text: 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.
  @cite_18: This paper presents a keyword extraction technique that can be used for tracking topics over time. In our work, keywords are a set of significant words in an article that gives high-level description of its contents to readers. Identifying keywords from a large amount of on-line news data is very useful in that it can produce a short summary of news articles. As on-line text documents rapidly increase in size with the growth of WWW, keyword extraction has become a basis of several text mining applications such as search engine, text categorization, summarization, and topic detection. Manual keyword extraction is an extremely difficult and time consuming task; in fact, it is almost impossible to extract keywords manually in case of news articles published in a single day due to their volume. For a rapid use of keywords, we need to establish an automated process that extracts keywords from news articles. We propose an unsupervised keyword extraction technique that includes several variants of the conventional TF-IDF model with reasonable heuristics.
summary: One group of methods is based on the “bag of words” model using algorithms such as tf-idf to adjust weights for terms in a representation of texts as vectors (cf. the introduction in ). Lee2008 proposed an unsupervised keyword extraction method by using a tf-idf model with some heuristics @cite_18 . Our approach uses similarity measures for finding a perfect match for each dataset reference in an article by comparing titles of datasets in a registry to sentences in articles. Dice, Jaccard and Cosine can be applied to a vector representation of a text easily (cf. ). The accuracy of algorithms based on such similarity measures can be improved by making them semantics-aware, e.g., representing a set of synonyms as a single vector space dimension.