Dark Web: E-Commerce Information Extraction Based on Name Entity Recognition Using Bidirectional-LSTM

Shah, Syed Afeef Ahmed; Masood, M. Ali; Yasin, Amanullah

doi:10.1109/access.2022.3206539

Cited by 6 publications

(3 citation statements)

References 56 publications

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“…In addition to the mentioned algorithms, there are other classic NLP algorithms used in ecommerce information retrieval, such as named entity recognition algorithms for identifying and extracting important information like product names, brands, and prices. Additionally, Syed Afeef Ahmed Shah and his colleagues present an innovative approach for the detection of e-commerce entities [10]. They employ a bidirectional LSTM model combined with a convolutional neural network (CNN) to effectively identify entities and entity groups associated with products available on the dark web.…”

Section: Application Of Nlp In E-commerce Information Retrievalmentioning

confidence: 99%

The application of NLP in information retrieval

Wang

2024

ACE

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The field of Natural Language Processing (NLP) has experienced impressive advancements and has found diverse applications. This paper presents a comprehensive review of the development of NLP in the field of information retrieval. It explores different stages of NLP techniques and methods, including keyword matching, rule-based approaches, statistical methods, and the utilization of machine learning and deep learning technologies. Furthermore, the paper provides detailed insights into the specific applications of NLP in domains such as academic information retrieval, medical information retrieval, travel information retrieval, and e-commerce information retrieval. It analyzes the current state of NLP applications in these domains, highlights their advantages, and discusses their associated limitations. Finally, the paper emphasizes the continuous advancement of the NLP field, with a particular focus on semantic understanding, personalized retrieval, and multimodal information retrieval, to better adapt to diverse data types and user requirements. The paper concludes by summarizing the main points discussed and providing future directions.

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Section: Application Of Nlp In E-commerce Information Retrievalmentioning

confidence: 99%

The application of NLP in information retrieval

Wang

2024

ACE

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“…It consists of a multi-head self-attention mechanism that allows the model to capture contextual information efficiently. BERT (Bidirectional Encoder Representations from Transformers) is a pre-trained Transformer-based model that has achieved state-of-the-art results in various natural language processing tasks, including NER & other applications via use of Multi-Agent Communication (MACs) [10,11,12].…”

Section: Literature Reviewmentioning

confidence: 99%

TMIANE: Design of an efficient Transformer-Based Model for Identification & Feature Analysis of Named Entities via Ensemble Operations

2024

Journal of Electrical Systems

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Named Entity Recognition (NER) is a critical task in natural language processing that involves identifying and categorizing named entities in text. These entities can include people, organizations, locations, and more. Extracting features from these entities is equally important as it enables downstream tasks like sentiment analysis, text classification, and more. In this research paper, we propose an efficient Transformer-based model for identifying named entities and analyzing their features through ensemble operations. Our proposed model leverages the power of Transformer models such as BERT and XLNet for identifying named entities. We then convert the identified entities into feature vector sets using a combination of BERT and XLNet. These features are classified using the GoogLeNet convolutional neural network for model validation operations. By combining these different models through ensemble operations, we aim to improve the accuracy, precision, recall, and delay of the model for different use cases. The need for such a model arises due to the limitations of existing models for named entity recognition and feature analysis. While these models have achieved significant success, they still suffer from low accuracy, precision, recall, and high delay. Our proposed model overcomes these limitations by using ensemble operations to combine the strengths of different models. We compare the performance of our proposed model with existing models on standard datasets and show that it outperforms these models in terms of accuracy, precision, recall, and delay. Our results demonstrate the potential of ensemble operations in developing efficient models for named entity recognition and feature analysis. Overall, this research paper contributes to the development of more accurate and efficient models for natural language processing tasks.

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“…Thirdly, deep learning models consisting of many stacked neural networks are widely used in NER, such as RNN [25,26], Long Short-Time Memory (LSTM) [27], Bidirectional LSTM [28,29], BiLSTM-CRF [30,31], BERT-BiLSTM-CRF [6,32], ALBERT-BiLSTM-CRF [8], and attention mechanisms [33], which have replaced statistical machine learning techniques. This method can handle high-dimensional data with excellent accuracy.…”

Section: Named Entity Recognition Techniquesmentioning

confidence: 99%

Chinese Named Entity Recognition in Football Based on ALBERT-BiLSTM Model

An,

Pan,

Liu

et al. 2023

Applied Sciences

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Football is one of the most popular sports in the world, arousing a wide range of research topics related to its off- and on-the-pitch performance. The extraction of football entities from football news helps to construct sports frameworks, integrate sports resources, and timely capture the dynamics of the sports through visual text mining results, including the connections among football players, football clubs, and football competitions, and it is of great convenience to observe and analyze the developmental tendencies of football. Therefore, in this paper, we constructed a 1000,000-word Chinese corpus in the field of football and proposed a BiLSTM-based model for named entity recognition. The ALBERT-BiLSTM combination model of deep learning is used for entity extraction of football textual data. Based on the BiLSTM model, we introduced ALBERT as a pre-training model to extract character and enhance the generalization ability of word embedding vectors. We then compared the results of two different annotation schemes, BIO and BIOE, and two deep learning models, ALBERT-BiLSTM-CRF and ALBERT BiLSTM. It was verified that the BIOE tagging was superior than BIO, and the ALBERT-BiLSTM model was more suitable for football datasets. The precision, recall, and F-Score of the model were 85.4%, 83.47%, and 84.37%, correspondingly.

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Dark Web: E-Commerce Information Extraction Based on Name Entity Recognition Using Bidirectional-LSTM

Cited by 6 publications

References 56 publications

The application of NLP in information retrieval

The application of NLP in information retrieval

TMIANE: Design of an efficient Transformer-Based Model for Identification & Feature Analysis of Named Entities via Ensemble Operations

Chinese Named Entity Recognition in Football Based on ALBERT-BiLSTM Model

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