The major change in the second edition of this book is the addition of a new chapter on probabilistic retrieval. This chapter has been included because I think this is one of the most interesting and active areas of research in information retrieval. There are still many problems to be solved so I hope that this particular chapter will be of some help to those who want to advance the state of knowledge in this area. All the other chapters have been updated by including some of the more recent work on the topics covered. In preparing this new edition I have benefited from discussions with Bruce Croft, The material of this book is aimed at advanced undergraduate information (or computer) science students, postgraduate library science students, and research workers in the field of IR. Some of the chapters, particularly Chapter 6 * , make simple use of a little advanced mathematics. However, the necessary mathematical tools can be easily mastered from numerous mathematical texts that now exist and, in any case, references have been given where the mathematics occur. I had to face the problem of balancing clarity of exposition with density of references. I was tempted to give large numbers of references but was afraid they would have destroyed the continuity of the text. I have tried to steer a middle course and not compete with the Annual Review of Information Science and Technology. Normally one is encouraged to cite only works that have been published in some readily accessible form, such as a book or periodical. Unfortunately, much of the interesting work in IR is contained in technical reports and Ph.D. theses. For example, most the work done on the SMART system at Cornell is available only in reports. Luckily many of these are now available through the National Technical Information Service (U.S.) and University Microfilms (U.K.). I have not avoided using these sources although if the same material is accessible more readily in some other form I have given it preference. I should like to acknowledge my considerable debt to many people and institutions that have helped me. Let me say first that they are responsible for many of the ideas in this book but that only I wish to be held responsible. My greatest debt is to Karen Sparck Jones who taught me to research information retrieval as an experimental science. Nick Jardine and Robin …

Information retrieval

The field of study of Natural Language Processing (NLP) has developed over the past 50 years or so, producing an array of now mature technology such as automatic morphological analysis, word sense disambiguation, parsing, anaphora resolution, natural language generation, named entity recognition, etc. The proliferation of large digital collections (evolving into Digital Libraries) and the emerging economic value of information demand efficient solutions for managing the information which is available, but which is not always easy to find. This chapter presents the requirements for handling documents in digital libraries and explains how existing NLP technology can be used to facilitate the task of document management.

NLP and Digital Library Management

This paper describes the analysis and processing programs for a set of natural language texts in a medical area (x-ray reports on patients with breast cancer). The programs convert the information in the text into a tabular form suitable for further automatic information processing (e.g., editing of records, question answering on the data collected, or statistical summaries of the data). To set up a tabular form appropriate for the data, we first perform a manual linguistic analysis on a sample of the texts. From this we obtain the word classes and the form of the table (called an information format) for this type of material. We then apply the series of processing programs to the sentences of the texts. Each sentence is parsed with the Linguistic String Parser English grammar in order to obtain its grammatical structure; certain standard English transformations are then applied to regularize the grammatical form of the sentence; and finally a set of "formatting transformations" map the words of the sentence into the slots of the format or table, in such a way that the sentence is reconstructible (up to paraphrase) from its representation in the table. The results of applying these programs to a corpus are described. This procedure enables us to convert a natural language corpus into a structured data base.

From text to structured information-Automatic processing of medical reports

Implementing electronic medical records during residency.

This paper describes the methods we used for our participation to CLEF eHealth 2017 Task 3 IRTask 1: ad-hoc search. This task aims at retrieving information relevant to people seeking health advice on the web. We present our work of using query reformulation techniques in this paper. We use cTAKES, a clinical natural processing system, to identify UMLS concepts in the original query. Query expansion techniques are then applied to the identified medical concepts. Query expansion based on UMLS meta-thesaurus or a Word2vec model trained with domain data is used in our work. We also use other techniques, like increasing the weight of the terms that are considered to catch the users’ need much more compared to other terms.

http://ceur-ws.org/Vol-1866/paper_105.pdf

UEvora at CLEF eHealth 2017 Task 3.

/pdf/applying-natural-language-processing-to-clinical-information-18zco22zid.pdf

Applying Natural Language Processing to Clinical Information Retrieval

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Applying Natural Language Processing to Clinical Information Retrieval