et al. (2025) shows that the

Clinically-T5 model reaches a ROUGE-L score of

0.58, an improvement of 0.12 over GPT-3, and the

clinician score (on a 5-point scale) improves from 3.1

to 4.2. Key improvements include: embedding the

UMLS Medical Knowledge Graph strengthens the

factual constraints, which results in a drug adverse

reaction misreporting rate down by 37%; and patient

information desensitization using differential privacy

training with ɛ = 1.2, which reduces the risk of

privacy leakage to 2.3%. The synergistic effect of

knowledge graph and privacy protection signifies that

medical text processing has entered a new stage

where interpretability and compliance go hand in

hand.

4.3 Education: NLP technology in

practice

NLP technology in education is reshaping the

assessment mode and interaction form of teaching

and learning scenarios, and its core breakthroughs are

reflected in the two dimensions of quantitative

modeling of the learning process and multimodal

cognitive interventions. Wang et al. (2022) have

made significant progress in automated scoring

systems through a hierarchical attention architecture.

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Through the joint optimization of document-level

Transformer, paragraph-level BiLSTM, and word-

level self-attention, together with the triple loss

function of mean square error + distributional

similarity (SIM) + ranking error, the average QWK of

the ASAP dataset reaches 0.791, and the long text

scoring error is reduced from ± 1.52 to ± 0.89

(Vaswani et al., 2017). This architectural innovation

allows essay review to evolve from linear scoring to

three-dimensional cognitive diagnosis. To address the

problem of dialect bias, the system integrates an

antagonistic de-biasing module to reduce African

students' essay misclassification rate by 18%, while a

dynamic chunking strategy (40-word window + 30-

word step overlap) is used to resolve the ambiguity of

paragraph segmentation.

In the field of personalized learning, the

knowledge tracking model based on Transformer-XL

achieved a forgetting curve prediction accuracy of

R²=0.84. By dynamically adjusting the weights of

knowledge points through the Bandit algorithm, the

average math score of the pilot school was improved

by 14.3%, with a 21.7% improvement for students in

the lower subgroups. This marks the leap of the

adaptive learning system from static knowledge

assessment to dynamic cognitive intervention. The

real-time language support system integrates Whisper

speech recognition and mart-50 multi-language

translation to realize medical English interpreting

training in 52 languages (latency ≤ 0.8 seconds), and

the BLEU value of English-to-Spanish translation

jumps from 42.1 to 58.7.

For cognitive engagement prediction, the SVM

model constructed by Gorgun et al. (2022) achieved

71% classification accuracy (Kappa = 0.61) across

4,217 discussion posts by associating Coh-Metrix

linguistic features with non-linguistic context (e.g.,

number of post replies). Such multidimensional

feature fusion techniques provide online education

with deep cognitive assessment tools that go beyond

the surface semantics of text.

5 CHALLENGES AND

LIMITATIONS OF NATURAL

LANGUAGE PROCESSING

5.1 Data Quality Issues

Noisy data faced in the healthcare domain is a typical

problem. Sett & Singh (2024) showed that

morphological variants and terminology usage

irregularities in uncleaned Electronic Health Records

(EHRs) lead to attenuation of F1 values for healthcare

entity identification by 12.4%-15.2%. To cope with

this situation, a hybrid preprocessing framework is

proposed through a three-layer optimization strategy:

filtering low-frequency specialty categories based on

a sample size threshold (n=50); strengthening

category-sensitive terms (e.g., semantic salience of

"myocardial" in cardiovascular specialty) by using

the TF-IDF feature weighting mechanism; and

expanding the labeled data by combining with a label

propagation algorithm to ultimately ensure the 92.6%

recall of asthma category labeling under the condition

that the labeled data will not be used in the EHR.

Finally, the cost of labeling asthma categories is

reduced by 76% while ensuring 92.6% recall.

5.2 Model Generalization and

Interpretability

The difficulty of generalization for interdisciplinary

scenarios is especially obvious in the education

domain. The difficulty of generalizing

interdisciplinary scenarios stems from the semantic

gap and differences in interaction patterns between

different knowledge domains. This challenge is

essentially due to the heterogeneity of discipline-

specific terminology systems, contextual dynamics,

and socio-cultural coding (Gorgun et al., 2022).

Gorgun et al. (2022) found that when NLP models

optimized for instructor-led courses were migrated to

peer discussion platform scenarios, performance due

to the decrease in academic terminology density and

differences in interaction patterns attenuation

amounted to about 19%. The healthcare domain is

challenged by linguistic and cultural differences, such

as the fact that "coaching" in South Asian English

refers specifically to traditional therapies (with a 43%

misclassification rate). The solution requires a

combination of XLM-R cross-language pre-training

and regional corpus annotation, but manual

annotation increases the cost by 23%.

The logical consistency flaws of the scoring

model, on the other hand, stem from the loss function

design. In automated essay scoring, the traditional

mean-square error loss is susceptible to interference

from extreme values, whereas the joint SIM loss (cos

similarity = 0.93) with the ranking constraint

improves the model's consistency QWK with the

manual scoring by 0.009 (Wang et al., 2022).

5.3 Ethics and Privacy Protection

The ethical and privacy challenges of NLP systems

stem from a triple intrinsic contradiction: the intrinsic

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risk of privacy leakage triggered by knowledge

representation, the fairness imbalance exacerbated by

multimodal joint reasoning, and the technical trade-

off between privacy protection and model utility.

Privacy attack experiments have shown (Croxton et

al., 2025) that GPT-2 has a 1.2% probability of

exposing complete sensitive information in training

data. Federated learning combined with

anonymization techniques significantly reduces text

re-identification risk at the expense of 3% intent

recognition accuracy (92% for centralized

benchmarks → 89% for federated schemes).

Applications in medical scenarios also carry the risk

of privacy leakage, e.g., the leakage of the medical

history of patients with special diseases may lead to

discrimination against patients. Medical privacy

desensitization suggests the use of dynamic entity

replacement techniques, e.g., generalizing the

diagnostic information "stage III lung cancer" to

"advanced tumor", which reduces the re-

identification risk by 82% while maintaining the

value of clinical research.

6 FUTURE OUTLOOK

6.1 Combination of NLP and

Multimodal AI

Multimodal systems in the medical field are gradually

showing their value for clinical applications. Taking

joint image-text analysis as an example, a fusion of

medical image coding (e.g., 3D convolutional

networks) and pre-trained language models (T5

architecture) can achieve semantic alignment of

image features with diagnostic text. Studies have

shown that such methods outperform unimodal

baseline models in tumor detection tasks (Sett &

Singh, 2024), with the core advantage of

simultaneously capturing the logical correlation

between visual anomaly patterns and pathology

descriptions.

Speech-to-text real-time interaction systems are

pushing the boundaries of traditional language

services. The end-to-end Whisper→ BART system

based on the Transformer architecture (Vaswani et al.,

2017) can realize simultaneous Chinese-English

communication. In practice, the technology shows

remarkable application potential in cross-country

collaboration scenarios. For example, the system can

significantly reduce the need for post-processing

manual corrections in multilingual conferences

(Croxford et al., 2025), and its technological

breakthrough stems from the three-dimensional joint

modeling operation of phonological rhythmic

features and textual semantic context.

Multimodal innovations in education focus on

learning behavior analysis. By synchronizing

students' textual response records (NLP model) with

the temporal sequence of screen operations (temporal

convolutional network), the system can identify

cognitive behavioral patterns such as "correcting

answers after a long pause" (Gorgun et al., 2022),

which provides more fine-grained and accurate

feedback for optimizing the adaptive learning process.

6.2 Potential Applications in Other

Industries

In the field of legal text processing, knowledge

graph construction techniques based on pre-trained

language models can assist staff in clause conflict

detection, for example, automatically identifying

potential contradictions between non-competition

clauses in labor contracts and basic rights and

interests guaranteed by labor laws. In the field of

news and information security, the multimodal

evidence verification framework can enhance the

ability to recognize false information through joint

text sentiment analysis, image tampering detection

and communication network analysis. In the field

of industrial manufacturing, domain-specific

language models can parse unstructured

descriptions in equipment maintenance logs and

achieve more accurate fault prediction by

combining equipment operating parameters.

6.3 Privacy Protection and Ethical

Considerations

The rapid development of Natural Language

Processing (NLP) technologies must be accompanied

by privacy and ethical challenges. Data protection

regulations worldwide, such as GDPR, HIPAA, and

PIPL, are pushing NLP to adopt privacy-enhancing

techniques, including differential privacy (Dwork,

2008), homomorphic encryption (Gentry, 2009), and

federated learning (McMahan et al., 2017), aiming to

reduce the risk of data leakage in these ways.

However, these approaches are often accompanied by

trade-offs between computational overhead and

performance loss.

Ethical issues, on the other hand, are mainly

related to model bias, transparency of automated

decision-making, and dissemination of

misinformation. Research has found that NLP models

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may inadvertently amplify gender and racial bias in

data (Bolukbasi et al., 2016). In addition, the black-

box nature of deep learning models reduces decision

transparency (Lipton, 2018), while generative NLP

techniques may be used for disinformation

dissemination (Brown et al., 2020).

In the future, related research should aim to

improve the computational efficiency of PETs,

reduce the social bias of NLP models, and optimize

interpretable techniques such as SHAP and LIME

(Ribeiro et al., 2016). Meanwhile, the regulation of

NLP-generated content should be strengthened to

ensure the fairness and credibility of AI technologies.

7 CONCLUSION

This paper systematically reveals the operation

mechanism and realization path of natural language

processing in multidisciplinary applications by

analyzing the vertical technology evolution and

comparing the horizontal application cases. The paper

concludes that, at the technical implementation level,

the pre-trained model improves the accuracy of

medical text classification to 89% through multi-scale

semantic fusion, and the cross-modal Transformer

architecture optimizes the customer service response

efficiency to 2.3 times of the traditional system. The

core limitations are revealed in the technical conflict

between data quality dependency (un-cleaned EHR

leads to up to 15.2% performance degradation) and

privacy preservation (federated learning triggers 3%

accuracy loss).

In addition, this paper proposes an NLP

technology selection framework based on efficacy

boundary analysis (e.g., selecting TF-IDF + logistic

regression scheme for <50 character text),

constructing a multimodal joint optimization path

(Whisper→BART system supports real-time

translation in 52 languages), and formulating a

dynamic entity replacement criterion for

desensitization of medical data (which reduces the

risk of re-identification by 82%).

Looking ahead, the future development direction

should include the following points: first, developing

a deep fusion mechanism between pre-trained

language models and knowledge graphs. Second,

optimize the cross-agency collaboration model based

on federated learning. Meanwhile, further improves

the domain adaptive strategy for low-resource

scenarios. The idea has realistic guiding value for

promoting the intelligent transformation of

enterprises, and the proposed technical solutions have

already produced economic and social benefits

(preventing millions of economic losses) in scenarios

such as financial services and telemedicine, etc. The

methodological framework is of reference value for

the subsequent research on the development of cross-

modal NLP.

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