Information Retrieval Topics Research Articles

Autumn School for Information Retrieval and Information Foraging (ASIRF 2017)

The Autumn School for Information Retrieval and Information Foraging (ASIRF) was held October 1-6, 2017 in Wadern, Germany. The school provided students with a review of topics in information retrieval and information behavior through lectures, student presentations, and a student project. ASIRF attracted thirty-one participants, from Asia, Europe, and North and South America.

RETRIEVAL—An Online Performance Evaluation Tool for Information Retrieval Methods

Performance evaluation is one of the main research topics in information retrieval. Evaluation metrics are used to quantify various performance aspects of a retrieval method. These metrics assist in identifying the optimum method for a specific retrieval challenge but also to allow its parameters fine-tuning in order to achieve a robust operation for a given set of requirements specification. In this work, we present RETRIEVAL, a Web-based integrated information retrieval performance evaluation platform. It offers a number of metrics that are popular within the scientific community, so as to compose an efficient framework for implementing performance evaluation. We discuss the functionality of RETRIEVAL by citing important aspects such as the data input approaches, the user-level performance metrics parameterization, the evaluation scenarios, the interactive plots, and the performance reports repository that offers both archiving and download functionalities.

A Two-Stage Machine learning approach for temporally-robust text classification

One of the most relevant research topics in Information Retrieval is Automatic Document Classification (ADC). Several ADC algorithms have been proposed in the literature. However, the majority of these algorithms assume that the underlying data distribution does not change over time. Previous work has demonstrated evidence of the negative impact of three main temporal effects in representative datasets textual datasets, reflected by variations observed over time in the class distribution, in the pairwise class similarities and in the relationships between terms and classes [1]. In order to minimize the impact of temporal effects in ADC algorithms, we have previously introduced the notion of a temporal weighting function (TWF), which reflects the varying nature of textual datasets. We have also proposed a procedure to derive the TWF’s expression and parameters. However, the derivation of the TWF requires the running of explicit and complex statistical tests, which are very cumbersome or can not even be run in several cases. In this article, we propose a machine learning methodology to automatically learn the TWF without the need to perform any statistical tests. We also propose new strategies to incorporate the TWF into ADC algorithms, which we call temporally-aware classifiers. Experiments showed that the fully-automated temporally-aware classifiers achieved significant gains (up to 17%) when compared to their non-temporal counterparts, even outperforming some state-of-the-art algorithms (e.g., SVM) in most cases, with large reductions in execution time.

10th European Summer School in Information Retrieval (ESSIR 2015)

The 10th European Summer School in Information Retrieval (ESSIR 2015) was held in Thessaloniki, Greece between August 31 and September 4, 2015. The summer school offered high quality lectures on 13 topics in Information Retrieval and related areas, a new edition of the Symposium on Future Directions in Information Access (FDIA), group activities, and a rich social programme. This report provides an overview of a successful international summer school that attracted a total of 51 participants from a wide range of countries in Europe and North America.

Evaluation methodologies in information retrieval dagstuhl seminar 13441

This paper reports on the Evaluation Methodologies in Information Retrieval Seminar [1] held from 27 October to 1 November 2013 at the Schloss Dagstuhl Leibniz Center for Informatics that is a world-wide renowned venue for informatics where scientists come together to exchange their knowledge and to discuss their research findings. Dagstuhl offers modern facilities and is located in the beautiful countryside of Saarland, Germany. Schloss Dagstuhl has previously hosted other seminars on information retrieval topics, e.g. the Seminar 09101 on Interactive Information Retrieval which took place in 2009, but this one was the first one devoted to the specific topic of information retrieval evaluation. The seminar was attended by 42 participants from thirteen different countries, including a large number of established researchers as well as some some promising young researchers, and also practitioners from industry. As it is well-known evaluation of information retrieval (IR) systems has a long tradition (see, for example, [2]), however, the test-collection based evaluation paradigm is of limited value for assessing today’s IR applications, since it fails to address major aspects of the IR process. Thus there is a need for new evaluation methodologies, which are able to deal with the following issues:

A Note on Optimal Function for Similarity Measure in Ontology Application

Ontology similarity calculation and ontology mapping are important research topics in information retrieval. One method for ontology similarity measure is using multi-dividing approach. Assume that the notation and terminology used but undefined in this paper can be found in [4] and [5]. We show that the assumption of strict ordering of xi*can be relaxed to allow some ties in the likelihood ratio. The same proof remains true if we consider equivalence classes defined by the likelihood ratio and relabel xi* as its equivalence class denoted by [xi*].

Supervised Multidimensional Scaling for Ontology Similarity Measure and Ontology Mapping via Multi-Dividing

Ontology similarity calculation and ontology mapping are crucial research topics in information retrieval. Ontology, as a concept structure model, is also widely used in biology, physics, geography and social sciences. In this paper, we propose new algorithms for ontology similarity measurement and ontology mapping using supervised multidimensional scaling and multi-dividing technologies. Two experimental results show that the proposed new algorithm has high accuracy and efficiency on ontology similarity calculation and ontology mapping in certain applications.

Term weighting for information retrieval based on term’s discrimination power

One of the most important research topics in Information Retrieval is term weighting for document ranking and retrieval, such as TFIDF, BM25, etc. We propose a term weighting method that utilizes past retrieval results consisting of the queries that contain a particular term, retrieval documents, and their relevance judgments. A term's Discrimination Power(DP) is based on the difference degree of the term's average weights obtained from between relevant and non-relevant retrieved document sets. The difference based DP performs better compared to ratio based DP introduced in the previous research. Our experimental result shows that a term weighting scheme based on the discrimination power method outperforms a TF*IDF based scheme.

A RELATIONAL RANKING METHOD WITH GENERALIZATION ANALYSIS

Recently, learning to rank, which aims at constructing a model for ranking objects, is one of the hot research topics in information retrieval and machine learning communities. Most of existing learning to rank approaches are based on the assumption that each object is independently and identically distributed. Although this assumption simplifies ranking problems, the implicit interconnections between objects are ignored. In this paper, a graph based ranking framework is proposed, which takes advantage of implicit correlations between objects. Furthermore, the derived relational ranking algorithm from this framework, called GRSVM, is developed based on the conventional algorithm RankSVM-primal. In addition, generalization properties of different relational ranking algorithms are analyzed using Rademacher Average. Based on the analysis, we find that GRSVM can achieve tighter generalization bound than existing relational ranking algorithms in most cases. Finally, a comparison of experimental results produced by improved and conventional algorithms shows the superior performance of the former.

Ontology Similarity Measure and Ontology Mapping via Learning Optimization SimilarityFunction

Ontology similarity calculation and ontology mapping are important research topics in information retrieval. By learning optimization similarity function, we propose the new algorithm for ontology similarity measure and ontology mapping. The stability of ontology algorithms is studied by adopting a strategy which adjusts the sample set by deleting one or two element from it. Relationship between uniform loss stability and uniform score stability is investigated. A sufficient condition for uniform score stability is given. The result of our work shows that if for any (v,v') ∈ V × V, a kernel function K((v,v'), (v,v')) has a limited upper bound, then the ontology algorithm which minimizes the regularization empirical l-error will have good uniform score stability. Also, two experiments results show that the proposed algorithm has high accuracy and efficiency for similarity calculation and ontology mapping. Index Terms—Ontology, similarity calculation, ontology mapping.

Opinion Search and Retrieval from WWW

Opinion retrieval has established itself as an important part of search engines ratings, opinion trends and representative opinions enrich the search experience ofusers when combined with traditional document retrieval by revealing more insights about a subject.In the past years we have witnessed Sentiment Analysis and OpinionMining becoming increasingly popular topics in InformationRetrieval and Web data analysis.With the rapid growth of the user-generated content on the Web.Opinion retrieval is a document retrieving and ranking process, a relevant document must be relevant to the query and contain opinions toward the query. Opinion polarity classification is an extension of opinion retrieval; it classifies the retrieved document as positive, negative or mixed, according to the overall polarity of the query relevant opinions in the document. In this study, we review the development of opinion search and retrieval during the last years, and also discuss the evolution of a relatively newresearch directionand we try to layout the futureresearch directions in the field.

Survey on mining subjective data on the web

In the past years we have witnessed Sentiment Analysis and Opinion Mining becoming increasingly popular topics in Information Retrieval and Web data analysis. With the rapid growth of the user-generated content represented in blogs, wikis and Web forums, such an analysis became a useful tool for mining the Web, since it allowed us to capture sentiments and opinions at a large scale. Opinion retrieval has established itself as an important part of search engines. Ratings, opinion trends and representative opinions enrich the search experience of users when combined with traditional document retrieval, by revealing more insights about a subject. Opinion aggregation over product reviews can be very useful for product marketing and positioning, exposing the customers' attitude towards a product and its features along different dimensions, such as time, geographical location, and experience. Tracking how opinions or discussions evolve over time can help us identify interesting trends and patterns and better understand the ways that information is propagated in the Internet. In this study, we review the development of Sentiment Analysis and Opinion Mining during the last years, and also discuss the evolution of a relatively new research direction, namely, Contradiction Analysis. We give an overview of the proposed methods and recent advances in these areas, and we try to layout the future research directions in the field.

A novel approach to the extraction of roots from Arabic words using bigrams

AbstractRoot extraction is one of the most important topics in information retrieval (IR), natural language processing (NLP), text summarization, and many other important fields. In the last two decades, several algorithms have been proposed to extract Arabic roots. Most of these algorithms dealt with triliteral roots only, and some with fixed length words only. In this study, a novel approach to the extraction of roots from Arabic words using bigrams is proposed. Two similarity measures are used, the dissimilarity measure called the “Manhattan distance,” and Dice's measure of similarity. The proposed algorithm is tested on the Holy Qu'ran and on a corpus of 242 abstracts from the Proceedings of the Saudi Arabian National Computer Conferences. The two files used contain a wide range of data: the Holy Qu'ran contains most of the ancient Arabic words while the other file contains some modern Arabic words and some words borrowed from foreign languages in addition to the original Arabic words. The results of this study showed that combining N‐grams with the Dice measure gives better results than using the Manhattan distance measure.

A Multi-User 3-D Virtual Environment with Interactive Collaboration and Shared Whiteboard Technologies

A multi-user 3-D virtual environment allows remote participants to have a transparent communication as if they are communicating face-to-face. The sense of presence in such an environment can be established by representing each participant with a vivid human-like character called an avatar. We review several immersive technologies, including directional sound, eye gaze, hand gestures, lip synchronization and facial expressions, that facilitates multimodal interaction among participants in the virtual environment using speech processing and animation techniques. Interactive collaboration can be further encouraged with the ability to share and manipulate 3-D objects in the virtual environment. A shared whiteboard makes it easy for participants in the virtual environment to convey their ideas graphically. We survey various kinds of capture devices used for providing the input for the shared whiteboard. Efficient storage of the whiteboard session and precise archival at a later time bring up interesting research topics in information retrieval.

Morpho-syntactic parsing for a text mining environment: An NP recognition model for knowledge visualization and information retrieval

Sidhom and Hassoun discuss the crucial role of NLP (Natural Language Processing) tools in Knowledge Extraction and Management as well as in the design of Information Retrieval Systems. The authors focus more specifically on the morpho-syntactic issues by describing their morpho-syntactic analysis platform, which has been implemented to cover the automatic indexing and information retrieval topics. To this end they implemented the Cascaded Augmented Transition Network (ATN). They used this formalism in order to analyse French text descriptions of Multimedia documents. An implementation of an ATN parsing automaton is briefly described. The Platform in its logical operation is considered as an investigative tool towards the knowledge organization (based on an NP -Noun Phrase- recognition model) and management of multiform e(electronic)-documents (text, multimedia, audio, image) using their text descriptions.