A Lexical Search Model based on word association norms

Reyes-Magaña, Jorge; Bel-Enguix, Gemma; Gómez-Adorno, Helena; Sierra, Gerardo

doi:10.3233/jifs-179010

Cited by 4 publications

(5 citation statements)

References 22 publications

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“…Te inverted index helped in collecting relevant information, while Word2vector computed news similarity. Te BM25, a probabilistic retrieval model developed by Stephen E. Robertson in the 1970s and 1980s, was used in the optimization scheme [32]. Te BM25 method is utilized to tokenize the user's keywords into distinct words and then apply a ranking function to arrange matching information based on their signifcance.…”

Section: Methodsmentioning

confidence: 99%

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News Classification and Categorization with Smart Function Sentiment Analysis

Nkongolo Wa Nkongolo

2023

International Journal of Intelligent Systems

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Search engines are tools used to find information on the Internet. Since the web has a plethora of websites, the engine queries the majority of active sites and builds a database organized according to keywords utilized in the search. Because of this, when a user types a few descriptive words on the home page of the search engine, the search function lists websites corresponding to these keywords. However, there are some problems with this search approach. For instance, if a user wants information about the word Jaguar, most search results are animals and cars. This is a polysemic problem that forces search engines to always provide the most popular but not the most relevant results. This article presents a study of using sentiment technology to help news classification and categorization and improve the classification accuracy. We have introduced a smart search function embedded into a search engine to tackle polysemic issues and record relevant results to determine their sentimentality. Therefore, this study presents a topic that involves several aspects of natural language processing (NLP) and sentiment analysis for news categorization and classification. A web crawler was used to collect British Broadcasting Corporation (BBC) news across the Internet, carried out preprocessing of text by using NLP, and applied sentiment analysis methods to determine the polarity of the processed text data. The sentimentality represents negative, positive, or neutral polarities assigned by the sentiment analysis algorithms. The research utilized the BBC news site to collect different information using a web crawler and a database to explore the sentimentality of BBC news. The natural language toolkit (NLTK) and BM25 indexed and preprocessed patterns in the database. The experimental results depict the proposed search function surpassing normal search with an accuracy rate of 85%. Moreover, the results show a negative polarity of BBC news using the Sentistrength algorithm. Furthermore, the Valence Aware Dictionary and sEntiment Reasoner (VADER) was the best-performing sentiment analysis model for news classification. This model obtained an accuracy of 85% using data collected with the proposed smart function.

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Section: Methodsmentioning

confidence: 99%

“…Te BM25 method is utilized to tokenize the user's keywords into distinct words and then apply a ranking function to arrange matching information based on their signifcance. Tis method utilizes a probabilistic retrieval approach to match patterns with their corresponding indexed information [32].…”

Section: Methodsmentioning

confidence: 99%

News Classification and Categorization with Smart Function Sentiment Analysis

Nkongolo Wa Nkongolo

2023

International Journal of Intelligent Systems

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“…Various approaches have been proposed in the literature to develop RDs, including Information Retrieval (IR) System-based Crawford and Crawford, 1997;El-Kahlout and Oflazer, 2004;Shaw et al, 2013), Graph-based (Dutoit and Nugues, 2002;Reyes Magaña et al, 2019;Thorat and Choudhari, 2016), Mental Dictionary-based (Zock and Schwab, 2008;Zock and Bilac, 2004), Vector Space Model-based Semantic Analysis (Calvo et al, 2016;Méndez et al, 2013), and Neural Language Model-based approaches (Agrawal et al, 2021;Hedderich et al, 2019;Hill et al, 2016;Morinaga and Yamaguchi, 2018;Morinaga and Yamaguchi, 2020;Pilehvar, 2019;Yan et al, 2020;Devlin et al, 2019).…”

Section: * Equal Contributionmentioning

confidence: 99%

“…Another study from (Reyes Magaña et al, 2019) uses word association norms to establish semantic connections between words in the context of designing an electronic RD. The authors used the corpus of human-definitions and graph-based techniques, specifically a measure of betweenness centrality, to perform searches in the knowledge graph.…”

Section: Graph-based Approachmentioning

confidence: 99%

KSAA-RD Shared Task: Arabic Reverse Dictionary

Al-Matham,

Alshammari,

AlOsaimy

et al. 2023

Proceedings of ArabicNLP 2023

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This paper outlines the first KSAA-RD shared task, which aims to develop a Reverse Dictionary (RD) system for the Arabic language. RDs allow users to find words based on their meanings or definition. This shared task, KSAA-RD, includes two subtasks: Arabic RD and cross-lingual reverse dictionaries (CLRD). Given a definition (referred to as a "gloss") in either Arabic or English, the teams compete to find the most similar word embeddings of their corresponding word. The winning team achieved 24.20 and 12.70 for RD and CLRD, respectively in terms of rank metric. In this paper, we describe the methods employed by the participating teams and offer an outlook for KSAA-RD.

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“…Madani et al used CNN to model sentences and completed English translation tasks on this basis with better results than traditional methods and achieved good results on several datasets [ 12 ]. Reyes-Magaña et al proposed the Tree-LSTM model for predicting the semantic relevance of text and English translation,which is used to predictthe semantic relevance of text and English classification [ 13 ]. In recent years, the Attention model in deep learning was first used for machine translation by Yuan Z et al Later, various variants of the Attention model were also applied to English translation work [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

Construction of English Translation Model Based on Neural Network Fuzzy Semantic Optimal Control

Zhang¹,

Liu

2022

Computational Intelligence and Neuroscience

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This work addresses four aspects of the English translation model: consistency, model structure, semantic understanding, and knowledge fusion. To solve the problem of lack of personality consistency in the responses generated by neural networks in English translation models, an English translation model with fuzzy semantic optimal control of neural networks is proposed in this study. The model uses a fuzzy semantic optimal control retrieval mechanism to obtain appropriate information from an externally set English information table; to further improve the effectiveness of the model in retrieving correct information, this work adopts a two-stage training method, using ordinary English translation data for model pretraining and then fine-tuning the model using English translation data with optimal control containing fuzzy semantic information. The model consists of two parts, a sequence generation network that can output the probability distribution of words and an evaluation network that can predict future whole-sentence returns. In particular, the evaluation network can evaluate the impact of currently generated words on whole sentences using deep inheritance features so that the model can consider not only the optimal solution for the current words, as in other generative models, but also the optimal solution for future generated whole sentences. The experimental results show that the English translation model with fuzzy semantic optimal control of the neural network proposed in this study can obtain better semantic feature representation by using a novel bidirectional neural network and a masked language model to train sentence vectors; the combination of semantic features and fuzzy semantic similarity features can obtain higher scoring accuracy and better model generalization. In English translation applications, there are large improvements in scoring accuracy and generality.

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A Lexical Search Model based on word association norms

Cited by 4 publications

References 22 publications

News Classification and Categorization with Smart Function Sentiment Analysis

News Classification and Categorization with Smart Function Sentiment Analysis

KSAA-RD Shared Task: Arabic Reverse Dictionary

Construction of English Translation Model Based on Neural Network Fuzzy Semantic Optimal Control

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