Information Retrieval Process Research Articles

"Revisiting information retrieval tasks with user behavior models" by Yiqun Liu and Jiaxin Mao with Martin Vesely as coordinator

Analyzing and modeling user behavior in the Information Retrieval (IR) process is important for IR research. In this paper, we propose a new research schema in IR research. In this schema, we first investigate humans' cognitive process when completing a specific IR task and build cognitive models for that task. The findings in the investigation and the proposed cognitive models are then utilized to improve machines' performance in the IR task. Through this research schema, we revisit three IR tasks. In the first study, we carefully analyze users' clicking behavior in mobile search and propose a mobile click model to extract relevance feedback from the mobile search logs. In the second study, we conduct an eye-tracking study to investigate how human assessors read a document during relevance judgment task and adopt the findings in building a novel retrieval model that can better approximate humans' relevance judgment. In the last study, we conduct another eye-tracking study to investigate humans' reading behavior when completing the reading comprehension task. We build a prediction model for user attention and leverage the predicted attention signals to improve the machine reading comprehension model. By successfully adopting this research schema to three different IR tasks, we demonstrate its effectiveness and generalizability.

Information Interactive Design Method for Smart City Construction

Information interaction design leads the healthy development of smart city Information interaction design intersects with various disciplines and is related to “a kind of conscious information control activity”. It is committed to the reasonable control and arrangement of information, “it pays close attention to the whole process of information expression, processing, retrieval, identification and communication activities”, so as to achieve the effective, convenient and accurate information transmission.

From acceleration to rhythmicity: Smartphone-assessed movement predicts properties of music

ABSTRACTQuerying music is still a disembodied process in Music Information Retrieval. Thus, the goal of the presented study was to explore how free and spontaneous movement captured by smartphone accelerometer data can be related to musical properties. Motion features related to tempo, smoothness, size, and regularity were extracted and shown to predict the musical qualities ‘rhythmicity’ (R² = .45), ‘pitch level + range’ (R² = .06) and ‘complexity (R² = .15). We conclude that (rhythmic) music properties can be predicted from movement, and that an embodied approach to MIR is feasible.

Web indexing and its usefulness in information management and retrieval process: A discussion

Web indexing or web marketing is the concept of two decades back in the fields of service operations, communications, academics, and research. It is also increasing importance in the LIS field comparing to the traditional marketing concepts as the library services are not confined within the four walls. Users access information breaking geographical restrictions from the information technology-based automated and digitized library. Indexing and the web indexing play important roles in the information management and retrieval process. It helps in making process of the documents more accessible and transparent. The importance of web indexing is increasing because of the gather of information. Considering greater importance to this topic, web marketing and its applicability in managing and disseminating digital information, this study has been undertaken for proper establishment. Â

Word Sense Disambiguation in Tamil using Indo Wordnet and Cross-Language Semantic Similarity

Word sense disambiguation is the way to compute the correct sense of a word. It is considered as one of the important subtasks in natural language processing, machine translation and information retrieval. WSD found improving the overall performances of these systems. The job of WSD is to eliminate all senses of a word except the appropriate one as per the given context. The work in Tamil linguistics domain for information retrieval or natural language processing is very less. WSD can be performed in supervised and unsupervised manner. Here, we have proposed an unsupervised approach to disambiguate Tamil words in a given context using the context words and their dictionary gloss definitions. We have proposed two variants of our approach. The first approach uses the number of word overlapping between the glosses of context words whereas the second one uses the similarity between the glosses of context words with that of the ambiguous word. The second one found best among the two. For our approach, we have used Tamil Indo-WordNet, Oxford Tamil Dictionary and English WordNet dictionary glosses. Our method achieves better result in recognising correct senses in Tamil text.

SIFRank: A New Baseline for Unsupervised Keyphrase Extraction Based on Pre-Trained Language Model

In the age of social media, faced with a huge amount of knowledge and information, accurate and effective keyphrase extraction methods are needed to be applied in information retrieval and natural language processing. It is difficult for traditional keyphrase extraction models to contain a large amount of external knowledge information, but with the rise of pre-trained language models, there is a new way to solve this problem. Based on the above background, we propose a new baseline for unsupervised keyphrase extraction based on pre-trained language model called SIFRank. SIFRank combines sentence embedding model SIF and autoregressive pre-trained language model ELMo, and it has the best performance in keyphrase extraction for short documents. We speed up SIFRank while maintaining its accuracy by document segmentation and contextual word embeddings alignment. For long documents, we upgrade SIFRank to SIFRank+ by position-biased weight, greatly improve its performance on long documents. Compared to other baseline models, our model achieves state-of-the-art level on three widely used datasets.

PENERAPAN SISTEM INFORMASI MANAJEMEN DALAM PENGOLAHAN DATA PESERTA DIDIK DI MI NASRUL HAQ

Abstract: The purpose of this study are:1) To find out the implementation of the operational system of data processing of students by using a management information system in the Madrasah Ibtidaiyah Nasrul Haq Makassar . 2) To find out the data processing process of students in the Madrasah Ibtidaiyah Nasrul Haq Makassar. 3) To find out the impact of the use of management information systems on data processing of students in the Madrasah Ibtidaiyah Nasrul Haq Makassar . The type of this study is a qualitative field research using data collection methods, namely interviews, observation and documentation. Data processing of students in Madrasah Ibtidaiyah Nasrul Haq based on management information systems is carried out using the online EMIS application, the process of data processing of students in the Madrasah Ibtidaiyah Nasrul Haq follows four main steps, namely data collection, data input, information storage and retrieval to be used and the use of management information systems in the management of student data in Nasrul Haq Ibtidaiyah Madrasah provides benefits in the form of ease of data storage, information retrieval processes, data security and access to student information wherever and whenever needed.

Building a Semantic Health Data Warehouse in the Context of Clinical Trials: Development and Usability Study.

BackgroundThe huge amount of clinical, administrative, and demographic data recorded and maintained by hospitals can be consistently aggregated into health data warehouses with a uniform data model. In 2017, Rouen University Hospital (RUH) initiated the design of a semantic health data warehouse enabling both semantic description and retrieval of health information.ObjectiveThis study aimed to present a proof of concept of this semantic health data warehouse, based on the data of 250,000 patients from RUH, and to assess its ability to assist health professionals in prescreening eligible patients in a clinical trials context.MethodsThe semantic health data warehouse relies on 3 distinct semantic layers: (1) a terminology and ontology portal, (2) a semantic annotator, and (3) a semantic search engine and NoSQL (not only structured query language) layer to enhance data access performances. The system adopts an entity-centered vision that provides generic search capabilities able to express data requirements in terms of the whole set of interconnected conceptual entities that compose health information.ResultsWe assessed the ability of the system to assist the search for 95 inclusion and exclusion criteria originating from 5 randomly chosen clinical trials from RUH. The system succeeded in fully automating 39% (29/74) of the criteria and was efficiently used as a prescreening tool for 73% (54/74) of them. Furthermore, the targeted sources of information and the search engine–related or data-related limitations that could explain the results for each criterion were also observed.ConclusionsThe entity-centered vision contrasts with the usual patient-centered vision adopted by existing systems. It enables more genericity in the information retrieval process. It also allows to fully exploit the semantic description of health information. Despite their semantic annotation, searching within clinical narratives remained the major challenge of the system. A finer annotation of the clinical texts and the addition of specific functionalities would significantly improve the results. The semantic aspect of the system combined with its generic entity-centered vision enables the processing of a large range of clinical questions. However, an important part of health information remains in clinical narratives, and we are currently investigating novel approaches (deep learning) to enhance the semantic annotation of those unstructured data.

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

Automatic text classification is a research focus and core technology in information retrieval and natural language processing. Different from the traditional text classification methods (SVM, Bayesian, KNN), the class-center vector method is an important text classification method, which has the advantages of less calculation and high efficiency. However, the traditional class-center vector method for text classification has the disadvantages that the class vector is large and sparse, and its classification accuracy is not high because of the lack of semantic information. To overcome these problems, this paper proposes a novel class-center vector model for text classification using dependencies and a semantic dictionary. We respectively use WordNet English semantic dictionary and Tongyici Cilin Chinese semantic dictionary to cluster the English or Chinese feature words in the class-center vector and to significantly reduce the dimension of class-center vector, thereby realizing a new class-center vector for text classification using dependencies and a semantic dictionary. Experiments show that, compared with traditional text classification algorithms, the improved class-center vector method has lower time complexity and higher accuracy on the 20Newsgroups English corpus, Fudan and Sogou Chinese corpus. This paper is an improved version of our NLPCC2019 conference paper.

Implementation of Computer-Based Systems in Efficient Credit Acceptance Decisions Applying the Additive Ratio Assessment (ARAS) Method

The development of computer-based systems, increasingly helping the banking world, in doing work efficiency in a bank. Especially in selecting credit recipients at the bank. This is needed so that the resulting decision can be valued objectively and effectively for management. In processing credit receipt decisions, using an information system known as the Decision Support System (SPK). The selection of credit recipients requires alternatives and criteria so that the information retrieval process can run well. By applying the Additive Ratio Assessment (ARAS) method, decision-makers can produce better calculations. ARAS is a method for decision-based support.

Report on the 4th Joint Workshop on Bibliometric-Enhanced Information Retrieval and Natural Language Processing for Digital Libraries at SIGIR 2019

The 4 th joint BIRNDL workshop was held at the 42nd ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2019) in Paris, France. BIRNDL 2019 intended to stimulate IR researchers and digital library professionals to elaborate on new approaches in natural language processing, information retrieval, scientometrics, and recommendation techniques that can advance the state-of-the-art in scholarly document understanding, analysis, and retrieval at scale. The workshop incorporated different paper sessions and the 5 th edition of the CL-SciSumm Shared Task.

Keyword extraction: Issues and methods

AbstractDue to the considerable growth of the volume of text documents on the Internet and in digital libraries, manual analysis of these documents is no longer feasible. Having efficient approaches to keyword extraction in order to retrieve the ‘key’ elements of the studied documents is now a necessity. Keyword extraction has been an active research field for many years, covering various applications in Text Mining, Information Retrieval, and Natural Language Processing, and meeting different requirements. However, it is not a unified domain of research. In spite of the existence of many approaches in the field, there is no single approach that effectively extracts keywords from different data sources. This shows the importance of having a comprehensive review, which discusses the complexity of the task and categorizes the main approaches of the field based on the features and methods of extraction that they use. This paper presents a general introduction to the field of keyword/keyphrase extraction. Unlike the existing surveys, different aspects of the problem along with the main challenges in the field are discussed. This mainly includes the unclear definition of ‘keyness’, complexities of targeting proper features for capturing desired keyness properties and selecting efficient extraction methods, and also the evaluation issues. By classifying a broad range of state-of-the-art approaches and analysing the benefits and drawbacks of different features and methods, we provide a clearer picture of them. This review is intended to help readers find their way around all the works related to keyword extraction and guide them in choosing or designing a method that is appropriate for the application they are targeting.

Combining a context aware neural network with a denoising autoencoder for measuring string similarities

Measuring similarities between strings is central for many established and fast-growing research areas, including information retrieval, biology, and natural-language processing. The traditional approach to string similarity measurements is to define a metric with respect to a word space that quantifies and sums up the differences between characters in two strings; surprisingly, these metrics have not evolved a great deal over the past few decades. Indeed, the majority of them are still based on making a simple comparison between character and character distributions without considering the words context. This paper proposes a string metric that encompasses similarities between strings based on (1) the character similarities between the words, including non-standard and standard spellings of the same words, and (2) the context of these words. We propose a neural network composed of a denoising autoencoder and what we call a context encoder, both specifically designed to find similarities between the words based on their context. Experimental results show that the resulting metrics have succeeded in 85.4% of the cases in finding the correct version of a non-standard spelling among the closest words, compared to 63.2% using the established Normalised-Levenshtein distance. We also show that by employing our approach, the words used in similar context are calculated to be more similar than words with different contexts, which is a desirable property lacking in established string metrics.

New labeled dataset of interconnected lexical typos for automatic correction in the bug reports

Large-scale and especially open-source projects use software triage systems like Bugzilla to manage their user’s requests like bugs, suggestions, and requirements. The software triage systems have many tasks like prioritizing, finding duplicate and assigning bug reports to developers automatically, which needs text mining, information retrieval, and natural language processing techniques. We already showed there are many typos in the bug reports which reduce the performance of artificial intelligence techniques. The connected terms were one of the most types of typos in the context of bug reports. Also, we introduce some algorithms to correct the connected terms earlier, but there was not any labeled dataset that can be used to evaluate the accuracy of process of typo correction. Now we made a new labeled dataset including 42,970 typos between 182,096 to can be used for the typo correction evaluation process. There are 52% connected typos in the labeled dataset, which show the previous results about the number of connected typos were correct. Then we used the typo correction algorithms which were introduced in prior studies to evaluate their accuracy. The experimental results show 81.6% and 83.3% accuracy in top-5 and top-10 suggestions of the list of typo corrections, respectively.

Health Insurance Data Processing using Linked Data

The data in the organization is distributed among multiple structured databases. The large database makes the process of risk analysis difficult, as data is distributed in the organization. Information gathering for Risk analysis is more subjective and therefore, processing incomplete information over distributed databases increase more fault in risk analysis. Linked Data representation helps to make structured, distributed data more related, combined and ready to be processed. Linked Data approach makes data interlinked and semantically rich, extracting meaning with the use of machines and eliminating the human subjectivity factor in assessing insurance risk. Using Linked data, information retrieval process can be easier as data or databases interlinked semantically. The proposed technique uses a linked data approach for risk analysis and related information retrieval methods over structured data. The work efficiency is also tested and found to be good.

A Bio-inspired Modified PSO Strategy for Effective Web Information Retrieval using RCV1 Datasets

Information retrieval is a key technology in accessing the vast amount of data present on today’s World Wide Web. Numerous challenges arise at various stages of information retrieval from the web, such as missing of plenteous relevant documents, static user queries, ever changing and tremendous amount of document collection and so forth. Therefore, more powerful strategies are required to search for relevant documents. In this paper, a PSO methodology is proposed which is hybridized with Simulated Annealing with the aim of optimizing Web Information Retrieval (WIR) process. Hybridized PSO has a high impact on reducing the query response time of the system and hence subsidizes the system efficiency. A novel similarity measure called SMDR acts as a fitness function in the hybridized PSO-SA algorithm. Evaluations measures such as accuracy, MRR, MAP, DCG, IDCG, F-measure and specificity are used to measure the effectiveness of the proposed system and to compare it with existing system as well. Ultimately, experiments are extensively carried out on a huge RCV1 collections. Achieved precision-recall rates demonstrate the considerably improved effectiveness of the proposed system than that of existing one.

Application of Pitch Derived Parameters to Speech and Monophonic Singing Classification

Speech and singing voice discrimination is an important task in the speech processing area given that each type of voice requires different information retrieval and signal processing techniques. This discrimination task is hard even for humans depending on the length of voice segments. In this article, we present an automatic speech and singing voice classification method using pitch parameters derived from musical note information and f 0 stability analysis. We applied our method to a database containing speech and a capella singing and compared the results with other discrimination techniques based on information derived from pitch and spectral envelope. Our method obtains good results discriminating both voice types, is efficient, has good generalisation capabilities and is computationally fast. In the process, we have also created a note detection algorithm with parametric control of the characteristics of the notes it detects. We compared the agreement of this algorithm with a state-of-the-art note detection algorithm and performed an experiment that proves that speech and singing discrimination parameters can represent generic information about the music style of the singing voice.

AggChecker

We demonstrate AggChecker, a novel tool for verifying textual summaries of relational data sets. The system automatically verifies natural language claims about numerical aggregates against the underlying raw data. The system incorporates a combination of natural language processing, information retrieval, machine learning, and efficient query processing strategies. Each claim is translated into a semantically equivalent SQL query and evaluated against the database. Our primary goal is analogous to that of a spell-checker: to identify erroneous claims and provide guidance in correcting them. In this demonstration, we show that our system enables users to verify text summaries much more efficiently than a standard SQL interface.

A reproducible survey on word embeddings and ontology-based methods for word similarity: Linear combinations outperform the state of the art

Human similarity and relatedness judgements between concepts underlie most of cognitive capabilities, such as categorisation, memory, decision-making and reasoning. For this reason, the proposal of methods for the estimation of the degree of similarity and relatedness between words and concepts has been a very active line of research in the fields of artificial intelligence, information retrieval and natural language processing among others. Main approaches proposed in the literature can be categorised in two large families as follows: (1) Ontology-based semantic similarity Measures (OM) and (2) distributional measures whose most recent and successful methods are based on Word Embedding (WE) models. However, the lack of a deep analysis of both families of methods slows down the advance of this line of research and its applications. This work introduces the largest, reproducible and detailed experimental survey of OM measures and WE models reported in the literature which is based on the evaluation of both families of methods on a same software platform, with the aim of elucidating what is the state of the problem. We show that WE models which combine distributional and ontology-based information get the best results, and in addition, we show for the first time that a simple average of two best performing WE models with other ontology-based measures or WE models is able to improve the state of the art by a large margin. In addition, we provide a very detailed reproducibility protocol together with a collection of software tools and datasets as supplementary material to allow the exact replication of our results.

Text Filtering through Multi-Pattern Matching: A Case Study of Wu–Manber–Uy on the Language of Uyghur

Given its generality in applications and its high time-efficiency on big data-sets, in recent years, the technique of text filtering through pattern matching has been attracting increasing attention from the field of information retrieval and Natural language Processing (NLP) research communities at large. That being the case, however, it has yet to be seen how this technique and its algorithms, (e.g., Wu–Manber, which is also considered in this paper) can be applied and adopted properly and effectively to Uyghur, a low-resource language that is mostly spoken by the ethnic Uyghur group with a population of more than eleven-million in Xinjiang, China. We observe that technically, the challenge is mainly caused by two factors: (1) Vowel weakening and (2) mismatching in semantics between affixes and stems. Accordingly, in this paper, we propose Wu–Manber–Uy, a variant of an improvement to Wu–Manber, dedicated particularly for working on the Uyghur language. Wu–Manber–Uy implements a stem deformation-based pattern expansion strategy, specifically for reducing the mismatching of patterns caused by vowel weakening and spelling errors. A two-way strategy that applies invigilation and control on the change of lexical meaning of stems during word-building is also used in Wu–Manber–Uy. Extra consideration with respect to Word2vec and the dictionary are incorporated into the system for processing Uyghur. The experimental results we have obtained consistently demonstrate the high performance of Wu–Manber–Uy.