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_8: This paper presents a simple unsupervised learning algorithm for recognizing synonyms, based on statistical data acquired by querying a Web search engine. The algorithm, called PMI-IR, uses Pointwise Mutual Information (PMI) and Information Retrieval (IR) to measure the similarity of pairs of words. PMI-IR is empirically evaluated using 80 synonym test questions from the Test of English as a Foreign Language (TOEFL) and 50 synonym test questions from a collection of tests for students of English as a Second Language (ESL). On both tests, the algorithm obtains a score of 74 . PMI-IR is contrasted with Latent Semantic Analysis (LSA), which achieves a score of 64 on the same 80 TOEFL questions. The paper discusses potential applications of the new unsupervised learning algorithm and some implications of the results for LSA and LSI (Latent Semantic Indexing).
summary: Corpus and Web based methods often use information about the co-occurrence of two texts in documents, and are used for measuring texts' semantic similarity. Turney2001 introduced a simple unsupervised learning algorithm for detecting synonyms @cite_8 , which searches queries through an online search engine and analyses the results. The quality of the algorithm depends on the number of search results returned.