Today, full-texts of scientific articles are often stored in different locations than the used datasets. Dataset registries aim at a closer integration by making datasets citable but authors typically refer to datasets using inconsistent abbreviations and heterogeneous metadata (e.g. title, publication year). It is thus hard to reproduce research results, to access datasets for further analysis, and to determine the impact of a dataset. Manually detecting references to datasets in scientific articles is time-consuming and requires expert knowledge in the underlying research domain.We propose and evaluate a semi-automatic three-step approach for finding explicit references to datasets in social sciences articles.We first extract pre-defined special features from dataset titles in the da|ra registry, then detect references to datasets using the extracted features, and finally match the references found with corresponding dataset titles. The approach does not require a corpus of articles (avoiding the cold start problem) and performs well on a test corpus. We achieved an F-measure of 0.84 for detecting references in full-texts and an F-measure of 0.83 for finding correct matches of detected references in the da|ra dataset registry.
Boland2012 proposed a pattern induction method for extracting dataset references from documents in order to overcome the necessity of such a large training set @cite_1 . Their algorithm starts with either the name of a dataset or with an abbreviation of this name, and then drives patterns of all phrases that contain that name or abbreviation in articles. The patterns are applied to articles in order to extract more dataset names and abbreviations. This process repeats with new abbreviations and names until no more datasets can be detected in articles. It derives patterns of phrases that contain dataset references iteratively by using a bootstrapping approach.
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Objective: To develop methods that automatically map abbreviations to their full forms in biomedical articles. @PARASPLIT Methods: The authors developed two methods of mapping defined and undefined abbreviations (defined abbreviations are paired with their full forms in the articles, whereas undefined ones are not). For defined abbreviations, they developed a set of pattern-matching rules to map an abbreviation to its full form and implemented the rules into a software program, AbbRE (for “abbreviation recognition and extraction”). Using the opinions of domain experts as a reference standard, they evaluated the recall and precision of AbbRE for defined abbreviations in ten biomedical articles randomly selected from the ten most frequently cited medical and biological journals. They also measured the percentage of undefined abbreviations in the same set of articles, and they investigated whether they could map undefined abbreviations to any of four public abbreviation databases (GenBank LocusLink, swissprot, LRABR of the UMLS Specialist Lexicon, and Bioabacus). @PARASPLIT Results: AbbRE had an average 0.70 recall and 0.95 precision for the defined abbreviations. The authors found that an average of 25 percent of abbreviations were defined in biomedical articles and that of a randomly selected subset of undefined abbreviations, 68 percent could be mapped to any of four abbreviation databases. They also found that many abbreviations are ambiguous (i.e., they map to more than one full form in abbreviation databases). @PARASPLIT Conclusion: AbbRE is efficient for mapping defined abbreviations. To couple AbbRE with abbreviation databases for the mapping of undefined abbreviations, not only exhaustive abbreviation databases but also a method to resolve the ambiguity of abbreviations in the databases are needed. Motivation: Biological literature contains many abbreviations with one particular sense in each document. However, most abbreviations do not have a unique sense across the literature. Furthermore, many documents do not contain the long forms of the abbreviations. Resolving an abbreviation in a document consists of retrieving its sense in use. Abbreviation resolution improves accuracy of document retrieval engines and of information extraction systems. Results: We combine an automatic analysis of Medline abstracts and linguistic methods to build a dictionary of abbreviation sense pairs. The dictionary is used for the resolution of abbreviations occurring with their long forms. Ambiguous global abbreviations are resolved using support vector machines that have been trained on the context of each instance of the abbreviation sense pairs, previously extracted for the dictionary set-up. The system disambiguates abbreviations with a precision of 98.9 for a recall of 98.2 (98.5 accuracy). This performance is superior in comparison with previously reported research work. Availability: The abbreviation resolution module is available at http: www.ebi.ac.uk Rebholz software.html Contact: [email protected] The recognition of names and their associated categories within unstructured text traditionally relies on semantic lexicons and gazetteers. The amount of effort required to assemble large lexicons confines the recognition to either a limited domain (e.g., medical imaging), or a small set of pre-defined, broader categories of interest (e.g., persons, countries, organizations, products). This constitutes a serious limitation in an information seeking context. In this case, the categories of potential interest to users are more diverse (universities, agencies, retailers, celebrities), often refined (e.g., SLR digital cameras, programming languages, multinational oil companies), and usually overlapping (e.g., the same entity may be concurrently a brand name, a technology company, and an industry leader). We present a lightly supervised method for acquiring named entities in arbitrary categories. The method applies lightweight lexico-syntactic extraction patterns to the unstructured text of Web documents. The method is a departure from traditional approaches to named entity recognition in that: 1) it does not require any start-up seed names or training; 2) it does not encode any domain knowledge in its extraction patterns; 3) it is only lightly supervised, and data-driven; 4) it does not impose any a-priori restriction on the categories of extracted names. We illustrate applications of the method in Web search, and describe experiments on 500 million Web documents and news articles. The location of the author of a social media message is not invariably the same as the location that the author writes about in the message. In applications that mine these messages for information such as tracking news, political events or responding to disasters, it is the geographic content of the message rather than the location of the author that is important. To this end, we present a method to geo-parse the short, informal messages known as microtext. Our preliminary investigation has shown that many microtext messages contain place references that are abbreviated, misspelled, or highly localized. These references are missed by standard geo-parsers. Our geo-parser is built to find such references. It uses Natural Language Processing methods to identify references to streets and addresses, buildings and urban spaces, and toponyms, and place acronyms and abbreviations. It combines heuristics, open-source Named Entity Recognition software, and machine learning techniques. Our primary data consisted of Twitter messages sent immediately following the February 2011 earthquake in Christchurch, New Zealand. The algorithm identified location in the data sample, Twitter messages, giving an F statistic of 0.85 for streets, 0.86 for buildings, 0.96 for toponyms, and 0.88 for place abbreviations, with a combined average F of 0.90 for identifying places. The same data run through a geo-parsing standard, Yahoo! Placemaker, yielded an F statistic of zero for streets and buildings (because Placemaker is designed to find neither streets nor buildings), and an F of 0.67 for toponyms.