The paper introduces a framework for representation and acquisition of knowledge emerging from large samples of textual data. We utilise a tensor-based, distributional representation of simple statements extracted from text, and show how one can use the representation to infer emergent knowledge patterns from the textual data in an unsupervised manner. Examples of the patterns we investigate in the paper are implicit term relationships or conjunctive IF-THEN rules. To evaluate the practical relevance of our approach, we apply it to annotation of life science articles with terms from MeSH (a controlled biomedical vocabulary and thesaurus).
Regarding the application of our framework to annotation of biomedical articles, a body of more or less recent works like @cite_8 , @cite_9 , @cite_5 , @cite_14 or @cite_0 exists (the second, third and fifth of the approaches are either used or considered for use as a support service for the professional annotators of the articles on PubMed, a biomedical literature repository). The state of the art methods, however, often require at least an indirect input from human users before they can produce annotations of new articles automatically. For instance, @cite_9 and @cite_0 require a large corpus of previously annotated articles for learning and ranking possible annotations of new resources. Other methods like @cite_5 require rather sophisticated tuning (e.g., experimenting with parameter settings or with the processing pipeline composition) for optimum performance on new data. This is not the case of our approach, as it can work in a purely unsupervised manner off-the-shelf.
@cite_14 @cite_8 @cite_9 @cite_0 @cite_5
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Motivation: We report on the development of a generic text categorization system designed to automatically assign biomedical categories to any input text. Unlike usual automatic text categorization systems, which rely on data-intensive models extracted from large sets of training data, our categorizer is largely data-independent. Methods: In order to evaluate the robustness of our approach we test the system on two different biomedical terminologies: the Medical Subject Headings (MeSH) and the Gene Ontology (GO). Our lightweight categorizer, based on two ranking modules, combines a pattern matcher and a vector space retrieval engine, and uses both stems and linguistically-motivated indexing units. Results and Conclusion: Results show the effectiveness of phrase indexing for both GO and MeSH categorization, but we observe the categorization power of the tool depends on the controlled vocabulary: precision at high ranks ranges from above 90 for MeSH to <20 for GO, establishing a new baseline for categorizers based on retrieval methods. Contact: Patrick.Ruch@sim.hcuge.ch For computational purposes documents or other objects are most often represented by a collection of individual attributes that may be strings or numbers. Such attributes are often called features and success in solving a given problem can depend critically on the nature of the features selected to represent documents. Feature selection has received considerable attention in the machine learning literature. In the area of document retrieval we refer to feature selection as indexing. Indexing has not traditionally been evaluated by the same methods used in machine learning feature selection. Here we show how indexing quality may be evaluated in a machine learning setting and apply this methodology to results of the Indexing Initiative at the National Library of Medicine. The Medical Text Indexer (MTI) is a program for producing MeSH indexing recommendations. It is the major product of NLM’s Indexing Initiative and has been used in both semi-automated and fully automated indexing environments at the Library since mid 2002. We report here on an experiment conducted with MEDLINE indexers to evaluate MTI’s performance and to generate ideas for its improvement as a tool for user-assisted indexing. We also discuss some filtering techniques developed to improve MTI’s accuracy for use primarily in automatically producing the indexing for several abstracts collections. Background Due to the high cost of manual curation of key aspects from the scientific literature, automated methods for assisting this process are greatly desired. Here, we report a novel approach to facilitate MeSH indexing, a challenging task of assigning MeSH terms to MEDLINE citations for their archiving and retrieval. @PARASPLIT Methods Unlike previous methods for automatic MeSH term assignment, we reformulate the indexing task as a ranking problem such that relevant MeSH headings are ranked higher than those irrelevant ones. Specifically, for each document we retrieve 20 neighbor documents, obtain a list of MeSH main headings from neighbors, and rank the MeSH main headings using ListNet–a learning-to-rank algorithm. We trained our algorithm on 200 documents and tested on a previously used benchmark set of 200 documents and a larger dataset of 1000 documents. @PARASPLIT Results Tested on the benchmark dataset, our method achieved a precision of 0.390, recall of 0.712, and mean average precision (MAP) of 0.626. In comparison to the state of the art, we observe statistically significant improvements as large as 39 in MAP (p-value <0.001). Similar significant improvements were also obtained on the larger document set. @PARASPLIT Conclusion Experimental results show that our approach makes the most accurate MeSH predictions to date, which suggests its great potential in making a practical impact on MeSH indexing. Furthermore, as discussed the proposed learning framework is robust and can be adapted to many other similar tasks beyond MeSH indexing in the biomedical domain. All data sets are available at: . The volume of biomedical literature has experienced explosive growth in recent years. This is reflected in the corresponding increase in the size of MEDLINE^(R), the largest bibliographic database of biomedical citations. Indexers at the US National Library of Medicine (NLM) need efficient tools to help them accommodate the ensuing workload. After reviewing issues in the automatic assignment of Medical Subject Headings (MeSH^(R) terms) to biomedical text, we focus more specifically on the new subheading attachment feature for NLM's Medical Text Indexer (MTI). Natural Language Processing, statistical, and machine learning methods of producing automatic MeSH main heading subheading pair recommendations were assessed independently and combined. The best combination achieves 48 precision and 30 recall. After validation by NLM indexers, a suitable combination of the methods presented in this paper was integrated into MTI as a subheading attachment feature producing MeSH indexing recommendations compliant with current state-of-the-art indexing practice.