Simple Information Retrieval Research Articles

Enhancing legal research through knowledge-infused information retrieval for Vietnamese labor law

<p>The role of intelligent information retrieval systems in legal research optimization has become increasingly recognized. There are many methods for exhibiting advance mentsin the proficient retrieval of legal documents. However, those methods fail to tackle the specific challenges encountered in real-world labor law searches. This research breaks new ground in Vietnamese labor law retrieval by leveraging a comprehensive dataset of 300,000 documents a cross diverse cat egories (20 document types and 27 legal fields) to train and evaluate retrieval models specifically designed for Vietnamese labor law. Unlike previous approaches, this work goes beyond simple information retrieval. It also constructs question & answer (Q&A) dataset specifically tailored to this legal domain. Be sides, this study introduces a novel approach of in corporating a legal ontology built from the data set itself. This knowledge in fusion significantly improves retrieval performance across legal search tasks, as demon strated through rigorous experimentation. These advancements empower intelligent systems to grasp the intricate semantic nuances of Vietnamese labor law.</p>

Deep Is Better? An Empirical Comparison of Information Retrieval and Deep Learning Approaches to Code Summarization

Code summarization aims to generate short functional descriptions for source code to facilitate code comprehension. While Information Retrieval (IR) approaches that leverage similar code snippets and corresponding summaries have led the early research, Deep Learning (DL) approaches that use neural models to capture statistical properties between code and summaries are now mainstream. Although some preliminary studies suggest that IR approaches are more effective in some cases, it is currently unclear how effective the existing approaches can be in general, where and why IR/DL approaches perform better, and whether the integration of IR and DL can achieve better performance. Consequently, there is an urgent need for a comprehensive study of the IR and DL code summarization approaches to provide guidance for future development in this area. This article presents the first large-scale empirical study of 18 IR, DL, and hybrid code summarization approaches on five benchmark datasets. We extensively compare different types of approaches using automatic metrics, we conduct quantitative and qualitative analyses of where and why IR and DL approaches perform better, respectively, and we also study hybrid approaches for assessing the effectiveness of integrating IR and DL. The study shows that the performance of IR approaches should not be underestimated, that while DL models perform better in predicting tokens from method signatures and capturing structural similarities in code, simple IR approaches tend to perform better in the presence of code with high similarity or long reference summaries, and that existing hybrid approaches do not perform as well as individual approaches in their respective areas of strength. Based on our findings, we discuss future research directions for better code summarization.

Coding peekaboom: a gaming mechanism for harvesting programming concepts.

There are plentiful online programming resources that enable learners to develop an understanding of conceptual knowledge and practical implementation. However, learners, especially novices, often experience difficulties locating the required information to solve the programming problems. Differ from natural language in syntax and convention, answers for programming languages may not be found just by simple text information retrieval. To address this issue, Coding Peekaboom, a game-based tagging was developed to help adequately index the critical concepts of a code segment. An EEG device was applied to measure participants’ mental states to identify their engagement during the gameplay. Study results include the effectiveness of appropriate concepts collected by participants whereas 47.15 concepts were collected on average in a game. The brainwave analysis and the questionnaire results reveal that participants were highly engaged in the tagging task via Coding Peekaboom. Correlations were found between the state of flow and the number of concepts selected, score, and time. Finally, the results of the flow theory and personal traits were reported to reflect the user experiences in the game.

Biomedical Literature Mining and Its Components.

The published biomedical articles are the best source of knowledge to understand the importance of biomedical entities such as disease, drugs, and their role in different patient population groups. The number of biomedical literature available and being published is increasing at an exponential rate with the use of large scale experimental techniques. Manual extraction of such information is becoming extremely difficult because of the huge number of biomedical literature available. Alternatively, text mining approaches receive much interest within biomedicine by providing automatic extraction of such information in more structured format from the unstructured biomedical text. Here, a text mining protocol to extract the patient population information, to identify the disease and drug mentions in PubMed titles and abstracts, and a simple information retrieval approach to retrieve a list of relevant documents for a user query are presented. The text mining protocol presented in this chapter is useful for retrieving information on drugs for patients with a specific disease. The protocol covers three major text mining tasks, namely, information retrieval, information extraction, and knowledge discovery.

Automated Disease Inference For Community Health Care Using Graph Induced Neural Network

One of the main problems in the area of community health care is to identify the disease based on the symptoms and lack of medical vocabulary among the users. There are limited number of doctors and medical practitioners available in these forums. The lack of medical professionals requires an automated solution, but this solution must be capable of bridging the vo- cabulary gap between the users and the medical terms. Therefore a simple medical information retrieval and file sequencing might not be the solution, we need a system capable of processing natural language and convert the necessary terms to a suitable medical term thus leading to accurate disease inference. There are many excellent text mining algorithms that are available such as GRU and LSTM , these algorithms work excellently on health records and documents with accurate description of diseases. The community data is very vast and more relatable to the data which will actually be used in day to day usage of the algorithm. Graph based algorithms have been widely used fr natural language processing, with neural network’s capability or retention of good information and elimination of noise can actually be of a great use.

A escolarização como contexto de inserção nos letramentos: um estudo do currículo real em contextos contrastados através de cadernos de alunos

Nesta pesquisa coletiva, interessamo-nos pelas adaptações curriculares em escolas socialmente contrastadas (favorecidas e desfavorecidas, em função da localização das escolas) por meio dos cadernos dos alunos do 6º ano. Os conteúdos dos cadernos foram analisados através de um tratamento quantitativo das atividades realizadas, dos textos produzidos e mobilizados, mas também das operações cognitivas solicitadas. Conhecimento, atividades, textos e operações cognitivas são considerados aqui como traços do currículo real objetivados nos cadernos. Essa comparação mostra um desnível nos objetivos de aprendizagem, que podem variar de um tipo de atividade para outro (entre atividades de exercício ou de avaliação, por exemplo), mas que são repetidas e cumulativas, tanto do ponto de vista das potencialidades cognitivas da escrita, que vão da mera cópia à produção de textos, quanto do ponto de vista das operações cognitivas solicitadas. Existem também diferenças significativas no tipo de operação cognitiva solicitada, que varia da simples localização de informações a paletas mais variadas, que incluem operações como a comparação, a elaboração e a problematização. Essas diferenças contribuem para moldar, dentro das classes e de acordo com a origem social do público, os contextos de inserção no letramento, restrito ou amplo, caracterizando um sistema educativo de múltiplas velocidades e que produz assim contextos educacionais diferenciadores.

Exploratory Search for Scientific Articles

It is intuitively clear that search for scientific publications often has many characteristics of exploratory search. The purpose of this paper is to formalize this intuitive understanding, explore which scientific search tasks can be classified as research search ones, what general approaches to the research search problem exist, and how they are implemented in specialized search engines for scientists. We overview the existing works that address the information-seeking behavior of scientists and a special variant of search called exploratory search. There are several types of search behavior typical for scientists; we show that most of them are exploratory ones. Exploratory search differs from information retrieval and requires special support from the search systems. We analyze seventeen search systems for academicians (from Google Scholar, Scopus, and Web of Science to ResearchGate) from the perspective of exploratory search support. We find that most of them do not go far beyond simple information retrieval, and there is room for further improvements, especially in collaborative search support.

SPIRITuS: a SimPle Information Retrieval regressIon Test Selection approach

Context:Regression Test case Selection (RTS) approaches aim at selecting only those test cases of a test suite that exercise changed parts of the System Under Test (SUT) or parts affected by changes.Objective:We present SPIRITuS (SimPle Information Retrieval regressIon Test Selection approach). It uses method code coverage information and a Vector Space Model to select test cases to be run. In a nutshell, the extent of a lexical modification to a method is used to decide if a test case has to be selected. The main design goals of SPIRITuS are to be: (i) easy to adapt to different programming languages and (ii) tunable via an easy to understand threshold.Method:To assess SPIRITuS, we conducted a large experiment on 389 faulty versions of 14 open-source programs implemented in Java. We were mainly interested in investigating the tradeoff between the number of selected test cases from the original test suite and fault detection effectiveness. We also compared SPIRITuS against well-known RTS approaches.Results:SPIRITuS selects a number of test cases significantly smaller than the number of test cases the other approaches select at the price of a slight reduction in fault detection capability.Conclusions:SPIRITuS can be considered a viable competitor of existing test case selection approaches especially when the average number of test cases covering a modified method increases (such information can be easily derived before test case selection takes place).

Исследовательский поиск научных статей

It is intuitively clear that the search for scientific publications often has many characteristics of a research search. The purpose of this paper is to formalize this intuitive understanding, explore which research tasks of scientists can be attributed to research search, what approaches exist to solve a research search problem in general, and how they are implemented in specialized search engines for scientists. We researched existing works regarding information seeking behavior of scientists and the special variant of a search called exploratory search. There are several types of search typical for scientists, and we showed that most of them are exploratory. Exploratory search is different to information retrieval and demands special support from search systems. We analyzed seventeen actual search systems for academicians (from Google Scholar, Scopus and Web of Science to ResearchGate) from the exploratory search support aspect. We found that most of them didn’t go far from simple information retrieval and there is a room for further improvements especially in the collaborative search support.

A Composite Natural Language Processing and Information Retrieval Approach to Question Answering Using a Structured Knowledge Base

With the inception of the World Wide Web, the amount of data present on the Internet is tremendous. This makes the task of navigating through this enormous amount of data quite difficult for the user. As users struggle to navigate through this wealth of information, the need for the development of an automated system that can extract the required information becomes urgent. This paper presents a Question Answering system to ease the process of information retrieval. Question Answering systems have been around for quite some time and are a sub-field of information retrieval and natural language processing. The task of any Question Answering system is to seek an answer to a free form factual question. The difficulty of pinpointing and verifying the precise answer makes question answering more challenging than simple information retrieval done by search engines. The research objective of this paper is to develop a novel approach to Question Answering based on a composition of conventional approaches of Information Retrieval (IR) and Natural Language processing (NLP). The focus is on using a structured and annotated knowledge base instead of an unstructured one. The knowledge base used here is DBpedia and the final system is evaluated on the Text REtrieval Conference (TREC) 2004 questions dataset.

Using Automated Keyword Extraction to Facilitate Team Discovery in a Digital Forensic Investigation of Electronic Communications

A major problem that often occurs in Digital Forensics (DF) is the huge volumes of data that has to be searched, filtered, and indexed to discover patterns that could lead to forensic evidence. The nature of, and the process by which the data gets collected, implies that the data also contain information about persons that are not implicated, or only incidentally involved in the crime under investigation. Privacy is therefore an important issue that needs to be managed in a DF investigation. This paper shows that techniques used in the Team Formation (TF) task can be successfully applied to address both the problems of data volume and privacy. The TF task can be re-formulated to fit the DF arena: to commit a crime, the culprit(s) may require the assistance of several other individuals, which implies that a team of some sort gets established. During a post-mortem DF analysis, an investigator may only have one, or a few names to start with. One of the key challenges is finding possible co-conspirators. From a TF point of view, the culprit is trying to find the best team to commit the crime, given some constraints. The TF task in DF requires the recording of skill-sets, and the generation and/or discovery of a graph depicting interaction between candidates. If the data consist of an email corpus and peoples’ roles in an organisation (such as in the Enron data), both of these are readily available. In this paper we consider the TF problem in general and extend it to the DF arena by considering the information that an investigator may have access to during the investigation. We also show that simple information retrieval and keyword extraction techniques (such as RAKE) can be used to automatically discover potential teams from the data, while preserving privacy; results from a series of experiments (using the new definitions of TF and the proposed information retrieval techniques) on the Enron data is then presented.

A Novel Fuzzy Document Based Information Retrieval Model for Forecasting

Information retrieval systems are generally used to find documents that are most appropriate according to some query that comes dynamically from users. In this paper a novel Fuzzy Document based Information Retrieval Model (FDIRM) is proposed for the purpose of Stock Market Index forecasting. The novelty of proposed approach is a modified tf-idf scoring scheme to predict the future trend of the stock market index. The contribution of this paper has two dimensions, 1) In the proposed system the simple time series is converted to an enriched fuzzy linguistic time series with a unique approach of incorporating market sentiment related information along with the price and 2) A unique approach is followed while modeling the information retrieval (IR) system which converts a simple IR system into a forecasting system. From the performance comparison of FDIRM with standard benchmark models it can be affirmed that the proposed model has a potential of becoming a good forecasting model. The stock market data provided by Standard & Poor’s CRISIL NSE Index 50 (CNX NIFTY-50 index) of National Stock Exchange of India (NSE) is used to experiment and validate the proposed model. The authentic data for validation and experimentation is obtained from http://www.nseindia.com which is the official website of NSE. A java program is under construction to implement the model in real-time with graphical users’ interface.

Generation of simple structured information retrieval functions by genetic algorithm without stagnation

Abstract This paper investigates an approach to construct new ranking models for Information Retrieval. The IR ranking model depends on the document description. It includes the term frequency and document frequency. The model ranks documents upon a user request. The quality of the model is defined by the difference between the documents, which experts assess as relative to the request, and the ranked ones. To boost the model quality a modified genetic algorithm was developed. It generates models as superpositions of primitive functions and selects the best according to the quality criterion. The main impact of the research if the new technique to avoid stagnation and to control structural complexity of the consequently generated models. To solve problems of stagnation and complexity, a new criterion of model selection was introduced. It uses structural metric and penalty functions, which are defined in space of generated superpositions. To show that the newly discovered models outperform the other state-of-the-art IR scoring models the authors perform a computational experiment on TREC datasets. It shows that the resulted algorithm is significantly faster than the exhaustive one. It constructs better ranking models according to the MAP criterion. The obtained models are much simpler than the models, which were constructed with alternative approaches. The proposed technique is significant for developing the information retrieval systems based on expert assessments of the query-document relevance.

A novel fuzzy document-based information retrieval scheme (FDIRS)

Information retrieval systems are generally used to find documents that are most appropriate according to some query that comes dynamically from the users. In this paper, a novel fuzzy document-based information retrieval scheme (FDIRS) is proposed for the purpose of Stock Market Index forecasting. The novelty of the proposed approach is the use of a modified tf-idf scoring scheme to predict the future trend of the stock market index. The contribution of this paper has two dimensions: (1) In the proposed system, the simple daily time series data are converted to an enriched fuzzy linguistic time series with a unique approach of incorporating information about the manner in which the OHLC (open, high, low, and close) price formation took place at every instance of the time series, and (2) A unique approach is followed while modeling the information retrieval (IR) system which converts a simple IR system into a forecasting system. The modified IR system provides us with a trend forecast and after which a crisp value is generated that becomes the forecast value that can be achieved in next few trading sessions. From the performance comparison of FDIRS with standard benchmark models, it can be affirmed that the proposed model has a potential of becoming a good forecasting model. Transaction data of CNX NIFTY-50 index of National Stock Exchange of India are used to experiment and validate the proposed model.

IEEE Access Special Section Editorial Smart Grids: a Hub of Interdisciplinary Research

The smart grid is an important hub of interdisciplinary research where researchers from different areas of science and technology combine their efforts to enhance the traditional electrical power grid. Due to these efforts, the traditional electrical grid is now evolving. The envisioned smart grid will bring social, environmental, ethical, legal and economic benefits. Smart grid systems increasingly involve machine-to-machine communication as well as human-to-human, or simple information retrieval. Thus, the dimensionality of the system is massive. The smart grid is the combination of different technologies, including control system theory, communication networks, pervasive computing, embedded sensing devices, electric vehicles, smart cities, renewable energy sources, Internet of Things, wireless sensor networks, cyber physical systems, and green communication. Due to these diverse activities and significant attention from researchers, education activities in the smart grid area are also growing.

Exhibiting the Exhibitors: Spatial Visualization for Heterogeneous Cinema Venue Data

Cinema data is characteristically complex, heterogeneous and interlinked. Rather than relying on simple information retrieval techniques, researchers are increasingly turning to the creative exploration and reapplication of data in order to more fully explore the meaning of newly available and diverse data sets. In this context, the cinema historian becomes the creator of visual texts which can be assessed for both their interpretive insight and their aesthetic qualities. This paper presents four research projects that use different spatio-temporal visualization techniques to understand the industrial dynamics of post-war film exhibition and distribution in Australia. The research integrates work by a group of inter-disciplinary investigators into the effectiveness of techniques such as dendritic mapping, Circos circular visualizations, animation, cartogram mapping, and multivariate visualization for the study of cinema circuits and operations at a number of scales.

Combining structured and unstructured data to identify a cohort of ICU patients who received dialysis

To develop a generalizable method for identifying patient cohorts from electronic health record (EHR) data-in this case, patients having dialysis-that uses simple information retrieval (IR) tools. We used the coded data and clinical notes from the 24,506 adult patients in the Multiparameter Intelligent Monitoring in Intensive Care database to identify patients who had dialysis. We used SQL queries to search the procedure, diagnosis, and coded nursing observations tables based on ICD-9 and local codes. We used a domain-specific search engine to find clinical notes containing terms related to dialysis. We manually validated the available records for a 10% random sample of patients who potentially had dialysis and a random sample of 200 patients who were not identified as having dialysis based on any of the sources. We identified 1844 patients that potentially had dialysis: 1481 from the three coded sources and 1624 from the clinical notes. Precision for identifying dialysis patients based on available data was estimated to be 78.4% (95% CI 71.9% to 84.2%) and recall was 100% (95% CI 86% to 100%). Combining structured EHR data with information from clinical notes using simple queries increases the utility of both types of data for cohort identification. Patients identified by more than one source are more likely to meet the inclusion criteria; however, including patients found in any of the sources increases recall. This method is attractive because it is available to researchers with access to EHR data and off-the-shelf IR tools.

From an expert-driven paper guideline to a user-centred decision support system: A usability comparison study

To assess whether a user-centred prototype clinical decision support system (CDSS) providing patient-specific advice better supports healthcare practitioners in terms of (a) types of usability problems detected and (b) effective and efficient retrieval of childhood cancer survivor's follow-up screening procedures compared to an expert-driven paper-based guideline. A user-centred design (UCD) process was employed to design a prototype CDSS. Usability problems in information retrieval with the paper-based guideline were assessed by think-aloud analysis with 13 participants. Both simple and more complex tasks were applied. The analysis provided input for the UCD process of the prototype. The usability of the prototype CDSS was subsequently evaluated by think-aloud analysis with the same participants. Usability problems of the paper-based guideline and the prototype CDSS were compared by using the classification of usability problems scheme. In addition, efficiency (time to complete task) and effectiveness (completeness of retrieved screening procedures) of information retrieval of participants in the expert-driven paper-based guideline and the user-centred prototype CDSS were compared. Usability problems in both the paper-based guideline and the CDSS prototype were mainly classified as 'incongruent with participants' mental model'. The prototype CDSS reduced this type of problem from 17 to 6 problems. The time to perform simple information retrieval tasks increased by 58 s when using the prototype CDSS, however, it resulted in a 58% improvement in task completeness compared to the paper-based guideline. The time to perform complex scenarios decreased by 3:50 min with the prototype CDSS, with 17% higher completeness compared to the paper-based guideline. Analysis showed that usability problems experienced by healthcare practitioners when using a paper-based guideline could be overcome by implementing the guideline in a user-centred CDSS design. Although different types of usability problems were experienced with the prototype CDSS, they did not inhibit effective and efficient performance of tasks in the system. The usability problem analysis of the paper-based guideline effectively supported comparison of usability problems found in the two information retrieval systems and it supported the UCD of the CDSS.

Programming for Serendipity

Serendipity, the faculty of making fortunate and unexpected discoveries by accident, is recognized today as playing a key role in sparking innovation. It does not seem, however, to be manageable by any means, which has kept it as a relatively unexplored subject of research. This paper claims that, although it may not be possible to program serendipity, it is quite possible to program for serendipity, that is, to induce serendipitous insights through the use of computers. The paper begins by commenting on the nature of serendipity and on its relationship to conventional creativity, before it presents Max, a software agent that uses simple information retrieval techniques and heuristic search to wander on the Internet and uncover information aimed at stimulating serendipitous insights.

SIMPRO: simple protein homology detection method by using indirect signals

Detecting homologous proteins is one of the fundamental problems in computational biology. Many tools to solve this problem have been developed, but development of a simple, effective and generally applicable method is still desirable. We propose a simple but effective information retrieval approach, named SIMPRO, to identify homology relationship between proteins. The key idea of our approach is that by accumulating and comparing indirect signals from conventional homology search methods, the search sensitivity can be increased. We tested the idea on the problem of detecting homology relationship between Pfam families, as well as detecting structural homologs based on SCOP, and found that our method achieved significant improvement. Our results indicate that simple manipulation of conventional homology search outputs by SIMPRO algorithm can remarkably improve homology search accuracy.