Query expansion using medical information extraction for improving information retrieval in French medical domain

Many users’ queries contain references to named entities, and this is particularly true in the medical field. Doctors express their information needs using medical entities as they are elements rich with information that helps to better target the relevant documents. At the same time, many resources have been recognized as a large container of medical entities and relationships between them such as clinical reports; which are medical texts written by doctors. In this paper, we present a query expansion method that uses medical entities and their semantic relations in the query context based on an external resource in OWL. The goal of this method is to evaluate the effectiveness of an information retrieval system to support doctors in accessing easily relevant information. Experiments on a collection of real clinical reports show that our approach reveals interesting improvements in precision, recall and MAP in medical information retrieval

On the evaluation and improvement of arabic wordnet coverage and usability

The final publication is available at Springer via http://dx.doi.org/10.1007/s10579-013-9237-0[EN] Built on the basis of the methods developed for Princeton WordNet and EuroWordNet, Arabic WordNet (AWN) has been an interesting project which combines WordNet structure compliance with Arabic particularities. In this paper, some AWN shortcomings related to coverage and usability are addressed. The use of AWN in question/answering (Q/A) helped us to deeply evaluate the resource from an experience-based perspective. Accordingly, an enrichment of AWN was built by semi-automatically extending its content. Indeed, existing approaches and/or resources developed for other languages were adapted and used for AWN. The experiments conducted in Arabic Q/A have shown an improvement of both AWN coverage as well as usability. Concerning coverage, a great amount of named entities extracted from YAGO were connected with corresponding AWN synsets. Also, a significant number of new verbs and nouns (including Broken Plural forms) were added. In terms of usability, thanks to the use of AWN, the performance for the AWN-based Q/A application registered an overall improvement with respect to the following three measures: accuracy (+9.27 % improvement), mean reciprocal rank (+3.6 improvement) and number of answered questions (+12.79 % improvement).The work presented in Sect. 2.2 was done in the framework of the bilateral Spain-Morocco AECID-PCI C/026728/09 research project. The research of the two first authors is done in the framework of the PROGRAMME D'URGENCE project (grant no. 03/2010). The research of the third author is done in the framework of WIQEI IRSES project (grant no. 269180) within the FP 7 Marie Curie People, DIANA-APPLICATIONS-Finding Hidden Knowledge in Texts: Applications (TIN2012-38603-C02-01) research project and VLC/CAMPUS Microcluster on Multimodal Interaction in Intelligent Systems. We would like to thank Manuel Montes-y-Gomez (INAOE-Puebla, Mexico) and Sandra Garcia-Blasco (Bitsnbrain, Spain) for their feedback on the work presented in Sect. 2.4. We would like finally to thank Violetta Cavalli-Sforza (Al Akhawayn University in Ifrane, Morocco) for having reviewed the linguistic level of the entire document.Abouenour, L.; Bouzoubaa, K.; Rosso, P. (2013). On the evaluation and improvement of arabic wordnet coverage and usability. Language Resources and Evaluation. 47(3):891-917. https://doi.org/10.1007/s10579-013-9237-0S891917473Abbès, R., Dichy, J., & Hassoun, M. (2004). The architecture of a standard Arabic lexical database: Some figures, ratios and categories from the DIINAR.1 source program. In Workshop on computational approaches to Arabic script-based languages, Coling 2004. Geneva, Switzerland.Abouenour, L., Bouzoubaa, K., & Rosso, P. (2009a). Structure-based evaluation of an Arabic semantic query expansion using the JIRS passage retrieval system. 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English Gigaword (3rd ed.). Philadelphia, USA: Linguistic Data Consortium.Hammou, B., Abu-salem, H., Lytinen, S., & Evens, M. (2002). QARAB: A question answering system to support the Arabic language. In Proceedings of the workshop on computational approaches to Semitic languages, ACL, (pp. 55–65), Philadelphia.Hearst, M. A. (1992). Automatic acquisition of hyponyms from large text corpora. In Proceedings of the 14th conference on Computational linguistics, COLING ‘92 (vol. 2, pp. 539–545).Kanaan, G., Hammouri, A., Al-Shalabi, R., & Swalha, M. (2009). A new question answering system for the Arabic language. American Journal of Applied Sciences, 6(4), 797–805.Kim, H., Chen, S., & Veale, T. (2006). Analogical reasoning with a synergy of HowNet and WordNet. In Proceedings of GWC’2006, the 3rd global WordNet conference, January, Cheju, Korea.Kipper-Schuler, K. (2006). VerbNet: A broad-coverage, comprehensive verb lexicon. Ph.D. Thesis.Mohammed, F. A., Nasser, K., & Harb, H. M. (1993). 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Enhancing the Performance of Text Mining

The amount of text data produced in science, finance, social media, and medicine is growing at an unprecedented pace. The raw text data typically introduces major computational and analytical obstacles (e.g., extremely high dimensionality) to data mining and machine learning algorithms. Besides, the growth in the size of text data makes the search process more difficult for information retrieval systems, making retrieving relevant results to match the users’ search queries challenging. Moreover, the availability of text data in different languages creates the need to develop new methods to analyze multilingual topics to help policymakers in governmental and health systems to make risk decisions and to create policies to respond to public health crises, natural disasters, and political or social movements. The goal of this thesis is to develop new methods that handle computational and analytical problems for complex high-dimensional text data, develop a new query expansion approach to enhance the performance of information retrieval systems, and to present new techniques for analyzing multilingual topics using a translation service. First, in the field of dimensionality reduction, we develop a new method for detecting and eliminating domain-based words. In this method, we use three different datasets and five classifiers for testing and evaluating the performance of our new approach before and after eliminating domain-based words. We compare the performance of our approach with other feature selection methods. We find that the new approach improves the performance of the binary classifier and reduces the dimensionality of the feature space by 90%. Also, our approach reduces the execution time of the classifier and outperforms one of the feature selection methods. Second, in the field of information retrieval, we design and implement a method that integrates words from a current stream with external data sources in order to predict the occurrence of relevant words that have not yet appeared in the primary source. This algorithm enables the construction of new queries that effectively capture emergent events that a user may not have anticipated when initiating the data collection stream. The added value of using the external data sources appears when we have a stream of data and we want to predict something that has not yet happened instead of using only the stream that is limited to the available information at a specific time. We compare the performance of our approach with two alternative approaches. The first approach (static) expands user queries with words extracted from a probabilistic topic model of the stream. The second approach (emergent) reinforces user queries with emergent words extracted from the stream. We find that our method outperforms alternative approaches, exhibiting particularly good results in identifying future emergent topics. Third, in the field of the multilingual text, we present a strategy to analyze the similarity between multilingual topics in English and Arabic tweets surrounding the 2020 COVID-19 pandemic. We make a descriptive comparison between topics in Arabic and English tweets about COVID-19 using tweets collected in the same way and filtered using the same keywords. We analyze Twitter’s discussion to understand the evolution of topics over time and reveal topic similarity among tweets across the datasets. We use probabilistic topic modeling to identify and extract the key topics of Twitter’s discussion in Arabic and English tweets. We use two methods to analyze the similarity between multilingual topics. The first method (full-text topic modeling approach) translates all text to English and then runs topic modeling to find similar topics. The second method (term-based topic modeling approach) runs topic modeling on the text before translation then translates the top keywords in each topic to find similar topics. We find similar topics related to COVID-19 pandemic covered in English and Arabic tweets for certain time intervals. Results indicate that the term-based topic modeling approach can reduce the cost compared to the full-text topic modeling approach and still have comparable results in finding similar topics. The computational time to translate the terms is significantly lower than the translation of the full text

Symbiosis between the TRECVid benchmark and video libraries at the Netherlands Institute for Sound and Vision

Audiovisual archives are investing in large-scale digitisation efforts of their analogue holdings and, in parallel, ingesting an ever-increasing amount of born- digital files in their digital storage facilities. Digitisation opens up new access paradigms and boosted re-use of audiovisual content. Query-log analyses show the shortcomings of manual annotation, therefore archives are complementing these annotations by developing novel search engines that automatically extract information from both audio and the visual tracks. Over the past few years, the TRECVid benchmark has developed a novel relationship with the Netherlands Institute of Sound and Vision (NISV) which goes beyond the NISV just providing data and use cases to TRECVid. Prototype and demonstrator systems developed as part of TRECVid are set to become a key driver in improving the quality of search engines at the NISV and will ultimately help other audiovisual archives to offer more efficient and more fine-grained access to their collections. This paper reports the experiences of NISV in leveraging the activities of the TRECVid benchmark

PREFERENCE BASED TERM WEIGHTING FOR ARABIC FIQH DOCUMENT RANKING

In document retrieval, besides the suitability of query with search results, there is also a subjective user assessment that is expected to be a deciding factor in document ranking. This preference aspect is referred at the fiqh document searching. People tend to prefer on certain fiqh methodology without rejecting other fiqh methodologies. It is necessary to investigate preference factor in addition to the relevance factor in the document ranking. Therefore, this research proposed a method of term weighting based on preference to rank documents according to user preference. The proposed method is also combined with term weighting based on documents index and books index so it sees relevance and preference aspect. The proposed method is Inverse Preference Frequency with α value (IPFα). In this method, we calculate preference value by IPF term weighting. Then, the preference values of terms that is equal with the query are multiplied by α. IPFα combined with the existing weighting methods become TF.IDF.IBF.IPFα. Experiment of the proposed method uses dataset of several Arabic fiqh documents. Evaluation uses recall, precision, and f-measure calculations. Proposed term weighting method is obtained to rank the document in the right order according to user preference. It is shown from the result with recall value reach 75%, precision 100%, and f-measure 85.7% respectively

A systematic literature review on Wikidata

To review the current status of research on Wikidata and, in particular, of articles that either describe applications of Wikidata or provide empirical evidence, in order to uncover the topics of interest, the fields that are benefiting from its applications and which researchers and institutions are leading the work