Is searching full text more effective than searching abstracts?

<p>Abstract</p> <p>Background</p> <p>With the growing availability of full-text articles online, scientists and other consumers of the life sciences literature now have the ability to go beyond searching bibliographic records (title, abstract, metadata) to directly access full-text content. Motivated by this emerging trend, I posed the following question: is searching full text more effective than searching abstracts? This question is answered by comparing text retrieval algorithms on MEDLINE^®abstracts, full-text articles, and spans (paragraphs) within full-text articles using data from the TREC 2007 genomics track evaluation. Two retrieval models are examined: <it>bm25 </it>and the ranking algorithm implemented in the open-source Lucene search engine.</p> <p>Results</p> <p>Experiments show that treating an entire article as an indexing unit does not consistently yield higher effectiveness compared to abstract-only search. However, retrieval based on spans, or paragraphs-sized segments of full-text articles, consistently outperforms abstract-only search. Results suggest that highest overall effectiveness may be achieved by combining evidence from spans and full articles.</p> <p>Conclusion</p> <p>Users searching full text are more likely to find relevant articles than searching only abstracts. This finding affirms the value of full text collections for text retrieval and provides a starting point for future work in exploring algorithms that take advantage of rapidly-growing digital archives. Experimental results also highlight the need to develop distributed text retrieval algorithms, since full-text articles are significantly longer than abstracts and may require the computational resources of multiple machines in a cluster. The MapReduce programming model provides a convenient framework for organizing such computations.</p

Factors affecting the effectiveness of biomedical document indexing and retrieval based on terminologies

International audienceThe aim of this work is to evaluate a set of indexing and retrieval strategies based on the integration of several biomedical terminologies on the available TREC Genomics collections for an ad hoc information retrieval (IR) task.Materials and methodsWe propose a multi-terminology based concept extraction approach to selecting best concepts from free text by means of voting techniques. We instantiate this general approach on four terminologies (MeSH, SNOMED, ICD-10 and GO). We particularly focus on the effect of integrating terminologies into a biomedical IR process, and the utility of using voting techniques for combining the extracted concepts from each document in order to provide a list of unique concepts.ResultsExperimental studies conducted on the TREC Genomics collections show that our multi-terminology IR approach based on voting techniques are statistically significant compared to the baseline. For example, tested on the 2005 TREC Genomics collection, our multi-terminology based IR approach provides an improvement rate of +6.98% in terms of MAP (mean average precision) (p < 0.05) compared to the baseline. In addition, our experimental results show that document expansion using preferred terms in combination with query expansion using terms from top ranked expanded documents improve the biomedical IR effectiveness.ConclusionWe have evaluated several voting models for combining concepts issued from multiple terminologies. Through this study, we presented many factors affecting the effectiveness of biomedical IR system including term weighting, query expansion, and document expansion models. The appropriate combination of those factors could be useful to improve the IR performance

Evaluating Information Retrieval and Access Tasks

This open access book summarizes the first two decades of the NII Testbeds and Community for Information access Research (NTCIR). NTCIR is a series of evaluation forums run by a global team of researchers and hosted by the National Institute of Informatics (NII), Japan. The book is unique in that it discusses not just what was done at NTCIR, but also how it was done and the impact it has achieved. For example, in some chapters the reader sees the early seeds of what eventually grew to be the search engines that provide access to content on the World Wide Web, today’s smartphones that can tailor what they show to the needs of their owners, and the smart speakers that enrich our lives at home and on the move. We also get glimpses into how new search engines can be built for mathematical formulae, or for the digital record of a lived human life. Key to the success of the NTCIR endeavor was early recognition that information access research is an empirical discipline and that evaluation therefore lay at the core of the enterprise. Evaluation is thus at the heart of each chapter in this book. They show, for example, how the recognition that some documents are more important than others has shaped thinking about evaluation design. The thirty-three contributors to this volume speak for the many hundreds of researchers from dozens of countries around the world who together shaped NTCIR as organizers and participants. This book is suitable for researchers, practitioners, and students—anyone who wants to learn about past and present evaluation efforts in information retrieval, information access, and natural language processing, as well as those who want to participate in an evaluation task or even to design and organize one

CITREC: An Evaluation Framework for Citation-Based Similarity Measures based on TREC Genomics and PubMed Central

Citation-based similarity measures such as Bibliographic Coupling and Co-Citation are an integral component of many information retrieval systems. However, comparisons of the strengths and weaknesses of measures are challenging due to the lack of suitable test collections. This paper presents CITREC, an open evaluation framework for citation-based and text-based similarity measures. CITREC prepares the data from the PubMed Central Open Access Subset and the TREC Genomics collection for a citation-based analysis and provides tools necessary for performing evaluations of similarity measures. To account for different evaluation purposes, CITREC implements 35 citation-based and text-based similarity measures, and features two gold standards. The first gold standard uses the Medical Subject Headings (MeSH) thesaurus and the second uses the expert relevance feedback that is part of the TREC Genomics collection to gauge similarity. CITREC additionally offers a system that allows creating user defined gold standards to adapt the evaluation framework to individual information needs and evaluation purposes.ye

Automatic medical encoding with SNOMED categories

BACKGROUND: In this paper, we describe the design and preliminary evaluation of a new type of tools to speed up the encoding of episodes of care using the SNOMED CT terminology. METHODS: The proposed system can be used either as a search tool to browse the terminology or as a categorization tool to support automatic annotation of textual contents with SNOMED concepts. The general strategy is similar for both tools and is based on the fusion of two complementary retrieval strategies with thesaural resources. The first classification module uses a traditional vector-space retrieval engine which has been fine-tuned for the task, while the second classifier is based on regular variations of the term list. For evaluating the system, we use a sample of MEDLINE. SNOMED CT categories have been restricted to Medical Subject Headings (MeSH) using the SNOMED-MeSH mapping provided by the UMLS (version 2006). RESULTS: Consistent with previous investigations applied on biomedical terminologies, our results show that performances of the hybrid system are significantly improved as compared to each single module. For top returned concepts, a precision at high ranks (P0) of more than 80% is observed. In addition, a manual and qualitative evaluation on a dozen of MEDLINE abstracts suggests that SNOMED CT could represent an improvement compared to existing medical terminologies such as MeSH. CONCLUSION: Although the precision of the SNOMED categorizer seems sufficient to help professional encoders, it is concluded that clinical benchmarks as well as usability studies are needed to assess the impact of our SNOMED encoding method in real settings. AVAILABILITIES : The system is available for research purposes on: http://eagl.unige.ch/SNOCat

Language modeling approaches to question answering

In today’s environment of information overload, Question Answering (QA) is a critically important research area. QA is the task of automatically extracting a precise answer from one or more data sources to a question posed in natural language. A twostage strategy is typically adopted when designing a QA system; the first stage is an Information Retrieval (IR) process which returns a set of candidate documents relevant to the question and the second stage narrows the information contained in those passages down to a single response (sentence or entity) that answers the question, typically using Information Extraction (IE) or Natural Language Processing methods. This research proposes novel techniques for QA by enhancing the user’s original query with latent semantic information from the corpus. This enhanced query is then applied to both the first and second stages of the QA architecture. To build the enhanced query, we propose the Aspect-Based Relevance Language Model as an approach that uses statistical language modeling techniques to measure the likelihood of relevance of a concept (oraspect as defined by Probabilistic Latent Semantic Analysis) to a question. We then use terms from the aspects that have the highest likelihood of relevance to design a model for a semantic Question Context, which includes sense-disambiguated terms than amplify the user’s query. Question Context is incorporated into the first state of QA as query expansion to improve recall. We then derive a novel measure called Answer Credibility from the Question Context. Answer Credibility may be thought of as a statistical measure of the reliability of a candidate answer with respect to a question and the source text from which the candidate answer was derived. We incorporate Answer Credibility in the Answer Validation process; the answer with the highest score after the application of Answer Credibility is returned to the user. Our techniques show performance improvements over state-of-the-art approaches, and have the advantage that they use statistical techniques to derive semantic information to aid the process of QA.Ph.D., Information Science and Technology -- Drexel University, 200

Information retrieval and text mining technologies for chemistry

Efficient access to chemical information contained in scientific literature, patents, technical reports, or the web is a pressing need shared by researchers and patent attorneys from different chemical disciplines. Retrieval of important chemical information in most cases starts with finding relevant documents for a particular chemical compound or family. Targeted retrieval of chemical documents is closely connected to the automatic recognition of chemical entities in the text, which commonly involves the extraction of the entire list of chemicals mentioned in a document, including any associated information. In this Review, we provide a comprehensive and in-depth description of fundamental concepts, technical implementations, and current technologies for meeting these information demands. A strong focus is placed on community challenges addressing systems performance, more particularly CHEMDNER and CHEMDNER patents tasks of BioCreative IV and V, respectively. Considering the growing interest in the construction of automatically annotated chemical knowledge bases that integrate chemical information and biological data, cheminformatics approaches for mapping the extracted chemical names into chemical structures and their subsequent annotation together with text mining applications for linking chemistry with biological information are also presented. Finally, future trends and current challenges are highlighted as a roadmap proposal for research in this emerging field.A.V. and M.K. acknowledge funding from the European Community’s Horizon 2020 Program (project reference: 654021 - OpenMinted). M.K. additionally acknowledges the Encomienda MINETAD-CNIO as part of the Plan for the Advancement of Language Technology. O.R. and J.O. thank the Foundation for Applied Medical Research (FIMA), University of Navarra (Pamplona, Spain). This work was partially funded by Consellería de Cultura, Educación e Ordenación Universitaria (Xunta de Galicia), and FEDER (European Union), and the Portuguese Foundation for Science and Technology (FCT) under the scope of the strategic funding of UID/BIO/04469/2013 unit and COMPETE 2020 (POCI-01-0145-FEDER-006684). We thank Iñigo Garciá -Yoldi for useful feedback and discussions during the preparation of the manuscript.info:eu-repo/semantics/publishedVersio

Knowledge mining over scientific literature and technical documentation

Abstract This dissertation focuses on the extraction of information implicitly encoded in domain descriptions (technical terminology and related items) and its usage within a restricted-domain question answering system (QA). Since different variants of the same term can be used to refer to the same domain entity, it is necessary to recognize all possible forms of a given term and structure them, so that they can be used in the question answering process. The knowledge about domain descriptions and their mutual relations is leveraged in an extension to an existing QA system, aimed at the technical maintenance manual of a well-known commercial aircraft. The original version of the QA system did not make use of domain descriptions, which are the novelty introduced by the present work. The explicit treatment of domain descriptions provided considerable gains in terms of efficiency, in particular in the process of analysis of the background document collection. Similar techniques were later applied to another domain (biomedical scientific literature), focusing in particular on protein- protein interactions. This dissertation describes in particular: (1) the extraction of domain specific lexical items which refer to entities of the domain; (2) the detection of relationships (like synonymy and hyponymy) among such items, and their organization into a conceptual structure; (3) their usage within a domain restricted question answering system, in order to facilitate the correct identification of relevant answers to a query; (4) the adaptation of the system to another domain, and extension of the basic hypothesis to tasks other than question answering. Zusammenfassung Das Thema dieser Dissertation ist die Extraktion von Information, welche implizit in technischen Terminologien und ähnlichen Ressourcen enthalten ist, sowie ihre Anwendung in einem Antwortextraktionssystem (AE). Da verschiedene Varianten desselben Terms verwendet werden können, um auf den gleichen Begriff zu verweisen, ist die Erkennung und Strukturierung aller möglichen Formen Voraussetzung für den Einsatz in einem AE-System. Die Kenntnisse über Terme und deren Relationen werden in einem AE System angewandt, welches auf dem Wartungshandbuch eines bekannten Verkehrsflugzeug fokussiert. Die ursprüngliche Version des Systems hatte keine explizite Behandlung von Terminologie. Die explizite Behandlung von Terminologie lieferte eine beachtliche Verbesserung der Effizienz des Systems, insbesondere was die Analyse der zugrundeliegenden Dokumentensammlung betrifft. Ähnliche Methodologien wurden später auf einer anderen Domäne angewandt (biomedizinische Literatur), mit einen besonderen Fokus auf Interaktionen zwischen Proteinen. Diese Dissertation beschreibt insbesondere: (1) die Extraktion der Terminologie (2) die Identifikation der Relationen zwischen Termen (wie z.B. Synonymie und Hyponymie) (3) deren Verwendung in einen AE System (4) die Portierung des Systems auf eine andere Domäne

Semi-Supervised Learning For Identifying Opinions In Web Content

Thesis (Ph.D.) - Indiana University, Information Science, 2011Opinions published on the World Wide Web (Web) offer opportunities for detecting personal attitudes regarding topics, products, and services. The opinion detection literature indicates that both a large body of opinions and a wide variety of opinion features are essential for capturing subtle opinion information. Although a large amount of opinion-labeled data is preferable for opinion detection systems, opinion-labeled data is often limited, especially at sub-document levels, and manual annotation is tedious, expensive and error-prone. This shortage of opinion-labeled data is less challenging in some domains (e.g., movie reviews) than in others (e.g., blog posts). While a simple method for improving accuracy in challenging domains is to borrow opinion-labeled data from a non-target data domain, this approach often fails because of the domain transfer problem: Opinion detection strategies designed for one data domain generally do not perform well in another domain. However, while it is difficult to obtain opinion-labeled data, unlabeled user-generated opinion data are readily available. Semi-supervised learning (SSL) requires only limited labeled data to automatically label unlabeled data and has achieved promising results in various natural language processing (NLP) tasks, including traditional topic classification; but SSL has been applied in only a few opinion detection studies. This study investigates application of four different SSL algorithms in three types of Web content: edited news articles, semi-structured movie reviews, and the informal and unstructured content of the blogosphere. SSL algorithms are also evaluated for their effectiveness in sparse data situations and domain adaptation. Research findings suggest that, when there is limited labeled data, SSL is a promising approach for opinion detection in Web content. Although the contributions of SSL varied across data domains, significant improvement was demonstrated for the most challenging data domain--the blogosphere--when a domain transfer-based SSL strategy was implemented