In search of an appropriate abstraction level for motif annotations

In: Proceedings of the 2012 Workshop on Computational Models of Narrative, (pp. 22-28).

Comparing Rule-based, Feature-based and Deep Neural Methods for De-identification of Dutch Medical Records

Unstructured information in electronic health records provide an invaluable resource for medical research. To protect the confidentiality of patients and to conform to privacy regulations, de-identification methods automatically remove personally identifying information from these medical records. However, due to the unavailability of labeled data, most existing research is constrained to English medical text and little is known about the generalizability of de-identification methods across languages and domains. In this study, we construct a varied dataset consisting of the medical records of 1260 patients by sampling data from 9 institutes and three domains of Dutch healthcare. We test the generalizability of three de-identification methods across languages and domains. Our experiments show that an existing rule-based method specifically developed for the Dutch language fails to generalize to this new data. Furthermore, a state-of-the-art neural architecture performs strongly across languages and domains, even with limited training data. Compared to feature-based and rule-based methods the neural method requires significantly less configuration effort and domain-knowledge. We make all code and pre-trained de-identification models available to the research community, allowing practitioners to apply them to their datasets and to enable future benchmarks.Comment: Proceedings of the 1st ACM WSDM Health Search and Data Mining Workshop (HSDM2020), 202

Morphology in Phonology : introduction

This fourth volume of Catalan Journal of Linguistics is devoted to a topic discussed at length in the literature but which nevertheless remains a challenge for any view of phonology: the morphology-phonology interaction. The papers collected address two related issues, the role of morphological information in phonology and the role of phonological information in morphology. The first six articles (i.e. McCarthy, Wheeler, Downing, van Oostendorp, José and Auger, and Rice) deal with the former topic; the last three (i.e. Bertinetto and Jetchev, Pérez Saldanya and Vallès, and Viaplana), with the latter. Several papers (Wheeler, van Oostendorp, Rice, Bertinetto and Jetchev, Pérez Saldanya and Vallès, and Viaplana) further discuss the role and concept of paradigms, an old Neogrammarian notion to which renewed attention has been payed both from the phonological perspective (cf. work by Benua 1995, 1997; Burzio 1994 and subsequent work; Kenstowicz 1996, 2002; Steriade 2000, and the articles in the recent volume edited by Downing et al. 2005, among others) and from the morphological perspective (cf. work by Aronoff 1994; Bauer 1997, 2001; Carstairs-McCarthy 1994, 1998; Stump 1991, 1997; van Marle 1985, 1994; Wurzel 1989, and several articles in the recent volume edited by Boucher 2002, among others)

Taxonomic corpus-based concept summary generation for document annotation.

Semantic annotation is an enabling technology which links documents to concepts that unambiguously describe their content. Annotation improves access to document contents for both humans and software agents. However, the annotation process is a challenging task as annotators often have to select from thousands of potentially relevant concepts from controlled vocabularies. The best approaches to assist in this task rely on reusing the annotations of an annotated corpus. In the absence of a pre-annotated corpus, alternative approaches suffer due to insufficient descriptive texts for concepts in most vocabularies. In this paper, we propose an unsupervised method for recommending document annotations based on generating node descriptors from an external corpus. We exploit knowledge of the taxonomic structure of a thesaurus to ensure that effective descriptors (concept summaries) are generated for concepts. Our evaluation on recommending annotations show that the content that we generate effectively represents the concepts. Also, our approach outperforms those which rely on information from a thesaurus alone and is comparable with supervised approaches

Content-Based Quality Estimation for Automatic Subject Indexing of Short Texts under Precision and Recall Constraints

Semantic annotations have to satisfy quality constraints to be useful for digital libraries, which is particularly challenging on large and diverse datasets. Confidence scores of multi-label classification methods typically refer only to the relevance of particular subjects, disregarding indicators of insufficient content representation at the document-level. Therefore, we propose a novel approach that detects documents rather than concepts where quality criteria are met. Our approach uses a deep, multi-layered regression architecture, which comprises a variety of content-based indicators. We evaluated multiple configurations using text collections from law and economics, where the available content is restricted to very short texts. Notably, we demonstrate that the proposed quality estimation technique can determine subsets of the previously unseen data where considerable gains in document-level recall can be achieved, while upholding precision at the same time. Hence, the approach effectively performs a filtering that ensures high data quality standards in operative information retrieval systems.Comment: authors' manuscript, paper submitted to TPDL-2018 conference, 12 page

Using Noun Phrases for Navigating Biomedical Literature on Pubmed: How Many Updates Are We Losing Track of?

Author-supplied citations are a fraction of the related literature for a paper. The “related citations” on PubMed is typically dozens or hundreds of results long, and does not offer hints why these results are related. Using noun phrases derived from the sentences of the paper, we show it is possible to more transparently navigate to PubMed updates through search terms that can associate a paper with its citations. The algorithm to generate these search terms involved automatically extracting noun phrases from the paper using natural language processing tools, and ranking them by the number of occurrences in the paper compared to the number of occurrences on the web. We define search queries having at least one instance of overlap between the author-supplied citations of the paper and the top 20 search results as citation validated (CV). When the overlapping citations were written by same authors as the paper itself, we define it as CV-S and different authors is defined as CV-D. For a systematic sample of 883 papers on PubMed Central, at least one of the search terms for 86% of the papers is CV-D versus 65% for the top 20 PubMed “related citations.” We hypothesize these quantities computed for the 20 million papers on PubMed to differ within 5% of these percentages. Averaged across all 883 papers, 5 search terms are CV-D, and 10 search terms are CV-S, and 6 unique citations validate these searches. Potentially related literature uncovered by citation-validated searches (either CV-S or CV-D) are on the order of ten per paper – many more if the remaining searches that are not citation-validated are taken into account. The significance and relationship of each search result to the paper can only be vetted and explained by a researcher with knowledge of or interest in that paper

Automated systems to identify relevant documents in product risk management

<p>Abstract</p> <p>Background</p> <p>Product risk management involves critical assessment of the risks and benefits of health products circulating in the market. One of the important sources of safety information is the primary literature, especially for newer products which regulatory authorities have relatively little experience with. Although the primary literature provides vast and diverse information, only a small proportion of which is useful for product risk assessment work. Hence, the aim of this study is to explore the possibility of using text mining to automate the identification of useful articles, which will reduce the time taken for literature search and hence improving work efficiency. In this study, term-frequency inverse document-frequency values were computed for predictors extracted from the titles and abstracts of articles related to three tumour necrosis factors-alpha blockers. A general automated system was developed using only general predictors and was tested for its generalizability using articles related to four other drug classes. Several specific automated systems were developed using both general and specific predictors and training sets of different sizes in order to determine the minimum number of articles required for developing such systems.</p> <p>Results</p> <p>The general automated system had an area under the curve value of 0.731 and was able to rank 34.6% and 46.2% of the total number of 'useful' articles among the first 10% and 20% of the articles presented to the evaluators when tested on the generalizability set. However, its use may be limited by the subjective definition of useful articles. For the specific automated system, it was found that only 20 articles were required to develop a specific automated system with a prediction performance (AUC 0.748) that was better than that of general automated system.</p> <p>Conclusions</p> <p>Specific automated systems can be developed rapidly and avoid problems caused by subjective definition of useful articles. Thus the efficiency of product risk management can be improved with the use of specific automated systems.</p

Mining document, concept, and term associations for effective biomedical retrieval - Introducing MeSH-enhanced retrieval models

Manually assigned subject terms, such as Medical Subject Headings (MeSH) in the health domain, describe the concepts or topics of a document. Existing information retrieval models do not take full advantage of such information. In this paper, we propose two MeSH-enhanced (ME) retrieval models that integrate the concept layer (i.e. MeSH) into the language modeling framework to improve retrieval performance. The new models quantify associations between documents and their assigned concepts to construct conceptual representations for the documents, and mine associations between concepts and terms to construct generative concept models. The two ME models reconstruct two essential estimation processes of the relevance model (Lavrenko and Croft 2001) by incorporating the document-concept and the concept-term associations. More specifically, in Model 1, language models of the pseudo-feedback documents are enriched by their assigned concepts. In Model 2, concepts that are related to users’ queries are first identified, and then used to reweight the pseudo-feedback documents according to the document-concept associations. Experiments carried out on two standard test collections show that the ME models outperformed the query likelihood model, the relevance model (RM3), and an earlier ME model. A detailed case analysis provides insight into how and why the new models improve/worsen retrieval performance. Implications and limitations of the study are discussed. This study provides new ways to formally incorporate semantic annotations, such as subject terms, into retrieval models. The findings of this study suggest that integrating the concept layer into retrieval models can further improve the performance over the current state-of-the-art models.Ye