scholarly journals A Study on Ranking Fusion Approaches for the Retrieval of Medical Publications

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 103
Author(s):  
Teofan Clipa ◽  
Giorgio Maria Di Nunzio
Keyword(s):  
Statistical Analysis ◽  
Relevance Feedback ◽  
Query Expansion ◽  
State Of The Art ◽  
Word Embeddings ◽  
Medical Topic ◽  
Medical Publication ◽  
Different Types ◽  
Text Preprocessing ◽  
Better Than

In this work, we compare and analyze a variety of approaches in the task of medical publication retrieval and, in particular, for the Technology Assisted Review (TAR) task. This problem consists in the process of collecting articles that summarize all evidence that has been published regarding a certain medical topic. This task requires long search sessions by experts in the field of medicine. For this reason, semi-automatic approaches are essential for supporting these types of searches when the amount of data exceeds the limits of users. In this paper, we use state-of-the-art models and weighting schemes with different types of preprocessing as well as query expansion (QE) and relevance feedback (RF) approaches in order to study the best combination for this particular task. We also tested word embeddings representation of documents and queries in addition to three different ranking fusion approaches to see if the merged runs perform better than the single models. In order to make our results reproducible, we have used the collection provided by the Conference and Labs Evaluation Forum (CLEF) eHealth tasks. Query expansion and relevance feedback greatly improve the performance while the fusion of different rankings does not perform well in this task. The statistical analysis showed that, in general, the performance of the system does not depend much on the type of text preprocessing but on which weighting scheme is applied.

scholarly journals Fighting Together against the Pandemic: Learning Multiple Models on Tomography Images for COVID-19 Diagnosis

AI ◽  
2021 ◽  
Vol 2 (2) ◽  
pp. 261-273
Author(s):  
Mario Manzo ◽  
Simone Pellino
Keyword(s):  
Network Architecture ◽  
State Of The Art ◽  
Ensemble Classification ◽  
Effective Vaccine ◽  
Rt Pcr ◽  
Neural Network Architecture ◽  
Experimental Phase ◽  
Different Types ◽  
Polymerase Chain ◽  
Better Than

COVID-19 has been a great challenge for humanity since the year 2020. The whole world has made a huge effort to find an effective vaccine in order to save those not yet infected. The alternative solution is early diagnosis, carried out through real-time polymerase chain reaction (RT-PCR) tests or thorax Computer Tomography (CT) scan images. Deep learning algorithms, specifically convolutional neural networks, represent a methodology for image analysis. They optimize the classification design task, which is essential for an automatic approach with different types of images, including medical. In this paper, we adopt a pretrained deep convolutional neural network architecture in order to diagnose COVID-19 disease from CT images. Our idea is inspired by what the whole of humanity is achieving, as the set of multiple contributions is better than any single one for the fight against the pandemic. First, we adapt, and subsequently retrain for our assumption, some neural architectures that have been adopted in other application domains. Secondly, we combine the knowledge extracted from images by the neural architectures in an ensemble classification context. Our experimental phase is performed on a CT image dataset, and the results obtained show the effectiveness of the proposed approach with respect to the state-of-the-art competitors.

scholarly journals Semantic expansion of search queries

2021 ◽  
Author(s):  
Andisheh Keikha
Keyword(s):  
Relevance Feedback ◽  
Query Expansion ◽  
Web Search ◽  
State Of The Art ◽  
Feature Learning ◽  
Correct Interpretation ◽  
Search Queries ◽  
Term Selection ◽  
Semantic Expansion ◽  
Pseudo Relevance Feedback

One of the major challenges in Web search pertains to the correct interpretation of users' intent. Query Expansion is one of the well-known approaches for determining the intent of the user by addressing the vocabulary mismatch problem. A limitation of the current query expansion approaches is that the relations between the query words and the expanded words is limited. In this thesis, we capture users' intent through query expansion. We build on earlier work in the area by adopting a pseudo-relevance feedback approach; however, we advance the state of the art by proposing an approach for feature learning within the process of query expansion. In our work, we specifically consider the Wikipedia corpus as the feedback collection space and identify the best features within this context for term selection in two supervised and unsupervised models. We compare our work with state of the art query expansion techniques, the results of which show promising robustness and improved precision.

scholarly journals An Empirical Study of Different Approaches for Protein Classification

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Loris Nanni ◽  
Alessandra Lumini ◽  
Sheryl Brahnam
Keyword(s):  
Tertiary Structure ◽  
State Of The Art ◽  
Support Vector ◽  
Protein Classification ◽  
Matrix Representations ◽  
New Methods ◽  
Multiple Datasets ◽  
Different Types ◽  
Scoring Matrix ◽  
Better Than

Many domains would benefit from reliable and efficient systems for automatic protein classification. An area of particular interest in recent studies on automatic protein classification is the exploration of new methods for extracting features from a protein that work well for specific problems. These methods, however, are not generalizable and have proven useful in only a few domains. Our goal is to evaluate several feature extraction approaches for representing proteins by testing them across multiple datasets. Different types of protein representations are evaluated: those starting from the position specific scoring matrix of the proteins (PSSM), those derived from the amino-acid sequence, two matrix representations, and features taken from the 3D tertiary structure of the protein. We also test new variants of proteins descriptors. We develop our system experimentally by comparing and combining different descriptors taken from the protein representations. Each descriptor is used to train a separate support vector machine (SVM), and the results are combined by sum rule. Some stand-alone descriptors work well on some datasets but not on others. Through fusion, the different descriptors provide a performance that works well across all tested datasets, in some cases performing better than the state-of-the-art.

scholarly journals Query Expansion menggunakan Word Embedding dan Pseudo Relevance Feedback

2019 ◽  
Vol 5 (1) ◽  
pp. 47 ◽  
Author(s):  
Evan Tanuwijaya ◽  
Safri Adam ◽  
Mohammad Fatoni Anggris ◽  
Agus Zainal Arifin
Keyword(s):  
Relevance Feedback ◽  
Query Expansion ◽  
Relevant Information ◽  
Word Embedding ◽  
Natural Languages ◽  
F Measure ◽  
Pseudo Relevance Feedback ◽  
Better Than

Kata kunci merupakan hal terpenting dalam mencari sebuah informasi. Penggunaan kata kunci yang tepat menghasilkan informasi yang relevan. Saat penggunaannya sebagai query, pengguna menggunakan bahasa yang alami, sehingga terdapat kata di luar dokumen jawaban yang telah disiapkan oleh sistem. Sistem tidak dapat memproses bahasa alami secara langsung yang dimasukkan oleh pengguna, sehingga diperlukan proses untuk mengolah kata-kata tersebut dengan mengekspansi setiap kata yang dimasukkan pengguna yang dikenal dengan Query Expansion (QE). Metode QE pada penelitian ini menggunakan Word Embedding karena hasil dari Word Embedding dapat memberikan kata-kata yang sering muncul bersama dengan kata-kata dalam query. Hasil dari word embedding dipakai sebagai masukan pada pseudo relevance feedback untuk diperkaya berdasarkan dokumen jawaban yang telah ada. Metode QE diterapkan dan diuji coba pada aplikasi chatbot. Hasil dari uji coba metode QE yang diterapkan pada chatbot didapatkan nilai recall, precision, dan F-measure masing-masing 100%; 70% dan 82,35 %. Hasil tersebut meningkat 1,49% daripada chatbot tanpa menggunakan QE yang pernah dilakukan sebelumnya yang hanya meraih akurasi sebesar 68,51%. Berdasarkan hasil pengukuran tersebut, QE menggunakan word embedding dan pseudo relevance feedback pada chatbot dapat mengatasi query masukan dari pengguna yang ambigu dan alami, sehingga dapat memberikan jawaban yang relevan kepada pengguna.  Keywords are the most important words and phrases used to obtain relevant information on content. Although users make use of natural languages, keywords are processed as queries by the system due to its inability to process. The language directly entered by the user is known as query expansion (QE). The proposed QE in this research uses word embedding owing to its ability to provide words that often appear along with those in the query. The results are used as inputs to the pseudo relevance feedback to be enriched based on the existing documents. This method is also applied to the chatbot application and precision, and F-measure values of the results obtained were 100%, 70%, 82.35% respectively. The results are 1.49% better than chatbot without using QE with 68.51% accuracy. Based on the results of these measurements, QE using word embedding and pseudo which gave relevance feedback in chatbots can resolve ambiguous and natural user’s input queries thereby enabling the system retrieve relevant answers.

scholarly journals A New End-to-End Multi-Dimensional CNN Framework for Land Cover/Land Use Change Detection in Multi-Source Remote Sensing Datasets

2020 ◽  
Vol 12 (12) ◽  
pp. 2010 ◽  
Author(s):  
Seyd Teymoor Seydi ◽  
Mahdi Hasanlou ◽  
Meisam Amani
Keyword(s):  
Remote Sensing ◽  
Change Detection ◽  
State Of The Art ◽  
Polarimetric Synthetic Aperture Radar ◽  
Different Types ◽  
Benchmark Datasets ◽  
Convolution Kernels ◽  
2D And 3D ◽  
Accuracy Indices ◽  
Better Than

The diversity of change detection (CD) methods and the limitations in generalizing these techniques using different types of remote sensing datasets over various study areas have been a challenge for CD applications. Additionally, most CD methods have been implemented in two intensive and time-consuming steps: (a) predicting change areas, and (b) decision on predicted areas. In this study, a novel CD framework based on the convolutional neural network (CNN) is proposed to not only address the aforementioned problems but also to considerably improve the level of accuracy. The proposed CNN-based CD network contains three parallel channels: the first and second channels, respectively, extract deep features on the original first- and second-time imagery and the third channel focuses on the extraction of change deep features based on differencing and staking deep features. Additionally, each channel includes three types of convolution kernels: 1D-, 2D-, and 3D-dilated-convolution. The effectiveness and reliability of the proposed CD method are evaluated using three different types of remote sensing benchmark datasets (i.e., multispectral, hyperspectral, and Polarimetric Synthetic Aperture RADAR (PolSAR)). The results of the CD maps are also evaluated both visually and statistically by calculating nine different accuracy indices. Moreover, the results of the CD using the proposed method are compared to those of several state-of-the-art CD algorithms. All the results prove that the proposed method outperforms the other remote sensing CD techniques. For instance, considering different scenarios, the Overall Accuracies (OAs) and Kappa Coefficients (KCs) of the proposed CD method are better than 95.89% and 0.805, respectively, and the Miss Detection (MD) and the False Alarm (FA) rates are lower than 12% and 3%, respectively.

Semantic expansion of search queries

2021 ◽  
Author(s):  
Andisheh Keikha
Keyword(s):  
Relevance Feedback ◽  
Query Expansion ◽  
Web Search ◽  
State Of The Art ◽  
Feature Learning ◽  
Correct Interpretation ◽  
Search Queries ◽  
Term Selection ◽  
Semantic Expansion ◽  
Pseudo Relevance Feedback

One of the major challenges in Web search pertains to the correct interpretation of users' intent. Query Expansion is one of the well-known approaches for determining the intent of the user by addressing the vocabulary mismatch problem. A limitation of the current query expansion approaches is that the relations between the query words and the expanded words is limited. In this thesis, we capture users' intent through query expansion. We build on earlier work in the area by adopting a pseudo-relevance feedback approach; however, we advance the state of the art by proposing an approach for feature learning within the process of query expansion. In our work, we specifically consider the Wikipedia corpus as the feedback collection space and identify the best features within this context for term selection in two supervised and unsupervised models. We compare our work with state of the art query expansion techniques, the results of which show promising robustness and improved precision.

scholarly journals Mapping Distributional Semantics to Property Norms with Deep Neural Networks

2019 ◽  
Vol 3 (2) ◽  
pp. 30 ◽  
Author(s):  
Dandan Li ◽  
Douglas Summers-Stay
Keyword(s):  
Language Processing ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
Feature Space ◽  
Semantic Space ◽  
Distributional Semantics ◽  
Word Embeddings ◽  
Different Types ◽  
Property Space ◽  
Psychology Literature

Word embeddings have been very successful in many natural language processing tasks, but they characterize the meaning of a word/concept by uninterpretable “context signatures”. Such a representation can render results obtained using embeddings difficult to interpret. Neighboring word vectors may have similar meanings, but in what way are they similar? That similarity may represent a synonymy, metonymy, or even antonymy relation. In the cognitive psychology literature, in contrast, concepts are frequently represented by their relations with properties. These properties are produced by test subjects when asked to describe important features of concepts. As such, they form a natural, intuitive feature space. In this work, we present a neural-network-based method for mapping a distributional semantic space onto a human-built property space automatically. We evaluate our method on word embeddings learned with different types of contexts, and report state-of-the-art performances on the widely used McRae semantic feature production norms.

Design, Development and Comparison of Heuristic Driven Algorithms Based on the Crossed Domain Products’ Reviews for User’s Summarization

2020 ◽  
Vol 13 (5) ◽  
pp. 884-892
Author(s):  
Sartaj Ahmad ◽  
Ashutosh Gupta ◽  
Neeraj Kumar Gupta
Keyword(s):  
Decision Making ◽  
Online Shopping ◽  
Recent Time ◽  
Design Development ◽  
Different Types ◽  
Feature Based ◽  
Use Of Internet ◽  
Day By Day ◽  
Better Than

Background: In recent time, people love online shopping but before any shopping feedbacks or reviews always required. These feedbacks help customers in decision making for buying any product or availing any service. In the country like India this trend of online shopping is increasing very rapidly because awareness and the use of internet which is increasing day by day. As result numbers of customers and their feedbacks are also increasing. It is creating a problem that how to read all reviews manually. So there should be some computerized mechanism that provides customers a summary without spending time in reading feedbacks. Besides big number of reviews another problem is that reviews are not structured. Objective: In this paper, we try to design, implement and compare two algorithms with manual approach for the crossed domain Product’s reviews. Methods: Lexicon based model is used and different types of reviews are tested and analyzed to check the performance of these algorithms. Results: Algorithm based on opinions and feature based opinions are designed, implemented, applied and compared with the manual results and it is found that algorithm # 2 is performing better than algorithm # 1 and near to manual results. Conclusion: Algorithm # 2 is found better on the different product’s reviews and still to be applied on other product’s reviews to enhance its scope. Finally, it will be helpful to automate existing manual process.

scholarly journals Multiple objects tracking in the UAV system based on hierarchical deep high-resolution network

2021 ◽  
Author(s):  
Wei Huang ◽  
Xiaoshu Zhou ◽  
Mingchao Dong ◽  
Huaiyu Xu
Keyword(s):  
High Resolution ◽  
Object Tracking ◽  
High Performance ◽  
State Of The Art ◽  
Class Imbalance ◽  
Unified Framework ◽  
Multiple Objects ◽  
Tracking Process ◽  
Objects Tracking ◽  
Different Types

AbstractRobust and high-performance visual multi-object tracking is a big challenge in computer vision, especially in a drone scenario. In this paper, an online Multi-Object Tracking (MOT) approach in the UAV system is proposed to handle small target detections and class imbalance challenges, which integrates the merits of deep high-resolution representation network and data association method in a unified framework. Specifically, while applying tracking-by-detection architecture to our tracking framework, a Hierarchical Deep High-resolution network (HDHNet) is proposed, which encourages the model to handle different types and scales of targets, and extract more effective and comprehensive features during online learning. After that, the extracted features are fed into different prediction networks for interesting targets recognition. Besides, an adjustable fusion loss function is proposed by combining focal loss and GIoU loss to solve the problems of class imbalance and hard samples. During the tracking process, these detection results are applied to an improved DeepSORT MOT algorithm in each frame, which is available to make full use of the target appearance features to match one by one on a practical basis. The experimental results on the VisDrone2019 MOT benchmark show that the proposed UAV MOT system achieves the highest accuracy and the best robustness compared with state-of-the-art methods.

Learning emotional word embeddings for sentiment analysis

2021 ◽  
pp. 1-13
Author(s):  
Qingtian Zeng ◽  
Xishi Zhao ◽  
Xiaohui Hu ◽  
Hua Duan ◽  
Zhongying Zhao ◽  
...  
Keyword(s):  
Sentiment Analysis ◽  
Language Processing ◽  
State Of The Art ◽  
Research Problem ◽  
Emotional Word ◽  
Classification Model ◽  
Data Sets ◽  
Word Embeddings ◽  
Real World Data ◽  
Text Documents

Word embeddings have been successfully applied in many natural language processing tasks due to its their effectiveness. However, the state-of-the-art algorithms for learning word representations from large amounts of text documents ignore emotional information, which is a significant research problem that must be addressed. To solve the above problem, we propose an emotional word embedding (EWE) model for sentiment analysis in this paper. This method first applies pre-trained word vectors to represent document features using two different linear weighting methods. Then, the resulting document vectors are input to a classification model and used to train a text sentiment classifier, which is based on a neural network. In this way, the emotional polarity of the text is propagated into the word vectors. The experimental results on three kinds of real-world data sets demonstrate that the proposed EWE model achieves superior performances on text sentiment prediction, text similarity calculation, and word emotional expression tasks compared to other state-of-the-art models.

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