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Natural Language Processing (NLP) and Applications—2nd Edition

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 May 2024 | Viewed by 4913

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Prof. Dr. Guilin Qi

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School of Computer Science and Engineering, Southeast University, Nanjing 211189, China
Interests: natural language processing; knowledge graph; multimodal learning
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Tong Xu

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Guest Editor

Lab of Big Data Analysis and Application, University of Science and Technology of China, Hefei 230027, China
Interests: natural language processing; social media analysis; multimodal intelligence
Special Issues, Collections and Topics in MDPI journals

Dr. Meng Wang

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Special Issue Information

Dear Colleagues,

Due to the success of the first edition of the Special Issue of Applied Science, entitled “Natural Language Processing (NLP) and Applications”, we have launched a second edition.

This Special Issue will showcase advances in NLP and its applications, including significant advances in sentiment analysis, machine translation, semantic understanding, and more. Large-scale pre-trained models such as BERT and GPT-3 have revolutionized NLP and provided a solid foundation for future advancement. The transformer design enhances cross-language and multi-modal intelligence. However, NLP still faces challenges such as unsupervised learning, model generalization, and linguistic diversity. Factors such as background, language and culture should be considered in real applications. This SI invites experts and scholars from around the world to share their latest research results and technological advances in order to provide more inspiration and ideas for the future development of NLP.

Prof. Dr. Guilin Qi
Prof. Dr. Tong Xu
Dr. Meng Wang
Guest Editors

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Keywords

natural language understanding
natural language generation
machine translation
knowledge graph
NLP for knowledge extraction
NLP for multimodal intelligence
NLP applications in specific domains, like life sciences, health, and medicine
eGovernment and public administration

Published Papers (7 papers)

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Research

18 pages, 2271 KiB

Open AccessArticle

Document Retrieval System for Biomedical Question Answering

by Harun Bolat and Baha Şen

Appl. Sci. 2024, 14(6), 2613; https://doi.org/10.3390/app14062613 - 20 Mar 2024

Viewed by 516

Abstract

In this paper, we describe our biomedical document retrieval system and answers extraction module, which is part of the biomedical question answering system. Approximately 26.5 million PubMed articles are indexed as a corpus with the Apache Lucene text search engine. Our proposed system consists of three parts. The first part is the question analysis module, which analyzes the question and enriches it with biomedical concepts related to its wording. The second part of the system is the document retrieval module. In this step, the proposed system is tested using different information retrieval models, like the Vector Space Model, Okapi BM25, and Query Likelihood. The third part is the document re-ranking module, which is responsible for re-arranging the documents retrieved in the previous step. For this study, we tested our proposed system with 6B training questions from the BioASQ challenge task. We obtained the best MAP score on the document retrieval phase when we used Query Likelihood with the Dirichlet Smoothing model. We used the sequential dependence model at the re-rank phase, but this model produced a worse MAP score than the previous phase. In similarity calculation, we included the Named Entity Recognition (NER), UMLS Concept Unique Identifiers (CUI), and UMLS Semantic Types of the words in the question to find the sentences containing the answer. Using this approach, we observed a performance enhancement of roughly 25% for the top 20 outcomes, surpassing another method employed in this study, which relies solely on textual similarity. Full article

(This article belongs to the Special Issue Natural Language Processing (NLP) and Applications—2nd Edition)

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14 pages, 1216 KiB

Open AccessArticle

Margin and Shared Proxies: Advanced Proxy Anchor Loss for Out-of-Domain Intent Classification

by Junhyeong Park, Byeonghun Kim, Sangkwon Han, Seungbin Ji and Jongtae Rhee

Appl. Sci. 2024, 14(6), 2312; https://doi.org/10.3390/app14062312 - 09 Mar 2024

Viewed by 467

Abstract

Out-of-Domain (OOD) intent classification is an important task for a dialog system, as it allows for appropriate responses to be generated. Previous studies aiming to solve the OOD intent classification task have generally adopted metric learning methods to generate decision boundaries in the embedding space. However, these existing methods struggle to capture the high-dimensional semantic features of data, as they learn decision boundary using scalar distances. They also use generated OOD samples for learning. However, such OOD samples are biased, and they cannot include all real-world OOD intents, thus representing a limitation. In the current paper, we attempt to overcome these challenges by using Advanced Proxy-Anchor loss, which introduces a margin proxy and shared proxy. First, to generate a decision boundary that has the high-dimensional semantic features of training data, we use a margin proxy for learnable embedding vectors. Next, the shared proxy, which is shared by all In-Domain (IND) samples, is introduced to make it possible to learn the discriminative feature between IND intents and OOD intent, ultimately leading to the improved classification of OOD samples. We conduct evaluations of the proposed method using three benchmark datasets. The experimental results demonstrate that our method achieved an improved performance compared to the methods described in previous studies. Full article

(This article belongs to the Special Issue Natural Language Processing (NLP) and Applications—2nd Edition)

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17 pages, 1401 KiB

Open AccessArticle

A Study on the Emotional Tendency of Aquatic Product Quality and Safety Texts Based on Emotional Dictionaries and Deep Learning

by Xingxing Tong, Ming Chen and Guofu Feng

Appl. Sci. 2024, 14(5), 2119; https://doi.org/10.3390/app14052119 - 04 Mar 2024

Viewed by 550

Abstract

The issue of aquatic product quality and safety has gradually become a focal point of societal concern. Analyzing textual comments from people about aquatic products aids in promptly understanding the current sentiment landscape regarding the quality and safety of aquatic products. To address the challenge of the polysemy of modern network buzzwords in word vector representation, we construct a custom sentiment lexicon and employ the Roberta-wwm-ext model to extract semantic feature representations from comment texts. Subsequently, the obtained semantic features of words are put into a bidirectional LSTM model for sentiment classification. This paper validates the effectiveness of the proposed model in the sentiment analysis of aquatic product quality and safety texts by constructing two datasets, one for salmon and one for shrimp, sourced from comments on JD.com. Multiple comparative experiments were conducted to assess the performance of the model on these datasets. The experimental results demonstrate significant achievements using the proposed model, achieving a classification accuracy of 95.49%. This represents a notable improvement of 6.42 percentage points compared to using Word2Vec and a 2.06 percentage point improvement compared to using BERT as the word embedding model. Furthermore, it outperforms LSTM by 2.22 percentage points and textCNN by 2.86 percentage points in terms of semantic extraction models. The outstanding effectiveness of the proposed method is strongly validated by these results. It provides more accurate technical support for calculating the concentration of negative emotions using a risk assessment system in public opinion related to quality and safety. Full article

(This article belongs to the Special Issue Natural Language Processing (NLP) and Applications—2nd Edition)

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25 pages, 545 KiB

Open AccessArticle

Applying Named Entity Recognition and Graph Networks to Extract Common Interests from Thematic Subfora on Reddit

by Jan Sawicki, Maria Ganzha, Marcin Paprzycki and Yutaka Watanobe

Appl. Sci. 2024, 14(5), 1696; https://doi.org/10.3390/app14051696 - 20 Feb 2024

Viewed by 616

Abstract

Reddit is the largest topically structured social network. Existing literature, reporting results of Reddit-related research, considers different phenomena, from social and political studies to recommender systems. The most common techniques used in these works, include natural language processing, e.g., named entity recognition, as well as graph networks representing online social networks. However, large-scale studies that take into account Reddit’s unique structure are scarce. In this contribution, similarity between subreddits is explored. Specifically, subreddit posts (from 3189 subreddits, spanning the year 2022) are processed using NER to build graph networks which are further mined for relations between subreddits. The evaluation of obtained results follows the state-of-the-art approaches used for a similar problem, i.e., recommender system metrics, and applies recall and AUC. Overall, the use of Reddit crossposts discloses previously unknown relations between subreddits. Interestingly, the proposed approach may allow for researchers to better connect their study topics with particular subreddits and shows promise for subreddit similarity mining. Full article

(This article belongs to the Special Issue Natural Language Processing (NLP) and Applications—2nd Edition)

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24 pages, 4014 KiB

Open AccessArticle

A Modular Framework for Domain-Specific Conversational Systems Powered by Never-Ending Learning

by Felipe Coelho de Abreu Pinna, Victor Takashi Hayashi, João Carlos Néto, Rosangela de Fátima Pereira Marquesone, Maísa Cristina Duarte, Rodrigo Suzuki Okada and Wilson Vicente Ruggiero

Appl. Sci. 2024, 14(4), 1585; https://doi.org/10.3390/app14041585 - 16 Feb 2024

Viewed by 688

Abstract

Complex and long interactions (e.g., a change of topic during a conversation) justify the use of dialog systems to develop task-oriented chatbots and intelligent virtual assistants. The development of dialog systems requires considerable effort and takes more time to deliver when compared to regular BotBuilder tools because of time-consuming tasks such as training machine learning models and low module reusability. We propose a framework for building scalable dialog systems for specific domains using the semi-automatic methods of corpus, ontology, and code development. By separating the dialog application logic from domain knowledge in the form of an ontology, we were able to create a dialog system for the banking domain in the Portuguese language and quickly change the domain of the conversation by changing the ontology. Moreover, by using the principles of never-ending learning, unsupported operations or unanswered questions create triggers for system knowledge demand that can be gathered from external sources and added to the ontology, augmenting the system’s ability to respond to more questions over time. Full article

(This article belongs to the Special Issue Natural Language Processing (NLP) and Applications—2nd Edition)

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20 pages, 8097 KiB

Open AccessArticle

VL-Few: Vision Language Alignment for Multimodal Few-Shot Meta Learning

by Han Ma, Baoyu Fan, Benjamin K. Ng and Chan-Tong Lam

Appl. Sci. 2024, 14(3), 1169; https://doi.org/10.3390/app14031169 - 30 Jan 2024

Viewed by 881

Abstract

Complex tasks in the real world involve different modal models, such as visual question answering (VQA). However, traditional multimodal learning requires a large amount of aligned data, such as image text pairs, and constructing a large amount of training data is a challenge for multimodal learning. Therefore, we propose VL-Few, which is a simple and effective method to solve the multimodal few-shot problem. VL-Few (1) proposes the modal alignment, which aligns visual features into language space through a lightweight model network and improves the multimodal understanding ability of the model; (2) adopts few-shot meta learning in the multimodal problem, which constructs a few-shot meta task pool to improve the generalization ability of the model; (3) proposes semantic alignment to enhance the semantic understanding ability of the model for the task, context, and demonstration; (4) proposes task alignment that constructs training data into the target task form and improves the task understanding ability of the model; (5) proposes generation alignment, which adopts the token-level training and multitask fusion loss to improve the generation ability of the model. Our experimental results show the effectiveness of VL-Few for multimodal few-shot problems. Full article

(This article belongs to the Special Issue Natural Language Processing (NLP) and Applications—2nd Edition)

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20 pages, 2767 KiB

Open AccessArticle

A Robust Chinese Named Entity Recognition Method Based on Integrating Dual-Layer Features and CSBERT

by Yingjie Xu, Xiaobo Tan, Xin Tong and Wenbo Zhang

Appl. Sci. 2024, 14(3), 1060; https://doi.org/10.3390/app14031060 - 26 Jan 2024

Viewed by 638

Abstract

In the rapidly evolving field of cybersecurity, the integration of multi-source, heterogeneous, and fragmented data into a coherent knowledge graph has garnered considerable attention. Such a graph elucidates semantic interconnections, thereby facilitating sophisticated analytical decision support. Central to the construction of a cybersecurity knowledge graph is Named Entity Recognition (NER), a critical technology that converts unstructured text into structured data. The efficacy of NER is pivotal, as it directly influences the integrity of the knowledge graph. The task of NER in cybersecurity, particularly within the Chinese linguistic context, presents distinct challenges. Chinese text lacks explicit space delimiters and features complex contextual dependencies, exacerbating the difficulty in discerning and categorizing named entities. These linguistic characteristics contribute to errors in word segmentation and semantic ambiguities, impeding NER accuracy. This paper introduces a novel NER methodology tailored for the Chinese cybersecurity corpus, termed CSBERT-IDCNN-BiLSTM-CRF. This approach harnesses Iterative Dilated Convolutional Neural Networks (IDCNN) for extracting local features, and Bi-directional Long Short-Term Memory networks (BiLSTM) for contextual understanding. It incorporates CSBERT, a pre-trained model adept at processing few-shot data, to derive input feature representations. The process culminates with Conditional Random Fields (CRF) for precise sequence labeling. To compensate for the scarcity of publicly accessible Chinese cybersecurity datasets, this paper synthesizes a bespoke dataset, authenticated by data from the China National Vulnerability Database, processed via the YEDDA annotation tool. Empirical analysis affirms that the proposed CSBERT-IDCNN-BiLSTM-CRF model surpasses existing Chinese NER frameworks, with an F1-score of 87.30% and a precision rate of 85.89%. This marks a significant advancement in the accurate identification of cybersecurity entities in Chinese text, reflecting the model’s robust capability to address the unique challenges presented by the language’s structural intricacies. Full article

(This article belongs to the Special Issue Natural Language Processing (NLP) and Applications—2nd Edition)

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