Semantic Similarity Retrieval System Based on the Chinese Minority Dictionary Wa Volume

2021 ◽  
pp. 312-326
Author(s):  
Xiangdong Meng ◽  
Jun Wang ◽  
Yiping Liufu ◽  
Zhaoxiang OuYang
Keyword(s):  
Semantic Similarity ◽  
Retrieval System ◽  
Similarity Retrieval

Improving semantic similarity retrieval with word embeddings

2018 ◽  
Vol 30 (23) ◽  
pp. e4489 ◽  
Author(s):  
Fengqi Yan ◽  
Qiaoqing Fan ◽  
Mingming Lu
Keyword(s):  
Semantic Similarity ◽  
Word Embeddings ◽  
Similarity Retrieval

An Approach of Semantic Similarity Measure between Documents Based on Big Data

2016 ◽  
Vol 6 (5) ◽  
pp. 2454 ◽  
Author(s):  
Mohammed Erritali ◽  
Abderrahim Beni-Hssane ◽  
Marouane Birjali ◽  
Youness Madani
Keyword(s):  
Big Data ◽  
Semantic Similarity ◽  
Retrieval System ◽  
Programming Model ◽  
State Of The Art ◽  
Distributed Processing ◽  
Similarity Measures ◽  
The State ◽  
Semantic Similarity Measure ◽  
Document Similarity

<p>Semantic indexing and document similarity is an important information retrieval system problem in Big Data with broad applications. In this paper, we investigate MapReduce programming model as a specific framework for managing distributed processing in a large of amount documents. Then we study the state of the art of different approaches for computing the similarity of documents. Finally, we propose our approach of semantic similarity measures using WordNet as an external network semantic resource. For evaluation, we compare the proposed approach with other approaches previously presented by using our new MapReduce algorithm. Experimental results review that our proposed approach outperforms the state of the art ones on running time performance and increases the measurement of semantic similarity.</p>

Lexical Co-Occurrence and Contextual Window-Based Approach with Semantic Similarity for Query Expansion

2017 ◽  
Vol 13 (3) ◽  
pp. 57-78 ◽  
Author(s):  
Jagendra Singh ◽  
Rakesh Kumar
Keyword(s):  
Semantic Similarity ◽  
Query Expansion ◽  
Ad Hoc ◽  
Retrieval System ◽  
Hybrid Approach ◽  
Information Retrieval System ◽  
Query Reformulation ◽  
Baseline Method ◽  
Benchmark Datasets ◽  
Pseudo Feedback

Query expansion (QE) is an efficient method for enhancing the efficiency of information retrieval system. In this work, we try to capture the limitations of pseudo-feedback based QE approach and propose a hybrid approach for enhancing the efficiency of feedback based QE by combining corpus-based, contextual based information of query terms, and semantic based knowledge of query terms. First of all, this paper explores the use of different corpus-based lexical co-occurrence approaches to select an optimal combination of query terms from a pool of terms obtained using pseudo-feedback based QE. Next, we explore semantic similarity approach based on word2vec for ranking the QE terms obtained from top pseudo-feedback documents. Further, we combine co-occurrence statistics, contextual window statistics, and semantic similarity based approaches together to select the best expansion terms for query reformulation. The experiments were performed on FIRE ad-hoc and TREC-3 benchmark datasets. The statistics of our proposed experimental results show significant improvement over baseline method.

Personalization Information Retrieval Based on Topic Directory

2013 ◽  
Vol 712-715 ◽  
pp. 2659-2663
Author(s):  
Yang Xin Yu ◽  
Yi Zhou Zhang
Keyword(s):  
Information Retrieval ◽  
Semantic Similarity ◽  
Retrieval System ◽  
User Profile ◽  
Information Retrieval System ◽  
Tree Structure ◽  
Experimental Results ◽  
User Profiles ◽  
User Interests ◽  
Computing Process

Personalization information retrieval is very useful in information retrieval system, the user profile can be used to represent the favorites or interests of user. This paper introduces how to automatically learn user interests, build user profiles and re-rank search results.A topic directory method is proposed to calculate the semantic similarity, which takes multi-inheritance into consideration, and then optimize the computing process based on the tree structure of inheritance relationship. Experiments are conducted to compare our method with the popular directory based search methods (e.g., Google Directory Search). Experimental results show that the proposed method in this paper can effectively capture personalization and improve the accuracy of personalized search over existing approaches.

scholarly journals TendeR-Sims - Similarity Retrieval System for Public Tenders

2018 ◽  
Author(s):  
Guilherme Q. Vasconcelos ◽  
Guilherme F. Zabot ◽  
Daniel M. de Lima ◽  
José F. Rodrigues Jr. ◽  
Caetano Traina Jr. ◽  
...  
Keyword(s):  
Retrieval System ◽  
Similarity Retrieval
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