MeSH-Based Semantic Indexing Approach to Enhance Biomedical Information Retrieval

2020 ◽  
Author(s):  
Hager Kammoun ◽  
Imen Gabsi ◽  
Ikram Amous
Keyword(s):  
Information Retrieval ◽  
State Of The Art ◽  
Research Paper ◽  
The State ◽  
Test Collection ◽  
Semantic Indexing ◽  
Effective Strategy ◽  
Biomedical Information Retrieval ◽  
Content Representation

Abstract Owing to the tremendous size of electronic biomedical documents, users encounter difficulties in seeking useful biomedical information. An efficient and smart access to the relevant biomedical information has become a fundamental need. In this research paper, we set forward a novel biomedical MeSH-based semantic indexing approach to enhance biomedical information retrieval. The proposed semantic indexing approach attempts to strengthen the content representation of both documents and queries by incorporating unambiguous MeSH concepts as well as the adequate senses of ambiguous MeSH concepts. For this purpose, our proposed approach relies on a disambiguation method to identify the adequate senses of ambiguous MeSH concepts and introduces four representation enrichment strategies so as to identify the best appropriate representatives of the adequate sense in the textual entities representation. To prove its effectiveness, the proposed semantic indexing approach was evaluated by intensive experiments. These experiments were carried out on OHSUMED test collection. The results reveal that our proposal outperforms the state-of-the-art approaches and allow us to highlight the most effective strategy.

scholarly journals Neural Embedding-Based Metrics for Pre-retrieval Query Performance Prediction

2021 ◽  
Author(s):  
Arabzadehghahyazi Negar
Keyword(s):  
Performance Prediction ◽  
State Of The Art ◽  
Learning To Rank ◽  
The State ◽  
Test Collection ◽  
Query Performance ◽  
Performance Predictors ◽  
Level Statistics ◽  
Ablation Study ◽  
Individual Specificity

file:///C:/Users/MWF/Downloads/Arabzadehghahyazi, Negar.Pre-retrieval Query Performance Prediction (QPP) methods are oblivious to the performance of the retrieval model as they predict query difficulty prior to observing the set of documents retrieved for the query. Among pre-retrieval query performance predictors, specificity-based metrics investigate how corpus, query and corpus-query level statistics can be used to predict the performance of the query. In this thesis, we explore how neural embeddings can be utilized to define corpus-independent and semantics-aware specificity metrics. Our metrics are based on the intuition that a term that is closely surrounded by other terms in the embedding space is more likely to be specific while a term surrounded by less closely related terms is more likely to be generic. On this basis, we leverage geometric properties between embedded terms to define four groups of metrics: (1) neighborhood-based, (2) graph-based, (3) cluster-based and (4) vector-based metrics. Moreover, we employ learning-to-rank techniques to analyze the importance of individual specificity metrics. To evaluate the proposed metrics, we have curated and publicly share a test collection of term specificity measurements defined based on Wikipedia category hierarchy and DMOZ taxonomy. We report on our extensive experiments on the effectiveness of our metrics through metric comparison, ablation study and comparison against the state-of-the-art baselines. We have shown that our proposed set of pre-retrieval QPP metrics based on the properties of pre-trained neural embeddings are more effective for performance prediction compared to the state-of-the-art methods. We report our findings based on Robust04, ClueWeb09 and Gov2 corpora and their associated TREC topics.

scholarly journals Symbolic Melodic Similarity: State of the Art and Future Challenges

2016 ◽  
Vol 40 (2) ◽  
pp. 70-83 ◽  
Author(s):  
Valerio Velardo ◽  
Mauro Vallati ◽  
Steven Jan
Keyword(s):  
Information Retrieval ◽  
Comparative Analysis ◽  
State Of The Art ◽  
Music Information Retrieval ◽  
The State ◽  
Information Retrieval Evaluation ◽  
Future Challenges ◽  
Music Information

Fostered by the introduction of the Music Information Retrieval Evaluation Exchange (MIREX) competition, the number of systems that calculate symbolic melodic similarity has recently increased considerably. To understand the state of the art, we provide a comparative analysis of existing algorithms. The analysis is based on eight criteria that help to characterize the systems, highlighting strengths and weaknesses. We also propose a taxonomy that classifies algorithms based on their approach. Both taxonomy and criteria are fruitfully exploited to provide input for new, forthcoming research in the area.

Sentiment Analysis of Code-Mixed Text: A Review

2021 ◽  
Vol 12 (3) ◽  
pp. 2469-2478
Author(s):  
Nurul Husna Mahadzir Et.al
Keyword(s):  
Information Retrieval ◽  
Text Mining ◽  
Sentiment Analysis ◽  
State Of The Art ◽  
The State ◽  
The Internet ◽  
Qualitative Comparison ◽  
Analysis Process ◽  
Active Research ◽  
Multiple Languages

In recent times, sentiment analysis has become one of the most active research and progressively popular areas in information retrieval and text mining. To date, sentiment analysis has been applied in various domains such as product, movie, sport and political reviews. Most of the previous work in this field has focused on analyzing only a single language, especially English. However, with the need of globalization and the increasing number of the Internet used worldwide; it is common to see the post written in multiple languages. Moreover, in an unstructured content like Twitter posts, people tend to mix languages in one sentence, which make sentiment analysis process even harder and more challenging. This paper reviews the state-of-the-art of sentiment analysis for code-mixed, which includes the detail discussions of each focus area, qualitative comparison and limitations of current approaches. This paper also highlights challenges along this line of research and suggests several recommendations for future works that should be explored.

Building Integrative Enterprise Knowledge Portals with Semantic Web Technologies

2005 ◽  
pp. 146-188 ◽  
Author(s):  
Torsten Priebe
Keyword(s):  
Information Retrieval ◽  
Semantic Web ◽  
State Of The Art ◽  
Content Management ◽  
The State ◽  
The Internet ◽  
Semantic Web Technologies ◽  
Research Results ◽  
Web Technologies ◽  
Integration Approach

The goal of this chapter is to show how Semantic Web technologies can help build integrative enterprise knowledge portals. Three main areas are identified: content management and metadata, global searching, and the integration of external content and applications. For these three areas the state-of-the-art as well as current research results are discussed. In particular, a metadata-based information retrieval and a context-based port let integration approach are presented. These have been implemented in a research prototype which is introduced in the Internet session at the end of the chapter.

Application of Advanced Speech Technology (AST) in Manned Penetration Bombers

1981 ◽  
Vol 25 (1) ◽  
pp. 48-52
Author(s):  
R.A. North ◽  
S.J. Mountford
Keyword(s):  
Information Retrieval ◽  
Human Factors ◽  
State Of The Art ◽  
The State ◽  
Speech Technology ◽  
Analysis Techniques ◽  
Factors Analysis ◽  
Human Factors Analysis

A methodology was developed to identify the most beneficial task candidates for speech technology in an airborne crewstation. The method combined human factors analysis techniques with knowledge of the state-of-the-art in speech technology to produce a tradeoff matrix that ranks each task on pilot utility and technological feasibility. Information retrieval from flight manuals was identified as a “high need” task that could be readily implemented with a speech system in the cockpit.

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