semantic association
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2022 ◽  
Vol 16 (4) ◽  
pp. 1-30
Author(s):  
Muhammad Abulaish ◽  
Mohd Fazil ◽  
Mohammed J. Zaki

Domain-specific keyword extraction is a vital task in the field of text mining. There are various research tasks, such as spam e-mail classification, abusive language detection, sentiment analysis, and emotion mining, where a set of domain-specific keywords (aka lexicon) is highly effective. Existing works for keyword extraction list all keywords rather than domain-specific keywords from a document corpus. Moreover, most of the existing approaches perform well on formal document corpuses but fail on noisy and informal user-generated content in online social media. In this article, we present a hybrid approach by jointly modeling the local and global contextual semantics of words, utilizing the strength of distributional word representation and contrasting-domain corpus for domain-specific keyword extraction. Starting with a seed set of a few domain-specific keywords, we model the text corpus as a weighted word-graph. In this graph, the initial weight of a node (word) represents its semantic association with the target domain calculated as a linear combination of three semantic association metrics, and the weight of an edge connecting a pair of nodes represents the co-occurrence count of the respective words. Thereafter, a modified PageRank method is applied to the word-graph to identify the most relevant words for expanding the initial set of domain-specific keywords. We evaluate our method over both formal and informal text corpuses (comprising six datasets), and show that it performs significantly better in comparison to state-of-the-art methods. Furthermore, we generalize our approach to handle the language-agnostic case, and show that it outperforms existing language-agnostic approaches.


Symmetry ◽  
2021 ◽  
Vol 13 (10) ◽  
pp. 1926
Author(s):  
Yiqi Xiao ◽  
Ke Miao ◽  
Chenhan Jiang

A stroke is the basic limb movement that both humans and animals naturally and repetitiously perform. Having been introduced into gestural interaction, mid-air stroke gestures saw a wide application range and quite intuitive use. In this paper, we present an approach for building command-to-gesture mapping that exploits the semantic association between interactive commands and the directions of mid-air unistroke gestures. Directional unistroke gestures make use of the symmetry of the semantics of commands, which makes a more systematic gesture set for users’ cognition and reduces the number of gestures users need to learn. However, the learnability of the directional unistroke gestures is varying with different commands. Through a user elicitation study, a gesture set containing eight directional mid-air unistroke gestures was selected by subjective ratings of the direction in respect to its association degree with the corresponding command. We evaluated this gesture set in a following study to investigate the learnability issue, and the directional mid-air unistroke gestures and user-preferred freehand gestures were compared. Our findings can offer preliminary evidence that “return”, “save”, “turn-off” and “mute” are the interaction commands more applicable to using directional mid-air unistrokes, which may have implication for the design of mid-air gestures in human–computer interaction.


Author(s):  
Vishnu VandanaKolisetty ◽  
Dharmendra Singh Rajput

AbstractThe process of integration through classification provides a unified representation of diverse data sources in Big data. The main challenges of big data analysis are due to the various granularities, irreconcilable data models, and multipart interdependencies between data content. Previously designed models were facing problems in integrating and analyzing big data due to highly complex and dynamic multi-source and heterogeneous information variation and also in processing and classifying the association among the attributes in a schema. In this paper, we propose an integration and classification approach through designing a Probabilistic Semantic Association (PSA) method to generate the feature pattern for the sources of big data. The PSA approach is trained to understand the data association and dependency pattern between the data class and incoming data to map the data objects accurately. It initially builds a data integration mechanism by transforming data into structured and learn to utilize the trained knowledge to classify the probabilistic association among the data and knowledge patterns. Later it builds a data analysis mechanism to analyze the mapped data through PSA to evaluate the integration efficiency. An experimental evaluation is performed over a real-time crime dataset generated from multiple locations having various events classes. The analysis of results confined that the utilization of knowledge patterns of accurate classification to enhance the integration of multiple source data is appropriate. The measure of precision, recall, fall-out rate, and F-measure approve the efficiency of the proposed PSA method. Even in comparison with the state-of-art classification method and with SC-LDA algorithm shows an improvisation in the prediction accuracy and enhance the data integration.


Author(s):  
Elisa Diniz de Lima ◽  
José Alberto Souza Paulino ◽  
Ana Priscila Lira de Farias Freitas ◽  
José Eraldo Viana Ferreira ◽  
Jussara da Silva Barbosa ◽  
...  

Objective: To assess three machine learning (ML) attribute extraction methods: radiomic, semantic and radiomic-semantic association on temporomandibular disorder (TMD) detection using infrared thermography (IT); and to determine which ML classifier, KNN, SVM and MLP, is the most efficient for this purpose. Methods and materials: 78 patients were selected by applying the Fonseca questionnaire and RDC/TMD to categorize control patients (37) and TMD patients (41). IT lateral projections of each patient were acquired. The masseter and temporal muscles were selected as regions of interest (ROI) for attribute extraction. Three methods of extracting attributes were assessed: radiomic, semantic and radiomic-semantic association. For radiomic attribute extraction, 20 texture attributes were assessed using co-occurrence matrix in a standardized angulation of 0°. The semantic features were the ROI mean temperature and pain intensity data. For radiomic-semantic association, a single dataset composed of 28 features was assessed. The classification algorithms assessed were KNN, SVM and MLP. Hopkins’s statistic, Shapiro–Wilk, ANOVA and Tukey tests were used to assess data. The significance level was set at 5% (p < 0.05). Results: Training and testing accuracy values differed statistically for the radiomic-semantic association (p = 0.003). MLP differed from the other classifiers for the radiomic-semantic association (p = 0.004). Accuracy, precision and sensitivity values of semantic and radiomic-semantic association differed statistically from radiomic features (p = 0.008, p = 0.016 and p = 0.013). Conclusion: Semantic and radiomic-semantic-associated ML feature extraction methods and MLP classifier should be chosen for TMD detection using IT images and pain scale data. IT associated with ML presents promising results for TMD detection.


Author(s):  
Ying Wang ◽  
Guoheng Huang ◽  
Lin Yuming ◽  
Haoliang Yuan ◽  
Chi-Man Pun ◽  
...  

2021 ◽  
Vol 17 (3) ◽  
pp. 300-306
Author(s):  
Sangwook Park ◽  
JungWan Kim

Purpose: For comparison of a semantic knowledge processing of the elderly, particularly the normal individuals and individuals with subjective memory impairment, this study aims to clarify what factors of semantic knowledge task could sensitively discriminate between the two groups by conducting various types of tasks and analyzing the aspects.Methods: High/low frequency category fluency test, concrete/abstract noun word defining test, and semantic association task were performed by 30 normal subjects and 30 subjective memory impaired subjects over 65 years old. Total and each subcategory scores were assessed for the category fluency test and word defining test, and correct response and reaction time were measured for the semantic association task.Results: It was found that there were significant differences between the two groups in the total score of the category fluency task (p < 0.001), low-frequency category score, abstract noun word defining task score (p < 0.05), and reaction time of semantic association task (p < 0.01).Conclusion: The result showed that in case of a target word with lower contact frequency and more abstract concept, the elderly with subjective memory impairment have difficulties in neural-networking activation of semantic knowledge and control of interruption stimulation when approaching a target word, with increased reaction time. This findings demonstrate that a semantic and lexical task has a clinical significance in discriminating a subjective memory impairment group.


2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Antoinette Schapper

Abstract In this article I demonstrate that there is a pervasive lexico-semantic association bones are strength in the languages of Melanesia, but that its linguistic expression is highly varied; languages are scattered along a lexical-to-clausal cline in their expression of the association between bone and strength, with a large number of language-specific idioms based on the association to be observed in Melanesia. I argue that the striking areality of this lexico-semantic association is readily missed in top-down approaches to lexical semantic typology that rely, for instance, on databases of word lists, or on narrow search domains limited to the meanings of simplex lexemes.


2021 ◽  
Author(s):  
Jipeng Li ◽  
Yujing Sun ◽  
Chenhui Li ◽  
Yanpeng Hu ◽  
Changbo Wang

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