CWI: A multimodal deep learning approach for named entity recognition from social media using character, word and image features

Asgari-Chenaghlu, Meysam; Feizi‐Derakhshi, Mohammad‐Reza; Farzinvash, Leili; Balafar, Mohammad Ali; Motamed, Cina

doi:10.1007/s00521-021-06488-4

Cited by 20 publications

(6 citation statements)

References 52 publications

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“…Calculating the mentioned probability for every node is the same as applying the random walk algorithm on the graph. It is proven that the probability is computable recursively and matrical using Equation (11).…”

Section: Segment Select Scoringmentioning

confidence: 99%

“…In general, there are two ways of processing dataflows: unimodal 7 and multimodal. 8,9 In unimodal, 10 only a single type of data, such as text or image, is being processed; however, in multimodal, 11 several types of data are being processed as input. Named entity recognition, or NER, systems are used in summarization systems, event recognition, 12 title and topic classification, 13 extraction of keywords, 14 locating positions 15 and so forth.…”

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confidence: 99%

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Named entities detection by beam search algorithm

Gholami-Dastgerdi

Feizi‐Derakhshi

Forouzandeh

2022

Concurrency and Computation

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Named entity recognition (NER) is a fundamental process in NLP and a requirement for most processes. This article aims to identify the named entities in the context of social networks. For this purpose, the idea of segmenting text into suitable and unsuitable expressions for the named entities has been used. So the contribution of this article is to process informal text in the Persian language by the Beam search algorithm to detect named entities. Due to the reproductive nature of language, new words and names are always produced, and available NER systems are inefficient in detecting new entities. The other contribution of this article is to make it possible to recognize the emerging named entity by applying dynamic external knowledge. According to a sense of the lack of datasets in low‐resource languages, N‐Gram and Wikipedia anchor datasets have been prepared for Persian and deployed as external knowledge. Also, a corpus of named entities in Persian from the telegram dataset has been generated. Three native experts have done labeling of this corpus. Evaluation of these three experts and the proposed method shows that the result of the proposed method is acceptable compared to the result of a human‐to‐human also to other methods.

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Section: Segment Select Scoringmentioning

confidence: 99%

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confidence: 99%

Named entities detection by beam search algorithm

Gholami-Dastgerdi

Feizi‐Derakhshi

Forouzandeh

2022

Concurrency and Computation

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“…While the approach yielded improved performance, the availability of visual data along with every piece of text is not practical for every webpage. Moreover, researchers filtered out short tweets containing less than three words from the dataset; therefore, the results are only representative of model performance on long text from social media posts ( Asgari-Chenaghlu et al, 2022 ). In another research multi-modal based named entity recognition technique has been proposed for short text.…”

Section: Literature Reviewmentioning

confidence: 99%

Autonomous schema markups based on intelligent computing for search engine optimization

Abbasi

Fatima²,

Mukhtar

et al. 2022

PeerJ Computer Science

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With advances in artificial intelligence and semantic technology, search engines are integrating semantics to address complex search queries to improve the results. This requires identification of well-known concepts or entities and their relationship from web page contents. But the increase in complex unstructured data on web pages has made the task of concept identification overly complex. Existing research focuses on entity recognition from the perspective of linguistic structures such as complete sentences and paragraphs, whereas a huge part of the data on web pages exists as unstructured text fragments enclosed in HTML tags. Ontologies provide schemas to structure the data on the web. However, including them in the web pages requires additional resources and expertise from organizations or webmasters and thus becoming a major hindrance in their large-scale adoption. We propose an approach for autonomous identification of entities from short text present in web pages to populate semantic models based on a specific ontology model. The proposed approach has been applied to a public dataset containing academic web pages. We employ a long short-term memory (LSTM) deep learning network and the random forest machine learning algorithm to predict entities. The proposed methodology gives an overall accuracy of 0.94 on the test dataset, indicating a potential for automated prediction even in the case of a limited number of training samples for various entities, thus, significantly reducing the required manual workload in practical applications.

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“…For example, a product such as cellphone has features in both numerical and image form. Multimodal prediction and learning is popular in other fields of AI too [15,16]. Even the text related to user comments can be investigated using different sentiment analysis and text classification methods [17].…”

Section: Introductionmentioning

confidence: 99%

Multimodal price prediction

Zehtab-Salmasi,

Feizi-Derakhshi,

Nikzad-Khasmakhi

et al. 2020

Preprint

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Valorization is one of the most heated discussions in the business community, and commodities valorization is one subset in this task. Features of a product is an essential characteristic in valorization and features are categorized into two classes: graphical and non-graphical. Nowadays, the value of products is measured by price. The goal of this research is to achieve an arrangement to predict the price of a product based on specifications of that. We propose five deep learning models to predict the price range of a product, one unimodal and four multimodal systems. The multimodal methods predict based on the image and non-graphical specification of product. As a platform to evaluate the methods, a cellphones dataset has been gathered from GSMArena. In proposed methods, convolutional neural network is an infrastructure. The experimental results show 88.3% F1-score in the best method.

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CWI: A multimodal deep learning approach for named entity recognition from social media using character, word and image features

Cited by 20 publications

References 52 publications

Named entities detection by beam search algorithm

Named entities detection by beam search algorithm

Autonomous schema markups based on intelligent computing for search engine optimization

Multimodal price prediction

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