Involving Information Retrieval Research Articles

SynoExtractor: A Novel Pipeline for Arabic Synonym Extraction Using Word2Vec Word Embeddings

Automatic synonym extraction plays an important role in many natural language processing systems, such as those involving information retrieval and question answering. Recently, research has focused on extracting semantic relations from word embeddings since they capture relatedness and similarity between words. However, using word embeddings alone poses problems for synonym extraction because it cannot determine whether the relation between words is synonymy or some other semantic relation. In this paper, we present a novel solution for this problem by proposing the SynoExtractor pipeline, which can be used to filter similar word embeddings to retain synonyms based on specified linguistic rules. Our experiments were conducted using KSUCCA and Gigaword embeddings and trained with CBOW and SG models. We evaluated automatically extracted synonyms by comparing them with Alma’any Arabic synonym thesauri. We also arranged for a manual evaluation by two Arabic linguists. The results of experiments we conducted show that using the SynoExtractor pipeline enhances the precision of synonym extraction compared to using the cosine similarity measure alone. SynoExtractor obtained a 0.605 mean average precision (MAP) for the King Saud University Corpus of Classical Arabic with 21% improvement over the baseline and a 0.748 MAP for the Gigaword corpus with 25% improvement. SynoExtractor outperformed the Sketch Engine thesaurus for synonym extraction by 32% in terms of MAP. Our work shows promising results for synonym extraction suggesting that our method can also be used with other languages.

Approximate Order-Sensitive k-NN Queries over Correlated High-Dimensional Data

The k Nearest Neighbor ( k -NN) query has been gaining more importance in extensive applications involving information retrieval, data mining, and databases. Specifically, in order to trade off accuracy for efficiency, approximate solutions for the k -NN query are extensively explored. However, the precision is usually order-insensitive, which is defined on the result set instead of the result sequence. In many situations, it cannot reasonably reflect the query result quality. In this paper, we focus on the approximate k -NN query problem with the order-sensitive precision requirement and propose a novel scheme based on the projection-filter-refinement framework. Basically, we adopt PCA to project the high-dimensional data objects into the low-dimensional space. Then, a filter condition is inferred to execute efficient pruning over the projected data. In addition, an index strategy named OR-tree is proposed to reduce the I/O cost. The extensive experiments based on several real-world data sets and a synthetic data set are conducted to verify the effectiveness and efficiency of the proposed solution. Compared to the state-of-the-art methods, our method can support order-sensitive k -NN queries with higher result precision while retaining satisfactory CPU and I/O efficiency.

VisualUrText: A Text Analytics Tool for Unstructured Textual Data

The growing amount of unstructured text over Internet is tremendous. Text repositories come from Web 2.0, business intelligence and social networking applications. It is also believed that 80-90% of future growth data is available in the form of unstructured text databases that may potentially contain interesting patterns and trends. Text Mining is well known technique for discovering interesting patterns and trends which are non-trivial knowledge from massive unstructured text data. Text Mining covers multidisciplinary fields involving information retrieval (IR), text analysis, natural language processing (NLP), data mining, machine learning statistics and computational linguistics. This paper discusses the development of text analytics tool that is proficient in extracting, processing, analyzing the unstructured text data and visualizing cleaned text data into multiple forms such as Document Term Matrix (DTM), Frequency Graph, Network Analysis Graph, Word Cloud and Dendogram. This tool, VisualUrText, is developed to assist students and researchers for extracting interesting patterns and trends in document analyses.

A topological embedding of the lexicon for semantic distance computation

AbstractWe show how a quantitative context may be established for what is essentially qualitative in nature by topologically embedding a lexicon (here, WordNet) in a complete metric space. This novel transformation establishes a natural connection between the order relation in the lexicon (e.g., hyponymy) and the notion of distance in the metric space, giving rise to effective word-level and document-level lexical semantic distance measures. We provide a formal account of the topological transformation and demonstrate the value of our metrics on several experiments involving information retrieval and document clustering tasks.

Research intelligence involving information retrieval – An example of conferences and journals

This paper reports a work that was intended to reveal the connection between topics investigated by conference papers and journal papers. This work selected hundreds of papers in data mining and information retrieval from well-known databases and showed that the topics covered by conference papers in a year often leads to similar topics covered by journal papers in the subsequent year and vice versa. This study used some existing algorithms and combination of these algorithms to proposed a new detective procedure for the researchers to detect the new trend and get the academic intelligence from conferences and journals. The goal of this research is fourfold: First, the research investigates if the conference papers’ themes lead the journal papers’. Second, the research examines how the new research themes can be identified from the conference papers. Third, the research looks at a specific area such as information retrieval and data mining as an illustration. Fourth, the research studies any inconsistencies of the correlation between the conference papers and the journal papers. This study explores the connections between the academic publications. The methodologies of information retrieval and data mining can be exploited to discover the relationships between published papers among all topics. By discovering the connections between conference papers and journal papers, researchers can improve the effectiveness of their research by identifying academic intelligence. This study discusses how conference papers and journal papers are related. The topics of conference papers are identified to determine whether they represent new trend discussed in journal papers. An automatic examination procedure based on information retrieval and data mining is also proposed to minimize the time and human resources required to predict further research developments. This study develops a new procedure and collects a dataset to verify those problems. Analytical results demonstrate that the conference papers submitted to journals papers are similar each year. Conference papers certainly affect the journal papers published over three years. About 87.23% of data points from papers published in 1991–2007 support our assumption. The research is intended to help researchers identify new trend in their research fields, and focus on the urgent topics. This is particularly valuable for new researchers in their field, or those who wish to perform cross-domain studies.

KaDo: An Advanced Enterprise Modeling, Database Design, Database Implementation, and Information Retrieval Case for the Accounting Information Systems Class

ABSTRACT: The KaDo case, designed for either an advanced undergraduate AIS class or a graduate AIS class, has four parts: enterprise modeling, relational database design, integration of legacy systems, and information retrieval. The first part exposes students to advanced enterprise modeling concepts such as subtypes and advanced REA concepts such as policy specifications. The second part asks students to translate advanced modeling concepts such as subtypes into relational database structures. The objective of the third part is for students to learn how to use database software to integrate legacy systems. Their task involves two steps: importing data and integrating data. The fourth part involves information retrieval where students map information requirements into queries that can be executed by the computer. The advanced query techniques that students apply include multi-column join definitions, union, and view hierarchies. KaDo is a dynamic case that allows instructors to create a version of the case adapted to their own learning objectives. KaDo's Web Environment (KWE) further enables them to create and deliver case materials online.

Arabic Text Data Mining: a Root-Based Hierarchical Indexing Model

The world has recently witnessed a tremendous growth in the volume of text documents available on the Internet, digital libraries, news sources, and company-wide intranets. Text data mining, as a multidisciplinary field involving information retrieval, text analysis, information extraction, clustering, categorization, linguistics, database technology, machine learning, and data mining, is becoming more significant, and efforts have been intensified in studies like information retrieval, practical applications of which are becoming more and more necessary to end users and to the scientific community itself, in order to fetch the increasingly available information efficiently. In the past few years, not only have new documents been produced directly in digital form, thus being suitable for automatic indexing, but also many of the older documents have been ported from their physical medium to the digital one. The meaning of a document is represented by a vector of features, which are weighted according to a measure that best estimate relevance. Text categorization presents unique challenges due to the large number of attributes present in the data set, large number of training samples, and attributes dependencies. This article focuses on speeding up the information retrieval process in Arabic document base by using a root-based hierarchical indexing model. Simulation results demonstrated that speed gain in the range of 50-100 can be achieved for typical queries.

Personal assistants

Already there are simple personal agents to help with some of our shopping. There are agents to track stocks in our portfolios, advise us on how to use particular software products, and arrange meetings within corporate workgroups. However, none of these agents takes more than one aspect of our activities into account, nor do they adapt easily to our preferences. Personal assistants, on the other hand, are agents that can represent individuals on the Web. They help users in their day-to-day activities, especially those involving information retrieval, negotiation, or coordination. A personal assistant might schedule a meeting and then, based on the meeting location, find the nearest babysitting service or the ATM with the lowest transaction fee. The paper discusses the interaction framework, negotiation, profiles and standards.