Search Queries in an Information Retrieval System for Arabic-Language Texts

Information retrieval aims to extract from a large collection of data a subset of information that is relevant to user’s needs. In this study, we are interested in information retrieval in Arabic-Language text documents. We focus on the Arabic language, its morphological features that potentially impact the implementation and performance of an information retrieval system and its unique characters that are absent in the Latin alphabet and require specialized approaches. Specifically, we report on the design, implementation and evaluation of the search functionality using the Vector Space Model with several weighting schemes. Our implementation uses the ISRI stemming algorithms as the underlying stemming technique and the general Arabic stop word list for building inverted indices for Arabic-language documents. We evaluate our implementation on a corpus consisting of selected technical papers published in Arabic-language journals. We use the Open Journal Systems (OJS) from the Public Knowledge Project as a repository for the corpus used in the evaluation. We evaluate the performance of our implementation of the search using a classic recall/precision approach and compare it to one of the default multilingual search functions supported in the OJS. Our experimental analysis suggests that stemming is an effective technique for searches in Arabic-language texts that improves the quality of the information retrieval system

The TXM Portal Software giving access to Old French Manuscripts Online

Texte intégral en ligne : http://www.lrec-conf.org/proceedings/lrec2012/workshops/13.ProceedingsCultHeritage.pdfInternational audiencehttp://www.lrec-conf.org/proceedings/lrec2012/workshops/13.ProceedingsCultHeritage.pdf This paper presents the new TXM software platform giving online access to Old French Text Manuscripts images and tagged transcriptions for concordancing and text mining. This platform is able to import medieval sources encoded in XML according to the TEI Guidelines for linking manuscript images to transcriptions, encode several diplomatic levels of transcription including abbreviations and word level corrections. It includes a sophisticated tokenizer able to deal with TEI tags at different levels of linguistic hierarchy. Words are tagged on the fly during the import process using IMS TreeTagger tool with a specific language model. Synoptic editions displaying side by side manuscript images and text transcriptions are automatically produced during the import process. Texts are organized in a corpus with their own metadata (title, author, date, genre, etc.) and several word properties indexes are produced for the CQP search engine to allow efficient word patterns search to build different type of frequency lists or concordances. For syntactically annotated texts, special indexes are produced for the Tiger Search engine to allow efficient syntactic concordances building. The platform has also been tested on classical Latin, ancient Greek, Old Slavonic and Old Hieroglyphic Egyptian corpora (including various types of encoding and annotations)

Single Muscle Fiber Proteomics Reveals Fiber-Type-Specific Features of Human Muscle Aging

Skeletal muscle is a key tissue in human aging, which affects different muscle fiber types unequally. We developed a highly sensitive single muscle fiber proteomics workflow to study human aging and show that the senescence of slow and fast muscle fibers is characterized by diverging metabolic and protein quality control adaptations. Whereas mitochondrial content declines with aging in both fiber types, glycolysis and glycogen metabolism are upregulated in slow but downregulated in fast muscle fibers. Aging mitochondria decrease expression of the redox enzyme monoamine oxidase A. Slow fibers upregulate a subset of actin and myosin chaperones, whereas an opposite change happens in fast fibers. These changes in metabolism and sarcomere quality control may be related to the ability of slow, but not fast, muscle fibers to maintain their mass during aging. We conclude that single muscle fiber analysis by proteomics can elucidate pathophysiology in a sub-type-specific manner

Multiple Retrieval Models and Regression Models for Prior Art Search

This paper presents the system called PATATRAS (PATent and Article Tracking, Retrieval and AnalysiS) realized for the IP track of CLEF 2009. Our approach presents three main characteristics: 1. The usage of multiple retrieval models (KL, Okapi) and term index definitions (lemma, phrase, concept) for the three languages considered in the present track (English, French, German) producing ten different sets of ranked results. 2. The merging of the different results based on multiple regression models using an additional validation set created from the patent collection. 3. The exploitation of patent metadata and of the citation structures for creating restricted initial working sets of patents and for producing a final re-ranking regression model. As we exploit specific metadata of the patent documents and the citation relations only at the creation of initial working sets and during the final post ranking step, our architecture remains generic and easy to extend