A Novel Class-Center Vector Model for Text Classification Using Dependencies and a Semantic Dictionary

Zhu, Xinhua; Xu, Qingting; Chen, Yishan; Chen, Hongchao; Wu, Tianjun

doi:10.1109/access.2019.2954106

Cited by 8 publications

(5 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, if the user's budget is 15000 rupees, a positive 5% threshold would translate to an extended range of 15750 rupees. After leveraging the flexible feature values, the leveraged features are set as the high impact features and the same are updated in user preferences ( R , it's time to personalize the product recommendations [39] further. This personalization step aims to refine the existing weights of products within set R based on the user's unique purchase history and profile information.…”

Section: Extended Partial Matchmentioning

confidence: 99%

FacetDRS: Multi-faceted Feature Matching for Personalized Product Recommendations in Dynamic E-Commerce

B. Tarakeswara Rao

2024

jes

View full text Add to dashboard Cite

Dynamic Product Recommender systems play a crucial role in e-commerce, guiding users towards products that align with their preferences and enhancing the shopping experience. However, existing systems often struggle to capture the nuances of user preferences, particularly when dealing with multiple features via keyword queries and filters. Additionally, scalability challenges arise as the volume of products grows with features and values. In addition, the static filters of existing recommender systems limit the exploration by strictly adhering to user selections, hindering discovery of potentially better features within the preferred budget or category. This can lead to user dissatisfaction and missed opportunities to discover more desirable options. This research addresses these limitations by introducing FacetDRS, a novel dynamic product recommendation system that leverages flexible feature matching and a scalable architecture. FacetDRS empowers the recommendations with three distinct matching methods to cater to diverse preference nuances. Exact Match ensures precise adherence to user specifications, while High-Impact Partial Match prioritizes core features with flexibility in non-critical aspects. Extended Partial Match suggests relevant options beyond initial preferences, leveraging predefined thresholds. This multi-faceted approach captures the full spectrum of user desires while maintaining accuracy. To evaluate the effectiveness of FacetDRS, a rigorous experimental setup was conducted, comparing its performance across Keyword Match Score, First Selections in Top-K and Average Response Time metrics with established recommender systems such as DeepRec, APGNN, and TADCF. The results demonstrate that FacetDRS achieves superior performance than its counterparts in all comparison aspects. The system's flexible matching methods, combined with its robust architecture, offer a valuable solution for enhancing user satisfaction and driving conversions in the mobile commerce landscape.

show abstract

Section: Extended Partial Matchmentioning

confidence: 99%

FacetDRS: Multi-faceted Feature Matching for Personalized Product Recommendations in Dynamic E-Commerce

B. Tarakeswara Rao

2024

jes

View full text Add to dashboard Cite

show abstract

“…WordNet is a large synonyms semantic dictionary based on cognitive linguistics and is designed and realized by psychologists, linguisticians and computer engineers in Princeton University [35]. It can be widely used for text classification [45] and semantic similarity calculation [46]. WordNet, in our CQACD system, is used to provide more semantic interpretation for student inputs based on BCKO ontology.…”

Section: Wordnetmentioning

confidence: 99%

CQACD: A Concept Question-Answering System for Intelligent Tutoring Using a Domain Ontology With Rich Semantics

Zhu

Zhang

2022

IEEE Access

Self Cite

View full text Add to dashboard Cite

In this study, a Concept Question Answering system applied to the Computer Domain (CQACD) for intelligent tutoring is proposed. This system is a dialogue-based Intelligent Tutoring System (ITS) that allows the tutor and student with mixed-initiative and natural language to ask each other questions concerning the basic computer knowledge in the Computer Basics course. CQACD is based on constructivist principles and encourages the learner to construct knowledge rather than merely receiving knowledge, which has the following characteristics: (a) this system employs a domain ontology with rich semantic relationships to model the basic computer knowledge and build up a concept-centric knowledge model, (b) uses a limited number of 80 input templates with description logics to acquire the intention of questions posed by students, (c) a textual entailment algorithm with semantic technologies is proposed to match the input template and assess the student's contribution to improve the flexibility of the system, and (d) an ontology-driven dialogue management mechanism is proposed, which can quickly form the conversational content and conversational sequence. The experimental results show that CQACD can replace the teachers' tutoring in large classes and can promote the learning of poor students in large classes better than teachers can. The paper reveals that the domain ontology with rich semantic relationships plays an important role in the Concept Question Answer System (CQAS). It can model CQAS's discipline knowledge, provide structured domain knowledge for student model, template design and matching, and provide basic architectural architecture for dialogue management.

show abstract

“…But it lacks the combination of text semantics to understand the text content. In order to overcome the above problems, this article refers to [33], introduces dependencies, and uses an improved TFIDF-based weight calculation algorithm to understand and optimize text features.…”

Section: Dependency Graphmentioning

confidence: 99%

“…For the word i, we calculate the number of times that the word i appears in the text and set it as n. Then, according to the result of the dependency syntax analysis implemented by Stanford Parser, it is obtained that the word i belongs to the m-th(1≤m≤n) sentence component in the text. And according to table 2 in the paper [33], classify the m-th occurrence of word i in the text into k i,m level, and assign weight w i,m to it. Then the improved frequency TF i the weight of word i in the text is calculated by formula (3).…”

Section: Dependency Graphmentioning

confidence: 99%

An Integration Model for Text Classification using Graph Convolutional Network and BERT

Xue

Zhu

Wang

et al. 2021

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Recently, Graph Convolutional Neural Network (GCN) is widely used in text classification tasks, and has effectively completed tasks that are considered to have a rich relational structure. However, due to the sparse adjacency matrix constructed by GCN, GCN cannot make full use of context-dependent information in text classification, and cannot capture local information. The Bidirectional Encoder Representation from Transformers (BERT) has been shown to have the ability to capture the contextual information in a sentence or document, but its ability to capture global information about the vocabulary of a language is relatively limited. The latter is the advantage of GCN. Therefore, in this paper, Mutual Graph Convolution Networks (MGCN) is proposed to solve the above problems. It introduces semantic dictionary (WordNet), dependency and BERT. MGCN uses dependency to solve the problem of context dependence and WordNet to obtain more semantic information. Then the local information generated by BERT and the global information generated by GCN are interacted through the attention mechanism, so that they can influence each other and improve the classification effect of the model. The experimental results show that our model is more effective than previous research reports on three text classification data sets.

show abstract

A Novel Class-Center Vector Model for Text Classification Using Dependencies and a Semantic Dictionary

Cited by 8 publications

References 20 publications

FacetDRS: Multi-faceted Feature Matching for Personalized Product Recommendations in Dynamic E-Commerce

FacetDRS: Multi-faceted Feature Matching for Personalized Product Recommendations in Dynamic E-Commerce

CQACD: A Concept Question-Answering System for Intelligent Tutoring Using a Domain Ontology With Rich Semantics

An Integration Model for Text Classification using Graph Convolutional Network and BERT

Contact Info

Product

Resources

About