Corpus-Based Text Retrieval and Adaptation for Learning System
The algorithm to adapt lexical complexity in the news article which can be used as materials for learning language presented in the paper. We consider words substitution retrieval according to wordnet-based and corpus-based semantic relatedness. Two corpus-based similarity measures empirically tested: Vector Space Model and Distributional Semantic Model. This language processing algorithm has created as a client-server application. It retrieves appropriate text from Web-resource. Next it performs adaptation procedure
Formal Concept Analysis (FCA) is a mathematically well-founded theory aimed at data analysis and classication, introduced and detailed in the book of Bernhard Ganter and Rudolf Wille, \Formal Concept Analysis", Springer 1999. The area came into being in the early 1980s and has since then spawned over 10000 scientic publications and a variety of practically deployed tools. FCA allows one to build from a data table with objects in rows and attributes in columns a taxonomic data structure called concept lattice, which can be used for many purposes, especially for Knowledge Discovery and Information Retrieval. The \Formal Concept Analysis Meets Information Retrieval" (FCAIR) workshop collocated with the 35th European Conference on Information Retrieval (ECIR 2013) was intended, on the one hand, to attract researchers from FCA community to a broad discussion of FCA-based research on information retrieval, and, on the other hand, to promote ideas, models, and methods of FCA in the community of Information Retrieval. This volume contains 11 contributions to FCAIR workshop (including 3 abstracts for invited talks and tutorial) held in Moscow, on March 24, 2013. All submissions were assessed by at least two reviewers from the program committee of the workshop to which we express our gratitude. We would also like to thank the co-organizers and sponsors of the FCAIR workshop: Russian Foundation for Basic Research, National Research University Higher School of Economics, and Yandex.
This book constitutes the refereed proceedings of the 20th International Symposium on String Processing and Information Retrieval, SPIRE 2013, held in Jerusalem, Israel, in October 2013. The 18 full papers, 10 short papers were carefully reviewed and selected from 60 submissions. The program also featured 4 keynote speeches. The following topics are covered: fundamentals algorithms in string processing and information retrieval; SP and IR techniques as applied to areas such as computational biology, DNA sequencing, and Web mining.
Recommender systems are becoming an inseparable part of many modern Internet web sites and web shops. The quality of recommendations made may significantly influence the browsing experience of the user and revenues made by web site owners. Developers can choose between a variety of recommender algorithms; unfortunately no general scheme exists for evaluation of their recall and precision. In this chapter, the authors propose a method based on cross-validation for diagnosing the strengths and weaknesses of recommender algorithms. The method not only splits initial data into a training and test subsets, but also splits the attribute set into a hidden and visible part. Experiments were performed on a user-based and item-based recommender algorithm. These algorithms were applied to the MovieLens dataset, and the authors found classical user-based methods perform better in terms of recall and precision.
This paper discusses approaches to the selection of keywords, used for information extraction of event frames. In particular, the innovation event is associated with different lexical items in different areas of knowledge. The paper evaluated the contribution of general and specific vocabulary in the representation of the frame in a particular subject area.
Name matching is a key component of systems for entity resolution or record linkage. Alternative spellings of the same names are a com- mon occurrence in many applications. We use the largest collection of genealogy person records in the world together with user search query logs to build name matching models. The procedure for building a crowd-sourced training set is outlined together with the presentation of our method. We cast the problem of learning alternative spellings as a machine translation problem at the character level. We use in- formation retrieval evaluation methodology to show that this method substantially outperforms on our data a number of standard well known phonetic and string similarity methods in terms of precision and re- call. Additionally, we rigorously compare the performance of standard methods when compared with each other. Our result can lead to a significant practical impact in entity resolution applications.
A vast amount of documents in the Web have duplicates, which is a challenge for developing efficient methods that would compute clusters of similar documents. In this paper we use an approach based on computing (closed) sets of attributes having large support (large extent) as clusters of similar documents. The method is tested in a series of computer experiments on large public collections of web documents and compared to other established methods and software, such as biclustering, on same datasets. Practical efficiency of different algorithms for computing frequent closed sets of attributes is compared.
Formal Concept Analysis (FCA) is an unsupervised clustering technique and many scientific papers are devoted to applying FCA in Information Retrieval (IR) research. We collected 103 papers published between 2003-2009 which mention FCA and information retrieval in the abstract, title or keywords. Using a prototype of our FCA-based toolset CORDIET, we converted the pdf-files containing the papers to plain text, indexed them with Lucene using a thesaurus containing terms related to FCA research and then created the concept lattice shown in this paper. We visualized, analyzed and explored the literature with concept lattices and discovered multiple interesting research streams in IR of which we give an extensive overview. The core contributions of this paper are the innovative application of FCA to the text mining of scientific papers and the survey of the FCA-based IR research.
Doctoral students were invited to the Doctoral Consortium held in conjunction with the main conference of ECIR 2013. The Doctoral Consortium aimed to provide a constructive setting for presentations and discussions of doctoral students’ research projects with senior researchers and other participating students. The two main goals of the Doctoral Consortium were: 1) to advise students regarding current critical issues in their research; and 2) to make students aware of the strengths and weakness of their research as viewed from different perspectives. The Doctoral Consortium was aimed for students in the middle of their thesis projects; at minimum, students ought to have formulated their research problem, theoretical framework and suggested methods, and at maximum, students ought to have just initiated data analysis. The Doctoral Consortium took place on Sunday, March 24, 2013, at the ECIR 2013 venue, and participation is by invitation only. The format was designed as follows: The doctoral students presents summaries of their work to other participating doctoral students and the senior researchers. Each presentation was followed by a plenary discussion, and individual discussion with one senior advising researcher. The discussions in the group and with the advisors were intended to help the doctoral student to reflect on and carry on with their thesis work.