Exploiting Siamese Neural Networks on Short Text Similarity Tasks for Multiple Domains and Languages

Souza, João Vitor Andrioli de; Oliveira, Lucas Emanuel Silva e; Gumiel, Yohan Bonescki; Carvalho, Déborah Ribeiro; Moro, Cláudia Maria Cabral

doi:10.1007/978-3-030-41505-1_34

Cited by 7 publications

(2 citation statements)

References 12 publications

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“…Siamese Neural network is a method for computing similarity with demanding less training data. An architecture with language-independent features for finding short text similarity detection in multiple languages and domains was proposed in [30]. They used these corpora from shared tasks: ASSIN 1 and ASSIN 2 with Portuguese journalistic texts and N2C2 (English clinical texts).…”

Section: Review Of State Of the Artmentioning

confidence: 99%

A Hybrid Model for Similarity Measurement of Twitter Profiles

Shoeibi¹,

Shoeibi²,

Chamoso³

et al. 2022

Preprint

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Social media platforms have been entirely an undeniable part of the lifestyle for the past decade. Analyzing the information being shared is a crucial step to understanding human behavior. Social media analysis aims to guarantee a better experience for the user and risen user satisfaction. For deriving any further conclusion, first, it is necessary to know how to compare users. In this paper, a hybrid model has been proposed to measure Twitter profiles’ similarity and quantifies the likeness degree of profiles by calculating features considering users’ behavioral habits. For this, first, the timeline of each profile has been extracted using the official TwitterAPI. Then, in parallel, three aspects of a profile are deliberated. Behavioral ratios are time-series-related information showing the consistency and habits of the user. Dynamic time warping has been utilized to compare the behavioral ratios of two profiles. Next, the audience network is extracted for each user, and for estimating the similarity of two sets, Jaccard similarity is used. Finally, for the Content similarity measurement, the tweets are preprocessed respecting the feature extraction method; TF-IDF and DistilBERT for feature extraction are employed and then compared using the cosine similarity method. Results have shown that TF-IDF has slightly better performance; therefore, the more straightforward solution is selected for the model. Similarity level of different profiles. As in the case study, a Random Forest classification model was trained on almost 20000 users revealed a 97.24% accuracy. This comparison enables us to find duplicate profiles with nearly the same behavior and content.

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Section: Review Of State Of the Artmentioning

confidence: 99%

A Hybrid Model for Similarity Measurement of Twitter Profiles

Shoeibi¹,

Shoeibi²,

Chamoso³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Siamese Neural network is a method for computing similarity with demanding less training data. An architecture with language-independent features for finding short text similarity detection in multiple languages and domains proposed in [24]. They used these corpora from shared tasks: ASSIN 1 and ASSIN 2 with Portuguese journalistic texts and N2C2 (English clinical texts).…”

Section: Review Of the State Of The Artmentioning

confidence: 99%

Similarity approximation of Twitter Profiles

Shoeibi¹,

Chamoso²,

Alizadehsani³

et al. 2021

Preprint

View full text Add to dashboard Cite

Social media platforms are entirely an undeniable part of the lifestyle from the past decade. Analyzing the information being shared is a crucial step to understand humans behavior. Social media analysis is aiming to guarantee a better experience for the user and risen user satisfaction. But first, it is necessary to know how and from which aspects to compare users with each other. In this paper, an intelligent system has been proposed to measure the similarity of Twitter profiles. For this, firstly, the timeline of each profile has been extracted using the official Twitter API. Then, all information is given to the proposed system. Next, in parallel, three aspects of a profile are derived. Behavioral ratios are time-series-related information showing the consistency and habits of the user. Dynamic time warping has been utilized for comparison of the behavioral ratios of two profiles. Next, Graph Network Analysis is used for monitoring the interactions of the user and its audience; for estimating the similarity of graphs, Jaccard similarity is used. Finally, for the Content similarity measurement, natural language processing techniques for preprocessing and TF-IDF for feature extraction are employed and then compared using the cosine similarity method. Results have presented the similarity level of different profiles. As the case study, people with the same interest show higher similarity. This way of comparison is helpful in many other areas. Also, it enables to find duplicate profiles; those are profiles with almost the same behavior and content.

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Develop corpora and methods for cross-lingual text reuse detection for English Urdu language pair at lexical, syntactical, and phrasal levels

Muneer

Nawab

2022

Lang Resources & Evaluation

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Exploiting Siamese Neural Networks on Short Text Similarity Tasks for Multiple Domains and Languages

Cited by 7 publications

References 12 publications

A Hybrid Model for Similarity Measurement of Twitter Profiles

A Hybrid Model for Similarity Measurement of Twitter Profiles

Similarity approximation of Twitter Profiles

Develop corpora and methods for cross-lingual text reuse detection for English Urdu language pair at lexical, syntactical, and phrasal levels

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